research paper about imagery

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 22 June 2020

A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia

• Alexei J. Dawes 1 ,
• Rebecca Keogh 1 ,
• Thomas Andrillon 1 , 2 &
• Joel Pearson 1  

Scientific Reports volume  10 , Article number:  10022 ( 2020 ) Cite this article

73k Accesses

107 Citations

429 Altmetric

Metrics details

• Neuroscience

For most people, visual imagery is an innate feature of many of our internal experiences, and appears to play a critical role in supporting core cognitive processes. Some individuals, however, lack the ability to voluntarily generate visual imagery altogether – a condition termed “aphantasia”. Recent research suggests that aphantasia is a condition defined by the absence of visual imagery, rather than a lack of metacognitive awareness of internal visual imagery. Here we further illustrate a cognitive “fingerprint” of aphantasia, demonstrating that compared to control participants with imagery ability, aphantasic individuals report decreased imagery in other sensory domains, although not all report a complete lack of multi-sensory imagery. They also report less vivid and phenomenologically rich autobiographical memories and imagined future scenarios, suggesting a constructive role for visual imagery in representing episodic events. Interestingly, aphantasic individuals report fewer and qualitatively impoverished dreams compared to controls. However, spatial abilities appear unaffected, and aphantasic individuals do not appear to be considerably protected against all forms of trauma symptomatology in response to stressful life events. Collectively, these data suggest that imagery may be a normative representational tool for wider cognitive processes, highlighting the large inter-individual variability that characterises our internal mental representations.

Similar content being viewed by others

Insights into embodied cognition and mental imagery from aphantasia

Individuals who rarely adopt observer perspectives report richer scene-based autobiographical memories

Qualitative analysis of hotspots and intrusive memories after viewing an aversive film highlights their sensory and spatial features

Introduction.

Visual imagery, or seeing with the mind’s eye, contributes to essential cognitive processes such as episodic memory 1 , future event prospection 2 , visual working memory 3 , and dreaming 4 . By allowing us to re-live the past and simulate hypothetical futures, visual imagery enables us to flexibly and adaptively interpret the events we experience in the world 5 , and by extension appears to be an important precursor to our ability to plan effectively and engage in guided decision-making. Consequently, the frequency and content of maladaptive visual imagery are often defining features of mental illness 6 and mental imagery is often elevated in disorders characterised by hallucinations 7 , 8 .

One of the most significant findings to date is that despite the prevalence of visual imagery use in the wider population, and despite its functional utility in cognition, certain individuals lack the ability to visualise altogether – a condition recently termed “aphantasia” 9 . Beyond self-report measures, this condition is characterised by stark differences between individuals who can and cannot visualise on an objective measure of imagery’s sensory strength 10 . This suggests that rather than reflecting inaccurate phenomenological reports or poor population-specific metacognition, aphantasia appears to represent a veridical absence of voluntarily generated internal visual representations.

The potential impact of visual imagery absence on wider cognition remains unknown. No research to date has empirically verified whether this phenomenology extends to other internal experiences and mental processes. This presents us with a rare opportunity to extend a cognitive fingerprint of aphantasia, in order to better clarify the role of visual imagery in wider psychological functioning and explore the impact of its absence on the subjective lives of individuals with a “blind mind”. Here we investigated whether individuals with aphantasia report reduced imagery in other multi-sensory domains, and assessed self-reports of episodic memory ability and trauma symptomatology in response to stressful life events, in addition to reported mind-wandering frequency and dreaming phenomenology.

Participants

We compared a group of self-identified aphantasic individuals with two independent control groups of individuals with self-reported intact visual imagery on a range of questionnaires. The current study was approved by the UNSW Human Research Ethics Advisory Panel (HREAP-C) in line with National Health and Medical Research Council (NHRMC) guidelines on ethical human research. All participants gave informed consent before completing the study.

Given the need for more research in this area, we sought to collect data on as many aphantasic participants as possible. With the limited number of previous studies on aphantasia using small sample sizes of N  = 10–20 9 , 10 , it was difficult to estimate required sample sizes for our study based on these results alone. We nevertheless used the limited data available to derive approximate effect sizes for group differences in these studies in the range of d  = 1.0–3.0. Effect sizes in small sample studies are often inflated, however, and we expected weaker effects across multiple comparisons in our study, especially in non-imagery domain comparisons. Establishing a comparatively moderate expected effect size of d  = 0.5, with 80% power and a highly conservative alpha of 0.0002 (see Statistical Analyses in Methods), we estimated that at least 170 participants would be required in each comparison group. Because our study was easily accessible online and received more participant responses than anticipated within our data collection window, we exceeded our sample size aim ( N  = 170) and ceased data collection for our aphantasic participant group at the sample size reported below. We then collected an equivalent number of participants for our independent control groups. Sample sizes for the aphantasia group, control group 1 and control group 2 were approximately equal after data cleaning and exclusions ( n  = 267, n  = 203 and n  = 197, respectively).

Aphantasia group

Aphantasic individuals in our study were recruited from online community research platforms ( https://www.facebook.com/sydneyaphantasiaresearch/ ) and participated in exchange for entry into a gift card prize draw. 317 aphantasic participants in total completed our study, of whom 33 participants were excluded from analysis due to missing data (not completing all questionnaires). An additional 17 participants were excluded from our aphantasic sample due to unclear reporting (e.g. scoring at ceiling on the Vividness of Visual Imagery Questionnaire (VVIQ; see Methods) in line with older versions of the scale that used reversed scoring compared to the current version of the scale). Our final sample of aphantasic individuals included for analysis contained 267 participants (48% females; mean age = 33.97 years, SD  = 12.44, range = 17–75 years).

Control group 1 (MTurk)

Participants in our main control group were recruited using Amazon Mechanical Turk (MTurk) and were remunerated to complete the study. This main control group sample comprised of 205 participants, two of whom were excluded from final analysis due to study incompletion. Our final sample for our main control group thus consisted of 203 participants (35% females; mean age = 33.82 years, SD  = 9.33, range = 20–70 years) who were matched on mean age with our aphantasic sample (mean age difference = 0.15 years, p  = 0.89, BF 10  = 0.107).

Control group 2 (Undergraduates)

A second control group of 193 first-year undergraduate psychology students were tested using the same experimental design. Participants in our second control group (73% females; mean age = 19.33 years, SD  = 3.69, range = 17–55 years) completed the study in exchange for course credit. All participants were included in final analysis (see section titled Control Group 2: Replication Analysis, in Results).

Aphantasia sample characteristics

Demographics.

A table of sample demographics for all groups can be found in the Supplementary Information (see Table  S1 ). Our sample population of aphantasic participants were recruited from online community research platforms dedicated to the topic of visual imagery ability and aphantasia. Both participants who did and didn’t identify with a history of visual imagery absence were invited to participate in the study. Of the 267 participants in our sample who reported aphantasia, a majority reported English as their first language (83%, n  = 220) and identified as White/Caucasian (88%, n  = 235). 31 countries of residence were listed, with a majority of participants originating from the United States of America.

Clinical history

Of the aphantasic sample, 24% of participants reported a history of mental illness (compared to 18% in control group 1; χ 2 1,470  = 3.644, p  = 0.06), 1% reported a history of epilepsy or seizures (compared to 8% in control group 1; χ 2 1,470  = 14.881, p  < 0.001), 4% reported a neurological condition (compared to 7% in control group 1; χ 2 1,470  = 1.765, p  = 0.184), 9% reported having suffered head injury or trauma at least once (compared to 9% in control group 1; χ 2 1,470  = 0.019, p  = 0.890), and 0.7% reported having once suffered a stroke (compared to 6% in control group 1; χ 2 1,470  = 10.634, p  < 0.01).

Imagery scores

Weak visual imagery ability is typically defined by a total score of 32 or less on the Vividness of Visual Imagery Questionnaire (VVIQ: see Imagery Questionnaires in Materials), a five-point Likert self-report scale which ranges from 16–80 9 , 11 . A total score of 32 is equivalent to rating one’s agreement on every questionnaire item at 2 (“Vague and dim”). On average, aphantasic participants in our sample scored 17.94 on the VVIQ (including 70% with total floor scores of 16), compared to 58.12 in control group 1 (see Imagery Results section) and 58.79 in control group 2 (see Table  S2 in Supplementary Information).

Experimental procedure

Questionnaires were administered online using the Qualtrics research platform, and presented to each participant in random order. All participants completed a total of 206 questions in eight questionnaires. These questionnaires assessed self-reported multi-sensory imagery, episodic memory and future prospection, spatial abilities, mind-wandering and dreaming propensity, and response to stressful life events, as detailed below.

Imagery questionnaires

The Vividness of Visual Imagery Questionnaire (VVIQ 11 ; Marks, 1973) is a 16-item scale which asks participants to imagine a person as well as several scenes and rate the vividness of these mental images using a 5-point scale ranging from 1 (“No image at all, you only ‘know’ that you are thinking of the object”) to 5 (“Perfectly clear and <as> vivid as normal vision”). A single mean score on the VVIQ was computed for each participant. The Questionnaire upon Mental Imagery (QMI 12 ; Sheehan, 1967) asks participants to rate the clarity and vividness of a range of imagined stimuli in seven sensory domains (visual, auditory, tactile, kinesthetic, taste, olfactory, emotion) on a 7-point scale ranging from 1 (“I think of it, but do not have an image before me”) to 7 (“Very vivid and as clear as reality”). There are 35 items on the QMI in total, with five items corresponding to each of the seven sensory domains. The Object and Spatial Imagery Questionnaire (OSIQ 13 ; Blajenkova, Kozhevnikov, & Motes, 2006) is a 50-item scale which requires participants to indicate how well each of several statements on object imagery ability (e.g. “When I imagine the face of a friend, I have a perfectly clear and bright image”) and spatial imagery ability (e.g. “I am a good Tetris player”) applies to them on a 5-point scale ranging from 1 (“Totally disagree”) to 5 (“Totally agree”). There are 25 items each comprising the Object and Spatial imagery domains of the OSIQ, averaged to form a mean score on each domain.

Memory questionnaires

The Episodic Memory Imagery Questionnaire (EMIQ; on request) is a custom designed, 16-item self-report questionnaire which aims to assess the subjective vividness of episodic memory. Items on the EMIQ were partially derived from the VVIQ 11 scale (Marks, 1973) and modified for context. The EMIQ asks participants to remember several events or scenes from their life and rate the vividness of these scenes using a 5-point scale ranging from 1 (“No image at all, I only ‘know’ that I am recalling the memory”) to 5 (“Perfectly clear and as vivid as normal vision”). A single mean score on the EMIQ was computed for each participant. The Survey of Autobiographical Memory (SAM 14 ; Palombo, Williams, Abdi, & Levine, 2013) is a 26-item scale which measures participant agreement with a number of statements related to general episodic memory ability on a 5-point scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). The scale is divided into 4 components: Event Memory (averaged across eight items, e.g. “When I remember events, in general I can recall people, what they looked like, or what they were wearing”), Future Events (averaged across six items; e.g. “When I imagine an event in the future, the event generates vivid mental images that are specific in time and place”), Factual Memory (averaged across six items; e.g. “I can learn and repeat facts easily, even if I don’t remember where I learned them”) and Spatial Memory (averaged across six items; e.g. “In general, my ability to navigate is better than most of my family/friends”).

Dreaming and daydreaming questionnaires

Part 1 of the Imaginal Process Inventory (IPI; 15 , 16 Giambra, 1980; Singer & Antrobus, 1963) consists of 24 items which assess the self-reported frequency of day dreams (or mind-wandering episodes) and night dreams on a 5-point agreement scale which differs on each question (e.g. “I recall my night dreams vividly”, ranging from a) “Rarely or never” through to e) “Once a night”). The Subjective Experiences Rating Scale (SERS 17 ; Kahan & Claudatos, 2016) comprises 39 questions which assess the qualitative content and subjective experience of participants’ night dreams generally (e.g. “During your dreams whilst asleep, <to what extent> do you experience colors”) on a 5-point rating scale ranging from 0 (“None”) to 4 (“A lot”). There are several sub-components of the scale which measure reported structural features of participants’ dreams (e.g. how bizarre one’s actions were, or how much perceived control participants experienced, during their dreams). The SERS is divided in our study into six dream components: Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity. These components reflect typical SERS scale divisions, with the exception of Lucidity (in which we merge two existing components (Awareness and Control) of the previously published SERS scale 17 in order to improve the readability of Fig.  2 ).

Trauma response questionnaire

The Post-Traumatic Stress Disorder (PTSD) Checklist for DSM-5 (PCL-5 18 ; Weathers et al ., 2013) measures self-reported responses to stressful life events. It asks participants to indicate how much they have been bothered by a problem related to a stressful life event on a 5-point scale ranging from 1 (“Not at all”) to 5 (“Extremely”). The PCL-5 contains 20 questions which are broken into four clinically relevant symptom categories: Intrusions (e.g. “Repeated, disturbing, and unwanted memories of the stressful experience”), Avoidance (e.g. “Avoiding memories, thoughts, or feelings related to the stressful experience”), Negative Alterations in Cognitions and Mood (e.g. “Blaming yourself or someone else for the stressful experience and what happened after it”), and Arousal and Reactivity (e.g. “Feeling jumpy or easily startled”). PTSD diagnosis can only be established by a professional practitioner in a structured clinical interview, and although cut-off scores on the PCL-5 are often used as an adjunct screening tool, the scale is not used for diagnostic purposes here.

Statistical analyses

Non-parametric Mann-Whitney U hypothesis tests were conducted in SPSS 25.0 for Mac OS using Bonferroni adjusted alpha levels of α = 0.0002 (0.05/206 where 206 is the total number of question items across all questionnaires) to correct for multiple comparisons. Estimates of effect sizes r were computed using the following formula:

where Z is the Mann-Whitney standardized test statistic, N the total sample size of the combined groups, and r the output effect size estimate (comparable with Cohen’s d effect size interpretations 19 ). Because we adopted a highly conservative adjusted alpha, Mann-Whitney tests were supplemented by Bayesian analyses conducted in JASP. For all Bayesian analyses, a Cauchy prior of 0.707 was used. Bayes factors were used to help compare the weight of evidence for between-group differences across test comparisons, whilst Mann-Whitney tests were used to make overall inferences about test direction and significance. Bayes factors were interpreted according to common threshold guidelines 20 , where 1 = “No evidence”, 1–3 = “Anecdotal evidence”, 3–10 = “Moderate evidence”, 10–30 = “Strong evidence”, 30–100 = “Very strong evidence”, and >100 = “Extreme evidence”.

Data transformation

All analyses were conducted on raw data. Data visualisation for Fig.  1 only, however, was carried out on median-centered raw questionnaire data using the following transformation:

where y is the transformed score; x the raw individual item score for scale S , and S.min and S.max the lowest and highest possible scores on that scale, respectively. This transformation allows us to graphically compare results across scales, with a value of −0 .5 representing the lowest possible score, 0 the median score, and 0.5 the maximum possible score on each scale.

Summary of self-reported cognition questionnaires for individuals with aphantasia (red, n = 267) and control group 1 participants with visual imagery (blue, n = 203). Violin plots of median-centred scale scores with median (bold line), lower and upper quartiles (thin lines) and kernel density-smoothed frequency distribution (shaded area) coloured by group. Each pair of violin plots represents transformed raw data (see Data Transformation, Method). Stars to the right of group plot segments indicate Mann-Whitney test significance at threshold p  < 0.0002.

We expected aphantasic individuals to report reduced visual imagery ability compared to controls, in line with previous findings 9 , 10 . There is some suggestion that auditory imagery may also be reduced in individuals who report visual imagery absence, however this evidence comes from case studies with limited sample sizes 1 . We therefore had no strong hypotheses regarding group differences in other multi-sensory imagery domains.

Given the proposed importance of mental imagery for the reliving of past life events 21 , we predicted that aphantasic individuals would report general alterations to episodic memory and future prospection processes, as well as reductions in episodic memory vividness.

Clinical research has traditionally placed heavy emphasis on the symptomatic role of visual imagery in mental health disorders including depression, social phobia, schizophrenia and post-traumatic stress disorder (PTSD), amongst others 6 . We therefore hypothesised that visual imagery absence might partially protect aphantasic individuals from experiencing some trauma symptomatology (such as vivid memory intrusions) in response to stressful past events.

Although neural measures suggest that dreaming is often characterised by vivid and objectively measurable internal visual experiences 4 , previous evidence on dreaming in aphantasia is somewhat inconclusive 22 . The overall impact of visual imagery absence on involuntary imagery processes (such as mind-wandering and dreaming whilst asleep) is therefore largely unclear, and we had no strong predictions regarding group differences in mind-wandering frequency, dream frequency, or dream phenomenology and content.

Lastly, we expected aphantasic self-reports of spatial imagery and spatial navigation abilities to align with data from previous studies suggesting that despite visual imagery absence, spatial abilities (as measured by questionnaires and performance on mental rotation and visuo-spatial tasks) appear to be largely preserved in aphantasia 10 , 22 .

The aim of the present study was to investigate the subjective impact of visual imagery absence on cognition. To achieve this, we compared self-reports of aphantasic individuals with those of general population individuals (with self-reported intact visual imagery) on several cognitive domains including multi-sensory imagery, episodic memory, trauma response, dreaming and daydreaming, and spatial abilities. The main results sections presented here all describe between-group tests comparing our aphantasic sample with our first control group of age-matched participants recruited from MTurk (see Tables  S2 – 6 in Supplementary Information). For replication comparisons with our second control group sample of undergraduates, see section at end of Results titled “Control Group 2: Replication Analysis”.

Control Group 1: Main Comparisons

Imagery results.

We first examined group differences in visual imagery vividness. As expected based on previous findings 9 , 10 , aphantasic participants rated their visual imagery ability on the VVIQ as being significantly lower (17.94 ± 0.223, with many (70%) scoring at floor, i.e. 16) compared to control group 1 (58.12 ± 0.888; Mann-Whitney U = 427.5, p  < 0.0002, r = 0.87, BF 10 = 1.41e 12 , 2-tailed; see Fig.  1 red section and Figure  S1 in Supplementary Information; Fig.  1 depicts median-centered data with the aphantasia group denoted by red plots and control group 1 by blue plots throughout; Figures  S1 – 5 show raw scale scores and distributions). This self-reported qualitative absence of visual imagery vividness was mirrored by significantly lower scores than controls on the object imagery component of the OSIQ (Mann-Whitney U = 372, p  < 0.0002, r = 0.85, BF 10 = 446,931.23, 2-tailed; see Fig.  1 red section and Fig. S1), which measures the perceived ability to use imagery as a cognitive tool in task-relevant scenarios. Our data also showed that individuals with aphantasia not only report being unable to visualise, but also report comparatively reduced imagery, on average, in all other sensory modalities (measured using the QMI), including auditory ( U = 6,152, BF 10 = 5.01e 11 ), tactile ( U = 4,473, BF 10 = 4.90e 9 ), kinesthetic ( U = 5,151, BF 10 = 1.04e 11 ), taste ( U = 3,069.5, BF 10 = 4.82e 26 ), olfactory ( U = 3,439.5, BF 10 = 2.73e 9 ) and emotion ( U = 6,670.5, BF 10 = 4.81e 12 ) domains (all Mann-Whitney U-tests, p  < 0.0002, r = 0.65–.78, 2-tailed; see Fig.  2a and Fig. S1). It is noteworthy, however, that despite reporting a near total absence of visual imagery on the QMI (Mann-Whitney U = 620.5, p  < 0.0002, r = 0.87, BF 10 = 1.07e 9 , 2-tailed; see Fig.  2a ) and significantly lower total QMI scores overall compared to controls (Mann-Whitney U = 1,868.5, p  < 0.0002, r = 0.79, BF 10 = 6.47e 12 , 2-tailed; see Fig.  1 red section, second panel from top), only 26.22% of aphantasic participants reported a complete lack of multi-sensory imagery altogether (rating each question in each QMI domain as “1: No sensory experience at all”). The remainder of our aphantasic sample (73.78%) reported some degree of imagery in non-visual sensory modalities (albeit significantly reduced compared to controls; see Fig.  1 red section, and Fig.  2a ), suggesting potential sub-categories of aphantasia.

Group differences in visual imagery ability on scale sub-components. Radar plots for ( a ) multi-sensory imagery; ( b ) trauma response; and ( c ) dreaming scales (SC. = Spatial Complexity; PSP. = Perspective; LUC. = Lucidity). Concentric dashed circles represent raw scale scores for each scale (e.g. a ; 1–7 Likert-type), with lowest possible item scores falling on innermost solid circle and highest possible item scores falling on outermost coloured circle; radial dashed lines denote item grouping for scale sub-components (e.g. c ; Intrusions, Avoidance, Negative Cognition and Mood, Arousal and Reactivity); central coloured lines (red = aphantasia group, blue = control group 1) represent raw total group scores on individual scale items, with translucent shading denoting standard-deviation.

Memory results

Aphantasic individuals described a significantly lower ability to remember specific life events in general (Event Memory component of the SAM; Mann-Whitney U = 8,865, p  < 0.0002, r = 0.58, BF 10 = 4.68e 10 , 2-tailed; see Fig.  1 blue section) and reported almost no ability to generate visual sensory details when actively remembering past events (memory vividness on the EMIQ; Mann-Whitney U = 2,186.5, p  < 0.0002, r = 0.81, BF 10 = 1.01e 15 , 2-tailed; see Fig.  1 blue section and Fig. S2 in Supplementary Information) compared to participants in control group 1. However, these self-reported reductions in reliving events were not confined to the past, with aphantasics as a group also reporting a near total inability to imagine future hypothetical events in any sensory detail (Future Events component of the SAM; Mann-Whitney U = 7,469.5, p  < 0.0002, r = 0.63, BF 10 = 2.97e 10 , 2-tailed; see Fig.  1 blue section and Fig. S2). Self-reported factual (or semantic) memory, which is traditionally thought to provide a kind of ‘scaffold’ for event memories more widely 23 , also appeared to be lower in individuals unable to visualise compared to controls (Factual Memory component of the SAM; Mann-Whitney U = 18,601.5, p  < 0.0002, r = 0.27, BF 10 = 156,732.50, 2-tailed; see Fig.  1 blue section and Fig. S2), although this effect was of a lower magnitude than the memory reductions reported above (see Fig.  1 blue section and Table  S7 in Supplementary Information). The fourth scale component of the SAM (Spatial Memory) is grouped with the Spatial Imagery component of the OSIQ in results below (see Spatial Ability Results).

Trauma response results

Our data did not directly support the hypothesis that visual imagery absence might protect aphantasic individuals from trauma symptomology in response to stressful life events, with the aphantasia group scoring comparatively to control group 1 on the PCL-5 overall (total PCL-5 scores; Mann-Whitney U = 27,515, p = 0.776, r = 0.01, BF 10 = 0.12, 2-tailed; see Fig.  1 grey section and Figure  S3 in Supplementary Information). An analysis of group differences on the four sub-components of this scale (Intrusions, Cognition and Mood, Avoidance, and Arousal) also revealed that there were no significant differences between the groups in reports of emotional arousal and reactivity associated with remembering stressful past events (Mann-Whitney U = 27,240, p = 0.924, r = 0.00, BF 10 = 0.11, 2-tailed; see Fig.  2b and Fig. S3). Compared to participants with visual imagery, individuals with aphantasia appeared to report fewer recurrent and involuntary memory intrusions (Mann-Whitney U = 22,739, p = 0.002, r = 0.14, BF 10 = 14.85, 2-tailed; see Fig.  2b and Fig. S3), lower engagement in avoidance behaviours (Mann-Whitney U = 23,164.5, p = 0.006, r = 0.13, BF 10 = 2.13, 2-tailed; see Fig.  2b and Fig. S3), and greater negative changes in cognition and mood (Mann-Whitney U = 30,960, p = 0.008, r = 0.12, BF 10 = 12.99, 2-tailed; see Fig.  2b and Fig. S3) in response to stressful life events, although none of these group differences survived Bonferroni correction for multiple comparisons, and effect size estimates were small ( r = 0.12–.14; see Table  S7 in Supplementary Information). Interestingly, however, Bayesian analyses indicated strong evidence in favour of group differences on the Intrusions (BF 10 = 14.85) and Cognition and Mood (BF 10 = 12.99) sub-scales of the PCL-5 reported above.

Day and night dream results

Here we found that although there was little evidence for or against (BF 10 = 1.93 and BF 01 = 0.518) a difference between groups in the reported frequency of day-dreaming (Mann-Whitney U = 23,001.5, p = 0.005, r = 0.13, 2-tailed, non-significant after Bonferroni correction; see Fig.  1 teal section and Figure  S4 in Supplementary Information), aphantasic individuals did report experiencing significantly fewer night dreams than controls (Imaginal Process Inventory (IPI); Mann-Whitney U = 15,828.5, p  < 0.0002, r = 0.37, BF 10 = 4.24e 6 , 2-tailed; see Fig.  1 teal section and Fig. S4). Interestingly, the reported qualitative content of these night dreams also differed between groups as measured by the SERS. Dream reports for aphantasic individuals reinforce a model of aphantasia as being primarily characterised by sensory deficits (Sensory; Mann-Whitney U = 15,087.5, p  < 0.0002, 0.38, BF 10 = 5.46e 6 , 2-tailed) across all dream modalities (including olfactory, tactile, taste and auditory domains; see Fig.  2c and Fig. S4). Interestingly, aphantasic individuals also reported experiencing lower awareness and control during their dreams (Lucidity; Mann Whitney U = 19,473.0, p  < 0.0002, r = 0.25, BF 10 = 1902.01, 2-tailed). We found some evidence that the dreams aphantasic participants report are characterised by less vivid emotions (Affective; Mann Whitney U = 23,463.0, p = 0.013, non-significant after Bonferroni correction, r = 0.11, BF 10 = 9.01, 2-tailed), and a less clear dreamer perspective (Perspective (PSP); Mann Whitney U = 22,070.5, p = 0.0004, r = 0.16, non-significant after Bonferroni correction, BF 10 = 127.28, 2-tailed) compared to participants in control group 1. However, there were no significant differences between the aphantasia group and control group 1 in the experience of within-dream cognition (e.g. planning or remembering (Cognitive); Mann Whitney U = 24,592.0, p = 0.085, r = 0.08, BF 10 = 1.05, 2-tailed) or the details of dreams’ spatial features (Spatial Complexity (SC); Mann Whitney U = 24,697.0, p = 0.092, r = 0.08, BF 10 = 0.31, 2-tailed). Interestingly, the only question on the SERS for which aphantasics scored significantly higher than control group 1 participants was an item in the Cognitive domain (see Fig.  2c ) which asks how much time participants spent thinking during their dreams (Mann-Whitney U = 34,401.5, p  < 0.0002, BF 10 = 3.53e 3 ), which accords well with a reduction in the sensory qualities of dreams in aphantasia in favour of semanticised contents.

