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Making a Decision: Using Conscious vs Unconscious Thinking to Solve the Problem

Approximately a decade ago, academic circles were set abuzz, as they are wont to do, by a set of studies that had produced some curious findings. The experiments went like this: a group of college students, with nothing better to do and eager to earn a little beer money, were split into 2 groups. Each participant was asked to select the “better” of 2 apartments or automobiles or some other common consumer product. Although one man’s palace can be another’s prison, the studies were designed such that one of the two options would be objectively superior — that way there would always be a right answer. Both groups of students were given the same information to consider before making their decision, although some were given data about a greater number of attributes, so that the researchers could distinguish between simple and complex decision making.

As it turned out, at least in some cases, the students made better decisions — that is, more frequently selected the superior item — if, before making their choice, they were subjected to a period of what is known as deliberation without attention. Basically, if the students were made to solve anagrams or word search puzzles for a few minutes — instead of thinking about the problem that they had been assigned — the quality of their decision making improved. What’s more, the effect seemed to become more pronounced as the complexity of the scenario increased.¹ – ³

This conclusion stands in stark contrast to what (we think) we know about thinking and problem solving. Intuitively, sophisticated problem solving requires deliberately, consciously considering the problem before rendering a conclusion. This wisdom is tied up in the aphorisms that we inculcate in young physicians : “Think before you act”; “Don’t just do something — stand there”; “Haste makes mistakes.” Still, doctors immediately took note, and rightfully so. We are constantly making complex decisions under conditions of risk and uncertainty — and, in our world, the consequences of a bad decision are a lot more severe than getting stuck with a car that’s a lemon for a few years. If we could improve decisions simply by replacing conscious deliberation with a brief period of distraction, it would represent a great — and essentially costless — step forward for us, and for our patients.

Alas, when things appear too good to be true, they usually are. Contrary to our hopes and dreams, the value of deliberation without attention has failed to be validated in anything resembling a clinical environment. In fact, substantial questions have since been raised about the existence of this effect in any context. A comprehensive meta-analysis, for instance, found that although a number of the supporting studies did demonstrate an effect in the hypothesized direction, they also very often failed to reach the level of statistical significance. That same meta-analysis also concluded that conscious thinkers were more successful at both identifying the top choice and distinguishing between more mediocre options. 4 Studies that have focused exclusively on clinical context have come to similar conclusions. The takeaway is that there is no reason to believe that practicing physicians would benefit from distracting themselves with irrelevant tasks before making patient-centered decision. 5 Too bad. I guess I shouldn’t have rushed out and bought that enormous book of word search puzzles. 

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When I was going over these studies, one particular finding jumped out at me. In one experiment, the participants were divided into expert and novice groups, and each group was given either simple or complex medical problems to solve. As I am sure you guessed, deliberation without attention was mostly found to be useless, but there was at least one exception: when novice doctors ( medical students , actually) tackled simple problems. It seemed that, compared with making an immediate judgment, taking a few minutes to complete some irrelevant mental exercise improved their outcomes substantially. 6

There is not much other literature corroborating this finding in a clinical context, so it is entirely possible that I am being led down the garden path by the streetlight effect, confirmation bias, or some other nefarious impediment to clear thinking. However, I think that this result makes good sense and says something about the how we teach young doctors to think. The unifying characteristic of young physicians — including and especially medical students — is that their fund of knowledge is poorly developed. They simply do not know as much about medicine and clinical decision making as experts do. That helps explain why unconscious deliberation is useful to them for simple problems but not for complex ones. The complex scenarios are likely to require information that the novice does not have, and will not be able to summon, no matter how long he or she spends trying. If that logic is correct — and ultimately verified by the scientific method — then we would do well to consider incorporating systematic non-attentive deliberation pauses into the process of effectuating the relatively simple decisions that we entrust to junior physicians.

More important though is what this study suggests about the various modern initiatives, such as night float regulations and the 80-hour work week, that have sought to draw a bright line between work and personal time. These ideas are rooted in our contemporary understanding of the importance of self-care as well as an acknowledgment that lifestyle considerations can very often dissuade talented people from pursuing demanding specialties. Yet, many smart practitioners bemoan the potential of these regulations, however well intentioned, to both damage the doctor-patient bond and diminish the extent to which any given physician feels responsible for his or her patient’s care. The notion that deliberation without attention might be valuable — even under limited circumstances — only serves to ratchet up that tension. The true value of the study, at least as far as I can tell, is as a reminder that, for their sake, we should always be thinking about our patients, even when we are not —which, come to think of it, is really just another way of re-stating the arguments against duty hour limits. These viewpoints might be impossible to reconcile, and I am not exactly sure how we determine the best path forward. I do have an idea, though. Why don’t we all take some time and sleep on it?

• Dijksterhuis A, Bos MW, Nordgren LF, van Baaren RB. On making the right choice: the deliberation-without-attention effect . Science. 2006; 311(5763):1005-1007.
• Dijksterhuis A, Meurs T. Where creativity resides: The generative power of unconscious thought . Conscious Cogn. 2006;15(1):135-146.
• Dijksterhuis A. Think different: the merits of unconscious thought in preference development and decision making . J Pers Soc Psychol. 2004:87(5):586-598.
• Acker, F. New findings on unconscious versus conscious thought in decision making: additional empirical data and meta-analysis . Judgment and Decision Making. 2008;3(4):292-303.
• Bonke B, Zietse R, Norman G, et al. Conscious versus unconscious thinking in the medical domain: the deliberation-without-attention effect examined . Perspect Med Educ. 2014;3(3):179-189.
• Mamede S, Schmidt HG, Rikers RM, Custers EJ, Splinter TA, van Saase JL. Conscious thought beats deliberation without attention in diagnostic decision-making: at least when you are an expert . Psychol Res. 2010;74(6):586-592. 

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ORIGINAL RESEARCH article

A fresh look at the unconscious thought effect: using mind-wandering measures to investigate thought processes in decision problems with high information load.

• 1 Department of Psychology, Heidelberg University, Heidelberg, Germany
• 2 Independent Researcher, Berlin, Germany

Unconscious Thought Theory ( Dijksterhuis, 2004 ) states that thinking about a complex problem unconsciously can result in better solutions than conscious deliberation. We take a fresh look at the cognitive processes underlying “unconscious” thought by analyzing data of 822 participants who worked on a complex apartment-evaluation task in three experiments. This task’s information-presentation and evaluation parts were separated by different kinds of filler-interval activities, which corresponded to standard conscious-thought and unconscious-thought manipulations. Employing experience-sampling methods, we obtained thought reports during and after filler-interval engagement. Evidence concerning the existence of the Unconscious Thought Effect was mixed, with such an effect being present in the first two experiments only. In these experiments, we further found less problem deliberation to be associated with better performance on the apartment task. Interestingly, this benefit disappeared when we probed participants’ thoughts during the filler interval. We suggested that explicit thought awareness diminishes the Unconscious Thought Effect.

Introduction

Life is full of situations requiring decisions. Some of them are rather simple, others are more complex. What should I make for dinner? Which college should I go to after high school? Should I buy this washing machine or another one? From a layman’s perspective, it sounds reasonable that in such situations serious, conscious deliberation should help us make good and satisfying choices. At the same time, other people might argue that – faced with a difficult decision – one should rather sleep on it, or at least stop thinking about it for a while, to get a fresh look at the situation or let our intuition guide us. Especially for complex decisions, Unconscious Thought Theory (UTT, Dijksterhuis et al., 2006 ; Dijksterhuis and Nordgren, 2006 ) recommends using the latter strategy. Unconscious thought is supposed to lead to better and more satisfying decisions when choosing, for example, between four apartments, which are characterized by a multitude of attributes. In the present work, we took a closer look at thought processes during conscious- and supposedly unconscious-thought intervals by applying methods used in current mind-wandering research within a standard UTT paradigm. In three experiments, retrospective thought protocols as well as thought reports collected online via thought probes offered insights into the cognitive processes leading to decisions within a complex apartment-evaluation task.

Unconscious thought, which is thought or processing in the absence of conscious attention being directed toward a pending problem, was proposed as a separate form of thought distinct from and, in specific situations, superior to conscious thought ( Dijksterhuis and Nordgren, 2006 ). In a typical UTT experiment, participants are introduced to several objects (e.g., apartments) which are characterized by a specific number of positive and negative attributes per object (e.g., “Apartment 1 has a balcony.”). The objectively best object possesses a relatively high number of positive attributes, the objectively worst object a relatively high number of negative attributes. Before evaluating the objects, participants face a distraction-task period or a period of conscious thought about the presented objects. Evidence for the Unconscious Thought Effect (UTE) comes in form of better decisions after distraction periods as compared to conscious thought periods. This effect occurs particularly in complex decision situations, that is, when objects are described by a high total number of attributes, for instance. According to the UTT, the power of the unconscious stems from its high information-processing capacity. The unconscious system is supposed to allow for large amounts of information to be integrated, whereas the conscious system suffers from a low information-processing capacity (e.g., Miller, 1956 ; Nørretranders, 1998 ). The latter refers to task-related cognitive processes that one is consciously aware of during task completion and has the advantage over the unconscious system of being rule-based and very precise ( Dijksterhuis, 2004 ). Consequently, when faced with simple decisions, the conscious system’s capacity is not exceeded and our choice benefits from rule-based cognition. We are able to consciously process all available information, which should result in the best possible decision. When faced with complex decision problems, however, its low information-processing capacity renders the conscious system less efficient because not all available information can be processed simultaneously. In these situations, decisions should benefit from the ability of the unconscious system to integrate a high number of decision-relevant attributes. Indeed, unconscious-thought advantages are most prevalently found for complex decision problems (e.g., Dijksterhuis et al., 2006 ; Strick et al., 2011 ).