Spatial ability results

Aphantasic participants reported slightly lower spatial imagery ability on the spatial sub-component of the OSIQ when compared to control group 1 (Mann-Whitney U = 24,462, p = 0.001, r = 0.15, BF 10 = 14.65, 2-tailed; see Fig.  1 purple section and Figure  S5 in Supplementary Information), although this effect was not significant after Bonferroni correction. Additionally, the scores of aphantasic individuals on the Spatial Memory component of the SAM (which includes items measuring reported spatial navigation and naturalistic spatial memory ability) were not significantly different from controls (SAM; Mann-Whitney U = 24,720, p = 0.1, r = 0.08, BF 10 = 0.23, 2-tailed; see Fig.  1 purple section and Fig. S5). These results demonstrate that overall there were no consistent differences in reported spatial abilities between aphantasic individuals and participants in control group 1.

Control Group 2: Replication Analysis

Although control group 1 was age-matched, it featured a higher ratio of males to females (see Table  S1 ) in contrast to our aphantasic sample (which comprised of more females than males). Some of the variables included in this study (such as spatial ability and PTSD susceptibility) are known to be influenced by gender. To address this potential issue, we ran a replication analysis with a second control group of first-year undergraduate psychology students using the same experimental design (their raw data is depicted alongside our original control group and aphantasic sample in Figures  S1 – 5 ).

Participants in our second control group ( n = 193) were recruited from a sample of undergraduate psychology students at the University of New South Wales, and completed the study in exchange for course credit. All participants in this second control group were included in final analysis (with no exclusions). These participants (mean age = 19.33 years, SD = 3.69, range = 17–55 years) were not matched on mean age with our aphantasic sample (mean age difference = 14.6 years, p  < 0.01, BF 10 = 1.23e 10 ), but instead featured a higher proportion of females to males (73% females, compared to 48% females in our aphantasic sample and 35% females in control group 1 (our main control group of MTurk responders).

Comparison with this second control group revealed a similar overall pattern of group differences to those reported above, with few effect changes in imagery and memory related domains in particular (see Figures  S1 – 5 and Tables  S2 – 6 in Supplementary Information for a comparison of test results, as well as Table  S7 for a comparison of effect sizes). Aphantasic participants scored significantly lower than control group 2 on all outcomes of the imagery and episodic memory questionnaires (all p  < 0.0002, all r  > 0.52, all BF 10  > 1.42e 8 ) with the exception of the factual memory component of the SAM (which was no longer significantly lower in aphantasics when compared to control group 2 after controlling for multiple comparisons; Mann-Whitney U = 21,496.0, p = 0.002, r = 0.14, BF 10 = 3.196, 2-tailed).

Although our Bayes analysis suggested strong evidence for higher total PCL-5 scores in control group 2 compared to the aphantasic group (Mann-Whitney U = 21,464.0, p = 0.002, r = 0.14, BF 10 = 12.76, 2-tailed), this effect was not significant after Bonferroni correction. However, the previously non-significant reduction in memory intrusions amongst aphantasic participants (compared to control group 1) was much stronger in this second group comparison (Mann-Whitney U = 15,134.5, p  < 0.0002, r = 0.35, BF 10 = 2.20e 7 , 2-tailed), as were lower reports of avoidance behaviours by aphantasic individuals compared to control group 2 (Mann-Whitney U = 18,494.5, p  < 0.0002, r = 0.24, BF 10 = 2494.67, 2-tailed). Compared to control group 2, however, aphantasic participants did not report significantly higher negative cognition and mood (Mann-Whitney U = 25,827.5, p = 0.97, r = 0.00, BF 10 = 0.12, 2-tailed) or arousal (Mann-Whitney U = 25,517.0, p = 0.12, r = 0.07, BF 10 = 0.34, 2-tailed) in response to stressful life events, in line with our main control group 1 comparisons.

Individuals with aphantasia reported significantly fewer night dreams than control group 2 (Mann-Whitney U = 17,156.0, p  < 0.0002, r = 0.74, BF 10 = 21,124.12, 2-tailed). However, they also reported significantly less frequent mind-wandering compared to participants in control group 2 (Mann-Whitney U = 19,271.5, p  < 0.0002, r = 0.29, BF 10 = 397.04, 2-tailed), in contrast to the results of our main analysis (which revealed no significant differences in mind-wandering reports between the aphantasic group and control group 1). Also in contrast to our initial dreaming results, aphantasic participants scored significantly lower than control group 2 on all components of the SERS (Sensory, Affective, Cognitive, Spatial Complexity, Perspective and Lucidity; all p  < 0.0002, all r  > 0.71, all BF 10  > 1.56e 7 ), including on some domains where there were no significant differences between aphantasic participants and age-matched participants in control group 1 (see Fig. S4 and Table  S5 ). However, these findings may be partially explained by age-related decline in dream frequency and subjective recall 24 .

Lastly, there were no significant differences in reported spatial imagery ability on the OSIQ (Mann-Whitney U = 22,635.5, p = 0.03, r = 0.10, BF 10 = 0.88, 2-tailed) or spatial navigation ability on the SAM (Mann-Whitney U = 23,760.5, p = 0.15, r = 0.07, BF 10 = 0.23, 2-tailed) between the aphantasic group and control group 2, reinforcing our initial results as well as previous findings of preserved spatial (but not object) imagery in aphantasic participant samples 10 , 22 .

Here we found that individuals with aphantasia report significant reductions in sensory simulation across a range of volitional and non-volitional mental processes, and overall appear to demonstrate a markedly distinct pattern of cognition compared to individuals with visual imagery. Notably, aphantasic individuals reported significantly reduced imagery across all sensory modalities (and not just visual). However, only 26.22% of aphantasic participants reported a total absence of multi-sensory imagery altogether, raising important questions about the primary aetiology of aphantasia and suggesting possible sub-categories of aphantasia within a heterogeneous group. Aphantasic individuals’ episodic memory and ability to imagine future events were also reported to be significantly reduced compared to the two control populations. These findings attest to the recently established functional and anatomical overlap in brain networks supporting the flexible, constructive simulation of episodic events (whether they be real past events or hypothetical future events) 25 , and suggest that visual imagery may be an essential and unifying representational format potentiating these processes.

Interestingly, our data aligns with that of previous studies demonstrating unaffected spatial imagery abilities in aphantasia 10 , 22 , suggesting an important distinction between object imagery (low-level perceptual features of objects and scenes) and spatial imagery (spatial locations and relations in mental images) 26 . This distinction is indeed reflected at a neural level, with disparate brain pathways used for perceptual object processing and spatial locations, respectively 27 . Strikingly, cognitive differences in aphantasia were not limited to processes where visual imagery is typically deliberate and volitional, with aphantasic individuals in our study reporting significantly less frequent and less vivid instances of spontaneous imagery such as night dreams. These data suggest that any cognitive function (voluntary or involuntary 28 ) involving a sensory visual component is likely to be reduced in aphantasic individuals, and it is this generalised reduction in the sensory simulation of complex events and scenes that is most striking in aphantasia.

This work used a large-sample design to investigate reports of altered cognitive processes as a function of visual imagery absence. However, due to the self-described nature of the phenomenon in our online sample, it is prudent to rule out alternative explanations for the between-group differences seen here. Some authors have appropriately highlighted that visual imagery absence does not always present congenitally, but may be acquired as an associated symptom of neurological damage or psychopathology 29 . As a result, it is arguable that some aspects of our results may be more parsimoniously attributed to underlying psychogenic factors. Whilst plausible, we do not believe the reports of our sample here are best explained by this account. Only 9 out of 267 (3%) participants in our aphantasic sample reported acquired imagery loss, with the majority of participants reporting having lacked visual imagery capacity since birth. Additionally, there were no significant differences between our aphantasic sample and our main control group in the number of participants reporting a history of mental illness, neurological condition, or head injury/trauma – in fact, significantly fewer aphantasic participants reported a history of stroke or history of epilepsy/seizures compared to participants in control group 1 (see Sample Characteristics in Method, and Table  S1 in Supplementary Information).

Importantly, a supplementary within-group analysis also showed that there were no significant differences between aphantasic participants with or without a reported history of mental illness/psychopathology on any of our primary imagery, memory, dreaming, or spatial ability outcome variables, after controlling for multiple comparisons (see Table  S8 in Supplementary Information). Furthermore, the only significant within-group differences that were revealed by this supplementary analysis (such as significantly higher scores on some PCL-5 components in aphantasic individuals with a mental illness history compared to those without; see Table  S8 ) are differences we might expect to find as a function of psychopathology status in any sample population, given the target variables of interest and clinical scope of the scale. Considering these factors together, it is unlikely that our main results are best explained by acquired or associated symptoms of psychogenic causes such as mental illness or psychopathology.

Aphantasic participants in our study were compared with two independent control groups of participants with visual imagery on a range of self-reported cognitive outcomes. It is important to note that that neither of these control groups were perfectly matched on demographic characteristics with our aphantasic sample. In our main group comparison, the ratio of females to males was significantly higher in the aphantasic group (48%) than in control group 1 (35%), despite these groups being matched on mean age. In order to account for the potential influence of sample characteristics (including gender) in our main control group of MTurk participants, we conducted a replication analysis with a second control group of undergraduate students featuring a higher ratio of females to males (73%). This second control group, however, was significantly younger in mean age (19 years) compared to the aphantasic sample (34 years; see Table  S1 in Supplementary Information).

Despite these demographic discrepancies, the results of our replication analysis with control group 2 revealed a remarkably similar pattern of between-group effects to our main analysis (see Tables  S2 – 6 in Supplementary Information). Additionally, a majority of the significant changes to our results that did occur are congruent with established effects of age and gender on cognitive outcomes. For example, our finding that undergraduate participants reported significantly more frequent memory intrusions and avoidance behaviours than aphantasic participants in response to stressful life events may be explained by the typically higher prevalence of PTSD diagnosis and symptomatology amongst females 30 (and younger females in particular 31 ). Similarly, our replication analysis results suggested that aphantasic participants reported significantly fewer mind-wandering episodes and qualitatively impoverished dream phenomenology in additional SERS domains, but only in comparison to the comparatively younger undergraduate control group 2 and not when compared to the age-matched control group 1 (see Fig. S4 and Table  S5 in Supplementary Information). This is a pattern of results which accords well with findings of age-related decline in spontaneous mind-wandering 32 and subjective dream phenomenology 24 , respectively.

The few divergences in results between our main analysis (with control group 1) and replication analysis (with control group 2) are therefore largely consistent with previous research on the roles of age and gender in cognition. The overall equivalence of our results across these independent control group comparisons (despite demographic discrepancies between groups) suggests that our major findings are unlikely to be artifacts of sampling bias. Nevertheless, the interaction between demographic characteristics, imagery and cognition is potentially complex, and future research should overcome this limitation of our study design by implementing more precise selection criteria for matched control samples.

It is also important to highlight that our study assessed intergroup differences in cognition by using self-report outcomes which might be influenced by response biases. If aphantasic participants were motivated to respond in line with a self-identified lack of imagery (or even with perceived generalised cognitive deficits), for example, we would expect them to indiscriminately report at floor on all self-report measures of cognition, or at least on all scales measuring cognitive abilities typically thought to be reliant on visual imagery use. Their pattern of responses on some scales (particularly those measuring reported spatial abilities) suggests otherwise. On the SAM, aphantasic individuals reported no consistent reduction in spatial memory (or navigation) ability compared to controls, despite reporting memory deficits on all other components of this scale (see Fig.  1 blue and purple sections). More convincingly, aphantasic participants selectively reported deficits in object imagery but not spatial imagery on the OSIQ in our study, despite items corresponding to these two components being presented in randomised order within the same scale (see Fig.  1 blue and red sections). Lastly, previous research has shown that participants with self-described aphantasia do not just score at floor on self-report imagery questionnaires, but also exhibit lower scores than control group participants on a behavioural measure of sensory imagery strength which bypasses the need for self reports 10 , suggesting that response bias is not a most parsimonious explanation for presentations of self-described aphantasia. Demand characteristics cannot be unequivocally ruled out in the current study (as with any study of self-reports), and our findings should be validated with objective measures in future experiments. However, this study provides useful population-level data in order to highlight the veridical subjective differences that exist in a range of cognitive domains as a function of visual imagery absence.

There is strong theoretical impetus for future assessments of aphantasia, and our work highlights several areas of relevance that should be prioritised by future studies. For example, it is noteworthy that whilst the PCL-5 assesses one’s general response to stressful life events, it does not assess responses to recalling specific traumatic events 18 , nor does it have good measurement sensitivity for the imagery-based re-experiencing of such events. Whilst the overall pattern of our results suggests that aphantasic individuals do not appear to be markedly protected against all forms of trauma symptomatology, it may remain the case that they discernibly benefit from a reduced susceptibility to re-living these events in vivid sensory detail. Similarly, the self-report nature of our study does not allow for an objective, content-driven account of episodic memory function and phenomenology in aphantasia. Whilst some of the questions presented to participants on the EMIQ and on the SAM do ask them to report upon the visual experience of their memories, the distinction between remembering past life events and visually representing them is one which is not well delineated. There is therefore considerable scope for future experimental research to tease apart these separable component processes of episodic memory, and their relation to visual imagery absence in aphantasia.

Many other questions about aphantasia remain unanswered, including its longitudinal stability, the relative contribution of genetic and developmental factors to its aetiology, and its exact contribution to individual cognitive profiles. Our research presents an extended cognitive fingerprint of aphantasia and helps to clarify the role that visual imagery plays in wider consciousness and cognition. Visual imagery is a cognitive tool often taken for granted – an assumed precursor to our ability to think, learn, and simulate the world around us. This work demonstrates that such tools are not shared by everyone, and shines light on the rich but often invisible variations that exist in the internal world of the mind.

Greenberg, D. L. & Knowlton, B. J. The role of visual imagery in autobiographical memory. Mem. Cognit. 42 , 922–934 (2014).

Article   Google Scholar  

D’Argembeau, A. & Van der Linden, M. Individual differences in the phenomenology of mental time travel: The effect of vivid visual imagery and emotion regulation strategies. Conscious. Cogn. 15 , 342–50 (2006).

Pearson, J. & Keogh, R. Redefining visual working memory: A cognitive-strategy, brain-region approach. Curr. Dir. Psychol. Sci . 28, 266–273 (2019).

Horikawa, T. et al . Neural decoding of visual imagery during sleep. Science 340 , 639–42 (2013).

Article   ADS   CAS   Google Scholar  

Moulton, S. T. & Kosslyn, S. M. Imagining predictions: Mental imagery as mental emulation. Philos. Trans. R. Soc. B Biol. Sci. 364 , 1273–1280 (2009).

Pearson, J., Naselaris, T., Holmes, E. A. & Kosslyn, S. M. Mental imagery: Functional mechanisms and clinical applications. Trends Cogn. Sci. 19 , 590–602 (2015).

Shine, J. M. et al . Imagine that: Elevated sensory strength of mental imagery in individuals with Parkinson’s disease and visual hallucinations. Proc. R. Soc. B Biol. Sci. 282 , 20142047–20142047 (2014).

Benson, T. L. & Park, S. Exceptional visuospatial imagery in schizophrenia; implications for madness and creativity. Front. Hum. Neurosci. 7 , 756 (2013).

Zeman, A., Dewar, M. & Della Sala, S. Lives without imagery - Congenital aphantasia. Cortex. 73 , 378–80 (2015).

Keogh, R. & Pearson, J. The blind mind: No sensory visual imagery in aphantasia. Cortex 105 , 53–60 (2018).

Marks, D. F. Visual imagery differences in the recall of picture. Br. J. Psychol. 64 , 17–24 (1973).

Sheehan, P. W. A shortened form of Betts’ questionnaire upon mental imagery. J. Clin. Psychol. 23 , 386–389 (1967).

Article   CAS   Google Scholar  

Blajenkova, O., Kozhevnikov, M. & Motes, M. A. Object-spatial imagery: A new self-report imagery questionnaire. Appl. Cogn. Psychol. 20 , 239–263 (2006).

Palombo, D. J., Williams, L. J., Abdi, H. & Levine, B. The survey of autobiographical memory (SAM): A novel measure of trait mnemonics in everyday life. Cortex 49 , 1526–1540 (2013).

Giambra, L. M. A factor analysis of the items of the imaginal processes inventory. J. Clin. Psychol. 36 , 383–409 (1980).

Singer, J. L. & Antrobus, J. S. A factor-analytic study of daydreaming and conceptually-related cognitive and personality variables. Percept. Mot. Skills 17 , 187–209 (1963).

Kahan, T. L. & Claudatos, S. Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS). Conscious. Cogn. 41 , 159–176 (2016).

Weathers, F. W. et al . The PTSD Checklist for DSM-5 (PCL-5). Scale available from Natl. Cent. PTSD www.ptsd.va.gov (2013).

Rosenthal, R. Parametric measures of effect size. (1994).

Jeffreys, H. Theory of Probability. (Clarendon Press, 1961).

Schacter, D. L., Addis, D. R. & Buckner, R. L. Remembering the past to imagine the future: The prospective brain. Nat. Rev. Neurosci. 8 , 657–661 (2007).

Zeman, A. Z. J. et al . Loss of imagery phenomenology with intact visuo-spatial task performance: A case of ‘blind imagination’. Neuropsychologia 48 , 145–155 (2010).

Irish, M., Addis, D. R., Hodges, J. R. & Piguet, O. Considering the role of semantic memory in episodic future thinking: Evidence from semantic dementia. Brain 135 , 2178–2191 (2012).

Mangiaruga, A., Scarpelli, S., Bartolacci, C. & De Gennaro, L. Spotlight on dream recall: The ages of dreams. Nat. Sci. Sleep 10 , 1–12 (2018).

Schacter, D. L. et al . The future of memory: Remembering, imagining, and the brain. Neuron 76 , 677–694 (2012).

Sheldon, S., Amaral, R. & Levine, S. Individual differences in visual imagery determine how event information is remembered. Memory 25 , 360–369 (2017).

Goodale, M. A. & Milner, A. D. Separate visual pathways for perception and action. Trends Neurosci. 15 , 20–25 (1992).

Pearson, J. & Westbrook, F. Phantom perception: Voluntary and involuntary nonretinal vision. Trends in Cognitive Sciences 19 , 278–284 (2015).

de Vito, S. & Bartolomeo, P. Refusing to imagine? On the possibility of psychogenic aphantasia. A commentary on Zeman et al. (2015). Cortex 74 , 334–335 (2016).

Tolin, D. F. & Foa, E. B. Sex differences in trauma and posttraumatic stress disorder: A quantitative review of 25 years of research. Psychol. Bull. 132 , 959–992 (2006).

Ditlevsen, D. N. & Elklit, A. The combined effect of gender and age on post traumatic stress disorder: Do men and women show differences in the lifespan distribution of the disorder? Ann. Gen. Psychiatry 9 , 32 (2010).

Maillet, D. & Schacter, D. L. From mind wandering to involuntary retrieval: Age-related differences in spontaneous cognitive processes. Neuropsychologia 8 , 142–156 (2016).

Download references

Acknowledgements

We thank Marcus Wicken for his helpful insight on this project and ongoing collaboration. We also thank the aphantasic participants who gave their time to participate in this study and contribute feedback on our research. This work was supported by Australian NHMRC grants APP1046198 and APP1085404; J. Pearson’s Career Development Fellowship APP1049596; and an ARC discovery project DP140101560. T. Andrillon is supported by the International Brain Research Organization and the Human Frontiers Science Program (LT000362/2018-L). A. Dawes is supported by an Australian Government Research Training Program (RTP) Scholarship.

Author information

Authors and affiliations.

School of Psychology, The University of New South Wales, Sydney, New South Wales, Australia

Alexei J. Dawes, Rebecca Keogh, Thomas Andrillon & Joel Pearson

School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia

Thomas Andrillon

You can also search for this author in PubMed   Google Scholar

Contributions

All authors developed the study concept. A. Dawes built the study design and collected the data. A. Dawes, R. Keogh and T. Andrillon performed data analysis. A. Dawes drafted the first version of the manuscript, and R. Keogh, T. Andrillon and J. Pearson provided critical revisions. All the authors approved the final manuscript for submission.

Corresponding author

Correspondence to Alexei J. Dawes .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Dawes, A.J., Keogh, R., Andrillon, T. et al. A cognitive profile of multi-sensory imagery, memory and dreaming in aphantasia. Sci Rep 10 , 10022 (2020). https://doi.org/10.1038/s41598-020-65705-7

Download citation

Received : 28 December 2019

Accepted : 06 May 2020

Published : 22 June 2020

DOI : https://doi.org/10.1038/s41598-020-65705-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Advertisement

Mental Imagery and its Relevance for Psychopathology and Psychological Treatment in Children and Adolescents: a Systematic Review

• Open access
• Published: 16 October 2020
• Volume 13 , pages 303–327, ( 2020 )

Cite this article

You have full access to this open access article

• Susan Schwarz 1 ,
• Dörte Grasmann 1 ,
• Franziska Schreiber 1 &
• Ulrich Stangier 1  

7645 Accesses

17 Citations

Explore all metrics

This review provides an overview of the current state of research concerning the role of mental imagery (MI) in mental disorders and evaluates treatment methods for changing MI in childhood. A systematic literature search using PubMed/Medline, Web of Science, and PsycINFO from 1872 to September 2020 was conducted. Fourteen studies were identified investigating MI, and fourteen studies were included referring to interventions for changing MI. Data from the included studies was entered into a data extraction sheet. The methodological quality was then evaluated. MI in childhood is vivid, frequent, and has a significant influence on cognitions and behavior in posttraumatic stress disorder (PTSD), social anxiety disorder (SAD), and depression. The imagery’s perspective might mediate the effect of MI on the intensity of anxiety. Imagery rescripting, emotive imagery, imagery rehearsal therapy, and rational-emotive therapy with imagery were found to have significant effects on symptoms of anxiety disorders and nightmares. In childhood, MI seems to contribute to the maintenance of SAD, PTSD, and depression. If adapted to the developmental stages of children, interventions targeting MI are effective in the treatment of mental disorders.

Similar content being viewed by others

Mental imagery in adolescent PTSD patients after child abuse: a comparison with matched healthy controls

Contents and characteristics of mental imagery and their association with emotional intensity in adolescents: a pilot study.

Harnessing Mental Imagery and Enhancing Memory Specificity: Developing a Brief Early Intervention for Depressive Symptoms in Adolescence

Avoid common mistakes on your manuscript.

Introduction

Mental imagery refers to the representation of sensory information without external stimulation. Mental images can occur in any sensory form, including visual, auditory, gustatory, and olfactory properties, physical sensations, and tactile impressions (Andrade et al. 2014 ). Besides verbal-based thoughts, mental images create a cognitive subsystem that is strongly linked to emotions (Paivio 1986 ) and induce physiological responses that are often beyond conscious control (Papadelis et al. 2007 ). In adults, it was found that mental images may trigger emotions like joy, sadness, anger, disgust, and fear (Blackwell 2019 ; Holmes and Mathews 2010 ). Thus, imagining a walk on the beach with all the associated sensory qualities can lead to emotional processing that is similar to an actual walk. Brain imaging studies indicate that neural representations of MI and perceptions of actual pictures are similar (Pearson et al. 2015 ). This can be helpful and relaxing, but MI is associated with extensive distress and strain if people think about unpleasant events. Mental images are based partially on memories, or contain parts of them, but MI may also refer to a future event. These “flash-forwards” could have various different themes and therefore evoke different emotions (Hales et al. 2011 ). For example, thinking about a future moment of glory is associated with positive emotions. In patients with depression, however, imagining one’s own suicide is associated with both positive and negative emotions, such as comfort and distress (Holmes et al. 2007b ; Moritz et al. 2014 ; Weßlau et al. 2015 ).

The potentially important role of negative MI as a maintenance factor of psychological disorders has been supported in studies with adults suffering from PTSD (Clark and Mackay 2015 ; Cockerham et al. 2016 ; Morina et al. 2013 ), obsessive-compulsive disorder (OCD; Da Silva 1986 ; Rachman 2007 ; Speckens et al. 2007 ), social anxiety disorder (SAD, e.g., Hackmann et al. 2000 ), agoraphobia (Day et al. 2004 ), body dysmorphic disorders (Osman et al. 2004 ), and depression (Holmes et al. 2008 ; Morina et al. 2011 ). The negative mental images may influence both behavior and cognitions. In SAD, the mental image of oneself blushing and embarrassing oneself in front of others leads to feelings of anxiety and increases self-focused attention in individuals. This in turn makes it difficult to correctly interpret environmental stimuli (Clark and Wells 1995 ) and could lead to severely distorted interpretations of ones’ appearance to others, resulting in elevated social anxiety. In PTSD, intrusive imagery seems to depend on characteristics of the traumata (Hackmann and Holmes 2004 ). These intrusive images can be triggered by various stimuli in the environment and cause emotional strain as the person relives parts of their trauma. To prevent the experience of these images, patients with PTSD avoid such triggers and therefore contribute to the maintenance of the disorder (Hackmann et al. 2012 ). Furthermore, a lack of positive MI and a greater amount of negative mental images are typical for depression. The repeated negative images are linked to cognitions about oneself, the world, and the future (Beck 1976 ; Morina et al. 2011 ; Holmes et al. 2016 ; Weßlau and Steil 2014 ).

In adults, it is evident that the vividness of the images (whether a mental image is perceived clearly and “real”) as well as the perspective (field- or observer perspective), in which the image is perceived, has an effect on the strength of triggered emotions (e.g., Kuyken and Moulds 2009 ). Furthermore, there is evidence that higher levels of vividness in negative MI are associated with greater distress (Holmes et al. 2016 ; Mathews et al. 2013 ; Moritz et al. 2014 ; Oertel et al. 2009 ). Regarding the perspective, research indicates that there are differences in the use and effect on emotions of imagery perspective among mental disorders. For example, an observer perspective is more often used in SAD (Hackmann et al. 1998 ). In depression, there is also a more frequent use of observer perspective, and it is associated with lower levels of affective content (e.g., Kuyken and Moulds 2009 ). In patients with PTSD, images are also more often experienced in an observer perspective and might be less affect-provoking than images perceived from a field perspective (McIsaac and Eich 2004 ). An observer-perspective might be part of cognitive avoidance, aiming at the reduction of aversive emotions (Kenny and Bryant 2007 ). By contrast, patients with OCD perceive the images more often from a field perspective (Lipton et al. 2010 ). Treatment methods such as imagery rescripting attempt to overwrite negative mental images and to implement a rescript of the aversive image, which does not provoke negative emotions (Hackmann et al. 2012 ). In adults, IR is most frequently used in the treatment of PTSD and SAD, but has also been applied in the treatment of body dysmorphic disorder, bulimia nervosa, OCD, and depression. A meta-analysis (Morina et al. 2017 ) yielded large pre- vs. posttreatment effect sizes for PTSD (Hedges- g  = 1.48) and SAD (Hedges- g  = 1.25 for SAD) and medium to large effect size for depression (Hedges- g  = 0.61).

These findings raise questions about the applicability in children and adolescents, who have less control over their conceptual images due to their cognitive development (Burnett-Heyes et al. 2013 ). Furthermore, there is research indicating that imagery vividness depends on both long-term memory and working memory (Baddeley and Andrade 2000 ). These abilities are not completely developed before the age of 14 (Kosslyn and Barrett 1990 ), which may explain the fact that unpleasant mental images occur more frequently at that age and manifest themselves as a result of emotional and cognitive defenselessness, because children and adolescents are unable to change and control their mental images (Burnett-Heyes et al. 2013 ). Mental disorders in adults can often be traced back to situations and certain factors in childhood (Caspi et al. 1996 ; Gregory and Eley 2007 ; Rutter 1984 ). Mental imagery could therefore be just such an important factor leading to a vulnerability to mental illness.

The aim of this systematic review is to provide an overview of the current state of research concerning the role of MI in mental disorders in children and adolescents, as well as to describe and evaluate treatment methods for changing MI. A wide range of mental disorders are included, so as to identify similarities and differences in the role of MI between the disorders. Furthermore, the review combines knowledge on mental imagery, developmental psychology, psychopathology, and treatment methods, and in addition, guidelines for future research are provided, as well as recommendations for therapists concerning the value of MI in psychotherapy with children and adolescents.

Literature Search

This review was prepared according to recommendations from Petticrew and Roberts ( 2006 ). A systematic literature search for papers in English and German, from the earliest indexed studies (year 1872) to September 2020, was conducted using the electronic databases PubMed/Medline, Web of Science, and PsycINFO. The following search terms were used for titles and abstracts: (“imagery” OR “mental imagery” OR “intrusive memories” OR “mental images”) AND (“mental disorders” OR “psychological disorders” OR “depression” OR “social anxiety” OR “anxiety disorder” OR “phobia” OR “PTSD” OR “bipolar disorder” OR “psychotic disorder” OR “borderline personality disorder” OR “eating disorder” OR “obsessive compulsive disorder” OR “agoraphobia” OR “body dysmorphic disorder” OR “ADHS” OR “autism” OR “imagery rescripting” OR “guided imagery” OR “cognitive bias modification” OR “MI training” OR “emotive therapy” OR “vividness” OR “assessment” OR “emotions”) AND (“children” OR “adolescents” OR “child” OR “youths” OR “development” OR “adults”). Furthermore, lists of references from review papers and other important articles were checked, and authors were asked for full-text versions, so as to obtain more useful information.

Inclusion Criteria

In order to be included in the present systematic review, studies had to meet the following criteria: publication in a peer reviewed journal or in a book; deal with the measurement of triggered emotions and/or MI vividness and/or other clinical aspects (such as symptom-strength) and/or developmental changes in MI; and include participants not older than 20 years.

In addition, intervention studies were included when imagery and symptom assessments include at least pre- and post-measurements and an imagery component was described and mentioned explicitly.

No restrictions were made concerning diagnoses or symptom stage. Healthy and clinical samples were included. Meta-analysis and randomized controlled trials were preferred, but quasi-experimental and cohort studies, as well as case studies, were also included, due to the general lack of rigorous studies.

Exclusion Criteria

Articles were excluded if they were not written in either English or German, or if they referred to non-psychological areas such as sport or language training, or if they were outside the timeframe.

After removing duplicates, the titles and abstracts of eligible studies were screened. Papers that did not meet the inclusion criteria were excluded. The full text of potentially relevant papers was examined, and 28 relevant papers were finally identified.

Data Extraction

Data was entered into a data extraction sheet. Variables extracted included: the author(s) of the study, study title and publication year, study design, inclusion/exclusion criteria, number and characteristics of participants (gender, age, ethnicity), characteristics of outcomes (e.g., effects on mood), characteristics of the intervention and of the comparison groups, and number of sessions and the main results. Methodological rigor (such as sample size, allocation to the groups, reliability of instruments used, and effect sizes) was assessed by the first author.

Data Synthesis

The included studies were highly diverse in terms of study design, characteristics, and outcome measures. Similarities and differences between study findings were analyzed with regard to characteristics of the studies themselves, characteristics of participants, characteristics of the intervention, outcome measures, and methodological rigor. Studies were grouped according to whether they investigated the characteristics of mental images across mental disorders, or whether they tested the effect of a treatment.

In total, 13,253 titles were identified using electronic databases. In the next step, 2553 were excluded as duplicates, and 10,700 were screened for inclusion in the review. Of these studies, 10,608 publications were excluded, as they did not meet the inclusion criteria, for example, covered a non-clinical topic or did not measure MI. Eight additional publications were identified through screening the reference lists of relevant papers and reviews or books. In total, 95 publications were screened full-text. Sixty-seven articles were excluded subsequently (because, for example, their sample group was not relevant; see Fig. 1 for details). Twenty-eight were included in the qualitative synthesis. Figure 1 depicts the literature search process.

Flowchart of selection of studies

The results of the literature search were assigned to two different topics, imagery in mental disorders of children and adolescents and mental imagery and psychotherapy. First, fourteen studies are described which investigate the characteristics of MI in children and adolescents with psychological disorders. Second, a summary of fourteen studies is provided which evaluate treatment methods targeting MI.

Imagery in Mental Disorders in Children and Adolescents

Fourteen studies investigating mental images and their effects on children’s and adolescents’ clinical symptoms were found. Six of the fourteen studies investigated a clinical sample, four of which had a healthy control group. The other eight studies examined clinical symptoms in analogue samples. Social anxiety symptoms and SAD were investigated in seven studies, depressive symptoms in three studies, autism spectrum disorder (ASD) and PTSD-symptoms (two studies each). PTSD and generalized anxiety symptoms (GAS) were both explored in one study. The age range across the studies is between 7 and 20 years. These results are summarized in Table 1 .

Posttraumatic Stress Disorder

Studies investigating MI and PTSD have focused on peri-traumatic aspects which are linked to occurrence and the sensoric qualities of MI in PTSD. Holmes et al. ( 2007a ) examined posttraumatic stress symptoms in 76 healthy schoolchildren (age range 10–11) who were exposed to the 9/11 attacks in 2001 on television. Mental imagery was related to posttraumatic stress symptoms, if combined with a high peri-traumatical anxiety level. Therefore, the authors recommend focusing on the improvement of imagery treatment techniques for children, given that the images may be important in the maintenance of PTSD-symptoms. Eksi et al. ( 2008 ) examined the sensoric qualities of MI in five children with PTSD after an earthquake (and after 20 months) who met the criteria of PTSD at the beginning of this observational study. Children with PTSD reported experiencing intrusive mental images and ones with various sensoric qualities, such as visual or auditory. However, the impact on emotions and long-term effects remained unclear. McKinnon et al. ( 2008 ) examined 75 children and adolescents in an observational study, who had experienced an injury which led to hospital treatment. It was shown that peri-traumatic thoughts were significantly associated with the encoding of the traumatic memories and linked to unpleasant intrusions. Furthermore, the level of anxiety due to intrusions was linked to how clearly the intrusive images appeared (McKinnon et al. 2008 ). In summary, MI might play a role in the development of PTSD, since some mental images relate to aspects of the trauma (Ehlers and Clark 2000 ).

Social Anxiety Disorder

All studies investigating MI in the context of social anxiety symptoms or SAD examined various aspects and effects of self-imagery. Schreiber and Steil ( 2013 ) investigated 31 adolescents with a diagnosis of SAD, and a control group without a mental disorder (also N  = 31), in order to explore the role of negative self-imagery in SAD. The participants were asked to imagine a previous anxiety-provoking or embarrassing situation and recall the negative self-image they had experienced. Negative self-imagery was linked to greater emotional distress, was more vivid, and more often perceived through an observer-perspective in patients with SAD, compared with the control group. In another study with a large adolescent sample, participants completed a questionnaire on negative self-images. Negative self-imagery was connected to social anxiety-level and therefore might be an important cognitive feature of social anxiety (Schreiber et al. 2012 ). These results supported the assumption of MI being a maintaining factor of SAD, according to Clark and Wells ( 1995 ). Alfano et al. ( 2008 ) examined self-imagery in 21 adolescents with SAD and compared it with a healthy control group during two social tasks (both entailing peer interaction in the classroom). The results indicated that having self-imagery at all is a substantial factor in SAD during adolescence, and that the valence or content of self-image plays only a minor role. This finding is in contrast to research results from the adult area, indicating that valence is important and that negative self-imagery plays a causal role in SAD (Hirsch et al. 2006 ).

In Vassilopoulos et al. ( 2012 ), participants (age 10–12) were asked to generate both positive and negative self-images and afterwards interpret social scenes. Negative self-imagery seems to be associated with more negative interpretations of ambiguous social scenes, and therefore seems to play a significant role in the maintenance of social anxiety (Vassilopoulos et al. 2012 ). Ranta et al. ( 2014 ) compared the results of an imagery interview of four groups (SAD, non-clinical SAD, high-anxiety levels, low-anxiety levels), and found that adolescents with high-anxiety-levels had more negative thoughts and more observer perspective images than adolescents with low-anxiety levels. These differences were even larger in both SAD-groups. Hignett and Cartwright-Hatton ( 2008 ) examined youths by means of a questionnaire about their self-images, including questions as to the perspective with which they generate a self-image. The results showed that high-anxiety-levels correlate with an increased use of observer-perspective. If one considers the studies in summary, it is evident that in SAD, the maintenance of the disorder seems to be influenced by MI, and that adolescents with SAD perceived the mental images more often through an observer perspective (Hignett and Cartwright-Hatton 2008 ; Ranta et al. 2014 ; Schreiber and Steil 2013 , as was also shown for adults: Hackmann et al. 1998 ). This is in accordance with results from a review by Chapman et al. ( 2020 ) who found studies including young people older than 20, which indicated that a higher social anxiety score is associated with more negative mental images, observer perspective images, and provided some evidence to support the cognitive models of SAD. It can therefore be assumed that these characteristics of mental images in SAD might remain constant over the life span.

Generalized Anxiety Disorder

Pile and Lau ( 2020 ) investigated intrusive prospective negative imagery (flash-forwards) and symptoms of GAS, depression, and social anxiety in 352 participants (age ranging from 11 to 16). A higher frequency and hyperarousal due to these flash-forwards was linked to GAS and depressive symptoms, but not to those of social anxiety. Furthermore, suppressing emotions seems to increase the relationship between generalized anxiety and the impact of intrusive prospective imagery.

Studies investigating MI in adolescent depression have focused on characteristics of MI and their links to depressive symptoms. Kuyken and Howell ( 2006 ) compared 31 adolescents without depression and 34 with depression, aged 12–18, with regard to autobiographical images. In this sample, they found that depressed adolescents are more likely to perceive memories as more vivid, more often from an observer perspective and experience more vivid negative memories than adolescents who have never suffered from depression.

Meiser-Stedman et al. ( 2012 ) investigated 231 high-school students, who had experienced a non-traumatic but unpleasant life event. It was found that the sensoric quality, as well as the frequency of intrusive images, seems to play an important role in the maintenance of depressive symptoms, as more intrusive images correlate with higher depression scores. Intrusive images seem to be linked to distress, and as adolescents with depressive symptoms have more intrusive images, they also might experience an elevated level of stress. In a study from Pile and Lau ( 2018 ), 375 adolescents (aged 11–16) imagined future events and described a past negative event. They also completed questionnaires about symptoms of anxiety and depression. Less vivid positive imagery, as well as more vivid negative imagery (future and past), was associated with symptoms of depression. In comparison, anxiety symptoms were only linked with increased vividness for past negative events. Additionally, adolescents who described future positive events that were less vivid also had higher depression scores and experienced the past negative event as more unpleasant (Pile and Lau 2018 ). With regard to depression, the results for children and adolescents indicate that maintenance seems to be influenced by MI.

Autism Spectrum Disorder

Ozsivadjian et al. ( 2017 ) investigated the effects of three images; a relaxed self-generated situation, a proposed anxious situation (either presenting to the class, or to the entire school), and a spontaneously generated anxious image experienced by children with ASD and non-ASD. Autistic children tend to have more images than healthy participants, and are more likely to have images which are based on their own imagination, rather than upon real events (Ozsivadjian et al. 2017 ). Difficulties in imagining among children with ASD seem to be specific to social stimuli (such as finding it difficult to imagine a funny-looking person) and not to imagination problems in general (Ozsivadjian et al. 2017 ; Ten Eycke and Müller 2015 ). Accordingly, it is evident that MI seems to have a different character in patients with ASD in contrast to children without ASD.

Mental Imagery and Psychotherapy

With respect to treatments of psychological disorders in children and adolescents, imagery can be used as an explicit part of the intervention (e.g., imagery rescripting, IR) or implicitly involved as an objective (e.g., video-feedback). Video-feedback aims at modifying of the distorted view of the social self in SAD by providing a realistic image (Warnock-Parkes et al. 2017 ). However, this review will focus only on interventions explicitly using imagery to tackle distressing images.

The use of MI in the treatment of children and adolescents has also been evaluated in 14 studies (age range 4 to 18). In these studies, the treatment methods differed as to how MI was used. Imagery rescripting is a method for modifying negative mental images. The patient is instructed to recall an aversive experience of the past and to implement a rescript of the aversive image, which does not provoke negative emotions (Hackmann et al. 2012 ). In emotive imagery (EI; Lazarus and Abramovitz 1962 ), the therapist helps the patient to develop a story, which deals with a feared object and makes it ridiculous (e.g., as in the Riddikulus-spell in Harry Potter). By contrast, with imagery rehearsal therapy (IRT), participants select a nightmare that they want to change, write down the nightmare, and are taught to convert it into a pleasant “new dream.”

Imagery Rescripting in Children and Adolescents

Three case studies with children and adolescents, (age ranging from 4 to 16) explored the effectiveness of IR. The treatments comprised between one and four sessions. In two studies, IR was incorporated into a cognitive behavioral treatment of sleeping disorders (Davis et al. 2003 ; Fernandez et al. 2013 ). Fernandez et al. ( 2013 ) treated two cases (8 and 11 years.) involving nightmares after experiencing a trauma, using IR, exposure (by talking or writing about, or drawing pictures of the nightmare) and instructions for progressive muscle relaxation. Both cases met the criteria for PTSD. After four sessions, the frequency of nightmares, sleep quality, and behavioral problems was found to be significantly improved.

Davis et al. ( 2003 ) demonstrated a reduction in nightmare frequency and intensity for a 16-year-old girl. The effects of imagery rescripting and reprocessing therapy (IRRT) were tested in a sample with mixed disorders (Nelius and Ahrens-Eipper 2017 ). The authors found a reduction and remission of mental disorders after IRRT embedded in a CBT treatment in 13 cases aged 4 to 14. Table 2 provides an overview of the studies investigating IR in children and adolescents.

Emotive Imagery

Emotive imagery (EI) is a technique which uses imagery for the treatment of childhood anxiety disorders and extends back to Lazarus and Abramovitz ( 1962 ). Accordingly, the therapist helps the patient to develop a story which deals with a feared object. In that story, the favorite superhero of the child helps to deal with the feared situation successfully, so that positive feelings are generated. One more possibility is that the child imagines becoming the superhero (with all his/her brave characteristics and superpowers). Table 3 gives an overview of these studies using EI.

In all studies, EI led to a reduction of anxiety and nightmares in children and adolescents. Muris et al. ( 2011 ) compared, in their experimental study, verbal and imaginative methods for reducing anxiety in 72 children, after inducing anxiety with respect to a fictional animal. After inducing this fear, the participants were randomly assigned to three conditions: receiving positive information about the fictional animal to reduce anxiety, to an imagery condition and a control group (which had to tell what they had heard about the animal). Both experimental conditions led to a higher reduction of anxiety than the control condition. However, the verbal technique was superior to the positive imagery. These results contrast to those from the adult area, where imaginative methods have a greater effect on emotions than verbal instructions (Holmes et al. 2006 , 2009 ; Holmes and Mathews 2005 ). A possible explanation is that verbally induced fears (as in Muris et al. 2011 ) could be reduced more effectively by methods at the verbal level as well (Muris et al. 2011 ).

Cornwall et al. ( 1996 ) treated children (aged 7 to 10) with fear of the dark, and compared the effects of EI with a waiting list control condition ( N  = 24). They showed a reduction of darkness fears and anxiety after six sessions of treatment and also in a follow-up after 3 months. Case studies investigating the effect of emotive imagery in phobia of dogs (cynophobia), nighttime fears, and school phobia (aka. school avoidance) (Chudy et al. 1983 ; Jackson and King 1981 ; King et al. 1989 ; Lazarus and Abramovitz 1962 ; Shepherd and Kuczynski 2009 ) also indicated that EI was effective.

Imagery Rehearsal Therapy and Rational-Emotive Therapy with Imagery

St-Onge et al. ( 2009 ), Simard and Nielsen ( 2009 ), and Krakow et al. ( 2001 ) investigated the effect of imagery rehearsal therapy (IRT) in children and adolescents suffering from nightmares (age ranging from 6 to 18). Sample sizes were 10 (Simard & Nielsen), 19 (Krakow et al.), and 20 participants (St-Onge et al.). All studies compared the results with a waiting list control group and had a clinical sample suffering from nightmares. Warren et al. ( 1984 ) explored the effects of rational-emotive therapy with imagery (REI) in a sample aged 12 to 16 with anxiety symptoms. This therapy method is based on the rational-emotive theory that negative emotions are mediated by irrational beliefs and self-verbalization. Warren et al. ( 1984 ) compared rational-emotive therapy (RET), rational-emotive imagery (REI), and relationship-oriented counseling in groups to a waiting list control condition. The REI-condition received the same treatment as the RET-condition (Ellis 1962 ), but had to practice the specified social scenes in their imagination and not only try to focus on changing their cognitions. Table 4 summarizes the four studies.

The IRT led to significant decreases in the frequency of nightmares ( d  = 1.41 according Krakow et al.; η 2  = 0.22 according St-Onge et al.) and nightmare distress ( d =  1.13 according Krakow et al.; η 2  = 0.42 according Simard and Nielsen) with large within-group effect sizes. Therefore, all studies showed a reduced nightmare frequency, also in the follow-up.

RET and RET with imagery were both more effective than the other two conditions, but only the RET with imagery had an effect on the self-measurements of anxiety (Warren et al. 1984 ). Therefore, a combination of RET with imagery seems to be important, as the treatment might have a more profound effect.

Mental imagery–based interventions in psychotherapy for adults are widely used, and empirical evidence supports its effectiveness. This is the first comprehensive review to summarize the empirical evidence regarding mental imagery (MI) in children and adolescent psychopathology, and related interventions for the treatment of children and adolescents.

Mental Imagery in Psychopathology

In PTSD, mental images seem to play a potential role in the development of the disorder in children and adolescents, as found in adults as well (e.g., disturbed recording of information under the influence of trauma, separation of semantic information, and sensory perception). The influence of MI on maintenance in children and adolescents is still unclear, but can be assumed, as patients with PTSD prevent the experience of these images by avoiding triggers (as shown in adults: Hackmann et al. 2012 ). For SAD, results for children and adolescents show that recurrent self-imagery is more frequent and more vivid in SAD and might be a maintenance factor, according to Clark and Wells ( 1995 ). In contrast to findings from the adult area (see Hirsch et al. 2006 ), having a self-image is a substantial factor in SAD during adolescence, and valence or content of self-images seems to play only a secondary role (Alfano et al. 2008 ). In GAS and depression, the impact of prospective negative mental imagery seems to be associated with these symptoms. In depression, the previous results for children and adolescents show that maintenance seems to be influenced by MI, as vivid positive imagery, as well as more vivid negative imagery (about the future and the past), was associated with symptoms of depression. In ASD, the characteristics of MI are typical for the disorder, as difficulties in generating MI seem to be specific to social stimuli, but conclusions due to the development or maintenance cannot be drawn.

MI may be an important factor in some mental disorders and should thus be targeted in the treatment (e.g., Holmes and Mathews 2010 ). However, longitudinal studies are needed to examine causal relationships between clinical symptoms and mental images. Moreover, the clinical aspects of imagery, the characteristics of re-experiencing intrusions or flashbacks, as well as the prospective imagery, and imagery bias across different mental disorders, are all important aspects for clinical research, as the results might be helpful in the therapy of mental disorders. Only one of the studies (Pile and Lau 2020 ) investigates the occurrence and effects of positive prospective images and flash-forwards in children and adolescents, yet flash-forwards seem to play a significant role in adults with depression, as images may appear of their own suicide, thus increasing the risk of acting out the suicidal ideation in reality (Holmes et al. 2007b ). This might be an important research field for adolescents, as the number of attempted and successful suicides in this age group is high (World Health Organization 2016 ). In cases of depression, positive, prospective images seem to help adults (Blackwell and Holmes 2017 ; Ji et al. 2017 ) and could therefore also be a suitable method for younger patient-groups. Because this is a complex and wide-ranging aspect, it seems promising to investigate disorders for which there is already evidence from studies with adults, in order to generate evidence across the lifespan. Accordingly, MI in PTSD, SAD, and depression seems to be the most appropriate disorders with which to commence.

As depressive symptoms were associated with less vivid positive MI, and more vivid and a higher frequency of negative MI (Kuyken and Howell 2006 ; Pile and Lau 2018 ), and comorbid depressive symptoms might mediate the effect of MI on symptoms (as in Karatzias et al. 2009 ; McTeague et al. 2009 ), the depressive symptoms in individuals must be controlled whenever MI is investigated.

In future studies, the perspective of mental images should be controlled. There are differences in the use and effect of imagery perspective between mental disorders, given that an observer perspective is present in SAD (adults: Hackmann et al. 1998 ; adolescents: Hignett and Cartwright-Hatton 2008 ; Ranta et al. 2014 ; Schreiber and Steil 2013 ) and depression (adults: Holmes et al. 2016 ; adolescents: Kuyken and Howell 2006 ). In depression, an observer perspective was also associated with lower levels of affective content in adults (Kuyken and Moulds 2009 ), which might be in contrast to the effect of observer perspective in SAD, as this perspective is more frequent in SAD, and MI is linked to higher emotional distress in SAD (Schreiber and Steil 2013 ). Thereby, it can be assumed that in SAD, the observer perspective is linked to higher levels of affect. In PTSD, images are also more often experienced in an observer perspective, and might therefore be less affect-provoking (McIsaac and Eich 2004 ). The observer perspective might be part of a cognitive avoidance tendency of adult patients with PTSD, as the observer perspective is associated with decreased emotions and may be less anxiety provoking than a field perspective (Kenny and Bryant 2007 ).

When investigating the perspective, it is important to note that adolescents need explicit guidance on how to adopt different perspective-options, because cognitive abilities, such as working-memory (Luna et al. 2004 ) and metacognitive accuracy (Weil et al. 2013 ), which are important in this context, still develop during adolescence. In addition to pre-selected standardized images, it seems relevant to investigate spontaneous images (Vassilopoulos et al. 2012 ) and the emotions triggered by these images.

As imagery rescripting (IR) seems to be the imagery technique with the most reliable results in adults (Morina et al. 2017 ), this method could be especially promising for the treatment of children and adolescents. However, for IR, the study situation for children and adolescents is unsatisfactory and more evidence is needed, as there have only been three studies investigating the effect of IR in children and adolescents. The results show that IR led to a decrease in the intensity and frequency of nightmares, reported behavior problems (Davis et al. 2003 ; Fernandez et al. 2013 ) and to a decrease or remission of mental disorders like PTSD, GAD, and specific phobia (Nelius and Ahrens-Eipper 2017 ). All studies investigating IR in childhood are case studies with a very low number of participants. Therefore, the external validity and the generalizability of the results are low. As the most reliable findings in adults are for PTSD, SAD, and depression in adults, as shown in the meta-analysis of Morina et al. ( 2017 ), there should be a future focus on studies examining these mental disorders in children and adolescents. Especially PTSD needs to be investigated, as the combination of intrusive imagery and peri-traumatic life threat is associated with symptom persistence (Holmes et al. 2007c ).

Furthermore, emotive imagery (EI) seems to be a promising method for treating specific phobias in children and adolescents, as there are two useful studies comparing the effect of EI with a waiting list control group. Studies comparing EI with the effectiveness of other interventions remain to be conducted. There is some evidence that this technique has a significant effect on symptoms of anxiety. However, most of the studies investigating the effect of EI adopted a case approach, which limits the external validity. Nevertheless, the effectiveness of EI regarding the number of sessions is impressive (large effect sizes were found).

Studies exploring the effects of imagery rehearsal therapy (IRT) and rational-emotive therapy (REI) with imagery have shown that imagery seems to be a component which enhances the effectiveness of REI (Warren et al. 1984 ), since it led to a reduction in nightmare frequency and the associated distress . The effect sizes were very large, despite the limited number of sessions. The intensity or severity of nightmares was not assessed in these studies and should be therefore considered in further studies. As there are only a few studies with a randomized design and control groups, and mostly case studies with no control group, the effects cannot be generalized and need to be examined further. As there were contradictory findings concerning whether verbal (Muris et al. 2011 ) or imaginative instructions (Holmes et al. 2006 ,  2009 ; Holmes and Mathews 2005 ) led to larger effects regarding the provoked anxiety, it might be worthwhile to examine and control whether and how children and young people generally use mental images during verbal instruction.

With respect to the assessment of MI, perceived vividness was assessed only in one study by Warren et al. ( 1984 ). Although this factor did not have a significant effect on reducing interpersonal anxiety, it may be an important mediator of treatment effects on emotions, as shown in a study with adult patients with depressive symptoms (Moritz et al. 2014 ). Having group differences in imagery vividness, as well as differences in the perspective and depression score, could falsify the effects of training and therefore needs to be controlled.

In conclusion, it seems useful to test the efficacy and effectiveness of these direct treatment methods in children and adolescents in further studies, as they can easily be formulated in a child-adapted manner, for instance, using a monster or a superhero to provide help in the mental image. These methods are more appropriate for children than complex cognitive processes. The cognitive ability of children to process abstract or logical information (e.g., Socratic dialog) is limited, whereas imagery provides information in a way that can easily be processed and help children with psychological disorders in a more targeted manner (De Voogd et al. 2017 ; Hautzinger et al. 2006 ). The therapist should use well-known situations and also pay attention to non-verbal reactions, as they are important sources of feedback. Furthermore, children have difficulties in describing their feelings during imagination processes and need guidance from the therapist (Rosenstiel and Scott 1977 ). Working with unpleasant images in therapy should be done more creatively, such as to paint it (as in Nelius and Ahrens-Eipper) or re-enact it with toys such as “Lego,” “Duplo,” clay, and plasticine and to change it in playful manner. Especially with children, externalizing the image seems to be important, as the concentration range is lower than in adults, and the cognitive ability to control images develops very slowly and is still poor at this age (Burnett-Heyes et al. 2013 ). The image should therefore become physical. Mental images and associated thoughts are often not concrete, and thus difficult for many children to become aware of and make more pleasant (Kosslyn and Barrett 1990 ). If the images have a tangible shape, it could pave the way for altering the image. It also might be helpful for the children to play a game, in which they have to control the image again and again. Examples might be to create an image with a positive ending, or to imagine a helping superhero. The age of participants in treatment studies using MI varied from 4 to 18 years, which is a remarkably wide range; considering the rapid social and cognitive development over that timeframe and still, the results indicate an effective treatment for all participants. Methods for changing MI might not only be important for the therapy of mental disorders. Given that MI might also play a role in the development of various mental disorders (such as PTSD), these methods should be included in prevention programs.