Some assumptions of the UTT have been recently criticized and, despite many successful replications, the UTE, which states that unconscious thought improves decision making in complex problem situations, does not always replicate (e.g., Acker, 2008 ; Calvillo and Penaloza, 2009 ; Rey et al., 2009 ; Newell and Rakow, 2011 ). According to Strick et al. (2011) , the debate concerning the UTE focuses on three main open questions: First, how stable and replicable is the UTE? Second, which boundary conditions are necessary for the UTE to appear? And third, what are the cognitive processes underlying periods of unconscious thought ( Damian and Sherman, 2013 ; Dijkstra et al., 2013 ; Abadie et al., 2016 , 2017 )? Not neglecting the first two, the present work primarily addresses the third question. We were interested in participants’ thoughts – unconscious as well as conscious – during distraction periods and intended to shed light on the question of which cognitive processes foster decision making or attitude formation within a standard UTT paradigm.

Schooler and Melcher (1995) suggested that incubation phases, that is phases during which a pending creative or complex problem is put aside to work on something else, cause a change of people’s “mental sets”: By doing so, one may get a fresh look at the situation, which eventually results in better problem solving. This view implicates a passive process of unconscious thought as something that “just happens” while a person is working on a different task. Dijksterhuis (2004) suggested that unconscious thought is rather active, as it renders mental representations more polarized as well as better organized and clustered. However, research on the UTE has been mostly output-centered so far. Measures such as choices, evaluations or attribute-memories have been the variables of interest used to draw conclusions concerning the cognitive processes during presumed unconscious-thought periods. For a long time, in-the-moment thought processes leading to specific manifestations of such output variables have been neglected, probably because they are difficult to assess with standard cognitive methods, making it challenging to directly address the third main question (see above) raised by Strick et al. (2011) . To overcome this problem, current mind-wandering research has been applying experience-sampling methods, which have been shown to be a valid instrument for the assessment of participants’ thought contents during all sorts of tasks (see Smallwood and Schooler, 2015 for an overview). Therefore, we argue that UTT research can benefit from the employment of such methods as they have the potential to offer a “fresh look” at the cognitive processes underlying unconscious- as well as conscious-thought periods.

Mind wandering can be described as disengaged or decoupled task-attention and has been intensively studied in recent years ( Smallwood and Schooler, 2006 ; Mason et al., 2007 ). Although handling many daily tasks requires our focused attention, it is a well know phenomenon that our thoughts drift off from time to time. Even though performance on the task at hand often suffers (e.g., Mrazek et al., 2012 ; Rummel and Boywitt, 2014 ), drifting thoughts also seem to be of adaptive value ( Mooneyham and Schooler, 2013 ; Smallwood and Andrews-Hanna, 2013 ). For example, mind wandering toward unsolved problems or tasks may be beneficial for problem solution or task fulfillment ( Baars, 2010 ; Steindorf and Rummel, 2017 ). Mind wandering is typically measured via self-reports. So-called thought probes interrupt ongoing tasks and ask participants to briefly describe and/or classify their current thoughts. Responses to these probes have proven to be valid ( McVay and Kane, 2012 ) and to be good estimates of mind-wandering frequency as they do not rely on thought-awareness ( Smallwood and Schooler, 2006 ). Finally, they are also often found to correlate with retrospective thought questionnaires (e.g., Steindorf and Rummel, 2020 ), which are a similar, yet distinct mind-wandering assessment method. In such questionnaires, after task completion, participants are asked to categorize the entirety of thoughts they had experienced while working on the task into several categories. In the present experiments, online as well as retrospective mind-wandering self-reports were employed to measure and quantify thought-contents occurring within a standard UTT paradigm.

Furthermore, we considered wandering thoughts as an alternative explanation for UTEs. Previous research concerning the underlying cognitive processes of complex-problem incubation suggests that either unconscious processes or short retrieval intervals during the incubation task foster post-incubation performance ( Damian and Sherman, 2013 ; Dijkstra et al., 2013 ; Abadie et al., 2016 , 2017 ; see also Sio and Ormerod, 2009 ). Automatically occurring, drifting thoughts concerning a still pending problem might represent such short retrieval intervals (cf. Steindorf and Rummel, 2017 ): Considering a typical UTT paradigm, one might argue that during the presentation of object-attribute combinations, an unsolved problem is activated within a participant’s cognitive system ( Watkins, 2008 ). This activation might lead to an increase in mental occupation with – or mind wandering toward – the problem during a period of distraction ( Zeigarnik, 1927 ). Since mind wandering might foster problem solving ( Baars, 2010 ), wandering apartment-thoughts during a distraction period might explain why problem-solving performance is improved for distracted participants. Moreover, the idea that too much deliberation can have destructive effects ( Waroquier et al., 2010 ) as a current problem might be “thought to pieces” led us to assume that mind-wandering episodes during distraction tasks might offer just the right amount of necessary problem engagement.

Further support for our assumptions comes from the combination of findings that, during more demanding tasks, lower levels of mind wandering are reported (e.g., Rummel and Boywitt, 2014 ) and that more demanding distraction tasks within UTT paradigms often lead to worse problem solving compared to less demanding tasks ( McMahon et al., 2011 ; Strick et al., 2011 ; Abadie et al., 2013 ; Waroquier et al., 2014 ). Focusing only on the latter finding, Strick et al. (2011) conclude that distraction tasks with high demands might compete with unconscious thoughts for resources. A different conclusion might refer to mind-wandering processes. That is, as high-demanding tasks do not leave a lot of room for mind wandering to occur, we assume that without this engagement in productive mind wandering toward the active problem, the benefit of a distraction-task period is reduced. In other words, high demands might compete with adaptive mind-wandering processes for attentional resources.

Whether high demands compete with unconscious-thought or with mind-wandering processes, both lines of argumentation suggest that during distraction tasks we allocate attentional resources toward a second ongoing cognitive process, contradicting the definition of unconscious thought as being deliberation-without-attention ( Dijksterhuis et al., 2006 ). Waroquier et al. (2014) offer a solution for this dilemma by considering that attention and consciousness are not identical to each other. One can allocate cognitive resources toward the processing of specific information (attention) without being aware of the process (consciousness, or meta-awareness as it is often termed in the mind-wandering literature). For this reason, Strick et al. (2011) suggested replacing the term deliberation-without-attention with the term deliberation-without-consciousness . Concerning mind wandering, it is found that off-task thoughts occur both with and without awareness ( Smallwood and Schooler, 2006 ). Sometimes we know and “feel” that our minds are drifting off, sometimes we might realize that we have been pondering tonight’s dinner plans only when being asked about our current thoughts. However, although at times we are not aware of our thoughts at the exact moment they are occupying our minds, we are able to put these thoughts into words later, suggesting that we have nevertheless allocated attentional resources toward them. During distraction-task periods within UTT experiments, aware as well as unaware cognitive processes could foster problem solving. Our thoughts might wander toward the still active, unsolved problem, with or without awareness. Attention-demanding wandering thoughts, which we are not aware of, might be what is referred to as “unconscious” within UTT. In the present work, using self-report methods, we intended to bring wandering thoughts into awareness by directly asking participants about their current thought processes. Especially thought probes, which do not rely on thought awareness, could reveal themselves as a promising method for gaining insight into the actual attentional processes occurring during distraction-task periods. That is, participants might experience a compound of aware and unaware problem-related mind wandering, which we intend to capture and to relate to problem-solving abilities.

In the following sections, we describe three experiments, in which we hypothesize UTEs to be mirrored by changes in mind-wandering behavior. More precisely, we expected the amount of apartment-thoughts during distraction-task incubation phases (i.e., typical unconscious-thought phases) to be related to post-incubation performance. In the first experiment, we relied on retrospective mind-wandering questionnaires for a first insight into participants’ thought processes during periods of distraction and conscious thought. In the second experiment, additional thought probes were employed to capture in-the-moment thoughts including unaware processes. The third experiment was conducted to take a closer look at awareness processes in UTT paradigms including thought probes. We first describe our general methods and plan of analyses before attending to the respective experiments.

General Methods and Plan of Analyses

In all following Methods and Results sections, we report how we determined our sample sizes and all data exclusions, manipulations, and measures in the study ( Simmons et al., 2012 ). Following the recommendations of Seli et al. (2018) , we conceptualized mind wandering as task-unrelated thought and explained the concept to our participants accordingly. We named experimental conditions in which participants were instructed to think about previously presented objects during a filler interval conscious thought conditions . Experimental conditions in which participants worked on a distraction task during a filler interval were named unconscious thought conditions . These labels refer to the standard thought-mode manipulations from the UTT literature and do not imply participants’ actual mode of thought, as the latter represents a to-be-examined variable in the reported experiments. Our data are available under https://osf.io/4375q/ (doi: 10.17605/OSF.IO/4375Q

Instruments

Apartment task.

In all three experiments, we used a German version of an apartment task 1 originally developed by Dijksterhuis (2004) to assess participants’ problem solving abilities in situations with high information load. Participants of this task are presented with information about four apartments. Imagining being on apartment hunt, they are supposed to familiarize themselves with and to visualize all apartments so that they will later be able to choose the best one. Each apartment is characterized by twelve attributes in total. The objectively best apartment is described by eight positive (e.g., “Apartment B has a balcony.”) and four negative (e.g., “Apartment B does not have a washing machine.”) attributes. The objectively worst apartment is described by eight negative and four positive attributes. The remaining two neutral apartments are described by six positive and six negative attributes each. For each apartment, attributes are assigned randomly from a list of twelve positive and twelve negative attributes with the only restriction being the number of positive/negative attributes. Apartment characteristics that are most essential in apartment-hunt situations (rental cost, apartment size, etc.) are not considered in the attribute list, so that they cannot overshadow other, intermediately essential, characteristics. The apartment task is typically divided into two phases, namely a presentation and an evaluation phase, which are separated by a filler interval. In the present studies’ presentation phases, the 48 apartment-attribute combinations were displayed sequentially and randomly intermixed for four seconds each, resulting in a total presentation time of 192 sec. In the later evaluation phases, participants were asked to indicate their attitude toward each of the apartments on a scale from one ( extremely negative ) to ten ( extremely positive ).