Finally, several limitations impact on the research reviewed, such as heterogeneity in the extent of imagery used during treatment (e.g., as the vividness was assessed in only one study) and the reliance on a low number of studies and small sample sizes for interventions. Comparisons across interventions are also difficult, as the methodological quality differs, which limits the ability to draw definite conclusions. For IR, only case studies were found. For ER, IRT, and RET with imagery, there are RCTs, but with a relatively small number of participants, which limits external validity. Especially for IR, it is surprising that there are no RCTs, as IR is a widely used treatment method in adults (Morina et al. 2017 ). Finally, it cannot be ignored that authors have missed potentially relevant papers and search terms. To assess the effectiveness and effectivity, a meta-analytic approach would be appropriate, as soon as there are sufficient reliable studies with reasonable sample sizes.

Mental images in children are vivid and probably exert an influence on the emotions of children and adolescents, as primarily studies in adults suggest. MI seems to play a role in the maintenance of PTSD, SAD, and depression in children and adolescents. Therefore, it is important to identify negative and disturbing mental images and treat them in therapy. Techniques which include MI should be adapted to the cognitive abilities of children and adolescents. The vividness of MI, the perspective in which the image is perceived, as well as the role of comorbid depressive symptoms, may influence the level of emotional distress due to MI, and need to be controlled in future studies. Using MI in helping children to control and change their mental images may be both an effective treatment and an effective measure of prevention, thus increasing resilience to mental disorders.

Alfano, C. A., Beidel, D. C., & Turner, S. M. (2008). Negative self-imagery among adolescents with social phobia: a test of an adult model of the disorder. Journal of Clinical Child and Adolescent Psychology, 37 (2), 327–336. https://doi.org/10.1080/15374410801955870 .

Article   Google Scholar  

Andrade, J., May, J., Deeprose, C., Baugh, S.-J., & Ganis, G. (2014). Assessing vividness of mental imagery: the Plymouth Sensory Imagery Questionnaire. British Journal of Psychology, 105 (4), 547–563. https://doi.org/10.1111/bjop.12050 .

Baddeley, A. D., & Andrade, J. (2000). Working memory and the vividness of imagery. Journal of Experimental Psychology. General, 129 (1), 126–145. https://doi.org/10.1037/0096-3445.129.1.126 .

Beck, A. T. (1976). Cognitive therapy and the emotional disorders . New York: International Universities Press.

Google Scholar  

Blackwell, S. E. (2019). Mental imagery: From basic research to clinical practice. Journal of Psychotherapy Integration, 29 (3), 235–247. https://doi.org/10.1037/int0000108 .

Blackwell, S. E., & Holmes, E. A. (2017). Brightening the day with flashes of positive mental imagery: a case study of an individual with depression. Journal of Clinical Psychology, 73 (5), 579–589. https://doi.org/10.1002/jclp.22455 .

Blake, D. D., Weathers, F. W., Nagy, L. M., Kaloupek, D. G., Gusman, F. D., Charney, D. S., & Keane, T. M. (1990). A clinician rating scale for assessing current and 950 lifetime PTSD: The CAPS-1. Behaviour Therapist, 13 , 187–188.

Burnett-Heyes, S., Lau, J. F., & Holmes, E. A. (2013). Mental imagery, emotion and psychopathology across child and adolescent development. Developmental Cognitive Neuroscience, 5 , 119–133. https://doi.org/10.1016/j.dcn.2013.02.004 .

Caspi, A., Moffitt, T. E., Newman, D. L., & Silva, P. A. (1996). Behavioral observations at age 3 years predict adult psychiatric disorders. Longitudinal evidence from a birth cohort. Archives of General Psychiatry, 53 (11), 1033–1039. https://doi.org/10.1001/archpsyc.1996.01830110071009 .

Chapman, J., Halldorsson, B., & Creswell, C. (2020). Mental imagery in social anxiety in children and young people: a systematic review. Clinical Child and Family Psychology Review, 23 , 379–392. https://doi.org/10.1007/s10567-020-00316-2 .

Chudy, J. F., Jones, G. E., & Dickson, A. L. (1983). Modified desensitization approach for the treatment of phobic behavior in children: a quasi-experimental case study. Journal of Clinical Child Psychology, 12 (2), 198–201. https://doi.org/10.1080/15374418309533131 .

Clark, I. A., & Mackay, C. E. (2015). Mental imagery and post-traumatic stress disorder: a neuroimaging and experimental psychopathology approach to intrusive memories of trauma. Frontiers in Psychiatry, 6 , 104. https://doi.org/10.3389/fpsyt.2015.00104 .

Clark, D. M., & Wells, A. (1995). A cognitive model of social phobia. In R. G. Heimberg, M. Liebowitz, D. Hope, & F. R. Schneier (Eds.), Social phobia: diagnosis, assessment, and treatment (pp. 69–93). New York: Guilford.

Cockerham, E. S., Cili, S., & Stopa, L. (2016). Investigating the phenomenology of imagery following traumatic burn injuries. Burns, 42 (4), 853–862. https://doi.org/10.1016/j.burns.2016.02.018 .

Cornwall, E., Spence, S. H., & Schotte, D. (1996). The effectiveness of emotive imagery in the treatment of darkness phobia in children. Behaviour Change, 13 , 223–233. https://doi.org/10.1017/S0813483900004824 .

Da Silva, P. (1986). Obsessional–compulsive imagery. Behaviour Research and Therapy, 24 (3), 333–350. https://doi.org/10.1016/0005-7967(86)90193-2 .

Davis, J. L., De Arellano, M., Falsetti, S. A., & Resnick, H. S. (2003). Treatment of nightmares related to post-traumatic stress disorder in an adolescent rape victim. Clinical Case Studies, 2 (4), 283–294. https://doi.org/10.1177/1534650103256289 .

Day, S. J., Holmes, E. A., & Hackmann, A. (2004). Occurence of imagery and its link with early memories in agoraphobia. Memory, 12 (4), 416–427. https://doi.org/10.1080/09658210444000034 .

Deeprose, C., & Holmes, E. A. (2010). An exploration of prospective imagery: the impact of future events scale. Behavioural and Cognitive Psychotherapy, 38 (2), 201–209. https://doi.org/10.1017/S1352465809990671 .

De Voogd, E. L., de Hullu, E., Burnett Heyes, S., Blackwell, S. E., Wiers, R. W., & Salemink, E. (2017). Imagine the bright side of life: a randomized controlled trial of two types of interpretation bias modification procedure targeting adolescent anxiety and depression. PLoS One, 12 (7), e0181147. https://doi.org/10.1371/journal.pone.0181147 .

Ehlers, A., & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38 (4), 319–345. https://doi.org/10.1016/S0005-7967(99)00123-0 .

Eksi, A., Peykerli, G., Saydam, R., Toparla, D., & Braun, K. L. (2008). Vivid intrusive memories in PTSD: responses of child earthquake survivors in Turkey. Journal of Loss and Trauma, 13 (2–3), 123–155. https://doi.org/10.1080/15325020701443925 .

Ellis, A. (1962). Reason and emotion in psychotherapy . Oxford: Lyle Stuart.

Fernandez, S., DeMarni Cromer, L., Borntrager, C., Swopes, R., Hanson, R. F., & Davis, J. L. (2013). A case series: cognitive-behavioral treatment (exposure, relaxation, and rescripting therapy) of trauma-related nightmares experienced by children. Clinical Case Studies, 12 (1), 39–59. https://doi.org/10.1177/1534650112462623 .

Gregory, A., & Eley, T. (2007). Genetic influences on anxiety in children: what we’ve learned and where we’re heading. Clinical Child and Family Psychology Review, 10 (3), 199–212. https://doi.org/10.1007/s10567-007-0022-8 .

Hackmann, A., Surawy, C., & Clark, D. M. (1998). Seeing yourself through others’ eyes: a study of spontaneously occurring images in social phobia. Behavioural and Cognitive Psychotherapy, 26 (1), 3–12. https://doi.org/10.1017/S1352465898000022 .

Hackmann, A., Bennett-Levy, J., & Holmes, E. A. (2012). Imaginationstechniken in der kognitiven Therapie . Beltz: Weinheim.

Hackmann, A., Clark, D. M., & McManus, F. (2000). Recurrent images and early memories in social phobia. Behaviour Research and Therapy, 38 (6), 601–610. https://doi.org/10.1016/S0005-7967(99)00161-8 .

Hackmann, A., & Holmes, E. A. (2004). Reflecting on imagery: a clinical perspective and overview of the special issue of memory on mental imagery and memory in psychopathology. Memory, 12 (4), 389–402. https://doi.org/10.1080/09658210444000133 .

Hales, S. A., Deeprose, C., Goodwin, G. M., & Holmes, E. A. (2011). Cognitions in bipolar affective disorder and unipolar depression: Imagining suicide. Bipolar Disorders, 13 (7–8), 651–661. https://doi.org/10.1111/j.1399-5618.2011.00954.x .

Hautzinger, M., Eimecke, S., & Mattejat, F. (2006). Lern- und kognitionspsychologische Grundlagen. 35-52. In Mattejat, F. (Hrsg.). Lehrbuch der Psychotherapie für die Ausbildung zum Kinder- und Jugendlichen-psychotherapeuten und für die ärztliche Weiterbildung . Band 4: Verhaltenstherapie mit Kindern, Jugendlichen und ihren Familien. München: CIP-Medien.

Hignett, E., & Cartwright-Hatton, S. (2008). Observer perspective in adolescence: the relationship with social anxiety and age. Behavioural and Cognitive Psychotherapy, 36 (4), 437–447. https://doi.org/10.1017/S1352465808004554 .

Hirsch, C. R., Mathews, A., Clark, D. M., Williams, R., & Morrison, J. A. (2006). The causal role of negative imagery in social anxiety: a test in confident public speakers. Journal of Behavior Therapy and Experimental Psychiatry, 37 (2), 159–170. https://doi.org/10.1016/j.jbtep.2005.03.003 .

Holmes, E. A., Arntz, A., & Smucker, M. R. (2007c). Imagery rescripting in cognitive behaviour therapy: images, treatment techniques and outcomes. Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 297–305. https://doi.org/10.1016/j.jbtep.2007.10.007 .

Holmes, E. A., Blackwell, S. E., Heyes, S. B., Renner, F., & Raes, F. (2016). Mental imagery in depression: phenomenology, potential mechanisms, and treatment implications. Annual Review of Clinical Psychology, 12 , 249–280. https://doi.org/10.1146/annurev-clinpsy-021815-092925 .

Holmes, E. A., Crane, C., Fennell, M. V., & Williams, J. G. (2007b). Imagery about suicide in depression—‘flash-forwards’? Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 423–434. https://doi.org/10.1016/j.jbtep.2007.10.004 .

Holmes, E. A., Creswell, C., & O’Connor, T. G. (2007a). Posttraumatic stress symptoms in London school children following September 11, 2001: an exploratory investigation of peri-traumatic reactions and intrusive imagery. Journal of Behavior Therapy and Experimental Psychiatry, 38 (4), 474–490. https://doi.org/10.1016/j.jbtep.2007.10.003 .

Holmes, E. A., Lang, T. J., & Deeprose, C. (2009). Mental imagery and emotion in treatment across disorders: using the example of depression. Cognitive Behaviour Therapy, 38 (1), 21–28. https://doi.org/10.1080/16506070902980729 .

Holmes, E. A., Lang, T. J., Moulds, M. L., & Steele, A. M. (2008). Prospective and positive mental imagery deficits in dysphoria. Behaviour Research and Therapy, 46 (8), 976–981. https://doi.org/10.1016/j.brat.2008.04.009 .

Holmes, E. A., & Mathews, A. (2005). Mental imagery and emotion: a special relationship? Emotion, 5 (4), 489–497. https://doi.org/10.1037/1528-3542.5.4.489 .

Holmes, E. A., & Mathews, A. (2010). Mental imagery in emotion and emotional disorders. Clinical Psychology Review, 30 (3), 349–362. https://doi.org/10.1016/j.cpr.2010.01.001 .

Holmes, E. A., Mathews, A., Dalgleish, T., & Mackintosh, B. (2006). Positive interpretation training: effects of mental imagery versus verbal training on positive mood. Behavior Therapy, 37 (3), 237–247. https://doi.org/10.1016/j.beth.2006.02.002 .

Jackson, H. J., & King, N. J. (1981). The emotive imagery treatment of a child’s trauma-induced phobia. Journal of Behavior Therapy and Experimental Psychiatry, 12 (4), 325–328. https://doi.org/10.1016/0005-7916(81)90072-0 .

Ji, J. L., Holmes, E. A., & Blackwell, S. E. (2017). Seeing light at the end of the tunnel: positive prospective mental imagery and optimism in depression. Psychiatry Research , 247155–247162. https://doi.org/10.1016/j.psychres.2016.11.025 .

Karatzias, T., Power, K., Brown, K., & McGoldrick, T. (2009). Vividness of mental imagery in posttraumatic stress disorder (PTSD): the role of depression. Journal of Behavior Therapy and Experimental Psychiatry, 40 (2), 352–358. https://doi.org/10.1016/j.jbtep.2009.02.002 .

Kenny, L. M., & Bryant, R. A. (2007). Keeping memories at an arm’s length: vantage point of trauma memories. Behaviour Research and Therapy, 45 , 1915–1920. https://doi.org/10.1016/j.brat.2006.09.004 .

King, N. J., Cranstoun, F., & Josephs, A. (1989). Emotive imagery and children’s night-time fears: a multiple baseline design evaluation. Journal of Behavior Therapy and Experimental Psychiatry, 20 (2), 125–135. https://doi.org/10.1016/0005-7916(89)90045-1 .

Kosslyn, S. M., & Barrett, A. M. (1990). Age differences in imagery ability. Child Development, 61 (4), 995–110. https://doi.org/10.1111/j.1467-8624.1990.tb02837.x .

Krakow, B., Sandoval, D., Schrader, R., Keuhne, B., McBride, L., Yau, C. L., et al. (2001). Treatment of chronic nightmares in adjudicated adolescent girls in a residential facility. Journal of Adolescent Health, 29 (2), 94–100. https://doi.org/10.1016/S1054-139X(00)00195-6 .

Kuyken, W., & Howell, R. (2006). Facets of autobiographical memory in adolescents with major depressive disorder and never-depressed controls. Cognition & Emotion, 20 (3–4), 466–487. https://doi.org/10.1080/02699930500342639 .

Kuyken, W., & Moulds, M. L. (2009). Remembering as an observer: how is autobiographical memory retrieval vantage perspective linked to depression? Memory, 17 (6), 624–634. https://doi.org/10.1080/09658210902984526 .

Lazarus, A. A., & Abramovitz, A. (1962). The use of ‘emotive imagery’ in the treatment of children’s phobias. The Journal of Mental Science, 108 (453), 191–195. https://doi.org/10.1192/bjp.108.453.191 .

Lipton, M. G., Brewin, C. R., Linke, S., & Halperin, J. (2010). Distinguishing features of intrusive images in obsessive–compulsive disorder. Journal of Anxiety Disorders, 24 (8), 816–822. https://doi.org/10.1016/j.janxdis.2010.06.003 .

Nelius, K., & Ahrens-Eipper, S. (2017). IRRT mit Kindern und Jugendlichen. Ein Fallbuch mi einer Einführung von Mervyn Schmucker . Halle: KJP Verlag.

Luna, B., Garver, E., Urban, T. A., Lazar, N. A., & Sweeney, J. A. (2004). Maturation of cognitive processes from late childhood to adulthood. Child Development, 75 (5), 1357–1372. https://doi.org/10.1111/j.1467-8624.2004.00745.x .

Mathews, A., Ridgeway, V., & Holmes, E. A. (2013). Feels like the real thing: Imagery is both more realistic and emotional than verbal thought. Cognition and Emotion, 27 (2), 217–229. https://doi.org/10.1080/02699931.2012.698252 .

McIsaac, H. K., & Eich, E. (2004). Vantage point in traumatic memory. Psychological Science, 15 , 248–253. https://doi.org/10.1111/j.0956-7976.2004.00660.x .

McKinnon, A. C., Nixon, R. V., & Brewer, N. (2008). The influence of data-driven processing on perceptions of memory quality and intrusive symptoms in children following traumatic events. Behaviour Research and Therapy, 46 (6), 766–775. https://doi.org/10.1016/j.brat.2008.02.008 .

McTeague, L. M., Lang, P. J., Laplante, M., Cuthbert, B. N., Strauss, C. C., & Bradley, M. M. (2009). Fearful imagery in social phobia: generalization, comorbidity, and physiological reactivity. Biological Psychiatry, 65 (5), 374–382. https://doi.org/10.1016/j.biopsych.2008.09.023 .

Meiser-Stedman, R., Dalgleish, T., Yule, W., & Smith, P. (2012). Intrusive memories and depression following recent non-traumatic negative life events in adolescents. Journal of Affective Disorders, 137 (1–3), 70–78. https://doi.org/10.1016/j.jad.2011.12.020 .

Morina, N., Deeprose, C., Pusowski, C., Schmid, M., & Holmes, E. A. (2011). Prospective mental imagery in patients with major depressive disorder or anxiety disorders. Journal of Anxiety Disorders, 25 (8), 1032–1037. https://doi.org/10.1016/j.janxdis.2011.06.012 .

Morina, N., Lancee, J., & Arntz, A. (2017). Imagery rescripting as a clinical intervention for aversive memories: a meta-analysis. Journal of Behavior Therapy and Experimental Psychiatry , 55 6–515. https://doi.org/10.1016/j.jbtep.2016.11.003 .

Morina, N., Leibold, E., & Ehring, T. (2013). Vividness of general mental imagery is associated with the occurrence of intrusive memories. Journal of Behavior Therapy and Experimental Psychiatry, 44 (2), 221–226. https://doi.org/10.1016/j.jbtep.2012.11.004 .

Moritz, S., Hörmann, C. C., Schröder, J., Berger, T., Jacob, G. A., Meyer, B., Holmes, E. A., Späth, C., Hautzinger, M., Lutz, W., Rose, M., & Klein, J. P. (2014). Beyond words: sensory properties of depressive thoughts. Cognition & Emotion, 28 (6), 1047–1056. https://doi.org/10.1080/02699931.2013.868342 .

Muris, P., Huijding, J., Mayer, B., van As, W., & van Alem, S. (2011). Reduction of verbally learned fear in children: a comparison between positive information, imagery, and a control condition. Journal of Behavior Therapy and Experimental Psychiatry, 42 (2), 139–144. https://doi.org/10.1016/j.jbtep.2010.11.006 .

Oertel, V., Rotarska-Jagiela, A., van de Ven, V., Haenschel, C., Grube, M., Stangier, U., Maurer, K., & Linden, D. J. (2009). Mental imagery vividness as a trait marker across the schizophrenia spectrum. Psychiatry Research, 167 (1–2), 1–11. https://doi.org/10.1016/j.psychres.2007.12.008 .

Osman, S., Cooper, M., Hackmann, A., & Veale, D. (2004). Spontaneously occurring images and early memories in people with body dysmorphic disorder. Memory, 12 (4), 428–436. https://doi.org/10.1080/09658210444000043 .

Ozsivadjian, A., Hollocks, M. J., Southcott, J., Absoud, M., & Holmes, E. (2017). Anxious imagery in children with and without autism spectrum disorder: an investigation into occurrence, content, features and implications for therapy. Journal of Autism and Developmental Disorders, 47 (12), 3822–3832. https://doi.org/10.1007/s10803-016-2840-3 .

Paivio, A. (1986). Mental representations: A dual coding approach . New York: Oxford University Press.

Papadelis, C., Kourtidou-Papadeli, C., Bamidis, P., & Albani, M. (2007). Effects of imagery training on cognitive performance and use of physiological measures as an assessment tool of mental effort. Brain and Cognition, 64 (1), 74–85. https://doi.org/10.1016/j.bandc.2007.01.001 .

Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental imagery: functional mechanisms and clinical applications. Trends in Cognitive Sciences, 19 (10), 590–602. https://doi.org/10.1016/j.tics.2015.08.003 .

Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences. A practical guide . Oxford: Blackwell Publishing.

Pile, V., & Lau, J. (2018). Looking forward to the future: impoverished vividness for positive prospective events characterises low mood in adolescence. Journal of Affective Disorders, 238 , 269–276. https://doi.org/10.1016/j.jad.2018.05.032 .

Pile, V., & Lau, J. Y. F. (2020). Intrusive images of a distressing future: links between prospective mental imagery, generalized anxiety and a tendency to suppress emotional experience in youth. Behaviour Research and Therapy, 124 , 103508. https://doi.org/10.1016/j.brat.2019.103508 .

Rachman, S. (2007). Unwanted intrusive images in obsessive compulsive disorders. Journal of Behavior Therapy and Experimental Psychiatry, 38 , 402–410. https://doi.org/10.1016/j.jbtep.2007.10.008 .

Ranta, K., Tuomisto, M. T., Kaltiala-Heino, R., Rantanen, P., & Marttunen, M. (2014). Cognition, imagery and coping among adolescents with social anxiety and phobia: testing the Clark and Wells model in the population. Clinical Psychology & Psychotherapy, 21 (3), 252–263. https://doi.org/10.1002/cpp.1833 .

Rosenstiel, A. K., & Scott, D. S. (1977). Four considerations in using imagery techniques with children. Journal of Behavior Therapy and Experimental Psychiatry, 8 (3), 287–290. https://doi.org/10.1016/0005-7916(77)90068-4 .

Rutter, M. (1984). Psychopathology and development: I. Childhood, antecedents of adult psychiatric disorder. Australian and New Zealand Journal of Psychiatry, 18 (3), 225–234. https://doi.org/10.3109/00048678409161295 .

Schreiber, F., Höfling, V., Stangier, U., Bohn, C., & Steil, R. (2012). A cognitive model of social phobia: applicability in a large adolescent sample. International Journal of Cognitive Therapy, 5 (3), 341–358. https://doi.org/10.1521/ijct.2012.5.3.341 .

Schreiber, F., Stangier, U., & Steil, R. (2009). Fragebogen zur Erfassung von Vorstellungsbildern bei Sozialer Phobie. Unpublished manuscript . Goethe-University, Frankfurt am Main.

Schreiber, F., & Steil, R. (2013). Haunting self-images? The role of negative self-images in adolescent social anxiety disorder. Journal of Behavior Therapy and Experimental Psychiatry, 44 (2), 158–164. https://doi.org/10.1016/j.jbtep.2012.10.003 .

Shepherd, L., & Kuczynski, A. (2009). The use of emotive imagery and behavioral techniques for a 10-year-old boy’s nocturnal fear of ghosts and zombies. Clinical Case Studies, 8 (2), 99–112. https://doi.org/10.1177/1534650108329664 .

Singer, J. A., & Moffitt, K. H. (1992). An experimental investigation of specificity and generality in memory narratives. Imagination, Cognition and Personality, 11 (3), 233–257. https://doi.org/10.2190/72A3-8UPY-GDB9-GX9K .

Simard, V., & Nielsen, T. (2009). Adaptation of imagery rehearsal therapy for nightmares in children: a brief report. Psychotherapy: Theory, Research, Practice, Training, 46 (4), 492–497. https://doi.org/10.1037/a0017945 .

Speckens, A., Hackmann, A., Ehlers, A., & Cuthbert, B. (2007). Imagery special issue: intrusive images and memories of earlier adverse events in patients with obsessive compulsive disorder. Journal of Behavior Therapy and Experimental Psychiatry, 38 , 411–422. https://doi.org/10.1016/j.jbtep.2007.09.004 .

St-Onge, M., Mercier, P., & De Koninck, J. (2009). Imagery rehearsal therapy for frequent nightmares in children. Behavioral Sleep Medicine, 7 (2), 81–98. https://doi.org/10.1080/15402000902762360 .

Ten Eycke, K., & Müller, U. (2015). Brief report: new evidence for a social-specific imagination deficit in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 45 (1), 213–220. https://doi.org/10.1007/s10803-014-2206-7 .

Vassilopoulos, S. P., Banerjee, R., & Prantzalou, C. (2009). Experimental modification of interpretation bias in socially anxious children: changes in interpretation, anticipated interpersonal anxiety, and social anxiety symptoms. Behaviour Research and Therapy, 47 (12), 1085–1089. https://doi.org/10.1016/j.brat.2009.07.018 .

Vassilopoulos, S. P., Moberly, N. J., & Douratsou, K. (2012). Social anxiety and the interaction of imagery and interpretations in children: an experimental test of the combined cognitive biases hypothesis. Cognitive Therapy and Research, 36 (5), 548–559. https://doi.org/10.1007/s10608-011-9382-y .

Warnock-Parkes, E., Wild, J., Stott, R., Grey, N., Ehlers, A., & Clark, D. M. (2017). Seeing is believing: using video feedback in cognitive therapy for social anxiety disorder. Cognitive and Behavioral Practice, 24 (2), 245–255. https://doi.org/10.1016/j.cbpra.2016.03.007 .

Warren, R., Smith, G., & Velten, E. (1984). Rational-emotive therapy and the reduction of interpersonal anxiety in junior high school students. Adolescence, 19 (76), 893–902.

Weil, L. G., Fleming, S. M., Dumontheil, I., Kilford, E. J., Weil, R. S., Rees, G., Dolan, R. J., & Blakemore, S.-J. (2013). The development of metacognitive ability in adolescence. Consciousness and Cognition: An International Journal, 22 (1), 264–271. https://doi.org/10.1016/j.concog.2013.01.004 .

Weßlau, C., & Steil, R. (2014). Visual mental imagery in psychopathology--implications for the maintenance and treatment of depression. Clinical Psychology Review, 34 (4), 273–281. https://doi.org/10.1016/j.cpr.2014.03.001 .

Weßlau, C., Cloos, M., Höfling, V., & Steil, R. (2015). Visual mental imagery and symptoms of depression - results from a large-scale web-based study. BMC Psychiatry, 15 , 308. https://doi.org/10.1186/s12888-015-0689-1 .

World Health Organization (2016). Suicide fact sheet (March 2018). Retrieved from http://www.who.int.proxy.ub.uni-frankfurt.de/mediacentre/factsheets/fs398/en/

Download references

Acknowledgments

We thank Dr. Brian Bloch for his comprehensive editing of the manuscript.

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and affiliations.

Department of Clinical Psychology and Psychotherapy, Goethe University, Varrentrappstraße 40-42, 60486, Frankfurt am Main, Germany

Susan Schwarz, Dörte Grasmann, Franziska Schreiber & Ulrich Stangier

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Susan Schwarz .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Schwarz, S., Grasmann, D., Schreiber, F. et al. Mental Imagery and its Relevance for Psychopathology and Psychological Treatment in Children and Adolescents: a Systematic Review. J Cogn Ther 13 , 303–327 (2020). https://doi.org/10.1007/s41811-020-00092-5

Download citation

Accepted : 06 October 2020

Published : 16 October 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s41811-020-00092-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental imagery
• Psychological disorders
• Adolescents
• Find a journal
• Publish with us
• Track your research

• Tools and Resources
• Customer Services
• Affective Science
• Biological Foundations of Psychology
• Clinical Psychology: Disorders and Therapies
• Cognitive Psychology/Neuroscience
• Developmental Psychology
• Educational/School Psychology
• Forensic Psychology
• Health Psychology
• History and Systems of Psychology
• Individual Differences
• Methods and Approaches in Psychology
• Neuropsychology
• Organizational and Institutional Psychology
• Personality
• Psychology and Other Disciplines
• Social Psychology
• Sports Psychology
• Share Facebook LinkedIn Twitter

Article contents

Psychological imagery in sport and performance.

• Krista J. Munroe-Chandler Krista J. Munroe-Chandler The University of Windsor
• , and  Michelle D. Guerrero Michelle D. Guerrero The University of Windsor
• https://doi.org/10.1093/acrefore/9780190236557.013.228
• Published online: 26 April 2017

Imagery, which can be used by anyone, is appealing to performers because it is executed individually and can be performed at anytime and anywhere. The breadth of the application of imagery is far reaching. Briefly, imagery is creating or recreating experiences in one’s mind. From the early theories of imagery (e.g., psychoneuromuscular) to the more recent imagery models (e.g., PETTLEP), understanding the way in which imagery works is essential to furthering our knowledge and developing strong research and intervention programs aimed at enhanced performance. The measurement of imagery ability and frequency provides a way of monitoring the progression of imagery use and imagery ability. Despite the individual differences known to impact imagery use (e.g., type of task, imagery perspective, imagery speed), imagery remains a key psychological skill integral to a performer’s success.

• mental imagery
• imagery ability
• imagery theories
• imagery models
• imagery perspective
• law enforcement

Introduction

All individuals, regardless of age, gender, or skill level, are capable of using imagery as a means to enhance cognitive, behavioral, and affective outcomes. In the sport domain, athletes use imagery in training, competition, and rehabilitation. Elsewhere, imagery has been widely utilized by other performers including military personnel, surgeons, and musicians.

Everything I make as a producer, I visualize it as a DJ first. And all those beats, I test them as a DJ. (David Guetta) I have a system of ridding my mind of negative thoughts. I visualize myself writing them down on a piece of paper. Then I imagine myself crumpling up the paper, lighting it on fire, and burning it to a crisp. (Bruce Lee)

The breadth of the application of imagery is far reaching, as demonstrated by these quotations from famous musician David Guetta and legendary martial artist Bruce Lee, illustrating that imagery can be used in different disciplines and for different functions. An often cited definition of imagery is:

an experience that mimics real experience. We can be aware of “seeing” an image, feeling movements as an image, or experiencing an image of smell, tastes, or sounds without actually experiencing the real thing. Sometimes people find that it helps to close their eyes. It differs from dreams in that we are awake and conscious when we form an image. (White & Hardy, 1998 , p. 389)

As just described, imagery is multisensory such that it can include the sense of sight, taste, sound, smell, and touch. This description provides insight into why the term imagery is used instead of “visualization,” which denotes only the sense of sight. In addition, the individual is awake and consciously aware when imaging and as such not dreaming. In essence, imagery is creating, or recreating, the entirety of an experience in one’s mind.

From early theories of imagery to more recent imagery models, the ways in which imagery is used to enhance performance will be explored. Measurement of imagery ability and frequency, which has been assessed primarily through the use of self-report, will be discussed, along with various factors influencing imagery use, including ability, speed, age, skill level and perspective. The uses of imagery in sport, exercise, and performance domains will be examined and avenues for future research suggested.

Theories and Models

For many years, researchers have been interested in the way in which imagery is used and applied by individuals. When individuals image they first retrieve information from memory to create or recreate an experience in their mind (Morris, Spittle, & Watt, 2005 ). Through a combination of imagery sub-processes, such as image transformation (e.g., rotation of an image), scanning (e.g., detecting details of an image), and maintenance (e.g., sustaining an image for some time), vivid and controllable images are generated. Despite the appeal of the simplistic explanation, a deeper understanding of how imagery works is necessary. As such, several theories have been proposed (psychoneuromuscular, bioinformational, triple code). Notwithstanding support and criticism of each of these theories, together they provide a foundation that continues to guide the development and refinement of imagery research and therefore warrant exploration and explanation. The most commonly discussed theories in sport, exercise, and performance psychology are presented along with an overview on the conceptual models of imagery.

The psychoneuromuscular theory (Jacobson, 1930 ) notes that when an individual mentally imagines a skill, the activated neural pathways are identical to those activated when physically performing the skill. The feedback one receives from the muscle innervation of the imagined skill enables the individual to make adjustments in motor behavior. Through measurement of electromyographical (EMG) activity, wherein the innervations when imaging are much smaller in magnitude than when physically performing, empirical support for the psychoneuromuscular theory has been found. Despite this, Hall ( 2001 ) has noted the failure of the psychoneuromuscular theory to examine the various types of imagery and Feltz and Landers ( 1983 ) have criticized the validity of this theory because of methodological concerns.

In bioinformation theory, Lang ( 1979 ) suggests that mental images comprise both stimulus proposition and stimulus response. Stimulus proposition refers to the content or characteristics of the image, such as a competitive swimmer imagining her surroundings and her opponents. Stimulus response, on the other hand, refers to the physiological and affective reaction experienced by the individual imaging. For example, that same swimmer may feel tightness in her shoulders due to the anxiety experienced when imagining the swim meet or she may neglect external stimuli such as the crowd cheering after imagining a personal best time. Images that contain both stimulus proposition and response are most effective in enhancing performance. Although not often acknowledged, Lang introduced the concept of meaning to the image, enhancing the relevance of the theory. Research supporting the bioinformational theory has found that imagery scripts containing more frequent use of response propositions, compared to stimulus propositions, elicit greater physiological reactions (Bakker, Boschker, & Chung, 1996 ). Although an improvement over earlier theories, the bioinformational theory lacks explanation regarding the motivational types of imagery (Hall, 2001 ).

Elaborating upon the bioinformational theory’s stimulus proposition and response characteristics, Ahsen’s ( 1984 ) tripe code theory added a third characteristic—the meaning of the image. Ahsen argued that no two people would have the same imagery experience even if provided with the same imagery instructions. Individuals bring their own unique set of experiences with them and view these experience through their individual lenses, thereby allowing for a different meaning of the image to emerge. As such, the most effective images are those that are realistic and vivid, evoke psychophysiological responses, and impart significance to the individual. However, as noted in the literature (Morris et al., 2005 ), this model neglects the cognitive effects of imagery, which is an important consideration for skill acquisition and learning.

The aforementioned concepts provide theoretical underpinning for imagery use; however, exploration of this topic also requires an examination of the different models of imagery, which are also essential for furthering our understanding of imagery use. Indeed, most of the recent performance imagery research (e.g., sport, exercise) has developed as a result of Paivio’s ( 1985 ) analytic model. It is well established that imagery has cognitive and motivational functions that operate at a general or specific level. The cognitive general (CG) function entails imaging strategies, game plans, or routines (e.g., a fast break in basketball), whereas the cognitive specific (CS) function involves imaging specific skills (e.g., follow through on a free throw). The motivational general (MG) function of imagery involves imaging physiological arousal levels and emotions (e.g., staying calm when taking a penalty shot), and the motivational specific (MS) function of imagery includes imaging individual goals (e.g., winning the championship). In an extension of Paivio’s work, Hall, Mack, Paivio, and Hausenblas ( 1998 ) further divided the motivational general function into a motivational general–arousal (MG-A) function, encompassing imagery associated with arousal and stress, and a motivational general–mastery (MG-M) function, representing imagery associated with being mentally tough, in control, and self-confident.

Guided by Paivio’s ( 1985 ) model, Martin, Moritz, and Hall ( 1999 ) developed the Applied Model of Imagery Use in Sport (AMIUS) to explain the way in which athletes use imagery to improve athletic performance. According to AMIUS, the sport situation influences the types of imagery used, which are then associated with various cognitive, affective, and behavioral outcomes. Further, the relationship between the imagery type (five functions of imagery as noted: CS, CG, MS, MG-A, MG-M) and the outcome is moderated by various individual differences, such as imagery ability.

As a model of imagery use, the AMIUS offers several benefits. From a research perspective, the AMIUS provides simple, practical, and testable relationships. From an applied perspective, the model offers guidance for imagery interventions. There is ample support for the AMIUS such that the type of imagery should match the desired outcome, or as summarized by Short, Monsma, and Short ( 2004 ), “what you see, is what you get” (p. 342). That is, if a performer wishes to improve his confidence, he should engage in MG-M imagery. However, some researchers (e.g., Bernier & Fournier, 2010 ; Nordin & Cumming, 2008 ) have found that images can serve multiple functions for an athlete and have argued that function (why athletes image) and content (what athletes image) are not identical and therefore should be separated. Indeed, the original belief that the type of imagery should match its intended outcome is not as clear as was once thought.

Drawing on the AMIUS, Munroe-Chandler and Gammage ( 2005 ) developed an applied model for exercise settings. The exercise model differs from the AMIUS in that the antecedents include factors beyond the physical setting (e.g., exerciser’s goals and experiences), efficacy beliefs mediate the function-outcome relationship, and the individual differences that moderate the relationship extend beyond imagery ability (e.g., frequency of exercise, age). This model has allowed for the refinement and development of exercise imagery research (e.g., Andersson & Moss, 2011 ; Najafabadi, Memari, Kordi, Shayestehfar, & Eshghi, 2015 ).

With over a decade of research guided by the AMIUS, Cumming and Williams ( 2013 ) proposed a revised model of deliberate imagery use applicable for many performers (e.g., athletes, dancers, musicians). The revised model considers “who” is imaging (age, gender, competitive level), “what” is being imaged (the type), and “why” performers use imagery (the function). Most important, however, the revised model recognizes the personal meaning as the link between the imagery type and function. Cumming and Williams note that the types of imagery are often combined to achieve a specific outcome (e.g., cognitive and motivational types of images are important sources of confidence; Levy, Perry, Nicholls, Larkin, & Davies, 2014 ), and therefore offers a more flexible framework than the original AMIUS.

Apart from the previously mentioned models, some sport psychology researchers have called for models of imagery to be grounded in neuroscience; the PETTLEP is one such model (Holmes & Collins, 2001 ). The PETTLEP model was developed to guide imagery interventions and is based on functional equivalence, which suggests that processes that occur in the brain during imagery mimic the processes that occur during actual movement. Seven key factors are identified to help guide imagery interventions; physical, environment, task, timing, learning, emotion, and perspective. Although there have been some studies examining the model’s components in isolation (e.g., O & Munroe-Chandler, 2008 ), more research is needed testing multiple elements of the model (cf., Smith, Wright, Allsopp, & Westhead, 2007 ) and in different contexts. Sophisticated neuroimaging techniques such as functional magnetic resonance imagery (fMRI) and positron emission tomography (PET), as well as mental chronometry (informs about the temporal coupling between real and simulated movements), have allowed researchers to test functional equivalence and to gain a greater understanding between imagery and movement.

Measurement

The measurement of imagery ability and imagery frequency have often been assessed in the sport, exercise, and performance imagery research. Given that imagery is an internal mental skill, its assessment has typically relied on the self-report questionnaires allowing individuals to subjectively report their imagery use and ability. More recent research, however, has combined self-report with other indices of imagery experiences such as chronometry or functional magnetic resonance imagery (fMRI) (Guillot & Collet, 2005 ).

As noted in the Applied Model of Imagery Use in Sport (AMIUS), imagery ability is one of the most important factors impacting imagery effectiveness. One’s ability to image includes various dimensions such as vividness, controllability, and maintenance (Morris, Spittle, & Watt, 2005 ). Although some performers may initially be better imagers than others, imagery is a skill that can be improved with practice (Rodgers, Hall, & Buckolz, 1991 ). From an applied perspective, the measurement of imagery ability is important as it leads to more individualized, and therefore effective, imagery interventions. Further, the measurement of imagery ability can be used as an imagery intervention screening procedure, thereby ensuring adequate imagery ability prior to the commencement of the intervention. Although there are numerous imagery ability questionnaires, the focus will be on the two most commonly used in the performance (sport) domain due to their inclusion of both movement and visual imagery.

The Movement Imagery Questionnaire (MIQ; Hall & Pongrac, 1983 ) assesses both visual and kinesthetic imagery. Although it was readily used for some time as a measure of imagery ability, Hall and Martin ( 1997 ) revised the MIQ (Movement Imagery Questionnaire–Revised; MIQ-R), reducing the number of items and thus minimizing the amount of time needed to complete the questionnaire. Those completing the MIQ-R are instructed to physically complete the movement sequence (i.e., knee raise, arm movement, waist bend, and jump) and then resume the starting position and recreate the experience using visual imagery, and finally using kinesthetic imagery. Participants are then asked to rate the quality of imagery on a 7-point Likert scale from 1 ( very easy to picture/feel) to 7 ( very difficult to picture/feel) . Given that the MIQ and MIQ-R did not distinguish between internal and external visual imagery perspective, Williams et al. ( 2012 ) developed the MIQ-3 to more fully capture an individual’s imagery ability. The MIQ-3 assesses external visual imagery (e.g., looking through your own eyes while performing the movement), internal visual imagery (e.g., watching yourself performing the movement), and kinesthetic imagery (e.g., feeling yourself do the movement). Although the MIQ-3 has shown to be a reliable and valid measure (Williams et al., 2012 ), because of the recentness of its development, more research is warranted using this measure.

The Vividness of Movement Imagery Questionnaire (Isaac, Marks, & Russell, 1986 ) assesses one’s ability to use visual imagery. It requires the participant to rate the 24 items on the vividness of imagery from 1 ( perfectly clear and as vivid as normal vision ) to 5 ( no image at all; you only know that you are thinking of the skill ). The revised VMIQ-2 (Roberts, Callow, Hardy, Markland, & Bringer, 2008 ) assesses the vividness of both visual and kinesthetic imagery. The 12-item VMIQ-2 scale asks respondents to imagine a variety of motor tasks (e.g., running, kicking a stone) and then rate the image on two perspectives of visual imagery (external and internal), as well as kinesthetically. All items are measured on a 5-point Likert scale ranging from 1 ( perfectly clear and as vivid as normal vision ) to 5 ( no image at all; you only know that you are thinking of the skill ). The VMIQ-2 has shown adequate reliability as well as adequate factorial, concurrent, and construct validity (Roberts et al., 2008 ).

All measurement tools are subject to criticism, and the imagery ability measures are not exempt. The instructions from the VMIQ-2 ask participants to draw on their memory of common movements, whereas the MIQ-3 requires participants to execute a movement first prior to imagining it, thereby relying on short-term memory. It may be argued that imaging a common movement (kicking a ball; VMIQ-2) may be easier for the participant than imaging a less common movement (raising your knee as high as possible so that you are standing on your left leg with your right leg flexed [bent] at the knee; MIQ-3). Conversely, a more common movement such as running up the stairs may elicit varying interpretations from the participant, thus leading to discrepancies in imagery content.

Gregg and Hall ( 2006 ) developed the Motivational Imagery Ability Measure for Sport (MIAMS) to assess motivational imagery abilities, which had yet to be included in any previous imagery ability measure. The MIAMS assesses the ability of an athlete to use MG-A and MG-M imagery, wherein the participant images the scene and then rates the image on an ease subscale 1 ( not at all easy to form ) to 7 ( very easy to form ) and an emotion subscale 1( no emotion ) to 7 ( very strong emotion ). Psychometric properties of the questionnaire have proved favorable, with acceptable model fit and adequate internal consistencies for the subscales (Gregg & Hall, 2006 ).

Of course, the various measures of imagery ability can be employed together to provide a more comprehensive assessment of an athlete’s overall imagery ability. Individuals who are more adept at imagery are more likely to engage these practices, and greater imagery use will likely result in enhanced imagery ability (Gregg, Hall, McGowan, & Hall, 2011 ). This is significant because research conclusively demonstrates that individual differences in imagery ability will have an impact on the effectiveness of imagery, and that high imagery ability leads to the ultimate goal: improved performance on a variety of motor tasks (Hall, 2001 ).

In addition to imagery ability, measuring a performer’s use of imagery allows researchers, and practitioners, to determine one’s frequency of a specific type of imagery and also enables them to see changes from pre- to post-intervention. The various questionnaires assessing the frequency of imagery use in sport, exercise, and active play will be addressed.

The Sport Imagery Questionnaire (SIQ; Hall, Mack, Paivio, & Hausenblas, 1998 ; Hall, Stevens, & Paivio, 2005 ) is the most widely used measure of imagery frequency in the sport domain (Morris et al., 2005 ). It is a general measure of imagery used for athletes of any sport at any competitive level. The self-report questionnaire comprises 30 items assessing the five functions of imagery (CS, CG, MS, MG-A, MG-M). All items are scored on a 7-point Likert scale anchored by 1 ( rarely ) and 7 ( often ). The SIQ has shown strong psychometric properties (i.e., reliability, validity) for athletes 14 years and older (Hall et al., 2005 ).

Given the research evidence supporting young athletes’ use of imagery (e.g., Munroe-Chandler, Hall, Fishburne, & Shannon, 2005 ), the Sport Imagery Questionnaire for Children (SIQ-C; Hall, Munroe-Chandler, Fishburne, O, & Hall, 2009 ) was developed for those young athletes aged 7–14 years. The SIQ-C includes 21 items, which assesses the same five functions as those identified in the adult version (CS, CG, MS, MG-A, MG-M). The items are rated on a 5-point Likert scale anchored at 1 ( not at all ) and 5 ( very often ), making it more appropriate for young children. Since its development, the SIQ-C has reported adequate internal consistencies for all subscales (Hall et al., 2009 ).

For researchers in the field of exercise imagery, two questionnaires have dominated: the Exercise Imagery Questionnaire (EIQ; Hausenblas, Hall, Rodgers, & Munroe, 1999 ) and the Exercise Imagery Inventory (EII; Giacobbi, Hausenblas, & Penfield, 2005 ). The nine-item EIQ was developed from qualitative responses from exercisers reporting their use imagery for three main purposes: appearance, energy, and technique. Exercisers are asked to rate their imagery use on the three aforementioned subscales using a 9-point scale, anchored by 1 ( never ) and 9 ( always ). Strong reliabilities are reported for all three subscales (Hausenblas et al., 1999 ; Rodgers, Munroe, & Hall, 2001 ).

The EII was developed as a result of qualitative evidence indicating exercisers’ use of imagery for purposes beyond those of appearance, energy, and technique. In fact, exercisers were found to use imagery for the following purposes: appearance or health, exercise technique, exercise self-efficacy, and exercise feelings. As a result of these findings, the EII includes questions that assess appearance, energy and technique imagery as well as exercise self-efficacy and exercise feeling imagery. The EII is a 19-item self-report measure of exercise frequency rated on a 7-point Likert scale (1 = rarely and 7 = often ). Support for the four-factor model across a variety of samples has been reported (Giacobbi et al., 2005 ).

The revised version of the EII (EII-R; Giacobbi, Tuccitto, Buman, & Munroe-Chandler, 2010 ) measures the same four subscales of the original version, in addition to exercise routines. This modification allowed for the measurement of the five functions of imagery, which were suggested in the applied model of exercise imagery use (Munroe-Chandler & Gammage, 2005 ). Results from a confirmatory factor analysis for the EII-R has demonstrated good fit indices (Giacobbi et al., 2010 ).

The Children’s Active Play Imagery Questionnaire (CAPIQ; Cooke, Munroe-Chandler, Hall, Tobin, & Guerrero, 2014 ) assesses the frequency of imagery use in children during their active play. The measure consists of 11 items, each rated on a 5-point Likert scale from 1 ( not at all ) to 5 ( very often ), assessing one of the three subscales (capability, fun, and social). Capability imagery refers to the practice of movements, social imagery refers to the engagement of active play activities either by oneself or with others, and fun imagery refers to feelings of satisfaction. The items were developed from active play research as well as qualitative focus groups with children examining their use of imagery during their leisure time physical activity (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013 ). The CAPIQ has demonstrated adequate internal consistencies for all three subscales (Cooke et al., 2014 ) and contributes to the measurement of imagery use in a physical activity setting other than organized sport.

Factors Affecting Imagery

Researchers have identified a wide range of factors that have been found to influence imagery effectiveness, including imagery ability, image speed, age, skill level, and perspective.

Both Martin, Moritz, and Hall ( 1999 ) and Munroe-Chandler and Gammage ( 2005 ) have proposed that the relationship between imagery use and desired outcome is moderated by various individual differences, especially the ability to image. That is, better imagery ability leads to better performance on a variety of motor tasks (Hall, 2001 ). This was supported in an applied study wherein tennis players with better imagery ability showed greater improvements in tennis serve return accuracy than those athletes with lower imagery ability (Robin et al., 2007 ). Individual differences in imagery ability has been noted in early imagery research (cf., MacIntyre, Moran, Collet, & Guillot, 2013 ). Some have noted that novice performers may not be as skilled at imagining given their lack of ability to develop knowledge of the spatial and kinesthetic requirements of the task (Driskell, Copper, & Moran, 1994 ). Regardless of individual differences in imagery ability, there is sufficient evidence to show that imagery ability can improve with practice (Cooley, Williams, Burns, & Cumming, 2013 ).

Cumming et al. ( 2016 ) developed a structured, imagery exercise known as layered stimulus and response training (LSRT) designed to improve imagery ability. By generating images in a layered fashion, starting with a simple image and gradually incorporating additional information in subsequent layers, imagery ability improves. After each layer, the individual evaluates the image by reflecting on various aspects of the image. For example, what aspects were strong, easy, vague, or difficult to image? Earlier studies have implemented LSRT in a single imagery session, with the intent of enhancing individuals’ imagery ability prior to receiving an imagery intervention (e.g., Cumming, Olphin, & Law, 2007 ), and more recently for improving actual motor skill performance (Williams, Cooley, & Cumming, 2013 ).

Image Speed

Regarding the Timing element of the PETTLEP model, Holmes and Collins ( 2001 ) have recommended that athletes image primarily in real-time speed, due to the accurate representation of movement tempo and relative timing duration in one’s images. In a large-scale study examining athletes’ voluntary use of image speed (O & Hall, 2009 ), both recreational and competitive athletes reported using three image speeds depending on the function of imagery being employed and the stage of learning of the athlete. Real-time images were used most often by athletes regardless of imagery function or stage of learning. However, when learning or developing a skill or strategy, slow-motion images were used most often (which supports recent findings with novice golfers; Shirazipour, Munroe-Chandler, Loughead, & Vander Laan, 2016 ), and when imaging skills or strategies that had been mastered fast-motion images were used most often. Subsequent qualitative research by O and Hall ( 2013 ) substantiated those findings and defined voluntary image speed manipulation as that which “occurs when an athlete consciously and purposefully selects a speed at which to image” (p. 11).

The cognitive development of the individual, most often distinguished by age, is another factor influencing imagery use. Much of the research conducted by Kosslyn and colleagues (e.g., Kosslyn, Margolis, Barrett, Goldknopf, & Daly, 1990 ) in the general psychology domain notes differences in imagery use between children and adults. More specifically, it is not until age 14 that children are able to image similarly to their adult counterparts. Age differences also holds true in the sport, exercise, and active play domain. For example, child-specific imagery measures have been developed to adequately assess their use of imagery in various domains (i.e., SIQ-C, CAPIQ). Findings from an imagery intervention study (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012 ) did identify age-related results, such that only the younger athletes (7–10 years) performed faster on a soccer task, when compared to the older athletes (10–14 years). Noted age differences are also evident in the active play setting such that only the older age cohorts (11–14 years) reported picturing themselves playing alone rather than with others (Tobin, Nadalin, Munroe-Chandler, & Hall, 2013 ). In the exercise domain, Milne, Burke, Hall, Nederhof, and Gammage ( 2006 ) found that younger exercisers ( M age = 22 years) reported using more appearance imagery than the older exercisers ( M age = 71 years). Although these findings offer some preliminary evidence for age differences, further research is needed in order to truly understand the effects of age on performers’ use of imagery.

Skill Level

One of the most consistent findings from the performance imagery literature is that higher skilled performers report using imagery more often than lower skilled performers (Cumming & Hall, 2002 ; Hall, Mack, Paivio, & Hausenblaus, 1998 ; Hausenblas, Hall, Rodgers, & Munroe, 1999 ). In the sport domain, although it had been suggested that novice athletes should use imagery more frequently than elite athletes, simply for the purposes of the learning, and development, of new strategies and skills (Hall, 2001 ), research supports benefits for highly skilled athletes (e.g., Arvinen-Barrow, Weigand, Thomas, Hemmings, & Walley, 2007 ). This finding is consistent in the exercise imagery field, wherein experienced exercisers use imagery more often than less experienced exercisers (Gammage, Hall, & Rodgers, 2000 ), and in the performing arts field wherein higher level ballet dancers report using more imagery than their lower level counterparts (Nordin & Cumming, 2008 ). Moving forward, researchers should consider other ways to assess skill level. Currently, skill level has been dichotomized as novice vs. elite or experienced vs non-experienced. This is problematic given the self-report nature of this dichotomy and the possibility that minimal differences in skill may exist between those two groups (Arvinen-Barrow et al., 2007 ). In the revised model of deliberate imagery use, Cumming and Williams ( 2013 ) suggest that in addition to the skill level of the athlete, other relevant individual characteristics to consider are experience with and confidence using imagery.

Imagery Perspective

Morris and Spittle ( 2012 ) noted that imagery perspective is a key factor impacting an athlete’s use of imagery. Indeed, a special issue of the Journal of Mental Imagery ( 2012 ) was dedicated solely to imagery perspective. Performers can image the execution of a skill from their own vantage point (internal imagery) or they can view themselves from the perspective of an external observer, as if they were a spectator in the stands watching a performance (external imagery). Early sport imagery researchers advocated the use of an internal perspective (Vealey, 1986 ), while others have found the perspective to be dependent upon the task. That is, tasks relying heavily on the use of form (e.g., gymnastics) are most effective when imaged from an external perspective (White & Hardy, 1995 ). Some researchers (Munroe, Giacobbi, Hall, & Weinberg, 2000 ; Smith, Wright, Allsopp, & Westhead, 2007 ) support athletes using a combination of internal and external perspectives. In the academic domain, Vasquez and Buehler ( 2007 ) found that students demonstrate increased motivation when they imagine the task from a third-person perspective. In a study examining imagery in five different disciplines (i.e., education, medicine, music, psychology, and sport), imagery was most often performed from an internal perspective (Schuster et al., 2011 ).