Filler Interval Activity

In the present experiments, two versions of the n -back task were used as distraction tasks within the filler interval between the apartment task’s two phases. In the n -back task, single letters are displayed consecutively. Participants of this task are supposed to press one key when the currently presented letter matches the one presented n trials earlier (target trials). For all other letters (non-target trials), they are supposed to press another key.

For the present implementations of this task, 20 different letters ( B C D F G H J K L M N P Q R S T V W Y Z ) were used and presented for 500 ms each in the center of the screen with a 300-ms inter-stimulus interval. Participants always performed one block of the n -back task consisting of 32 non-target and 16 target trials. The B -key was used as the response key for non-targets and was labeled with a red sticker. The green-labeled C -key was used as the target key. In the conditions with demanding distraction n equaled three, resulting in more letters to be constantly monitored compared to the conditions with undemanding distraction, in which n equaled one. Including one short introduction screen, the distraction task lasted approximately three min. Instead of working on a distraction task, the participants in the conscious thought conditions were asked to consciously think about their attitudes toward four previously presented apartments for 3 min during the filler interval.

Retrospective Thought Assessment

To assess the amounts of task-related and task-unrelated thoughts during the filler interval, participants of all three experiments were asked to retrospectively categorize the entirety of thoughts they had experienced during this interval into several categories using percentage scores. Participants in the conscious-thought conditions were given two response categories: (1) thoughts about the apartment task and (2) thoughts about something completely unrelated. In addition to these categories, participants in all other conditions, that is conditions including an n -back distraction-task, were asked to indicate the percentage of (3) thoughts about the distraction task and (4) thoughts about their performance on the distraction task. For all participants, category (2) corresponded to general off-task thoughts and was explained accordingly using multiple examples. For conscious-thought participants, category (1) corresponded to on-task thoughts. For all other participants, category (1) corresponded to a special kind of off-task thoughts and category (3) to on-task thoughts. Category (4) described task-related interferences.

General Procedure

The general procedure (excluding instructions and practice) is illustrated in Figure 1 . At the beginning of each experiment, participants signed a consent form and provided demographic information. Afterward, all participants received instructions for a distraction task they would have to perform later. They were then presented with the to-be evaluated apartments. The apartment task’s presentation phase was followed by a filler interval that differed between conditions and experiments. Next, participants indicated their attitudes toward the previously presented apartments in the evaluation phase. Finally, participants’ thought contents during the filler interval were retrospectively assessed, before participants were debriefed and dismissed. Detailed procedure descriptions for each experiment are provided below.

Figure 1. Experimental procedure. All participants worked on the apartment task’s presentation phase and evaluation phase, with both phases being separated by a filler interval. The filler interval activity varied between experimental conditions and experiments. After evaluating the apartments, participants filled in a retrospective thought-assessment questionnaire, which related to their thoughts during the filler interval. Participants in the immediate evaluation condition (Experiment 2) made an exception to this general procedure, as they worked on both apartment task phases one after the other without a filler interval.

General Plan of Analyses

In all three reported experiments, we realized a conscious thought and several unconscious thought conditions, which differed with regard to the filler interval activities and thought assessment methods. Filler interval activities differed regarding their difficulty and in some conditions, participants’ current thoughts were probed during the task. In all conditions, retrospective thought reports were (additionally) employed after the task.

We employed a consistent plan of analyses for all experiments regarding the main dependent variables, which were apartment-task performance, amounts of apartment-related thoughts during the filler interval, and amounts of task-unrelated thoughts during the filler interval. We always first ran a one-factorial ANOVA with the experimental condition as fixed factor testing for overall group differences. We then conducted follow-up analyses using Helmert contrasts, for which the first contrast always tested for an overall UTE and the following contrast(s) for the experiment-specific manipulations within the unconscious thought conditions. When necessary, we finally conducted additional simple comparisons to further disentangle significant effects.

Since we worked with experimental designs, the focus of our analyses was on group differences. Full correlation tables for each experimental condition from Experiments 1 to 3 as well as from a joint data set (see below) can be found in our Supplementary Material under https://osf.io/4375q/ (doi: 10.17605/OSF.IO/4375Q

General Measures

The performance on the distraction task ( n -back) was defined in terms of the sensitivity index d’ . The performance on the apartment task was defined as the difference in the subjective attitude values between the objectively best and worst apartment (see for example Dijksterhuis, 2004 ; Dijksterhuis and Nordgren, 2006 ). Higher values thus represent a better performance. The amounts of retrospectively reported task-unrelated and apartment-related thought during the filler interval were specified using percentage scores. Thoughts that were completely unrelated to any of the study’s tasks and thus corresponded to response category (2) were considered as task-unrelated thoughts (TUTs) in all analyses. Thoughts that were related to the apartment task [response category (1)] were considered as apartment thoughts (ATs). 2 Thoughts about the distraction task itself and the performance on this task are both distraction-task related. Because they directly result from TUTs and ATs [% distraction – task related thoughts = 100% – (% TUTs + % ATs )] and because the latter two were our variables of interest, we only analyzed TUTs and ATs.

Experiment 1

We ran the first experiment to establish the apartment-task paradigm and to gain initial insights into thought processes during conscious and unconscious thought filler intervals. For this purpose, we employed a standard UTT experiment, in which one group of participants was supposed to consciously think about previously presented apartments before evaluating them. Two other groups worked on a distraction task instead during the filler interval. To additionally examine the influence of the distraction task’s difficulty on thought reports as well as the apartment-task performance, we employed both an undemanding and a demanding version of the distraction task. In Experiment 1, retrospective thought reports were supposed to provide insights into participant’s cognitive processes leading to the solution of the apartment problem.

Participants and Design

An a priori power analysis with the software G ∗ Power ( Faul et al., 2007 ) was conducted to determine the required sample size for the planned contrast analysis central to our hypotheses that would allow to reveal medium-size effects with an α = 5% and 1-β = 80%. This analysis suggested a required sample-size of at least N = 128. To cover for potential drop-outs, we tested a total of 153 participants at Heidelberg University, Germany in groups no larger than six. Data of five participants were excluded due to poor performance on the distraction task ( d ’ < 0). Comparing these participants’ performances to the non-excluded participants’ good performances (mean d’ = 1.99 for the demanding distraction task, mean d’ = 3.00 for the undemanding distraction task), we assumed that the excluded participants did not pay sufficient attention to the distraction task, potentially changing their mind-wandering behavior and/or influencing possible unconscious thought processes. Another three participants’ data were excluded due to missing values on at least one of the dependent variables of interest. Missing values resulted from participants not properly filling out the though-assessment questionnaire or the apartment evaluation. Analyses were thus executed with N = 145 ( M age = 21.82, SD age = 3.96; 123 female). We employed a one-factorial design with thought condition being manipulated between participants: conscious thought ( n = 48), unconscious thought with demanding distraction ( n = 49), and unconscious thought with undemanding distraction ( n = 48).

The three experimental conditions differed regarding the filler interval activity. Accordingly, at the beginning of the experiment, the participants in the unconscious thought condition with demanding distraction received instructions for the 3-back task and were presented with practice trials. The participants in the unconscious thought condition with undemanding distraction and in the conscious thought condition (to keep the procedure equal for all conditions) read instructions for and practiced the 1-back task. Having finished the practice trials, all participants were told that they would later work on more trials of the task. For the current moment, however, they would work on a different task. All participants were instructed regarding the apartment task and the presentation of apartment-attribute combinations started. After the apartment task’s presentation phase, the participants in both unconscious thought conditions worked on the respective version of the distraction task while participants in the conscious thought condition were asked to actively think about their attitude concerning all previously presented apartments. Then, in the evaluation phase of the apartment task, all participants indicated their attitude toward each of the presented apartments. Finally, they filled out the retrospective thought-assessment questionnaire.

Distraction-Task Performance

The performance in the condition with a demanding distraction task (3-back, M = 1.99, SD = 0.70) was significantly worse than the performance in the condition with an undemanding distraction task (1-back, M = 3.00, SD = 0.66), t (95) = 7.37, p < 0.001, d = 1.50, reflecting the fact that the demanding task was more difficult than the undemanding one.

Apartment-Task Performance

As illustrated in Figure 2 , there was a marginally significant difference between the three experimental conditions regarding the apartment-task performance, F (2, 142) = 2.96, p = 0.055, η 2 p = 0.04. Helmert contrasts indicated an UTE. That is, performance was generally worse in the conscious thought condition compared to the two unconscious thought conditions, F (1, 142) = 5.33, p = 0.022. Between the two unconscious thought conditions, the apartment-task performance did not differ, F (1, 142) = 0.57, p = 0.451. Thus, employing a distraction task during the filler interval, regardless of this task’s difficulty, fostered participants’ problem solving abilities in comparison to those participants who actively thought about the problem during the filler interval.

Figure 2. Descriptive data for the main analyses. Columns represent the respective experiment, rows the respective variable. The bars’ colors stand for the thought-mode manipulations employed, with darker gray representing conscious-thought manipulations and lighter gray unconscious-thought manipulations. The immediate-evaluation condition of Experiment 2 is displayed in white. Patterns of results are explained and discussed within the running text. Error bars represent standard errors of the means.