Other Factors

Scholars have recently acknowledged the scant research assessing the influence of personality characteristics on imagery use and its effectiveness (Roberts, Callow, Hardy, Woodman, & Thomas, 2010 ). In an effort to fill this gap, Roberts et al. ( 2010 ) examined the interactive effects of imagery perspective and narcissism on motor performance. Given that narcissists enjoy looking at themselves from the point of others, it was hypothesized that those high in narcissism would score higher on external visual imagery and better on their motor performance when compared to those low in narcissism. This hypothesis was supported using two independent samples. As such, it seems as though personality characteristics (i.e., narcissism) may influence the effectiveness of psychological skills and thereby require additional investigation.

Another factor that has recently been examined within the imagery domain is emotion regulation. Anuar, Cumming, and Williams ( 2016 ) believed that athletes’ emotion regulation may be associated with their imagery ability given that both imagery and emotion regulation are linked with emotions and memory. Indeed, their results indicated that athletes who change how they think about a particular situation scored higher on imagery ability. This study is the first of its kind, and future research examining individual characteristics and imagery is warranted.

Imagery as a Means to Improving Performance

Drawing on the various imagery models and empirical support, athletes use imagery for various motivational purposes (i.e., motivational general–mastery [MG-M], motivational general–arousal [MG-A], motivational specific [MS]). Most of the motivational imagery interventions have targeted the MG-M imagery function, and results from these studies are promising. In one study, a MG-M imagery intervention was implemented with four elite junior badminton players (Callow, Hardy, & Hall, 2001 ). The imagery scripts were designed to elicit images of being focused and confident, and included both response and stimulus propositions. Following the completion of the intervention, all but one badminton player showed significant improvements in their sport confidence. Other researchers employing single-subject multiple-baseline designs have found that MG-M imagery improved young squash players’ self-efficacy (O, Munroe-Chandler, Hall, & Hall, 2014 ) and high-performance golfers’ flow states (Nicholls, Polman, & Holt, 2005 ). Recently, MG-M imagery sessions were delivered to young athletes with an intellectual disability in an attempt to increase their perceptions of their sport competence (Catenacci, Harris, Langdon, Scott, & Czech, 2016 ). Results indicated that perceptions of sport competence improved from baseline to post-intervention for three of the five athletes, with two of the three athletes maintaining these changes upon commencement of the intervention. The benefits of MG-M imagery have also been underscored in several cross-sectional studies, providing evidence for a positive link between MG-M imagery and performance, state and trait sport confidence, self-efficacy, collective efficacy (see Cumming & Ramsey, 2009 , for review), and mental toughness (Mattie & Munroe-Chandler, 2012 ).

Imagery has also been used as a means to achieve desirable somatic and emotional experiences associated with sport-related stress, arousal, and anxiety (MG-A imagery). It is generally argued that MG-A imagery may be more beneficial for athletes who experience debilitative interpretations of pre-competitive anxiety as opposed to those who experience facilitative interpretations (Martin, Moritz, & Hall, 1999 ). For example, a female fencer who is feeling unusually sluggish prior to competition might use MG-A imagery to psych herself up, while a male mixed martial arts fighter who is abnormally restless before the start of a competition might use MG-A imagery to reduce his anxiety. Though MG-A images have been negatively associated with athletes’ self-reported cognitive and somatic anxiety (Monsma & Overby, 2004 ), few studies have examined the direct effects of MG-A imagery on competitive anxiety. Investigators of past studies have typically delivered multicomponent interventions, which have included MG-A imagery along with other psychological skills (e.g., relaxation, breathing; Thomas, Maynard, & Hanton, 2007 ). Adopting a multicomponent psychological skills package makes it virtually impossible to determine precisely how much MG-A imagery contributed to any observed changes. Nevertheless, findings from other studies have contributed to researchers’ existing understanding of the MG-A imagery–competitive anxiety relationship (Cumming, Olphin, & Law, 2007 ; Mellalieu, Hanton, & Thomas, 2009 ). Specifically, imagery scripts that contained MG-A images (psyching up imagery, anxiety imagery, and coping imagery) led to greater increases in athletes’ heart rate and anxiety intensity (Cumming et al., 2007 ), while individualized MG-A imagery scripts led to more facilitative interpretations of symptoms related to competitive anxiety (Mellalieu et al., 2009 ).

Within the sport psychology literature, few interventions have focused exclusively on goal-based images (MS imagery). This is likely because goal- or outcome-based images (e.g., qualifying for a competition, winning a medal) are least often used by athletes. Rather, coaches and sport practitioners often encourage their athletes to focus on process goals (e.g., completing stretching exercises prior to competition) rather than outcome goals. In a sample with beginner golfers, participants who imaged executing the perfect stroke as well as sinking the golf ball (performance and outcome imagery group) had better performance and set higher goals for themselves compared to participants who imaged executing the perfect stroke only (performance group) and the participants who received no intervention (control group; Martin & Hall, 1995 ). Additionally, athletes who used MS imagery more frequently also reported greater goal achievement, state and trait sport confidence, and self-efficacy (Cumming & Ramsey, 2009 ).

In addition to motivational purposes, athletes have reported using imagery for cognitive purposes (i.e., cognitive specific [CS] and cognitive general [CG]). Using cognitive imagery to enhance skill acquisition and performance (CS imagery) has received the most attention among researchers (Morris, Spittle, & Watt, 2005 ). Investigators examining the positive effects of CS imagery have found significant improvements in young soccer players’ time to complete a soccer task (Munroe-Chandler, Hall, Fishburne, Murphy, & Hall, 2012 ) as well as adult equestrian riders’ performance and self-efficacy for a specific skill (Davies, Boxall, Szekeres, & Greenlees, 2014 ). In another study, 7- to 10-year-old athletes who imaged the proper execution of a table tennis serve significantly improved their serve accuracy and quality (Li-Wei, Qi-Wei, Orlick, & Zitzelsberger, 1992 ). Furthermore, CS imagery has been positively associated with gymnasts’ performance at competition (Simonsmeier & Buecker, 2017 ) and trait confidence (Abma, Fry, Li, & Relyea, 2002 ).

Evidence for imagery as a means to learn and improve execution of strategies, game plans, and routines (CG imagery) has been equivocal (see Westlund, Pope, & Tobin, 2012 , for review). For instance, while improvements in basketball athletes’ strategy execution were observed following a CG imagery intervention (Guillot, Nadrowska, & Collet, 2009 ), soccer athletes who participated in a seven-week CG imagery intervention showed no improvements in strategy execution from baseline to post-intervention (Munroe-Chandler, Hall, Fishburne, & Shannon, 2005 ). However, researchers adopting correlational-based studies have shown that athletes who used CG imagery reported higher levels of confidence, self-efficacy, imagery ability, and cohesion in team sports (Westlund et al., 2012 ).

Imagery has long been recognized as a viable psychological technique that can directly modify exercise-related cognitions. Self-efficacy is a particularly good example of one cognition that continues to receive attention in literature. Weibull, Cumming, Cooley, Williams, and Burns ( 2015 ) examined whether a brief (one week) imagery intervention could increase barrier self-efficacy among a group of women who were interested in becoming more active. Findings indicated that participants who performed daily imagery for one week (experimental group) reported greater increases in barrier self-efficacy compared to those who did not perform imagery (control group). Note, however, that when preexisting exercise levels were controlled, there were no significant differences in barrier efficacy between groups. Nevertheless, findings from this study support the notion that imagery can have an influential effect on barrier self-efficacy in a short time frame. Evidence for the effectiveness of using imagery to increase exercise self-efficacy has also been found in other intervention studies, including Duncan, Rodgers, Hall, and Wilson ( 2011 ).

Imagery has also been used to modify individuals’ motivation toward exercise. Duncan, Hall, Wilson, and Rodgers ( 2012 ) implemented an eight-week imagery intervention and found that participants who listened to guided imagery scripts showed significantly greater increases in self-determined motivation than those who listened to health information sessions. In another study, imagery scripts combined with peer-mentoring led to significantly greater increases in self-determined motivation to exercise at the end of the intervention compared to those whose participation was limited to peer-mentoring only (Giacobbi, Dreisbach, Thurlow, Anand, & Garcia, 2014 ). Additional benefits of employing imagery in an exercise domain include increased revitalization and post-exercise valence (Stanley & Cumming, 2010 ) and implicit attitudes toward exercise (Markland, Hall, Duncan, & Simatovic, 2015 ).

Beyond changing individuals’ attitudes toward exercise, imagery can also significantly impact exercise behavior. For example, audio-administered imagery scripts led to significantly greater increases in self-reported exercise behavior in both adult (Andersson & Moss, 2011 ) and older adult (Kim, Newton, Sachs, Giacobbi, & Glutting, 2011 ) samples. Chan and Cameron ( 2012 ) also tested the effects of different imagery content on physical activity participation by looking at imagery’s impact on a group of inactive adults. Their findings indicated that imagery scripts linking images of participation in physical activity with achievement of goals were most effective in increasing self-reported physical activity as well as greater increases in goal orientation, intentions, and action planning.

Although few imagery interventions have utilized objective measures of physical activity, the research that has been conducted in this area illustrates positive impact of imagery. In a sample of adolescent girls, Najafabadi et al. ( 2015 ) developed imagery scripts that focused on benefits obtained from exercise (e.g., improved appearance, enhanced energy). Following the intervention, significantly greater levels of physical activity (as measured by accelerometers) and physical self-concept were found among females in the imagery group compared to those in the control group. In a separate study, school-aged children who were assigned to an imagery group showed greater levels of active play and self-determined motivation following a four-week intervention compared to children assigned to a control group (Guerrero, Tobin, Munroe-Chandler, & Hall, 2015 ).

The effects of mental imagery with video-modeling on front squat strength and self-efficacy was recently examined in a sample of adults (Buck, Hutchinson, Winter, & Thompson, 2016 ). From pre-test to post-test, participants who received the imagery script and video-modeling showed significant increases in their self-efficacy and front squat performance compared to those who received no intervention. In a recent systematic review examining the effects of various cognitive strategies (e.g., imagery) on strength performance, imagery was found to positively influence maximal strength (Tod, Edwards, McGuigan, & Lovell, 2015 ).

Performance

Along with sporting arenas and fitness facilities, researchers have explored the effects and application of imagery in other performance domains. For example, in musical settings, imagery use coupled with physical practice increased pianists’ and trombonists’ movement timing, music memorization, and self-efficacy (see Wright, Wakefield, & Smith, 2014 , for review). Imagery use, in the absence of physical practice, has also shown to have promising effects on performance. In this respect, auditory practice (listening to an audio recording and imagining finger movements) led to significantly fewer errors in pianists’ performance than with those who did not engage in auditory practice (Highben & Palmer, 2004 ). More recently, Braden, Osborne, and Wilson ( 2015 ) tested the effectiveness of a multi-component, preventative skills-based program in reducing musical performance anxiety. The intervention program in this study comprised various components, including psychoeducation, cognitive restructuring, relaxation techniques, identification of strengths, goal-setting, positive self-talk, and imagery. Students who received the eight-week program reported significantly less musical performance anxiety than participants who did not receive the program.

In medical settings, researchers have employed imagery interventions to improve two primary outcomes: skill acquisition and levels of stress. With respect to skill acquisition, researchers found that medical students who received two imagery sessions demonstrated greater skill in performing surgery on live rabbits than students who had studied a textbook (Sanders et al., 2008 ). Similar findings were established in a study with gynecology residents, with those in the imagery group showing significantly better performance of cystoscopies as well as higher self-perceived level of preparedness compared to those in the control group (Komesu et al., 2009 ). In another study, student nurses who received PETTLEP training performed significantly better on a psychomotor skill (i.e., blood pressure measurement) than those who did not (Wright, Hogard, Ellis, Smith, & Kelly, 2008 ).

Given its successful use in the medical context, it is perhaps unsurprising that imagery has also been shown to be an effective stress management technique for other healthcare professionals (Arora et al., 2011 ) who also experience high levels of performance stress (Prabhu, Smith, Yurko, Acker, & Stefanidis, 2010 ). Compared to their control counterparts, novice surgeons who received imagery training demonstrated reduced self-reported stress as well as decreased objective stress (heart rate and salivary cortisol; Arora et al., 2011 ). In a very recent intervention study, Ignacio et al. ( 2016 ) developed, implemented, and evaluated an imagery intervention designed to improve nursing students’ clinical performance and reduce stress. Although no changes in subjective or objective stress were found, participants did significantly improve their performance from pre- to post-test.

Similar to healthcare professionals, police officers are often faced with a variety of stressors and potentially traumatic events, making imagery an appropriate psychological technique for members of law enforcement. Arnetz, Arble, Backman, Lynch, and Lubin ( 2013 ) implemented a 10-week imagery and relaxation intervention designed to help police officers develop effective coping skills. Compared to those in the control group, participants who received imagery training reported better general health and problem-based coping as well as reduced stomach problems, sleep difficulties, and exhaustion. Similarly, an imagery training program with rookie police officers led to significantly less negative mood and stress compared to standard police training (Arnetz, Nevedal, Lumley, Backman, & Lubin, 2009 ). Additionally, participants who received imagery training also demonstrated better performance during a live critical incident simulation (Arnetz et al., 2009 ).

Future Directions

That imagery is a powerful psychological technique is undeniable. Imagery allows individuals to search through, skip over, and select images from their memories in order to re-experience past events. Imagery also allows individuals to travel through time to create and manipulate never-experienced events. As illustrated, there is ample evidence documenting the effectiveness of imagery in sport, exercise, and performance settings. However, less is known about the potential negative consequences of imagery. For instance, engaging in self-generated imagery of a task requiring physical self-control (i.e., handgrip squeeze) led to performance decreases in a subsequent handgrip task for those who performed imagery compared to those who rested quietly (Graham, Sonne, & Bray, 2014 ). Furthermore, under certain conditions, imagery has been shown to have a negative effect on golf putting performance (Beilock, Afremow, Rabe, & Carr, 2001 ) and levels of aspirations and academic performance (Pham & Taylor, 1999 ). Together these findings indicate that there may be a dark side to imagery that should be explored to ensure that potential deleterious practices do not counteract the positive benefits associated with imagery use. Thus, future research should specifically explore possible negative effects of imagery on behavior and cognitions, including whether specific types of imagery should be avoided in certain environments and, if so, whether this caveat would hold true for all performers (e.g., professional dancer vs. surgeon)? While some researchers have begun to answer these questions (e.g., Nordin & Cumming, 2005 ), a more thorough examination of when and what imagery types facilitate or hinder performance would certainly contribute to the existing imagery research.

Along similar lines, there is a considerable gap in the imagery research investigating the impact involuntary, intrusive images have on performance. Imagery is considered to be intrusive as it can capture attention, cause distractions, and provoke unpleasant physiological and emotional reactions (Brewin, Gregory, Lipton, & Burgess, 2010 ). Indeed, there is some evidence indicating that performers do experience intrusive images (e.g., Nordin & Cumming, 2005 ; Parker, Jones, & Lovell, 2015 ). For instance, professional dancers reported experiencing irrelevant images, which may be intrusive, spontaneous, and debilitative (Nordin & Cumming, 2005 ). More recently, a small percentage of university students who participated on either recreational, university, county, or national competition levels reported experiencing intrusive visual imagery (Parker et al., 2015 ). Clearly, more research is needed in order to develop a greater understanding of the existence and effect of intrusive imagery within performance settings.

While the body of literature on imagery in performance settings continues to grow, more research exploring the usefulness and applicability of imagery among diverse performers is needed. Virtually all individuals, regardless of their occupations, are required to perform at some point or another. Successful lawyers need to deliver persuading and emotionally moving closing statements to the members of the jury; stand-up comedians are required to provide entertainment by mastering the pace and timing of every joke. Individuals of such occupations could undoubtedly benefit from imagery. Furthermore, as eSports (online competitive gaming) and competitive eating continue to gain popularity, exploring the potential for imagery as a performance enhancement technique for competitive gamers and eaters appears timely. Competitive gamers could use imagery to learn or improve their ability to make crucial decisions and to effectively cope with pressure, whereas competitive eaters could use imagery to improve execution of new strategies and maintain motivation during a contest.

Further Reading

• Catenacci, K. L. , Harris, B. S. , Langdon, J. , Scott, M. K. , & Czech, D. R. (2015). Using a MG-M imagery intervention to enhance the sport competence of young Special Olympics athletes. Journal of Imagery Research in Sport and Physical Activity . Advance online publication.
• Cumming, J. , & Williams, S. E. (2013). Introducing the revised applied model of deliberate imagery use for sport, dance, exercise, and rehabilitation. Movement & Sport Sciences , 82 (4), 69–81.
• Guerrero, M. D. , Tobin, D. , Munroe-Chandler, K. J. , & Hall, C. R. (2015). Tigers and lions, oh my! Effect of a guided imagery intervention on children’s active play. Journal of Applied Sport Psychology , 27 , 412–429.
• Williams, S. E. , Cooley, S. J. , Newell, E. , Weibull, F. , & Cumming, J. (2013). Seeing the difference: Advice for developing effective imagery scripts for athletes. Journal of Sport Psychology in Action , 4 , 109–121.
• Abma, C. L. , Fry, M. D. , Li, Y. , & Relyea, G. (2002). Differences in imagery content and imagery ability between high and low confident track and field athletes. Journal of Applied Sport Psychology , 14 , 67–75.
• Ahsen, A. (1984). ISM: The triple code model for imagery and psychophysiology. Journal of Mental Imagery , 8 , 15–42.
• Andersson, E. K. , & Moss, T. P. (2011). Imagery and implementation intention: A randomised controlled trial of interventions to increase exercise behavior in the general population. Psychology of Sport and Exercise , 12 , 63–70.
• Anuar, N. , Cumming, J. , & Williams, S. (2016). Emotion regulation predicts imagery ability. Imagination, Cognition and Personality: Consciousness in Theory, Research, and Clinical Practice . Advance online publication.
• Arnetz, B. B. , Arble, E. , Backman, L. , Lynch, A. , & Lubin, A. (2013). Assessment of a prevention program for work-related stress among urban police officers. International Archives of Occupational and Environment Health , 86 , 79–88.
• Arnetz, B. B. , Nevedal, D. C. , Lumley, M. A. , Backman, L. , & Lubin, A. (2009). Trauma resilience training for police: Psychophysiological and performance effects. Journal of Police and Criminal Psychology , 24 , 1–9.
• Arora, S. , Aggarwal, R. , Moran, A. , Sirimanna, P. , Crochet, P. , Darzi, A. , et al. (2011). Mental practice: Effective stress management training for novice surgeons. American College of Surgeons , 212 , 225–233.
• Arvinen-Barrow, M. , Weigand, D. A. , Thomas, S. , Hemmings, B. , & Walley, M. (2007). Elite and novice athletes’ imagery use in open and closed sports. Journal of Applied Sport Psychology , 19 , 93–104.
• Bakker, F. C. , Boschker, M. S. J. , & Chung, T. (1996). Changes in muscular activity while imagining weightlifting using stimulus or response propositions. Journal of Sport & Exercise Psychology , 18 , 313–324.
• Beilock, S. L. , Afremow, J. A. , Rabe, A. L. , & Carr, T. H. (2001). “Don’t miss!” The debilitating effects of suppressive imagery on golf putting performance. Journal of Sport and Exercise Psychology , 23 , 200–221.
• Bernier, M. , & Fournier, J. F. (2010). Functions of mental imagery in expert golfers. Psychology of Sport and Exercise , 11 , 444–452.
• Braden, A. M. , Osborne, M. S. , & Wilson, S. J. (2015). Psychological intervention reduces self-reported performance anxiety in high school music students. Frontiers in Psychology , 6 , 1–9.
• Brewin, C. R. , Gregory, J. D. , Lipton, M. , & Burgess, N. (2010). Intrusive images in psychological disorders: Characteristics, neural mechanisms, and treatment implication. Psychological Review , 117 , 210–232.
• Buck, D. J. , Hutchinson, J. C. , Winter, C. R. , & Thompson, B. A. (2016). The effect of mental imagery with video-modeling on self-efficacy and maximal front squat ability. Sports , 4 , 1–10.
• Callow, N. , Hardy, L. , & Hall, C. R. (2001). Types of imagery associated with sport confidence in netball players of varying skill levels. Journal of Applied Sport Psychology , 13 , 1–17.
• Catenacci, K. L. , Harris, B. S. , Langdon, J. , Scott, M. K. , & Czech, D. R. (2016). Using a MG-M imagery intervention to enhance the sport competence of young Special Olympics athletes. Journal of Imagery Research in Sport and Physical Activity , 11 (1), 1–12.
• Chan, C. K. Y. , & Cameron, L. D. (2012). Promoting physical activity with goal-oriented mental imagery: A randomized controlled trial. Journal of Behavioral Medicine , 35 , 347–363.
• Colin, L. , Nieuwenguys, A. , Visser, A. , & Oudejans, R. R. D. (2014). Positive effects of imagery on police officers’ shooting performance under threat. Applied Cognitive Psychology , 28 , 115–121.
• Cooke, L. M. , Munroe-Chandler, K. J. , Hall, C. R. , Tobin, D. , & Guerrero, M. D. (2014). Development of the Children’s Active Play Imagery Questionnaire. Journal of Sport Sciences , 32 , 860–869.
• Cooley, S. J. , Williams, S. E. , Burns, V. E. , & Cumming, J. (2013). Methodological variations in guided imagery interventions using movement imagery scripts in sport: A systematic review. Journal of Imagery Research in Sport and Physical Activity , 8 , 1–22.
• Cumming, J. , Cooley, S. J. , Anuar, N. , Kosteli, M. , Quinton, M. L. , Weibull, F. , & Williams, S. E. (2016). Developing imagery ability effectively: A guide to layered stimulus response training. Journal of Sport Psychology in Action . Advance online publication.
• Cumming, J. , & Hall, C. (2002a). Athletes’ use of imagery in the off-season. Sport Psychologist , 16 , 160–172.
• Cumming, J. , Olphin, T. , & Law, M. (2007). Self-reported psychological states and physiological responses to different types of motivational general imagery. Journal of Sport & Exercise Psychology , 29 , 629–644.
• Cumming, J. , & Ramsey, R. (2009). Imagery interventions in sport. In S. D. Mellalieu & S. Hanton (Eds.), Advances in Applied Sport Psychology: A review (pp. 5–36). New York: Routledge.
• Davies, J. , Boxall, S. , Szekeres, Z. , & Greenlees, I. (2014). Developing equestrian training quality and self-efficacy using cognitive-specific imagery. Sport & Exercise Psychology Review , 10 , 25–40.
• Duncan, L. R. , Hall, C. R. , Wilson, P. M. , & Rodgers, W. M. (2012). The use of a mental imagery intervention to enhance integrated regulation for exercise among women commencing an exercise program. Motivation and Emotion , 36 , 452–464.
• Duncan, L. R. , Rodgers, W. M. , Hall, C. R. , & Wilson, P. M. (2011). Using imagery to enhance three types of exercise self-efficacy among sedentary women. Applied Psychology: Health and Well-Being , 3 , 107–126.
• Driskell, J. E. , Copper, C. , & Moran, A. (1994). Does mental practice enhance performance? Journal of Applied Psychology , 79 , 481–492.
• Feltz, D. L. , & Landers, D. M. (1983). The effect of mental practice on motor skill learning and performance: A meta-analysis. Journal of Sport Psychology , 2 , 211–220.
• Gammage, K. L. , Hall, C. R. , & Rodgers, W. M. (2000). More about exercise imagery. Sport Psychologist , 14 , 348–359.
• Giacobbi, P. R., Jr. , Dreisbach, K. A. , Thurlow, N. M. , Anand, P. , & Garcia, F. (2014). Mental imagery increases self-determined motivation to exercise with university enrolled women: A randomized controlled trial using a peer-based intervention. Psychology of Sport and Exercise , 15 , 374–381.
• Giacobbi, P. R., Jr. , Hausenblas, H. A. , & Penfield, R. D. (2005). Further refinements in the measurement of exercise imagery: The exercise imagery inventory. Measurement in Physical Education and Exercise Science , 9 , 251–266.
• Giacobbi, P. R., Jr. , Tuccitto, D. , Buman, M. , & Munroe-Chandler, K. M. (2010). A measurement and conceptual investigation of exercise imagery establishing construct validity. Research Quarterly for Exercise and Sport , 18 , 485–493.
• Graham, J. D. , Sonne, M. W. L. , & Bray, S. R. (2014). It wears me out just imagining it! Mental imagery leads to muscle fatigue and diminished performance of isometric exercise. Biology Psychology , 103 , 1–6.
• Gregg, M. & Hall, C. R. (2006). Measurement of motivational imagery abilities in sport. Journal of Sports Sciences , 24 , 961–971.
• Gregg, M. , Hall, C. , McGowan, E. , & Hall, N. (2011). The relationship between imagery ability and imagery use among athletes. Journal of Applied Sport Psychology , 23 , 129–141.
• Guillot, A. , & Collet, C. (2005). Duration of mentally simulated movement: A review. Journal of Motor Behavior , 37 , 10–20.
• Guillot, A. , Nadrowska, E. , & Collet, C. (2009). Using motor imagery to learn tactical movements in basketball. Journal of Sport Behavior , 32 , 189–206.
• Hall, C. R. (2001). Imagery in sport and exercise. In R. N. Singer , H. A. Hausenblas , & C. M. Janelle (Eds.), Handbook of research on sport psychology (2d ed., pp. 529–549). New York: Wiley.
• Hall, C. R. , Mack, D. E. , Paivio, A. , & Hausenblas, H. A. (1998). Imagery use by athletes: Development of the Sport Imagery Questionnaire. International Journal of Sport Psychology , 29 , 73–89.
• Hall, C. R. , & Martin, K. A. (1997). Measuring movement imagery abilities: A revision of the Movement Imagery Questionnaire. Journal of Mental Imagery , 21 , 143–154.
• Hall, C. R. , Munroe-Chandler, K. J. , Fishburne, G. O. J. , & Hall, N. D. (2009). The Sport Imagery Questionnaire for Children. Measurement in Physical Education and Exercise Science , 13 , 93–107.
• Hall, C. R. , & Pongrac, J. (1983). Movement imagery questionnaire . London: University of Western Ontario.
• Hall, C. R. , Stevens, D. , & Paivio, A. (2005). The sport imagery questionnaire: Test manual . Morgantown, WV: Fitness Information Technology.
• Hausenblas, H. A. , Hall, C. R. , Rodgers, W. M. , & Munroe, K. J. (1999). Exercise imagery: Its nature and measurement. Journal of Applied Sport Psychology , 11 , 171–180.
• Highben, Z. , & Palmer, C. (2004). Effects of auditory and motor mental practice in memorized piano performance. Bulletin of the Council for Research in Music Education , 159 , 58–65.
• Holmes, P. S. , & Collins, D. J. (2001). The PETTLEP approach to motor imagery: A functional equivalence model for sport psychologists. Journal of Applied Sport Psychology , 13 , 60–83.
• Ignacio, J. , Dolmans, D. , Scherpbier, A. , Rethans, J. , Lopez, V. , & Ying Liaw, S. (2016). Development, implementation, and evaluation of a mental rehearsal strategy to improve clinical performance and reduce stress: A mixed methods study. Nurse Education Today , 37 , 27–32.
• Isaac, A. , Marks, D. , & Russell, E. (1986). An instrument for assessing imagery or movement: The Vividness of Movement Imagery Questionnaire (VMIQ). Journal of Mental Imagery , 10 , 23–30.
• Jacobson, E. (1930). Electrical measurement of neuromuscular states during mental activities. American Journal of Physiology , 94 , 24–34.
• Kim, B. H. , Newton, R. A. , Sachs, M. L. , Giacobbi, P. R., Jr. , & Glutting, J. J. (2011). The effect of guided relaxation and exercise imagery on self-reported leisure-time exercise behaviors in older adults. Journal of Aging and Physical Activity , 19 , 137–146.
• Komesu, Y. , Urwitz-Lane, R. , Ozel, B. , Lukban, J. , Kahn, M. , Muir, T. , et al. (2009). Does mental imagery prior to cystoscopy make a difference? A randomized controlled trial. American Journal of Obstetrics & Gynecology , 201 , 218.e1–218.e9.
• Kosslyn, S. M. , Margolis, J. A. , Barrett, A. M. , Goldknopf, E. J. , & Daly, P. F. (1990). Age differences in imagery abilities. Child Development , 61 , 995–1010.
• Lang, P. J. (1979). A bio-informational theory of emotional imagery. Psychophysiology , 16 , 495–512.
• Levy, A. R. , Perry, J. , Nicholls, A. R. , Larkin, D. , & Davies, J. (2014). Sources of sport confidence, imagery type and performance among competitive athletes: the mediating role of sports confidence. Journal of Sports Medicine and Physical Fitness , 55 , 835–844.
• Li-Wei, Z. , Qi-Wei, M. , Orlick, T. , & Zitzelsberger, L. (1992). The effect of mental-imagery training on performance enhancement with 7–10-year-old children. Sport Psychologist , 6 , 230–241.
• MacIntyre, T. E. , Moran, A. P. , Collet, C. , & Guillot, A. (2013). An emerging paradigm: A strength-based approach to exploring mental imagery. Frontiers in Human Neuroscience , 7 , 104.
• Malouin, F. , Richards, C. , Durand, A. , & Doyon, J. (2008). Reliability of mental chronometry for assessing motor imagery ability after stroke. Archives of Physical Medicine and Rehabilitation , 89 , 311–319.
• Markland, D. , Hall, C. R. , Duncan, L. R. , & Simatovic, J. (2015). The effects of an imagery intervention on implicit and explicit exercise attitudes. Psychology of Sport and Exercise , 17 , 24–31.
• Martin, K. A. , & Hall, C. R. (1995). Using mental imagery to enhance intrinsic motivation. Journal of Sport & Exercise Psychology , 17 , 54–69.
• Martin, K. A. , Moritz, S. E. , & Hall, C. R. (1999). Imagery use in sport: A literature review and applied model. Sport Psychologist , 13 , 245–268.
• Mattie, P. , & Munroe-Chandler, K. J. (2012). Examining the relationship between mental toughness and imagery use. Journal of Applied Sport Psychology , 24 , 144–156.
• Mellalieu, S. D. , Hanton, S. , & Thomas, O. (2009). The effects of a motivational general-arousal imagery intervention upon preperformance symptoms in male rugby union players. Psychology of Sport and Exercise , 10 , 175–185.
• Milne, M. I. , Burke, S. M. , Hall, C. R. , Nederhof, E. , & Gammage, K. L. (2006). Comparing the imagery use of younger and older adult exercisers. Imagination, Cognition, and Personality , 25 , 59–67.
• Monsma, E. V. , & Overby, L. Y. (2004). The relationship between imagery and competitive anxiety in ballet auditions. Journal of Dance Medicine and Science , 8 , 11–18.
• Morris, T. , & Spittle, M. (2012). A default hypothesis of the development of internal and external imagery perspectives. Journal of Mental Imagery , 3 , 1.
• Morris, T. , Spittle, M. , & Watt, A. P. (2005). Imagery in sport . Champaign, IL: Human Kinetics.
• Munroe, K. J. , Giacobbi, P. R. , Hall, C. R. , & Weinberg, R. (2000). The four Ws of imagery use: Where, when, why, and what. Sport Psychologist , 14 , 119–137.
• Munroe-Chandler, K. J. , & Gammage, K. L. (2005). Now see this: A new vision of exercise imagery. Exercise and Sport Sciences Reviews , 33 , 201–205.
• Munroe-Chandler, K. J. , Hall, C. R. , Fishburne, G. J. , Murphy, L. , & Hall, N. D. (2012). Effects of a cognitive specific imagery intervention on the soccer skill performance of young athletes: Age group comparisons. Psychology of Sport and Exercise , 13 , 324–331.
• Munroe-Chandler, K. J. , Hall, C. R. , Fishburne, G. J. , & Shannon, V. (2005). Using cognitive general imagery to improve soccer strategies. European Journal of Sport Science , 5 , 41–49.
• Najafabadi, M. G. , Memari, A. , Kordi, R. , Shayestehfar, M. , & Eshghi, M. (2015). Mental training can improve physical activity behavior in adolescent girls. Journal of Sport and Health Science . Advance online publication.
• Nicholls, A. R. , Polman, R. C. , & Holt, N. L. (2005). The effects of individualized imagery interventions on golf performance and flow states. Athletic Insight: The Online Journal of Sport Psychology , 7 , 43–66.
• Nordin, S. M. , & Cumming, J. (2005). Professional dancers describe their imagery: Where, when, what, why, and how. Sport Psychologist , 19 , 395–416.
• Nordin, S. M. , & Cumming, J. (2008). Comparison of dancers and aesthetic sport athletes’ imagery use. Journal of Applied Sport Psychology , 20 , 1–17.
• O, J. , & Hall, C. R. (2009). A quantitative analysis of athletes’ voluntary use of slow motion, real time, and fast motion images. Journal of Applied Sport Psychology , 2 , 15–30.
• O, J. , & Hall, C. R. (2013). A qualitative analysis of athletes’ voluntary image speed use. Journal of Imagery Research in Sport and Physical Activity , 8 , 1–12.
• O, J. , & Munroe-Chandler, K. J. (2008). The effects of image speed on the performance of a soccer task. Sport Psychologist , 22 , 1–17.
• O, J. , Munroe-Chandler, K. J. , Hall, C. R. , & Hall, N. D. (2014). Using motivational general-mastery imagery to improve the self-efficacy of youth squash players. Journal of Applied Sport Psychology , 26 , 66–81.
• Paivio, A. (1985). Cognitive and motivational functions of imagery in human performance. Canadian Journal of Applied Sport Sciences , 10 , 22–28.
• Parker, J. K. , Jones, M. I. , & Lovell, G. P. (2015). An investigation into athletes’ intrusive visual imagery. Sport & Exercise Psychology Review , 11 , 34–42.
• Pham, L. B. , & Taylor, S. E. (1999). From thought to action: Effects of process- versus outcome-based mental simulations on performance. Personality and Social Psychology Bulletin , 25 , 250–260.
• Prabhu, A. , Smith, W. , Yurko, Y. , Acker, C. , & Stefanidis, D. (2010). Increased stress levels may explain the incomplete transfer of simulator-acquired skill to the operating room. Surgery , 147 , 640–645.
• Roberts, R. , Callow, N. , Hardy, L. , Markland, D. , & Bringer, J. (2008). Movement imagery ability: Development and assessment of a revised version of the vividness of movement imagery questionnaire. Journal of Sport & Exercise Psychology , 30 , 200–221.
• Roberts, R. , Callow, N. , Hardy, L. , Woodman, T. , & Thomas, L. (2010). Interactive effects of different visual imagery perspectives and narcissism on motor performance. Journal of Sport & Exercise Psychology , 32 , 499–517.
• Robin, N. , Dominique, L. , Toussaint, L. , Blandin, Y. , Guillot, A. , & Le Her, M. (2007). Effects of motor imagery training on service return accuracy in tennis: The role of imagery ability. Journal of Sport and Exercise Psychology , 2 , 175–186.
• Rodgers, W. , Hall, C. R. , & Buckolz, E. (1991). The effect of an imagery training program on imagery ability, imagery use, and figure skating performance. Journal of Applied Sport Psychology , 3 , 109–125.
• Rodgers, W. M. , Munroe, K. J. , & Hall, C. R. (2001). Relations among exercise imagery, self-efficacy, exercise behavior, and intentions. Imagination, Cognition, and Personality , 21 , 55–65.
• Sanders, C. W. , Sadoski, M. , van Walsum, K. , Bramson, R. , Wiprud, R. , & Fossum, T. W. (2008). Learning basic surgical skills with mental imagery: Using the simulation centre in the mind. Medical Education , 42 , 607–612.
• Schuster, C. , Hilfiker, R. , Amft, O. , Scheidhauer, A. , Andrews, B. , Butler, J. , et al. (2011). Best practice for motor imagery: A systematic literature review on motor imagery training elements in five different disciplines. BMC Medicine , 9 , 1–35.
• Shirazipour, C. , Munroe-Chandler, K. J. , Loughead, T. , & Vander Laan, A. G. (2016). The effect of image speed on novice golfers’ performance in a putting task. Journal of Imagery Research in Sport and Physical Activity , 11 (1), 13–24.
• Short, S. E. , Monsma, E. V. , & Short, M. W. (2004). Is what you see really what you get? Athletes’ perceptions of imagery’s functions. Sport Psychologist , 18 , 341–349.
• Simonsmeier, B. A. , & Buecker, S. (2017). Interrelations of imagery use, imagery ability, and performance in young athletes. Journal of Applied Sport Psychology , 29 (1), 32–43.
• Smith, D. , Wright, C. , Allsopp, A. , & Westhead, H. (2007). It’s all in the mind: PETTLEP-based imagery and sports performance. Journal of Applied Sport Psychology , 19 , 80–92.
• Stanley, D. M. , & Cumming, J. (2010). Not just how one feels, but what one images? The effect of imagery use on affective responses to moderate exercise. International Journal of Sport and Exercise Psychology , 8 , 343–359.
• Thomas, O. , Maynard, I. , & Hanton, S. (2007). Psychological preparation for elite performance II: A temporal multimodal intervention with athletes debilitated by their preperformance symptoms. Journal of Applied Sport Psychology , 19 , 398–418.
• Tobin, D. , Nadalin, E. , Munroe-Chandler, K. J. , & Hall, C. R. (2013). Children’s active play imagery. Psychology of Sport & Exercise , 14 , 371–378.
• Tod, D. , Edwards, C. , McGuigan, M. , & Lovell, G. (2015). A systematic review of the effect of cognitive strategies on strength performance. Sport Medicine , 11 , 1589–1602.
• Vasquez, N. A. , & Buehler, R. (2007). Seeing future success: Does imagery perspective influence achievement motivation? Personality and Social Psychology Bulletin , 33 (10), 1392–1405.
• Vealey, R. E. (1986). Imagery training for performance enhancement. In J. M. Williams (Ed.), Applied sport psychology: Personal growth to peak performance (pp. 209–231). Mountain View, CA: Mayfield.
• Weibull, F. , Cumming, J. , Cooley, S. J. , Williams, S. E. , & Burns, V. E. (2015). Walk this way: A brief exercise imagery intervention increases barrier self-efficacy in women. Current Psychology , 34 , 477–490.
• Westlund, N. , Pope, P. , & Tobin, D. (2012). Cognitive general imagery: The forgotten imagery function? Journal of Imagery Research in Sport and Physical Activity , 7 , Article 1.
• White, A. , & Hardy, L. (1995). Use of different imagery perspectives on the learning and performance of different motor skills. British Journal of Psychology , 86 , 169–180.
• White, A. , & Hardy, L. (1998). An in-depth analysis of the uses of imagery by high-level slalom canoeists and artistic gymnasts. Sport Psychologist , 12 , 387–403.
• Williams, S. E. , Cooley, S. J. , & Cumming, J. (2013). Layered stimulus response training improves motor imagery ability and movement execution. Journal of Sport & Exercise Psychology , 35 , 60–71.
• Williams, S. E. , Cumming, J. , Ntoumanis, N. , Nordin-Bates, S. M. , Ramsey, R. & Hall, C. (2012). Further validation and development of the Movement Imagery Questionnaire. Journal of Sport & Exercise Psychology , 34 , 621–646.
• Wright, C. , Hogard, E. , Ellis, R. , Smith, D. , & Kelly, C. (2008). Effect of PETTLEP imagery training on performance of nursing skills: Pilot study. Journal of Advanced Nursing , 63 , 259–265.
• Wright, D. J. , Wakefield, C. J. , & Smith, D. (2014). Using PETTLEP imagery to improve music performance: A review. Musicae Scientiae , 18 , 448–463.

Related Articles

• Relaxation and Recovery in Sport and Performance
• Self-Talk in Sport and Performance
• Surgical Performance From a Psychological Perspective
• Psychological Considerations for Paralympic Athletes
• Strength Training and Sport Psychology
• Psychological Skills Training and the Impact on Military Performance Readiness

Printed from Oxford Research Encyclopedias, Psychology. Under the terms of the licence agreement, an individual user may print out a single article for personal use (for details see Privacy Policy and Legal Notice).

date: 27 September 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [195.158.225.230]
• 195.158.225.230

Character limit 500 /500

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

The Multiple Uses of Guided Imagery

Affiliation.

• 1 Vanderbilt University School of Nursing, 6809 Highland Park Drive, Nashville, TN 37205, USA. Electronic address: [email protected].
• PMID: 33131625
• DOI: 10.1016/j.cnur.2020.06.013

Guided imagery is a therapeutic approach that has been used for centuries. Through the use of mental imagery, the mind-body connection is activated to enhance an individual's sense of well-being, reduced stress, and reduced anxiety, and it has the ability to enhance the individual's immune system. There are research and data to support the use of guided imagery for these patient outcomes.

Keywords: Complimentary medicine intervention; Guided imagery; Visualization.

Copyright © 2020 Elsevier Inc. All rights reserved.

PubMed Disclaimer

Similar articles

• Self-Guided Humorous Imagery Mitigates Pain and Anxiety: A Repeated Measures Pilot Study. Greenberg MJ. Greenberg MJ. J Holist Nurs. 2024 Jun;42(2):121-132. doi: 10.1177/08980101231181257. Epub 2023 Aug 9. J Holist Nurs. 2024. PMID: 37559400 No abstract available.
• Relaxation-guided imagery reduces perioperative anxiety and pain in children: a randomized study. Vagnoli L, Bettini A, Amore E, De Masi S, Messeri A. Vagnoli L, et al. Eur J Pediatr. 2019 Jun;178(6):913-921. doi: 10.1007/s00431-019-03376-x. Epub 2019 Apr 3. Eur J Pediatr. 2019. PMID: 30944985 Clinical Trial.
• The effect of guided imagery on the quality and severity of pain and pain-related anxiety associated with dressing changes in burn patients: A randomized controlled trial. Aghakhani N, Faraji N, Alinejad V, Goli R, Kazemzadeh J. Aghakhani N, et al. Burns. 2022 Sep;48(6):1331-1339. doi: 10.1016/j.burns.2021.11.020. Epub 2021 Nov 26. Burns. 2022. PMID: 34924224 Clinical Trial.
• Underlying Mechanisms of Psychological Interventions in Magnetic Resonance Imaging and Image-Guided Radiology Procedures. Chadderdon AL, Carns DR, Pudalov LR, McKernan LC, Honce JM. Chadderdon AL, et al. Top Magn Reson Imaging. 2020 Jun;29(3):157-163. doi: 10.1097/RMR.0000000000000239. Top Magn Reson Imaging. 2020. PMID: 32568978 Review.
• Guided Imagery for Pain Management in Postoperative Orthopedic Patients: An Integrative Literature Review. Carpenter JJ, Hines SH, Lan VM. Carpenter JJ, et al. J Holist Nurs. 2017 Dec;35(4):342-351. doi: 10.1177/0898010116675462. Epub 2016 Oct 23. J Holist Nurs. 2017. PMID: 30208778 Review.
• Integrative Approaches for Cancer Pain Management. Martini RS, Brown T, Singh V, Woodbury A. Martini RS, et al. Curr Oncol Rep. 2024 Jun;26(6):691-705. doi: 10.1007/s11912-024-01536-3. Epub 2024 Apr 29. Curr Oncol Rep. 2024. PMID: 38683254 Review.
• Guided, motor, and mental imagery adjuncts to teaching central venous access. Cryder S, Jensen S, McCarthy J, Whitworth K, Kennelley GE, Nowak AS, Hysell MK. Cryder S, et al. AEM Educ Train. 2023 Apr 11;7(2):e10867. doi: 10.1002/aet2.10867. eCollection 2023 Apr. AEM Educ Train. 2023. PMID: 37064489 Free PMC article.

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources.

• ClinicalKey Nursing
• Elsevier Science
• W.B. Saunders
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier Sponsored Documents

Mental Imagery: Functional Mechanisms and Clinical Applications

Joel pearson.

1 School of Psychology, The University of New South Wales, Sydney, Australia

Thomas Naselaris

2 Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA

Emily A. Holmes

3 Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK

4 Department for Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

Stephen M. Kosslyn

5 Minerva Schools at the Keck Graduate Institute, San Francisco, CA, USA

Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment.

Recent research suggests that visual mental imagery functions as if it were a weak form of perception.

Evidence suggests overlap between visual imagery and visual working memory – those with strong imagery tend to utilize it for mnemonic performance.

Brain imaging work suggests that representations of perceived stimuli and mental images resemble one another as early as V1.

Imagery plays a pivotal role in many mental disorders and clinicians can utilize imagery to treat such disorders.

Mental Imagery

Mental imagery has played a central role in discussions of mental function for thousands of years. Many have argued that it is one of the primary human mental events that allow us to remember, plan for the future, navigate, and make decisions. In addition, mental imagery plays a core role in many mental health disorders and plays an increasingly important role in their treatment.

We use the term ‘mental imagery’ to refer to representations and the accompanying experience of sensory information without a direct external stimulus. Such representations are recalled from memory and lead one to re-experience a version of the original stimulus or some novel combination of stimuli. Note that not all mental imagery need be voluntary; external events or internal associations also can trigger a mental image, even if one does not want to experience the image at that time [1] . Mental imagery can clearly involve all of the senses, but in this review we focus on visual mental imagery, given that most empirical work has addressed this sensory domain.

Historically, mental imagery research suffered for both practical and theoretical reasons. Methodological constraints caused by imagery's inherently private nature put practical limits on the types of mechanistic investigation that could be performed. Furthermore, the second half of the 20th century saw the rise of behaviorism in psychology. This theoretical orientation rejected the study of internal representations, including mental imagery. The combination of these two impediments is largely responsible for the comparative lack of mental imagery research relative to related topics such as visual attention and visual working memory [2] .

Such constraints are now lifting, with increasingly sophisticated research techniques leading to many new discoveries about imagery. In recent years, new objective research methods have permitted more direct investigations into the mechanisms and neural substrates of mental imagery. Results from these methods shed light on mental imagery's role in perception, cognition, and mental health. Findings have cemented our understanding of visual mental imagery as a depictive internal representation with strong and unexpected ties to visual perception, effectively ending the so-called ‘imagery debate’ [3] . Moreover, studies reveal that mental imagery plays a pivotal role in clinical disorders such as anxiety. This upsurge in fundamental and clinical science regarding mental imagery is revealing the central role that mental imagery plays in everyday behavior as well as in human mental function and dysfunction.

Mental Imagery and Weak Perception

Much of the work on imagery and perception in the 1990s and 2000s revealed that imagery shares processing mechanisms with like-modality perception. For example, researchers showed that imagined visual patterns interact with a concurrent perceptual stimulus to boost sensory performance in a detection task [4] . Many studies converged in demonstrating that mental imagery could function much like afferent sensory perception. Imagining oriented lines can induce an orientation aftereffect [5] or imagining a moving stimulus can induce a motion aftereffect on a subsequent perceptual stimulus, much like normal perception [6] .

Mental images can also take the place of perceptual stimuli during various types of learning. Perceptual learning typically involves repeated performance of a perceptual detection or discrimination task that leads to increases in performance. However, imagining the crucial components of such a task, instead of actually performing them on a perceptual stimulus, can also enhance performance on the perceptual task [7] . For example, when participants repeatedly imagine a vertical line between two perceptual lines they subsequently perform better in discriminating the distances between three perceptual lines [7] . Similarly, classical conditioning can occur with voluntarily formed visual imagery in place of perceptual stimuli [8] . In both of these examples the imagery-based learning is later tested with perceptual stimuli, which demonstrates generalization from the imagined to the perceptual content.

One important requirement in mental imagery research is to ensure that the effect of visual imagery on concurrent perception is not merely being driven by visual attention. Many studies have demonstrated that applying attention to a particular stimulus, or part of one, can change multiple dimensions of sensory perception. For example, attention alone can increase stimulus contrast, color, or coherency [9–11] . Studies using the ‘ binocular rivalry ’ technique (see Glossary ) have demonstrated contrasting effects of imagery and attention. When participants visualize one of two patterns, the imaged pattern has a much higher probability of being perceptually dominant in a subsequent brief single binocular rivalry presentation [2,12,13] . In other words, the content of the mental image primes subsequent dominance in binocular rivalry, just as a weak or low-contrast perceptual stimulus would do. Moreover, these effects grow stronger with longer image generation times, whereas increasing periods of applying attention to a particular stimulus does not modulate this priming effect [12] . Furthermore, particular experimental manipulations can attenuate the priming effect of imagery while leaving intact the effect of prior attention [12] . Thus, imagery can be dissociated from visual attention along at least two different dimensions.

An emerging consensus from multiple behavioral studies is that the influence of prior perceptual stimuli on subsequent perceptual tasks depends on the ‘perceptual energy’ or strength of the prior stimulus [12,14–16] . Facilitation is more likely if the preceding stimulus is short and/or low contrast, whereas suppression is more likely when a prior stimulus is high contrast and/or is shown for a long duration [12,14–16] . Hence, the facilitative effect of a prior stimulus increases as the strength or presentation duration increases until it reaches a tipping point, when the effect reverses and leads to reduced facilitation and increased suppression ( Figure 1 ) [12,15,16] . Evidence suggests a single continuous mechanism that depends on the visual ‘energy’ of the prior stimulus; that is, its sensory strength. Behavioral data thus far show that the effect of imagery on subsequent perception is limited to the facilitation range, and not suppression ( Figure 1 B, right panel) (for a review see [2] ).

Imagery Resembles a Weak Version of Perception. (A) A useful way to conceptualize mental imagery is as a weak form of sensory perception. (B) A schematic illustration of the effects of prior perceptual stimuli at different strengths and of imagery on subsequent perception. The left graph shows hypothetical data for prior perceptual stimuli at different strengths (e.g., contrasts). Low-contrast prior stimulation facilitates subsequent detection [16] or binocular rivalry dominance [12,15] , whereas high-contrast prior stimulation will induce a suppressive aftereffect. By contrast, on the right graph, imagery only facilitates subsequent perception. Overall, imagery acts much like weak perception. Schematic data plots are based on data from [12,15] .

If mental imagery is conceived of as a type of top-down perception, visual features such as luminance or brightness should also be preserved in imagined representations and should have similar effects on physiology. Indeed a recent study demonstrated exactly that: the brightness of the imagined stimulus had a reliable and predictable effect on pupil constriction, as it does during perception [17] .

In addition, brain imaging work has provided compelling evidence that visual mental images arise from activation of the same types of visual features that are activated during visual perception. Several studies have explicitly modeled the representations encoded in activity during perception and then used the model to decode mental images from brain activity. To our knowledge this explicit modeling approach – known as voxel -wise modeling and decoding (VM) – was first applied to mental imagery in 2006 [18] . This landmark study designed a voxel-wise model of tuning to retinotopic location (i.e., a receptive field model) and then used it to decode mental images of high-contrast blobs in various configurations. Consistent with the evidence for retinotopic organization in mental imagery [19] , models of retinotopic tuning during visual perception could be used to identify and even reconstruct mental images of the stimuli ( Figure 2 ).

Activity Patterns Evoked by Visual Perception and Visual Mental Imagery Are Increasingly Similar with Ascension of the Processing Hierarchy. This diagram summarizes an organizing principle that is implicit in the fMRI literature on visual mental imagery. Here the ventral stream is coarsely grouped into early visual areas (shaded brain region, left panels) that represent low-level visual features (e.g., edges, textures) and higher-level visual areas (shaded region, right panels) that represent scene-level information and object categories. For purposes of illustration, we consider hypothetical multivoxel populations comprising just two voxels in the early visual cortex (left) and two voxels in the higher visual cortex (right). Activity patterns are represented as vectors in a 2D space in which the axes correspond to the two hypothetical voxels. In the early visual areas, activity associated with mental imagery has a lower signal-to-noise ratio (SNR) than activity associated with perception. This means that the mean activity vector (black arrow) evoked by visualizing a particular stimulus is shorter than the mean activity vector evoked by actually seeing a corresponding stimulus, while the spread of activity patterns around the mean activity vector (arc length) is larger. In the higher visual areas, the SNR associated with mental imagery is not as severely attenuated.

It could be argued that imagining simple, blob-like stimuli that are optimized to engage area V1 is a special case of mental imagery that may be unrelated to the rich, complex imagery we generate and use daily. However, the representation of low-level visual features is conserved even when people visualize complex, multi-object scenes such as photographs and artwork [20] . A model that was tuned to retinotopic location, spatial frequency, and orientation [21] picked out mental images of specific works of art from among thousands of other randomly selected images – and even from other examples of the artists’ own work. Performance was lower than that for actual perception, but still much better than that expected by chance. Thus, representations of retinotopy and spatial frequency – quintessential ‘visual’ representations – are encoded in activity even during rich and complex mental imagery.

Furthermore, sensitivity to both perceptual orientation and location in visual space has been linked to the anatomy of V1 [22,23] . Likewise, mental imagery precision of spatial orientation and location in retinotopic space are both associated with the size of V1 [24] . In fact, the precision of both mental imagery and visual perception is correlated with the size of area V1, providing further support for the commonalties between the two.

Together, these studies show that activity patterns in the visual cortex are not merely similar across visual mental imagery and perception: activity patterns encode a common set of visual representations. When considered in light of the behavioral evidence reviewed above, these results lend further support to the conceptualization of visual mental imagery as a weak or noisy form of top-down perception that can in some cases take the place of bottom-up perception.

Mental Imagery and Visual Working Memory

Although mental imagery and visual working memory both involve the ability to represent and manipulate visual information, research on the two topics has diverged into two separate literatures that rarely reference one another [25] . Because of the different behavioral measures and tasks used, it has proved challenging to establish the degree of commonality between the two functions.