Retrospective Thought Reports

The amount of TUTs (see Figure 2 ) differed significantly between the three experimental conditions, F (2, 142) = 50.58, p < 0.001, η 2 p = 0.42. Helmert contrasts showed that the percentage of TUTs in the conscious thought condition was generally higher than in the two unconscious thought conditions, F (1, 142) = 98.17, p < 0.001. Between the two unconscious thought conditions the percentage of TUTs only differed marginally, F (1, 142) = 2.78, p = 0.097, with a numerically higher number in the condition with undemanding distraction (cf. Rummel and Boywitt, 2014 ). The amount of ATs (see Figure 2 ) also varied with experimental conditions, F (2, 142) = 99.59, p < 0.001, η 2 p = 0.58. Helmert contrasts indicated that there were more ATs in the conscious thought condition than in the two unconscious thought conditions, F (1, 142) = 194.80, p < 0.001. The percentage of ATs in the undemanding unconscious thought condition was still higher than in the demanding unconscious thought condition, F (1, 142) = 4.04, p = 0.046. That is, participants in the conscious thought condition thought about the apartments for roughly half of the time. During the other half, they thought about unrelated matters. Participants in both unconscious thought conditions spent the majority of their filler interval time thinking about the distraction task. However, there was still room for TUTs and ATs, especially for participants working on the undemanding task.

In Experiment 1, we established the apartment task paradigm producing an UTE. This effect was independent of the distraction task’s difficulty, although thought patterns differed between both unconscious thought groups. Overall, distracted participants reported lower levels of ATs and performed better on the apartment task. Moreover, conscious-thought participants, who showed the highest levels of apartment thought, performed worse on the apartment task. Using mind-wandering assessment methods, we were able to demonstrate that a distraction task does not leave a lot of room for deliberation about the apartment-task problem. Hinting toward a competition for attentional resources of task-related and adaptive-mind-wandering processes, we found the lowest levels of ATs for participants in the demanding distraction condition. We had additionally expected worse problem-solving performance for participants in this condition as a result of this competition. However, participants of the demanding condition performed comparably well as those of the undemanding condition on the apartment task, challenging our assumptions about apartment-related mind wandering being an alternative explanation for the UTE. Still, because all participants working on any kind of a distraction task showed lower levels of ATs as well as a better apartment task performance than conscious-thought participants, the possibility remains that the unconscious thought conditions fostered “just the right amount” of ATs, which may be necessary and sufficient for a good apartment task performance.

Participants in the conscious thought condition indicated high levels of general TUTs. They only spent about half of the filler-interval time thinking about the apartments, which was their actual task. This finding might indicate that the filler interval was too long, so that participants had too much time and “thought the apartment problem to pieces.” Thus, a possible alternative explanation for our findings might be that unconscious thoughts, or fewer apartment thoughts, do not generally lead to better evaluations. Rather, too intensive conscious thought about the apartment problem may have had destructive effects on the apartment task solution.

Experiment 2

In the second experiment, we included an additional baseline condition to be better able to interpret the effects of conscious thought manipulations. In this condition, participants did not have time to consciously (or unconsciously) think about the previously presented apartments before evaluating them. The objective was to investigate whether a high amount of ATs in the conscious thought condition would result in poorer apartment-task performance due to overthinking compared to no ATs in a condition without a filler interval.

Apart from including this new condition, the structure of Experiment 2 resembled the first experiment’s structure with unconscious thought conditions differing in task demands for the distraction task. Additionally, to examine thought processes in the exact moment they are happening and to capture possible unaware processes, we employed online thought probes during the filler interval. As stated in the introduction section, thought probes are frequently used in mind-wandering experiments and interrupt participants who are working on a task by asking them to briefly describe and/or classify their current thoughts’ content. Another advantage is that they do not rely on thought awareness ( Smallwood and Schooler, 2006 ), so that they might be able to capture thought processes that participants are unaware of, which possibly are not captured as well by retrospective thought reports. Thought probes proved to be reasonably valid mind-wandering indicators (e.g., McVay and Kane, 2012 ) and do not interfere with performance on ongoing cognitive tasks ( Wiemers and Redick, 2019 ). Yet, it may well be that asking participants to report on their thoughts during the filler interval might disrupt unconscious thought processes. For this purpose, we additionally employed a condition without such task-interruptions to control for possible reactive effects on apartment-task performance. Furthermore, such a condition ensures comparability with regard to Experiment 1.

In groups of up to six, we tested 152 participants at Heidelberg University, Germany, and 162 participants at Mannheim University 3 , Germany, ensuring that participants had not participated in Experiment 1 and that group sizes were comparable to those in Experiment 1. Data of seven participants were discarded due to poor performance ( d’ < 0) in the distraction task (to compare, means for the non-excluded participants, d’ = 2.14 for the demanding distraction task, d’ = 2.89 for the undemanding distraction task). Another four participants’ data were excluded due to missing values on at least one of the dependent variables of interest. Analyses were thus executed with N = 303 ( M age = 22.78, SD age = 4.06; 219 female). We employed a one-factorial design with the thought condition being manipulated between participants [immediate evaluation ( n = 59), conscious thought ( n = 61), and three unconscious thought conditions: demanding distraction with thought probes ( n = 60), undemanding distraction with thought probes ( n = 60), undemanding distraction without thought probes ( n = 63)].

As in Experiment 1, we employed the procedure outlined in the General Procedure section with the experimental conditions differing regarding the filler interval activity. We used a 1-back task as the filler interval distraction task for the two unconscious thought conditions with undemanding distraction and a 3-back task for the unconscious thought condition with demanding distraction. While working on these tasks, the participants in the conditions including thought probes were interrupted after each sequence of 12 trials (resulting in a total of four thought probes) and asked about their current thoughts. They were supposed to categorize their current thoughts’ content as being n -back-task-related, related to their performance on the n -back task, related to the apartment task, or unrelated to any task in the current study. Participants in the conscious thought condition were supposed to actively think about their attitude toward all previously presented apartments during the filler interval.

Having been presented with distraction-task instructions, the apartment task’s presentation phase, the filler interval activity, and the apartment task’s evaluation phase in this order, participants were asked about their thoughts during the filler interval. We employed the same retrospective thought-assessment questionnaire as in Experiment 1, but for Heidelberg participants only. We decided to include this questionnaire on short notice at a time point at which data collection was already ongoing in Mannheim. The participants in the conscious thought condition filled out the version with two response options, that is, (1) thoughts about the apartment task, and (2) thoughts about something completely unrelated. The participants in all other conditions filled out the version with four response options, that is, (1), (2) as well as (3) thoughts about the distraction task, and (4) thoughts about their performance on the distraction task.

The participants in the immediate-evaluation condition worked on the individual parts of the experiment in a different order. They were asked to indicate their attitude toward each of the presented apartments directly after the presentation phase of the apartment task. That is, for these participants there was no filler interval between the apartment task’s two phases. After completing the evaluation phase, they worked on the 1-back version of the n -back task including thought probes and filled in the retrospective thought-assessment questionnaire afterward.

As for Experiment 1, we ran an ANOVA for each of our dependent variables of interest to test for overall group-differences between the experimental conditions. The performance on the distraction task varied between the four experimental conditions in which participants worked on the n-back filler task, F (3, 238) = 15.55, p < 0.001, η 2 p = 0.16. All other ANOVAs included all five experimental conditions. The performance on the apartment task (see Figure 2 ) also varied with experimental conditions, F (4, 298) = 2.43, p = 0.048, η 2 p = 0.03. Because we had retrospective thought data available for (most of) the Heidelberg participants only, we ran the analyses concerning retrospectively reported TUTs and ATs (see Figure 2 ) with N = 144. The amount of retrospectively reported TUTs differed significantly between the experimental conditions, F (4, 139) = 31.63, p < 0.001, η 2 p = 0.48, as did the amount of retrospectively reported ATs, F (4, 139) = 25.72, p < 0.001, η 2 p = 0.43.

The amounts of online reported TUTs and ATs were defined as the sum of thought probes in which participants self-categorized their thoughts as being task-unrelated or apartment-related, respectively. Online reports of TUTs and ATs were collected in three conditions only (immediate evaluation, unconscious thought with undemanding distraction and thought probes, and unconscious thought with demanding distraction and thought probes). Online reported TUTs varied between groups, F (2, 176) = 16.67, p < 0.001, η 2 p = 0.16, as did online reported ATs, F (2, 176) = 5.86, p = 0.003, η 2 p = 0.06.

In the following sections, we report planned contrasts that were carried out based on the above reported ANOVAs.

Immediate Evaluation Condition

For comparability (between experiments) and clarity reasons, we prepend reporting all planned contrasts, which include the immediate evaluation condition as a reference condition. Our main objective for including this condition was to test whether a large amount of ATs in the conscious thought condition would result in poorer apartment-task performance due to overthinking compared to no ATs in the immediate evaluation condition. A first planned contrast showed, however, that the immediate evaluation and the conscious thought condition achieved a comparable apartment-task performance, F (1, 298) = 1.09, p = 0.298. Furthermore, within following contrasts, we found that there were no significant differences between each of the unconscious thought conditions and the immediate evaluation condition regarding the apartment-task performance, all F s ≤ 1.93, all p s ≥ 0.166. That is, a 3-min distraction interval within the apartment task paradigm did not result in better apartment evaluations compared to evaluations submitted right after apartment presentation.

Next, we contrasted the immediate evaluation condition with the unconscious thought conditions with undemanding distraction, because participants in these conditions worked on the same task (1-back) as immediate-evaluation participants. Concerning filler-interval measures, we did not find significant differences regarding distraction-task performance, all F s ≤ 0.92, all p s ≥ 0.338. We also did not find any significant differences regarding online, F (1, 176) = 0.86, p = 0.356, and retrospective TUTs (all F s < 0.40, all p s ≥ 0.530) as well as retrospective ATs (all F s ≤ 3.11, all p s ≥ 0.080). However, thought-probe results indicated that there were more online-reported ATs, F (1, 176) = 7.53, p = 0.007, in the unconscious thought condition with undemanding distraction ( M = 0.48, SD = 0.68) than in the immediate evaluation condition ( M = 0.22, SD = 0.42). Participants who performed the distraction task after the apartment task was finished showed fewer ATs than participants who performed it before they made their apartment judgments. This pattern could be interpreted as a Zeigarnik-like effect ( Zeigarnik, 1927 ).

All in all, besides this Zeigarnik-like effect, there were no significant differences between participants in the immediate evaluation condition and participants in the conditions featuring a delayed evaluation of apartments. As stated above, we next specified planned contrasts comparing the delayed evaluation conditions, analogously to the analyses conducted in Experiment 1. The immediate evaluation condition was always weighted with zero for these analyses.

Helmert contrasts revealed that the performance in the unconscious thought condition with a demanding (3-back) distraction task ( M = 2.14, SD = 0.86) differed significantly from the performance in the two unconscious thought conditions with an undemanding (1-back) distraction task, F (1, 238) = 37.59, p < 0.001. Performance between the undemanding-distraction condition without thought probes ( M = 2.90, SD = 0.68) and that with thought probes ( M = 2.82, SD = 0.76) did not differ, F (1, 238) = 0.34, p = 0.560. As in Experiment 1, performance on the demanding distraction task was worse than on the undemanding one. The presence of thought probes did not affect the distraction task performance.

The first Helmert contrast (conscious thought versus the three unconscious thought conditions) did not indicate an UTE, F (1, 298) = 0.43, p = 0.514. That is, participants who performed a distraction task during the filler interval did not generally perform better than participants who consciously thought about the apartment problem during the filler interval. Further contrasting the three unconscious thought conditions with each other, we found participants who worked on the demanding version of the distraction task to achieve a similar apartment-task performance as participants who worked on the undemanding distraction task, F (1, 298) = 2.35, p = 0.127. When contrasting both undemanding distraction conditions, we found participants who were not probed while working on the distraction task to performed better than those who were probed, F (1, 298) = 6.16, p = 0.014. Finally, employing further simple planned contrasts to compare the conscious thought condition with each of the unconscious-thought conditions, we found an UTE for the condition with undemanding distraction without thought probes only, F (1, 298) = 4.98, p = 0.026, both other F s ≤ 0.13 and p s ≥ 0.724. This pattern suggests that unconscious thought participants whose thoughts were not probed during the filler interval found better solutions to the apartment problem than participants who consciously thought about the apartments.

Helmert contrasts revealed that conscious thought participants reported more TUTs than participants in the unconscious thought conditions, F (1, 139) = 116.17, p < 0.001. Participants who had worked on the demanding distraction task showed fewer TUTs than participants who had worked on the undemanding distraction task, F (1, 139) = 8.12, p = 0.005. Undemanding distraction-task participants who had received thought probes showed similar levels of TUTs as participants who did not receive any thought probes, F (1, 139) = 0.71, p = 0.401.

Regarding the amount of ATs, Helmert contrasts showed that more ATs were reported in the conscious thought condition than in the unconscious thought conditions, F (1, 139) = 84.77, p < 0.001. Participants who had performed the demanding distraction task showed fewer ATs than participants who had performed the undemanding distraction task, F (1, 139) = 7.30, p = 0.008. Moreover, undemanding-distraction participants, who had received thought probes while working on the distraction task, showed similar levels of ATs as participants who did not receive thought probes, F (1, 139) = 0.14, p = 0.710.

These findings suggest that conscious thought participants had followed our instructions to consciously think about the apartments. However, they only did so for roughly 40 percent of the time. The high demands of the 3-back task almost entirely kept participants in the demanding unconscious thought condition from thinking about the apartments and unrelated matters whereas participants in both undemanding unconscious thought conditions still thought about the apartments and other issues from time to time. Thought probes had no influence on reported thoughts.

Online Thought Reports

Unconscious-thought participants who had performed the demanding 3-back version of the distraction task ( M = 0.42, SD = 0.59) reported significantly fewer TUTs than unconscious-thought participants who had performed the undemanding 1-back version ( M = 1.17, SD = 1.04), F (1, 176) = 20.10, p < 0.001. The same pattern was found for ATs, with fewer ATs for participants who had worked on the demanding version ( M = 0.18, SD = 0.43) compared to those who had worked on the undemanding version of the distraction task ( M = 0.48, SD = 0.68), F (1, 176) = 9.88, p = 0.001. Overall, patterns of online thought reports mirrored retrospective thought reports patterns. Even numerically, percentages of TUTs and ATs were very similar whether measured by online or by retrospective methods 4 .

In Experiment 2, we found an UTE only for participants who did not receive thought probes while performing the distraction task during the filler interval. These participants performed better in the apartment task than participants who consciously thought about the apartments during the filler interval. They also retrospectively reported less apartment-related thoughts compared to conscious thought participants, replicating the findings from Experiment 1. The second experiment’s results also ruled out the possibility that conscious thought participants might overthink the apartment problem, as they did not perform worse than participants who were not given time to consciously or unconsciously think about the apartments before evaluating them.

In Experiment 2, we validated the retrospective thought reports employed in Experiment 1. Thought probes provided similar thought descriptions as retrospective questionnaires. This finding suggests that after and during task completion, participants seem to be well aware of their recent thought processes. Because thought probes were found to be valid mind-wandering-frequency indicators ( McVay and Kane, 2012 ) which do not rely on thought awareness because they capture participants’ thoughts in the moment they are happening ( Smallwood and Schooler, 2006 ), we conclude that we might have captured a compound of aware as well as unaware wandering thoughts. A distinction between the two kinds of mind-wandering assessment might be that retrospective reports were not intrusive, due to them capturing mind wandering after distraction-task completion, that is, without interfering with any thought processes during the task. Thought probes, however, might have altered participants’ thought experiences during the distraction task by bringing the unaware portions of the thought compound into awareness. Results obtained using the retrospective mind-wandering questionnaires should, however, be interpreted with caution. We acknowledge that the statistical power to detect effects on this measure was certainly limited because only participants from one out of two universities provided retrospective thought reports resulting in a smaller sample size for this measure compared to all other measures.

Unexpectedly, we found that the UTE disappeared when we employed thought probes during the filler interval, leading us to assume a detrimental nature of such probes to the processes producing the UTE. It has already been found that changing thought-probe characteristics within mind-wandering experiments can lead to differences in results ( Seli et al., 2013 ; Weinstein et al., 2018 ; Robison et al., 2019 ), which made it reasonable to assume that the mere presence of thought probes might have interfered with (unconscious) thoughts for at least two reasons ( Steindorf et al., 2020 ): One reason could be that thought probes may have interrupted the ongoing task and thereby ongoing (unconscious) thought processes. Alternatively or additionally, thought probes may have made participants more aware of their current states of thought during the distraction task, whereas an absence of thought awareness might be a necessary criterion for an UTE to appear. To test these two competing assumptions and to further replicate the negative association between AT-levels and apartment-task performance, we conducted a third experiment.

Experiment 3

In the third experiment our aim was to, once more, replicate the UTE and its negative association with the number of ATs. For this reason, we included the same conscious thought condition as well as the same unconscious thought condition (undemanding distraction task without thought probes) as in the first and second experiments. To take a closer look at the effect of thought probes on thought processes as well as on apartment-task performance, we included one condition with an undemanding distraction task and thought probes (see Experiment 2) and one new condition in which participants were only interrupted during the undemanding distraction task, but not asked about their current thoughts. If mere interruption was responsible for the lack of an UTE when including thought probes as found in Experiment 2, the UTE should be absent in this condition. If, however, thought awareness was a necessary condition for the effect to vanish, we would expect a better apartment-task performance in this condition compared to both the condition including regular thought probes and the conscious thought condition. A lack of thought awareness as a necessary condition for the UTE would further support the assumption of a deliberation-without-consciousness effect ( Strick et al., 2011 ). Thought probes might add consciousness/awareness to the deliberation part, diminishing its beneficial effect on problem solving performance.

In groups of up to six, we tested 289 participants at Heidelberg University, Germany, and 108 participants at the University of Mannheim, Germany 5 . To be able to calculate correlations between AT-levels and apartment-task performance within experimental conditions, we substantially increased the group sizes for this experiment. Participants of Experiment 3 had not taken part in Experiment 1 and 2. Data of one participant were discarded due to poor performance ( d’ < 0) in the distraction task (to compare, mean for the non-excluded participants, d’ = 3.07). Another five participants’ data were excluded because they were handed out no or a wrong retrospective thought-assessment questionnaire. Yet another 17 participants’ data were excluded due to missing values on at least one of the dependent variables of interest. Analyses were thus executed with N = 374 ( M age = 21.64, SD age = 3.19; 292 female). We employed a one-factorial design with the thought condition 6 being manipulated between participants. We had a conscious thought ( n = 96) and three different unconscious thought conditions with undemanding distraction: one without thought probes ( n = 92), one with thought probes ( N = 94), and one with trivia probes ( n = 92).

We adhered to the general procedure and implemented the differences between experimental conditions within the filler interval as follows: We employed the 1-back version of the n -back task as the filler interval distraction task for all three unconscious thought conditions. The two conditions with and without thought probes were equivalent to those employed in Experiment 2. Instead of being presented with thought probes, participants in the trivia-probe condition were presented with a trivia question after each sequence of 12 trials (as was the case for the thought probes). Each trivia question had four response options, only one of which was correct [e.g., Who wrote the fantasy novels “Lord of the Rings”? (1) John Ronald Reul Tolkien (2) Joanna Kathleen Rowling (3) Pete Johnson (4) Jeff Kinney ]. As in the previous experiments, participants in the conscious thought condition were supposed to actively think about their attitude toward all previously presented apartments during the filler interval.