When participants in visual working memory experiments are asked to describe the strategies they use to complete the memory task, they tend to describe one of two different strategies. One involves creating a mental image to compare with the subsequent test stimuli [26–28] ; the other strategy involves picking out particular details of a scene or array and encoding them phonologically or verbally, which is then compared with the test stimuli [26,28,29] .

Recent behavioral work supports these subjective reports of different strategies [29,30] (but see [31] ). This behavioral work directly compared the sensory strength of mental imagery and different measures of visual working memory. Individuals with stronger mental imagery had greater precision and higher capacity in visual working memory tasks, but not in iconic or verbal working memory tasks [29,30] . Furthermore, only those participants with strong sensory imagery were perturbed by the passive presence of uniform background luminance during visual working memory storage, but not in a verbal working memory task. Importantly, the creation of visual mental images is also perturbed by the presence of uniform passive luminance [12,32] . In addition, in a similar vein to visual imagery, the content of visual working memory can bias perception [33] and can facilitate detection in the neglected hemifield of visual extinction patients [34] .

Taken together, these behavioral data suggest that those with relatively strong mental imagery utilize it to perform a visual working memory task whereas those with weaker imagery tend to rely on nonvisual strategies.

Brain imaging work has demonstrated overlap in the neural representation of visual working memory and mental imagery. For example, in one study [35] , on some trials, participants were required to hold an oriented grating pattern in visual working memory until their memory performance was tested with a probe stimulus; on other trials, the same participants had to form and rotate a mental image of the same grating in accordance with a given cue. BOLD activity patterns in area V1 enabled accurate decoding of which pattern was being held in visual working memory and in the mental rotation (imagery) condition. When the classifier was trained on data from the working memory condition and then applied to decode data from the imagery condition, performance was just as high [35] . This generalization of decoding from memory to imagery is evidence for commonalities in the spatial pattern of BOLD activity during the two tasks. This in turn is evidence for representational overlap between mental imagery and visual working memory. Recent results also show that both visual working memory capacity and imagery strength and precision are associated with the surface size of V1 [24,36] .

The combination of behavioral and brain imaging data shows that, despite clear task differences (‘Hold this visual information in memory and we will subsequently test you on it’ vs ‘Create a mental image of this’), mental imagery and visual working memory can share common neural mechanisms in the sensory cortex. In many tasks, participants have to decide for themselves how best to maximize their memory performance. Depending on the ‘mental tools’ at hand, this might be with mental imagery or a propositional strategy. Recent work suggests that imagery strength and the neural networks underlying imagery may play a role in how individuals perform such tasks [37] .

The key to unlocking the mechanistic relationship between visual imagery and visual working memory may lie in the individual differences across the population in visual representational strength, physiology, and even anatomy [36] . If a subset of the population tends to utilize imagery to aid memory performance, as the evidence suggests, whereas another subset of people who lack strong imagery utilize a different strategy, collapsing across these two groups could induce inconsistencies in visual working memory data. Separating participants into these groups, based the strength of their imagery, may be a good starting point for gaining clarity on the neural machinery used in visual working memory.

Graded, System-Wide Activation of Visual Cortex during Mental Imagery

The human visual system is a constellation of functionally distinct areas. These areas are conceived of as being organized in a hierarchy. Activation in areas toward the top of the hierarchy – so-called ‘high-level’ visual areas – is sensitive to changes in the semantic content of visual scenery and is invariant to visual detail. These areas are located in the ventral temporal lobe and representations encoded in the activity of these areas become increasingly abstract toward the anterior pole. Areas toward the bottom of the hierarchy – the ‘early’ visual areas – are located in the occipital cortex and are exquisitely sensitive to visual detail (e.g., retinotopic location, spatial frequency, edges).

Given this organization and the fact that early visual areas both send projections to and receive projections from high-level visual areas, many researchers have predicted that the role of the early visual areas in mental imagery is to flesh out visual detail.

Between 1993 and 2010, at least 20 studies attempted to test this hypothesis by using brain scanning to compare the amplitude of activity in early visual areas during visual mental imagery with the amplitude of activity during perception (or rest). Many studies reported no significant activity above baseline in the early visual cortex during mental imagery [38–45] , but a slightly larger number of studies reported significant activity [46–57] . The discrepancy may be explained by differences in experimental factors [58] and variation in the vividness of mental imagery across individuals [55] . Meanwhile, the evidence for activation in high-level areas during imagery is uncontested; studies published over a decade from different groups have shown comparable levels of activity across visual mental imagery and visual perception in high-level visual areas [38,49,59,60] . Although activation in higher areas during mental imagery is more robust than in early visual areas, the vividness of mental images appears to be most tightly coupled to activity in the early visual areas [55] . See Box 1 for other evidence.

Brain Damage Studies

Many studies have related measures of imagery to damage in particular parts of the brain. For example, in one study researchers [112] asked patients with unilateral neglect to imagine standing at either end of a well-known piazza in Milan, Italy. The primary symptom of unilateral neglect is that when patients look at a perceptual scene in front of them they tend to neglect one side of space. When asked to describe the piazza in their mind's eye, the patients described landmarks on one side of the square only. To ensure that this was not due to memory deficits, patients were asked to imagine the piazza from the opposite vantage point; the patients could describe the details of the previously neglected side, but now neglected the other side. This result was taken as evidence that imagery and perception share common neural processes at the level of attentional deployment.

Damage to the early visual cortex has also been diagnostic in the role of area V1 in imagery generation. In one study researchers were able to test the size of visualized objects both before and after a unilateral occipital lobe resection for epilepsy treatment [113] . Using a particular method that involves imagining an object and then walking toward it in the mind's eye until the object fills the entire visual field, and then reporting the distance between the individual and the object, researchers are able to infer the maximal image size. Here they found that after surgery the patient's maximal image size shrunk in the horizontal dimension compared with the image size before surgery.

Such data suggest that area V1 plays a functional role in visual mental imagery. However, other studies have demonstrated that it is possible to have intact and even vivid mental imagery, both behaviorally and when assessed using brain imaging, despite near-complete blindness due to cortical grey matter damage in the calcarine sulcus (V1) [114] . Hence, damage to V1 will impair mental images but, even with V1 almost completely gone, mental imagery remains possible ( Box 2 ).

Thus, the results from studies of patients with brain damage are consistent with the results from fMRI and behavioral studies noted in the text: early visual areas can contribute to imagery, but other areas also play key roles. This inference is consistent with the idea that mental images, like visual percepts, rely on representations that are collaboratively constructed by visual areas at all stages of the visual processing hierarchy.

However, activation is only part of the picture. Recent studies using multivariate pattern classifiers (MVPCs) have shown that the same pattern classifiers that accurately discriminate stimuli by analyzing patterns of activity in visual areas V1 and V2 during perception of simple external stimuli can also discriminate the same stimuli during mental imagery [35,61,62] . This suggests that, although overall levels of activation in V1 during imagery are relatively low, the patterns of activity across imagery and perception in V1 and V2 are similar. This finding again supports the hypothesis of a shared representational format in imagery and perception.

MVPC studies have also revealed similarity in activity patterns across visual perception and imagery in high-level visual areas [59,60,63] . Consistent with activation studies, decoding performance is typically more robust in high-level areas than in early visual areas. Thus, MVPC studies of visual mental imagery support the claim that patterns of activity in perception and imagery become increasingly similar with ascension of the visual hierarchy (see Boxes 1 and 2 ).

Primary Visual Cortex and Mental Imagery

During visual perception, area V1 is distinguished both by its anatomical location and by the visual features that are encoded in its activity. This area is anatomically privileged because it is a gatekeeper of retinal information into the cortex. It receives more direct connections from the lateral geniculate nucleus than any other part of the visual cortex. However, during mental imagery its proximity to the retina does not make it special. The source of mental imagery is unknown, but it is likely that memory-encoding structures in the medial temporal lobe (MTL) and executive structures in the prefrontal cortex are critical. In addition, area V1 is distinguished by its representation of low-level visual features. Feed-forward models of perception treat these low-level features as the building blocks of object representation. However, feedback models treat them not as foundational for constructing representations of objects but as a tool for error-checking predictions about what objects are present in the immediate environment [115] .

The anatomical importance of V1 during mental imagery may be derived from its topographical organization, which allows it to make explicit and accessible geometric properties that are only implicit in representations stored in long-term memory. In other words, the role of V1 in imagery may be determined by the kinds of inferences it allows to be drawn from a mental image. For example, if we want to infer whether a German shepherd dog has pointed or floppy ears [116] we may need to invoke a V1-like representation as a component of our mental image. If we simply want to infer whether an elephant is bigger than a mouse, it may suffice to invoke representations in any one of the many visual and/or parietal areas that are topographically mapped. This idea is perfectly consistent with findings that imply that the extent of V1 activation during mental imagery is task dependent.

In addition, the role of V1 in imagery is likely to vary enormously across individuals. Area V1 may make very different contributions to mental images for different people, depending on how important its representations are to the way in which each person imagines objects and/or scenes [117] . Recent research has documented that the size of area V1 predicts the sensory strength and precision of visual imagery [24] . Such relationships dovetail nicely with capacity limitation theories that propose an interaction between the content and anatomical restrictions due to the 2D layout of structures like V1, which support the representations [118] .

Taken together, the MVPC and activation studies indicate that activity patterns associated with matched external and imagined stimuli begin to resemble one another as early as area V1. The resemblance increases with ascension of the visual hierarchy, although vividness of imagery appears to be most closely associated with early visual areas.

This general picture of how mental imagery engages the visual cortex satisfies many intuitions about how mental images are generated. For example, mental imagery is presumably based on the recall and recombination of memories. Because high-level areas are physically (and synaptically) closer to memory-encoding structures in the medial temporal lobe than are earlier visual areas, it makes sense that the activity patterns associated with perceived and imagined images should more closely resemble one another in high-level than in early visual areas. This may also explain why the semantic aspects of mental images tend to be less ambiguous than visual details (e.g., we can know for certain that we are imagining a zebra and not a horse, even if we are not able to imagine the zebra's individual stripes). Lastly, it makes sense that the parts of the visual system responsible for visual detail should be most closely coupled to the visual vividness of mental images.

Mental Imagery in Mental Disorders and Their Treatment

In a similar time frame to the burgeoning fundamental mechanistic investigations we have discussed so far, mental imagery has also been found to play a pivotal role in many mental and neurological disorders and their treatments. For example, intrusive, emotional mental imagery causes distress across a range of psychiatric disorders, from post-traumatic stress disorder (PTSD) and other anxiety disorders to bipolar disorder and schizophrenia [64] . However, those psychological therapies primarily based on verbal exchanges have historically neglected imagery, primarily focusing on the patient's verbal thoughts.

After a psychologically traumatic event, a significant proportion of people develop PTSD [65] . PTSD – characterized by re-experiencing the traumatic event through unwanted and recurring intrusive memories and nightmares – provides a hallmark illustration of clinically relevant mental imagery. An example of an intrusive memory is re-experiencing a vivid visual and auditory mental image of the moment a red car hit a child on the sidewalk. Such distressing images may be only fleeting and may occur only a handful of times per week, but their impact can be profound. The patient may avoid reminders of the traumatic event such as cars, children, or walking down a street and may feel a sense of current threat and a racing heart. These imagery-based memories are not a mere epiphenomenon of having PTSD but a cognitive mechanism driving the maintenance of the ongoing clinical disorder [66] . In other words, the intrusive images can strongly affect behavior and physiology.

Recent years have witnessed an explosion of research suggesting that mental imagery plays a role across a wide range of mental disorders [64,67–69] . Distressing and unwanted emotional imagery has been shown to occur in many mental disorders and the imagery content matches the core concern of people with the disorder. For example, a patient with arachnophobia (fear of spiders) may report imagery of large, hairy spiders with fangs. A patient with obsessive compulsive disorder (OCD) may have images of contaminated grubs boring into his skin and therefore feel dirty, fuelling the behavior of repeated washing. During conversation, a patient with social phobia (fear of public speaking) may experience concurrent imagery of how she appears to her conversational partner, envisioning herself as red and sweating. A patient with bipolar disorder may have future-oriented imagery and ‘flash forward’ to a suicidal act [70] . Conversely, in depression, people can report difficulties imagining a positive future [71] (see Figure 3 ).

Imagery Is a Key Part of Symptoms in Mental Disorders – From the Intrusive Memories of Trauma in Post-traumatic Stress Disorder (PTSD) to the Lack of Positive Future Imagery in Depression. It presents a cognitive mechanism driving psychopathology, and thus imagery can also be targeted as a process – and harnessed as a tool – in psychological treatment.

The Clinical Relevance of Mental Imagery

Given the centrality of intrusive emotional imagery in such a wide variety of mental disorders, a basic understanding of mental imagery could prove instrumental in the development of new treatments. Potentially critical issues include the relative emotional impact of different representational formats, imagery ‘realness’, and the perceived likelihood of imagined events occurring.

Until recently, surprisingly little research had tested the relative impact of picture-like formats versus language-like descriptive formats on emotion; that is, mental imagery versus verbal thought (for an exception see [72] ). Recent experiments support the hypothesis that, compared with verbal processing of the same content, mental imagery elicits stronger emotion. For example, in one experiment participants were given negative scenarios with instructions that promoted either verbal processing or mental imagery [73] . Imagery led to a greater increase in anxiety. When presented with positive scenarios, imagery again amplified (positive) emotion [74,75] . Such data are consistent with the finding that emotional memories have more sensory–perceptual features than do non-emotional memories [76] .

Other imagery properties are also important. Compared with verbal thoughts of similar content, mental images are rated as more ‘real’ [77] . Many patients report that their imagery ‘feels real’ despite having the knowledge that they are not real, the images have a profound impact on their behavior. The apparent realness of clinical imagery seems to add to its power, influencing not only behavior and emotion but also beliefs. Hallucinations in schizophrenia are defined as mental experiences believed to be external percepts. Both schizophrenia [78] and Parkinson's disease [79] involve involuntary sensory hallucinations. In Parkinson's disease the degree of visual hallucination is well predicted by the sensory strength of an individual's voluntary mental imagery [79] .

Repeatedly imagining a future event increases its perceived likelihood of occurrence [80] . This simulation heuristic effect also occurs for anxiety-provoking future events [81] , increasing anxiety levels. Conversely, imagining an event that supposedly occurred in the past (even if it did not) inflates a person's confidence that the event actually did occur [82] .

Imagined rehearsal of an action influences the likelihood that a person will complete that action [83] . Although such promotion of a behavior might be useful when actions are desired such as in sports psychology, one can see how its consequences can be maladaptive in psychopathology; for example, by increasing washing behavior in OCD. Similarly, imagery of a desired substance may contribute to cravings and thereby drive addictive behaviors [84,85] . In depression, imagining suicidal acts may even increase the risk of suicide [86] . Conversely, impaired ability to simulate positive future events is related to depression [87,88] (a disorder characterized by pessimism) whereas trait optimism is associated with greater ability to mentally simulate positive future events [119] .

Mental Imagery in Clinical Treatments

These intriguing results on the emotional and behavioral impact of mental imagery offer insights into the development of new treatments for anxiety disorders. It is difficult to treat problematic emotional imagery with purely verbal discussion in therapy: to reduce imagery symptoms effectively, therapeutic techniques should include an imagery-focused component. Mental imagery techniques are currently used in some evidence-based treatments. For example, cognitive behavioral therapy (CBT) often includes ‘imaginal exposure’, which involves having the patient repeatedly imagine the feared object or context (e.g., contaminated hands) until his or her anxiety level subsides [89] . Imaginal exposure is a key technique, used across anxiety disorders.

Another technique, ‘imagery rescripting’ [68] , aims to transform the imagery content. For example, in social phobia the negative outcome of mental imagery (e.g., performing badly) is changed to a new, more adaptive image such as performing competently [90] . ‘Systematic desensitization’ uses gradual exposure to images of feared objects or situations, whereby the imagery is paired with an incompatible response to the fear – such as physical relaxation – until the image no longer evokes negative emotion [91] . A form of therapy called ‘eye movement desensitization and reprocessing’ (EMDR) promotes lateral eye movements during the recall of emotional memories; this technique appears to dampen the vividness and emotionality of imagery [92] .

These imagery-focused therapeutic techniques reduce the powerful impact of dysfunctional imagery on emotion and/or reduce the frequency of associated intrusive imagery. It is noteworthy that imagery-focused CBT, as reviewed in clinical guidelines [120] has the strongest documented impact on treating PTSD and social phobia, with some trials showing success rates of up to 75%.

Future Mental Imagery Treatments

How might mental imagery research lead to future treatment innovations? First, we can import existing imagery techniques to clinical areas where imagery has been neglected. For example, treatments for bipolar disorder have shown little improvement since the discovery of lithium many decades ago. Perhaps advances can be made by leveraging the fact that people with bipolar disorder show high spontaneous use of imagery and intrusive imagery [70,93] . By considering the possible role of imagery in this disorder, new treatments could be devised by importing imagery techniques from those used to treat anxiety disorders. Another example is addressing hopeless, pessimistic future orientation by training patients with depression to generate more adaptive mental imagery and simulate future positive events. An initial randomized controlled trial including computerized positive imagery training in depressed patients showed some promising results [94] (though see also [71] ), requiring further research.

Second, basic science studies of mental imagery may inform the development of new imagery treatment techniques by focusing on the depictive, pictorial format of imagery itself. For example, as discussed above, concurrent perception may interfere with image generation [2,12,95] . This finding is consistent with the fact that strategically applied visual tasks, such as the computer game Tetris, performed soon after an experimental trauma (in the time window for memory consolidation), reduce the frequency of intrusive images [96] ; this technique has recently been extended to reconsolidation [97] . Such findings may open ways to prevent the accumulation of intrusive images, which is important because we need preventative treatments for PTSD [98] . Linking studies of emotional imagery with neural mechanisms may also be useful [99,100] .

Overall, discoveries about mental imagery can contribute to our understanding of the cognitive and neural mechanisms underlying psychopathology and of which mechanisms to target in improving treatments [101] . Even the best treatments do not work for everyone and effective treatments are not yet available for all mental disorders. Science-driven mental imagery treatment techniques could greatly help and may even offer treatment innovations that look little like traditional talking therapies. Such treatments would capitalize on the principle that imagery involves a depictive format with its own set of properties [3] ( Box 2 ).

Concluding Remarks

The many new methods touched on here not only offer new mechanistic insights into mental imagery but also offer new tools for future research. Recent work has demonstrated how imagery can ‘stand in’ for an afferent visual representation of an external stimulus. Specifically, mental images seem to behave much like weak versions of externally trigged perceptual representations. Functional brain imaging work supports the behavioral evidence by demonstrating that common sets of neural structures are employed during both events. Further, both representations seem to be encoded using a common set of basic visual features, which in many visual areas are organized topographically.

An increasingly important component of imagery research now and in the future is the translation of the fundamental science into the clinic. Clinical research shows that many different mental disorders involve symptomatic imagery, and incorporating imagery into behavioral treatments is proving beneficial. Bridges from fundamental research to emotional imagery will be critical for the systemic understanding of mental representations in dysfunction. Similarly, the characteristics and function of mental representations in everyday cognition will help form a fuller understanding of human mental events.

The main functions of mental imagery include simulating possible future scenarios and ‘reliving’ past experiences [83,102,103] . From this perspective, imagery should perhaps be studied not only in its own right but in many types of cognitive tasks. Beyond visual working memory, we know that imagery plays a role in affective forecasting [104] , eye witness memory [105] , making certain moral decisions [106] , prior expectation templates to aid in predictable visual tasks [107] , and facilitating emotion [108] . Mental simulations are now used to detect consciousness in vegetative state patients [109] and can be decoded using brain imaging during the early stages of dreams [110] . One interesting proposition is that all forms of cognition involve modality-specific mental simulations, known as embodied or grounded cognition [111] . Such theories imply that imagery plays a functional role in all cognitive events. It is exciting to begin to see the detailed, ubiquitous, and multifaceted role imagery plays in our everyday lives, both in function and dysfunction.

Outstanding Questions

How do perception and mental imagery differ? There are clear phenomenological and epistemological differences between external perceptual and mental images, and patterns of activity measured during imagery and perception of the same stimulus are not identical. A first step toward answering this question will be to discover whether imagery-induced neural activity patterns are simply weaker or noisy versions of the activity during the perception of matched external stimuli or whether they encode systematically distorted representations.

Are individuals able to exploit the differences between mental imagery and perception?

How does mental imagery differ from other forms of top-down activity? Visual perception is heavily influenced by working memory, attention, and expectation. Clearly mental imagery is related to these disparate cognitive phenomena, but more work is needed to elucidate the networks and patterns of neural activity that distinguish mental imagery from these and other modes of cognition and perception.

Can mental imagery be involuntary, as clinical theory proposes? Or do individuals simply lack conscious awareness of the voluntary process (e.g., have poor metacognition)? The type of imagery prevalent in many mental disorders is typically described as involuntary, or not under the individual's control (see the clinical section). Little is known about the mechanisms that distinguish voluntary and involuntary imagery.

Can mental images be generated non-consciously?

What functional mechanisms dictate individual differences in imagery strength?

There are many examples of visual illusions that create a conscious visual experience without a direct stimulus. Might the involuntary nature of such phantom perceptual experience offer a novel way to study the involuntary elements of imagery?

Disclaimer Statement

The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the United Kingdom Medical Research Council.

Acknowledgments

J.P. is supported by Australian NHMRC grants APP1024800, APP1046198, and APP1085404, Career Development Fellowship APP1049596, and ARC Discovery Project DP140101560. E.A.H. is supported by the Medical Research Council (United Kingdom) Intramural Programme (MC-A060-5PR50), a Wellcome Trust Clinical Fellowship (WT088217), and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre Programme. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Funding to pay the Open Access publication charges for this article was provided by the United Kingdom Medical Research Council.

School of Writing, Literature, and Film

• BA in English
• BA in Creative Writing
• About Film Studies
• Film Faculty
• Minor in Film Studies
• Film Studies at Work
• Minor in English
• Minor in Writing
• Minor in Applied Journalism
• Scientific, Technical, and Professional Communication Certificate
• Academic Advising
• Student Resources
• Scholarships
• MA in English
• MFA in Creative Writing
• Master of Arts in Interdisciplinary Studies (MAIS)
• Low Residency MFA in Creative Writing
• Undergraduate Course Descriptions
• Graduate Course Descriptions
• Faculty & Staff Directory
• Faculty by Fields of Focus
• Promoting Your Research
• 2024 Spring Newsletter
• Commitment to DEI
• Twitter News Feed
• 2022 Spring Newsletter
• OSU - University of Warsaw Faculty Exchange Program
• SWLF Media Channel
• Student Work
• View All Events
• The Stone Award
• Conference for Antiracist Teaching, Language and Assessment
• Continuing Education
• Alumni Notes
• Featured Alumni
• Donor Information
• Support SWLF

The CLA websites are currently under construction and may not reflect the most current information until the end of the Fall Term.

What is Imagery? || Definition & Examples

"what is imagery" a guide for english essays.

View the full series: The Oregon State Guide to English Literary Terms

• Guide to Literary Terms
• BA in English Degree
• BA in Creative Writing Degree
• Oregon State Admissions Info

What is Imagery? Transcript (English and Spanish Subtitles Available, Click HERE for Spanish Transcript.)

By Raymond Malewitz , Oregon State University Associate Professor of American Literature

24 April 2019

As human beings, we understand the world through our senses—what we see, what we hear, what we smell, what we taste, and what we touch.  To represent this process in their literary works, storytellers and poets use vivid language designed to appeal to these senses.  This language is called imagery.   Let me give you one example.

In Kate Chopin’s short story “The Story of an Hour,” a woman named Mrs. Mallard is told that her husband has just been killed in a railroad accident.  After retreating to her room to grieve, she looks out her window.  Chopin writes:

"She could see in the open square before her house the tops of trees that were all aquiver with new spring life.  The delicious breath of rain was in the air.  In the street below a peddler was crying his wares.  The notes of a distant song which someone was singing reached her faintly, and countless sparrows were twittering in the eaves."

imagery_kate_chopin_the_story_of_an_hour.jpg

In this passage, Chopin’s imagery appeals to a variety of senses: the sight of quivering trees, the smell of rain, the sound of twittering sparrows, and so on.

As this passage suggests, imagery often does more than simply present sensory impressions of the world: it also conveys tone , or the attitude of a character or narrator towards a given subject.  By concentrating on what Mrs. Mallard experiences at this moment-- quivering trees, singing birds, and smells of rain –Chopin’s narrator allows readers to understand the complex way in which Mrs. Mallard views her husband’s death—as both a tragic event and a rebirth of sorts in which the spring imagery conveys the freedom she imagines beyond the confines of her marriage. 

Instead of telling us these thoughts through exposition or explanation, Chopin’s narrator shows us the worldview of her character and encourages us to interpret what this imagery means.  This difference is crucial for students interested using the term “imagery” in their literary essays.  Rather than writing that imagery is good or bad, vivid or dull, students should instead try to connect imagery to the thoughts of a character, narrator, or speaker. 

Want to cite this?

MLA Citation: Malewitz, Raymond. "What is Imagery?" Oregon State Guide to English Literary Terms, 24 Apr. 2019, Oregon State University, https://liberalarts.oregonstate.edu/wlf/what-imagery-definition-examples. Accessed [insert date].

Further Resources for Teachers

H.D.'s short poem "Oread" and Leslie Marmon Silko's short story "The Man to Send Rain Clouds" offer students two different good opportunities to practice linking imagery to the worldview of certain speaker. 

Writing Prompt #1: In H.D.'s poem, a forest nymph sees the waves of the sea as "pointed pines," which is a very strange metaphor. How does this imagery provide insight into ways that that creature experiences the world?

Writing Prompt #2: In Silko's story (which was published under the name Leslie Chapman), the fourth section drops into what might be called a "close" third-person aligned with the priest's perspective on the ritual he is performs. But instead of providing his actual thoughts, Silko chooses to present how he sees the world through detailed imagery.  What does this imagery convey about his thoughts on the ritual and why might Silko has chosen this oblique or indirect style to convey it?

Interested in more video lessons? View the full series:

The oregon state guide to english literary terms, contact info.

Email: [email protected]

College of Liberal Arts Student Services 214 Bexell Hall 541-737-0561

Deans Office 200 Bexell Hall 541-737-4582

Corvallis, OR 97331-8600

liberalartsosu liberalartsosu liberalartsosu liberalartsosu CLA LinkedIn

• Dean's Office
• Faculty & Staff Resources
• Research Support
• Featured Stories
• Undergraduate Students
• Transfer Students
• Graduate Students
• Career Services
• Internships
• Financial Aid
• Honors Student Profiles
• Degrees and Programs
• Centers and Initiatives
• School of Communication
• School of History, Philosophy and Religion
• School of Language, Culture and Society
• School of Psychological Science
• School of Public Policy
• School of Visual, Performing and Design Arts
• School of Writing, Literature and Film
• Give to CLA