Having been presented with instructions, the apartment task’s presentation phase, the filler interval activity, and the apartment task’s evaluation phase in this order, participants were asked about their thoughts during the filler interval. We employed the same retrospective thought-assessment questionnaire as in Experiments 1 and 2. The participants in the conscious thought condition filled in the version with two response options, that is, (1) thoughts about the apartment task, (2) thoughts about something completely unrelated. The participants in all other conditions filled in the version with four response options, that is, (1), (2), (3) thoughts about the distraction task, and (4) thoughts about their performance on the distraction task.

All unconscious thought conditions featured the same 1-back distraction task in Experiment 3. As expected, there was no significant difference between the unconscious thought conditions concerning the performance on the distraction task, F (2, 275) = 1.26, p < 0.29, η 2 p = 0.009.

There was no significant main effect of the experimental condition for the performance on the apartment task (see Figure 2 ), F (3, 370) = 0.38, p = 0.765, η 2 p = 0.00. Simple contrasts further indicated that performance in each unconscious thought condition was comparable to the performance in the conscious thought condition, all F s ≤ 0.90, all p s ≥ 0.343.

As in Experiments 1 and 2, we analyzed TUTs and ATs as reported on the retrospective questionnaires (see Figure 2 ). The amount of TUTs during the filler interval varied between experimental conditions, F (3, 370) = 41.94, p < 0.001, η 2 p = 0.25. The first Helmert contrast indicated that the percentage of TUTs was higher in the conscious thought condition compared to all three unconscious thought conditions, F (1, 370) = 114.92, p < 0.001. Further comparing the unconscious thought conditions with each other, we found that participants who had received thought probes reported higher levels of TUTs than participants who had received trivia probes or no thought probes, F (1, 370) = 11.06, p = 0.001 7 . The latter two conditions did not differ from each other concerning the amount of TUTs, F (1, 370) = 0.21, p = 0.649.

The amount of ATs also varied between experimental conditions, F (3, 370) = 228.71, p < 0.001, η 2 p = 0.65. Helmert contrasts indicated higher AT levels in the conscious thought compared to all other conditions, F (1, 370) = 683.56, p < 0.001, but no differences between thought-probed, trivia-probed and unprobed unconscious-thought conditions, all F s ≤ 2.41, all ps ≥ 0.122.

Thought probes were employed in one condition only. Participants reported numerically similar levels of TUTs ( M = 1.22, SD = 1.01) and ATs ( M = 0.38, SD = 0.55) as in the corresponding condition in Experiment 2.

Correlational Analyses

To more directly test for a relation between ATs during the filler interval and later apartment-task performance, we correlated these measures within conditions. Retrospectively reported ATs did not correlate with the apartment-task performance in any of the experimental conditions, all p s ≥ 0.242. The same held for the correlation between retrospectively reported TUTs and the apartment-task performance, all p s ≥ 0.287, except for the unconscious thought condition without thought probes in which TUTs correlated negatively with the apartment-task performance, r = −0.24, p = 0.022. We can only speculate about the interpretation of this single significant correlation and therefore refrain from further expanding on this result.

In Experiment 3, we did not replicate the UTE, which we had previously found in both Experiments 1 and 2. In addition, comparisons of apartment-task performance between the unconscious thought conditions were not conclusive. We did not replicate the detrimental effect that thought probes had had in Experiment 2. Consequently, we cannot draw any conclusions concerning a lack of thought awareness as a necessary criterion for the UTE to appear. However, thought probes had an influence on retrospectively reported general TUTs, with more wandering thoughts found in participants who had been explicitly asked about their state of thought compared to participants whose thoughts had not been probed. This influence of thought probes goes beyond mere interruption, because participants who were merely interrupted did not show such increased levels of TUTs. One could speculate that the online thought probes made participants more aware of mind wandering instances during the distraction task which, in turn, resulted in higher levels of retrospectively reported TUTs.

Joint Analysis of the Unconscious Thought Effect

Having employed the same conscious thought and the same unconscious thought (undemanding distraction without thought probes) condition in all three experiments allowed us to collapse the data for these conditions to conduct a joined analysis of the UTE with N = 408 ( n conscious = 205, n unconscious = 203). We ran a 2 (experimental conditions) x 3 (experiments) ANOVA for the apartment-task performance, which revealed no significant main effect of experiment, F (2, 402) = 1.23, p = 0.295, η 2 p = 0.01, but a significant effect of experimental condition, F (1, 402) = 4.33, p = 0.038, η 2 p = 0.01, suggesting that, overall, there was an UTE: Participants in the unconscious thought condition ( M = 2.73, SD = 2.69) performed better on the apartment task than participants in the conscious thought condition ( M = 2.33, SD = 2.66), which equates to a small effect with a Cohen’s d of 0.15. Notably, the interaction was significant, F (2, 402) = 3.42, p = 0.034, η 2 p = 0.02, bolstering the cross experimental observation that the UTE was present in Experiments 1, t (94) = −1.71, p = 0.045 (one-tailed), and 2, t (122) = −2.08, p = 0.020 (one-tailed), but not in Experiment 3, t (186) = 0.95, p = 0.173 (one-tailed).

Concerning ATs, there was a significant main effect of the experimental condition, F (1, 337) = 240.33, p < 0.001, η 2 p = 0.42, but no significant main effect of the experiment, F (2, 337) = 2.89, p = 0.057, η 2 p = 0.02, as revealed by a 2 × 3 ANOVA. On average, participants in the conscious-thought conditions ( M = 49.84, SD = 24.18) reported having experienced more ATs than participants in the unconscious-thought conditions ( M = 11.46, SD = 11.89). Notably, as for the apartment-task performance, there was a significant interaction, F (2, 337) = 5.15, p = 0.006, η 2 p = 0.03. This interaction did, however, not concern the presence or absence of a significant difference in the amount of ATs between the conscious- and the unconscious-thought conditions, but the extent of this difference.

Concerning TUTs, we also ran a 2 (experimental conditions) × 3 (experiments) ANOVA. There was a significant main effect of the experimental condition, F (1, 337) = 168.57, p < 0.001, η 2 p = 0.33, and of the experiment factor, F (2, 337) = 3.71, p = 0.026, η 2 p = 0.02. The interaction remained non-significant, F (2, 337) = 2.14, p = 0.119, η 2 p = 0.01. On average, participants in the conscious-thought conditions ( M = 50.10, SD = 24.14) reported having experienced more ATs than participants in the unconscious-thought conditions ( M = 18.26, SD = 20.01). Average TUT levels also varied significantly between experiments (M Experiment 1 = 36.20, SD Experiment 1 = 28.82; M Experiment 2 = 40.15, SD Experiment 2 = 30.66; M Experiment 3 = 32.05, SD Experiment 3 = 25.16).

Within-condition Pearson correlations between all measures of interest for the joint data set are provided in the Supplementary Materials under https://osf.io/4375q/ (doi: 10.17605/OSF.IO/4375Q ). As none of the bivariate correlations was significant, we did not conduct any further correlation or mediation analyses.

In this joint analysis of the UTE, we found that participants in an unconscious thought condition performed better on the apartment-evaluation task than participants in a conscious thought condition. However, this effect was of a small size only. This finding is in line with a meta-analysis by Strick et al. (2011) , which supports the existence of the UTE as a small effect. However, our sample sizes in the single experiments were based on a medium-sized effect, thus hinting toward power issues and advising caution when interpreting (especially null-) effects. Even though we tested as many as 822 participants overall, and included more participants per condition than most of the previous studies on the UTE, sample sizes should have been even larger to identify small effects in the present data. This especially holds for the analyses of the thought reports for Experiment 2 which only participants from one out of two universities provided. However, sample sizes for the detection of small effects may not easily be achieved in laboratory studies and running experiments online comes with additional caveats regarding data quality ( Chmielewski and Kucker, 2020 ). Furthermore, it is debatable to which extent small effects would be of practical relevance even when they are detected with sufficient power. Consequently, we find the present results worth to be discussed although we acknowledge that the statistical power to detect small-sized effects was certainly limited.

In the joint analyses, we also found that participants in the conscious-thought conditions of all three experiments experienced more ATs (and more TUTs) compared to participants in the unconscious-thought conditions with undemanding distraction and without thought probes. These results qualify as a check that the manipulation of conscious versus unconscious thought was successful. Additionally, however, these results represent a critical test of the UTT’s fundamental assumptions. Participants who were instructed to consciously think about the apartments actually did so, even though they spent about 50 percent of the time thinking about unrelated things. Thus, the filler interval did not seem to represent an episode of uninterruptedly ongoing conscious apartment-thought. Participants in the unconscious-thought conditions spent significantly less time thinking about the apartments. Still, on average (as found in the joint analysis), they experienced ATs for a little more than 10 percent of their distraction time. Thus, the filler interval in the unconscious thought condition did not seem to represent pure unconscious thought. Of course, as proposed in the UTT, there might still be unconscious thought at play. How both types of thought relate to the apartment-task performance, cannot be inferred from our results. However, it is still an interesting finding that a larger amount of ATs (conscious condition) does not improve the apartment-task performance. This finding implies that this kind of conscious thought does indeed not seem to be helpful when it comes to complex decisions supporting one of the fundamental assumptions of the UTT.

Notably, in our joint analyses of the apartment-task performance and thought contents, there were significant main effects of and interactions with the experiment factor outlining a notable degree of variability concerning the observed effects and their sizes. Especially when it comes to the apartment-task-performance measure, our study’s three experiments represent a successful as well as a failed (see also Acker, 2008 ; Calvillo and Penaloza, 2009 ; Rey et al., 2009 ; Newell and Rakow, 2011 ) replication attempt of the UTE (for similar meta-analytic findings, see also Nieuwenstein et al., 2015 ).

General Discussion

Unconscious thought effect.

Making tough decisions without any cognitive effort sounds like a good deal. Because of this obvious appeal, the UTT has been extensively studied. A meta-analysis by Strick et al. (2011) identified the UTE as a real effect, which is, however, moderated by many factors such as distraction-task features, problem-presentation features, and filler-interval length. A later meta-analysis by Nieuwenstein et al. (2015) heavily criticized the UTT, stating that there exists no support for its notions. Adding to this ongoing discussion, we ran three experiments with the goal to get a fresh look at the UTE and its underlying cognitive processes. Overall, in our joined analysis, we found an UTE of a small effect size. However, even though we employed similar methods in all three experiments, our findings were still mixed. For all experiments, we used the same materials, and we collected three independent samples with quite similar characteristics (German university students). An UTE was present in Experiments 1 and 2, but absent in Experiment 3. The possibility remains that due to Type II errors this is a natural occurrence when running nearly the same experiment repeatedly. We also want to acknowledge that we had some difficulties collecting enough “naive” participants for the third study as most members of our participant pool had already participated in earlier UT studies. Thus, it may be that Experiment-3 participants were less motivated than the ones included in the earlier experiments.

However, furthermore challenging the UTT, we found that unconscious thought participants did not produce significantly better apartment evaluations compared to immediate-evaluation participants in Experiment 2. This might either be an issue of statistical power as participants in the unconscious-thought condition with undemanding-distraction showed numerically better apartment-task performance than immediate-evaluation participants. Or, as for example Waroquier et al. (2010) stated, it might be just as good to trust your first intuition as to think unconsciously. However, studies with larger sample sizes are needed to resolve this issue. It further needs to be determined whether immediate-evaluation participants actually simply “trust their gut feeling” or rely on analytical thoughts or heuristics to make their evaluations.

Thought Processes Within a UTT Paradigm

Current mind-wandering research has been applying self-report methods such as online thought probes or retrospective questionnaires. These instruments allow scientists to assess participants’ internal thought processes, which is the reason why we implemented them within a standard UTT paradigm. We believed that an insight into participants’ thoughts during unconscious- as well as conscious-thought periods might help us to better understand the processes leading to decisions within a complex-problem scenario.

Across all three experiments, we found that conscious-thought instructions indeed led to considerably higher levels of mental occupation with a previously presented evaluation problem. Still, participants did not use all of the available time for conscious problem deliberation. Indeed, they spent roughly about half of the time thinking about the problem and the other half thinking about unrelated matters. It appeared as if the deliberation time had been too long so that people’s minds started wandering. However, Experiment 2 ruled out the possibility that participants in a conscious condition would overthink the problem at hand, possibly deteriorating evaluation quality: Participants in the conscious-thought condition performed similarly well as participant in the immediate-evaluation condition. However, this also implies that high amounts of conscious thought about the evaluation problem did not lead to better evaluations compared to those formed only by first impressions, so that one could go so far as to describe conscious thought as unnecessary. In addition, the results of Experiments 1 and 2 revealed that conscious-thought participants showed worse performances evaluating the apartments than unconscious-thought participants (unless thought probed), who showed significantly lower levels of problem-related thought. Only taking into account these two experiments, one could indeed argue that lower levels of problem deliberation foster higher quality evaluations, as is assumed by the UTT. However, the results from Experiment 3 put this notion into perspective, as evaluation performance did not differ between groups, although the extent of problem deliberation differed.

In a survey concerning real life purchase decisions, Dijksterhuis et al. (2006) asked participants how much they had thought about a product they had recently bought. For complex products, the authors found that a higher amount of conscious product-thought was associated with lower satisfaction with the product. To directly test for such an association within our paradigm, we correlated the amount of problem-related thought with problem-solution quality within the experimental conditions in Experiment 3. These correlations were around zero (and not significant). That is, we did not find evidence for a relationship between the amount of ATs and decision performance within conditions on an individual level. It may still be, however, that the individual variations in ATs within each condition were just too small to affect decision performance.

In the present work, we constantly found that distraction-tasks with high demands did not leave as much room for mind wandering, problem-related or unrelated, as tasks with low demands. Such effects of task demands are found to be stable within the mind-wandering literature (e.g., Rummel and Boywitt, 2014 ) and suggest that task demands compete with wandering thoughts for attentional resources. Concerning a UTT paradigm, our results suggest that demanding filler-interval tasks occupy attentional resources to a higher degree than undemanding tasks, leading to fewer conscious problem-related thoughts. Before, we considered wandering thoughts as an alternative explanation for UTEs and argued that high filler-task demands would compete with adaptive mind-wandering processes for attentional resources. Further, we argued that without engagement in productive mind wandering toward the pending evaluation problem, the benefit of a distraction-task period would be reduced. Although the results within the different unconscious-thought conditions mirrored the first part of this line of reasoning, there was no evidence for a connection between higher amounts of problem-related mind wandering and better evaluations, qualifying our alternative explanation for UTEs. Yet, because working on any kind of distraction resulted in lower amounts of problem-related thoughts as well as better evaluations than engaging in conscious problem thought (at least in Experiments 1 and 2) in general, the possibility remains that “less is better,” or that there is “just the right amount” of problem-related thought which is necessary and sufficient for good decision making. Research showing that self-paced conscious thought periods are shorter than experimenter-paced conscious thought periods whilst also leading to better decisions supports this assumption ( Payne et al., 2008 ).

Furthermore, the question remains whether what we called ATs in all our experimental conditions is qualitatively the same across conditions. Instructing participants to consciously think about a solution to the apartment problem might lead to other kinds of thought than asking them to work on a letter-task. Conscious-thought participants might have actively tried to engage in various strategies such as remembering attributes and weighting them, which might not be the best strategy within a complex decision situation given the conscious’ system’s low information-processing capacity. By contrast, while working on a distraction-task, ATs might have been of a completely different nature, possibly more focused on a holistic visualization of or a feeling evoked by a respective apartment. Such potential differences of qualitative nature should be addressed in further research, which could possibly employ qualitative methods and more detailed thought reports.

Finally, as there was no stable relation between thought condition and performance on the apartment task across all three experiments, no strong conclusions should be drawn concerning the engagement of mind-wandering processes regarding problem-deliberation and their success for complex-problem solving. With our experiments being the first using mind-wandering measures within UTE designs, further research is needed to look into the relation between different thought modes and the UTE. This includes the variation of the experimental paradigm, for example by including a mere distraction condition without the intention to further process the apartments ( Bos et al., 2008 ), which should influence mind-wandering levels as well as the UTE 8 .

Effects of Thought Awareness

Another issue which we addressed in the present work was whether UTEs are really the result of deliberation without attention ( Dijksterhuis et al., 2006 ). Strick et al. (2011) already suggested replacing this term with the term deliberation-without-consciousness, focusing on the distinction between attention and consciousness, or rather awareness. Participants of an unconscious-thought experiment might allocate attentional resources toward the active problem during a filler interval, without being aware of this process. The same is true for mind wandering in general, which is found to occur with and without awareness ( Smallwood and Schooler, 2006 ). By applying thought probes in Experiment 2, we aimed to capture both aware as well as unaware thoughts in order to relate them to problem-evaluation quality. The results indicated that self-reports from online thought probes mirrored those from retrospective questionnaires, thereby validating each other. After and during task completion, participants seem to be well aware of their recent thought processes, when asked. Although both mind-wandering assessment methods produced similar estimates on thought variables, evaluation performance varied between the two conditions which differed in nothing but the mind-wandering assessment method. A distinction between the two kinds of assessment might be that thought probes are intrusive and might bring thought processes into awareness during the task. A lack of thought awareness might, however, be a necessary criterion for an UTE to appear, which would explain the increase in evaluation performance for the condition without thought probes only. However, results from Experiment 3, which was supposed to test this assumption, were inconclusive.

Depending on the choice of mind-wandering assessment method, results of studies applying thought probes have been found to fluctuate ( Seli et al., 2013 ; Weinstein, 2018 ; Weinstein et al., 2018 ). Given that changing probe characteristics can lead to considerable differences in results, one might argue that the mere presence of probes produces similar or even larger discrepancies. As we recently found in our lab ( Steindorf et al., 2020 ), probing participants’ thoughts during an incubation interval within a creativity task resulted in fewer creativity-task-related thoughts (reported retrospectively) compared to not applying any probes or applying trivia probes (cf. present Experiment 3). Because trivia probes interrupt participants just as thought probes do, this effect cannot be attributed to mere task-interruption. We interpreted the findings as an awareness-effect: Thought probes might have made participants more aware and more cautious of their mind-wandering behavior, thereby changing the experience itself. Also, in the present work’s third experiment, thought reports were affected by differences in mind-wandering assessment methods. Participants who were thought-probed retrospectively reported more general TUTs compared to those who were trivia-probed or not probed at all. Asking people about their current thoughts might either change their in-the-moment mind wandering behavior or their recollection of mind-wandering instances when asked after task completion. Further research will be needed to investigate the potentially intrusive nature of thought probes for complex decisions in more detail.

To gain further insights into thought-awareness processes during UTT experiments, further research could rely on self-caught mind-wandering assessment. Employing such methods, a researcher asks participants to monitor their awareness of mind-wandering instances ( Cunningham et al., 2000 ). Unlike probe-caught mind wandering, it requires participants to be aware of their thought experiences ( Smallwood and Schooler, 2006 ). Employing both measures within one and the same experiment could help disentangle actual mind-wandering instances and awareness of such instances (e.g., Sayette et al., 2009 ).

Across three experiments, we examined the nature of thoughts during UTT experiments. We relied on retrospective mind-wandering questionnaires as well as on online thought probes (i.e., methods used in current mind-wandering research) to gain insights into participants’ cognitive processes during distraction and conscious-thought periods. We demonstrated that typical UTT manipulations within the here applied apartment task change the participants’ though contents. When they were instructed to consciously think about the apartments, they spent a lot of time doing so. Distracting them from thinking about the apartments – especially with difficult filler tasks – led to considerably smaller amounts of apartment thoughts. These results represent successful critical tests of the UTT’s fundamental assumptions. Thought reports further demonstrated that filler intervals in the conscious-thought conditions do not seem to represent episodes of uninterruptedly ongoing conscious apartment-thought. Similarly, filler intervals in the unconscious-thought conditions do not seem to represent episodes of pure unconscious thought. Participants seem to experience a mixture of different thought modes, which does not rule out the possibility of unconscious thought being the driving force behind the UTE. Although we theoretically argued for mind-wandering processes as an alternative explanation for UTEs, our results do not support this notion. Both ATs and TUTs did not relate to the performance on the apartment task across all experiments as well as in the joint analyses.

Finally, we found (some) evidence for the existence of an UTE in the first two experiments. Results of the third experiments were inconclusive. As the debate concerning the existence and nature of UTEs is still ongoing, our experiments represent important pieces for the puzzle that is the UTT.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: our data are available under https://osf.io/4375q/ (doi: 10.17605/OSF.IO/4375Q ).

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

All authors developed the initial research idea and the experiments reported here. LS ran the statistical analysis and drafted the manuscript. JR and CDB edited and commented on the draft.

The present research was supported by a grant from the German Research Foundation (DFG) to the second and third authors (Grant No: RU1996/1-1). We acknowledge financial support from the publication fonts of the Baden-Württemberg Ministry of Science, Research and the Arts and Heidelberg University.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.545928/full#supplementary-material

• ^ We thank Arndt Bröder and his lab at the University of Mannheim, Germany for sharing their materials as well as their expertise concerning the implementation of the apartment task with us.
• ^ We analyzed the absolute amount of ATs and TUTs to test if they would vary across groups and be related to the performance in the apartment task. An alternative variable of interest, however, might be the relative propensity with which people engaged in apartment thoughts when they were mind wandering. Therefore, we also conducted analyses of relative AT proportions, which can be found in the Supplementary Material under https://osf.io/4375q/ (doi: 10.17605/OSF.IO/4375Q ).
• ^ Because university affiliation did not affect our measures of interest, we omitted this factor from all analyses for the sake of clarity.
• ^ Calculating percentages from the total values yielded 10.50% TUTs and 4.50% ATs out of 100% for participants in the condition with demanding distraction including thought probes. In the condition with undemanding distraction, 29.25% TUTs and 11.50% ATs were reported.
• ^ Because university affiliation did not affect our measures of interest (except for a main effect on the distraction-task performance), we collapsed across this factor for all further analyses.
• ^ While running the experiment, we added a fifth condition and collected data from 42 additional participants in this condition with modified thought probes . Only slightly deviating from the procedure with regular thought probes, participants in the condition with modified thought probes were supposed to categorize their current thoughts’ content as being task-related (related to the 1-back task), related to their task performance, or task-unrelated. That is, we did not mention the apartment task in any of the response options to the modified thought probes. This group’s data [apartment-task performance ( M = 3.00, SD = 0.61), TUTs ( M = 18.64, SD = 16.54), ATs ( M = 9.83, SD = 11.84)] will not be further considered, because it did not deviate from the regular online thought-probe condition and we thus would not have gained any new insights from adding it to our design.
• ^ The relative proportion of ATs to overall mind wandering (reflecting the propensity with which people engaged in apartment thoughts when mind wandering) was significantly lower for participants receiving thought probes than for all other unconscious-thought conditions (see Supplementary Material under https://osf.io/4375q/ ).
• ^ We thank one of our reviewers for pointing this out.

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Keywords : mind wandering, task-unrelated thought, Unconscious Thought Effect, consciousness, unconscious thought advantage

Citation: Steindorf L, Rummel J and Boywitt CD (2021) A Fresh Look at the Unconscious Thought Effect: Using Mind-Wandering Measures to Investigate Thought Processes in Decision Problems With High Information Load. Front. Psychol. 12:545928. doi: 10.3389/fpsyg.2021.545928

Received: 26 March 2020; Accepted: 17 May 2021; Published: 24 June 2021.

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Copyright © 2021 Steindorf, Rummel and Boywitt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lena Steindorf, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Unconscious Problem Solving

This method relies on the unconscious mind to be continually processing the various sensory inputs stored in short-term and long-term memory.

Using your unconscious to solve problems is a process of listening and a readiness to record ideas as they percolate into your conscious mind.

Some of the greatest thinkers were great relaxers. Einstein was a daydreamer and spent much of his relaxation time sailing on a lake. Ralph Waldo Emerson enjoyed fishing.

It's all very well to work hard on a problem under the stressful pressure of deadlines, but the opposite condition of relaxation and not working on a problem is very valuable. A practical application of this technique is to saturate yourself in the problem and then take a break. Write down the problem on a writing pad and leave it by your bedside. The next morning, take that pad and start writing down your ideas. Aim to write three full pages of anything that comes to mind. Explore your dreams. We all dream, and we all dream a lot more than we think we do. As you get into bed, say out loud: "Tonight I am going to dream about " (including a brief description of the problem). When you wake up, lie and bed and think some more about the problem. The important thing is not to try too hard. Go with the flow. Incubate.

Last updated: 18th October 1996

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What is not someting that our unconcious mind helps us solve?

One possibility is complex mathematical calculations involving advanced formulas or equations. While our unconscious mind may assist in simpler arithmetic or solving basic math problems, intricate mathematical problems often require conscious effort, logical reasoning, and deliberate thinking.

Which if the follow is not something that our unconscious mind helps us solve?

Math problems

What does our unconscious mind help solve problems?

Our unconscious mind helps solve problems by helping us access and apply our intuition and creativity, taking shortcuts in problem solving, and helping us to find creative solutions to problems. It helps us to process information quickly and come up with creative solutions to problems.

Insight Problem Solving and Unconscious Analytic Thought. New Lines of Research

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• Laura Macchi 12 ,
• Veronica Cucchiarini 12 ,
• Laura Caravona 12 &
• Maria Bagassi 12  

Part of the book series: Studies in Applied Philosophy, Epistemology and Rational Ethics ((SAPERE,volume 49))

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Several studies have been interested in explaining which processes underlie the solution of insight problems. Our contribution analyses and compares the main theories on this topic, focusing on two contrasting perspectives: the business - as - usual view (conscious and analytical processes) and the special process view (unconscious automatic associations). Both of these approaches have critical aspects that reveal the complexity of the issue on hand. In our view, the insight problem solution derives from an unconscious analytic thought , where the unconscious process is not merely associative (by chance), but is achieved by a covert thinking process, which includes a relevant, analytic, goal-oriented search.

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The main theoretical views are the executive attention view (e.g. Engle 2002 ), the binding hypothesis (e.g., Oberauer 2009 ) and the primary and secondary memory view (Unsworth and Engle 2007 ).

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Macchi, L., Cucchiarini, V., Caravona, L., Bagassi, M. (2019). Insight Problem Solving and Unconscious Analytic Thought. New Lines of Research. In: Nepomuceno-Fernández, Á., Magnani, L., Salguero-Lamillar, F., Barés-Gómez, C., Fontaine, M. (eds) Model-Based Reasoning in Science and Technology. MBR 2018. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-32722-4_8

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Unconscious processing modulates creative problem solving: evidence from an electrophysiological study

Affiliations.

• 1 Key Laboratory of Cognition and Personality, Ministry of Education, School of Psychology, Southwest University, Chongqing 400715, China.
• 2 Key Laboratory of Cognition and Personality, Ministry of Education, School of Psychology, Southwest University, Chongqing 400715, China. Electronic address: [email protected] .
• PMID: 24674758
• DOI: 10.1016/j.concog.2014.03.001

Previous behavioral studies have identified the significant role of subliminal cues in creative problem solving. However, neural mechanisms of such unconscious processing remain poorly understood. Here we utilized an event-related potential (ERP) approach and sandwich mask technique to investigate cerebral activities underlying the unconscious processing of cues in creative problem solving. College students were instructed to solve divergent problems under three different conditions (conscious cue, unconscious cue and no-cue conditions). Our data showed that creative problem solving can benefit from unconscious cues, although not as much as from conscious cues. More importantly, we found that there are crucial ERP components associated with unconscious processing of cues in solving divergent problems. Similar to the processing of conscious cues, processing unconscious cues in problem solving involves the semantic activation of unconscious cues (N280-340) in the right inferior parietal lobule (BA 40), new association formation (P350-450) in the right parahippocampal gyrus (BA 36), and mental representation transformation (P500-760) in the right superior temporal gyrus (BA 22). The present results suggest that creative problem solving can be modulated by unconscious processing of enlightening information that is weakly diffused in the semantic network beyond our conscious awareness.

Keywords: Creative problem solving; Creativity; Divergent thinking; Event-related potential; Unconscious processing.

Copyright © 2014 Elsevier Inc. All rights reserved.

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The Unconscious Tug-of-War: Exploring the Effect of Stimulus Selection Bias on Creative Problem Solving with Multiple Unconscious Stimuli

Chengzhen liu.

1 Department of Psychology, Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, 410081, People’s Republic of China

2 School of Humanities and Management Science, Southwest Medical University, Luzhou, 626000, People’s Republic of China

3 Department of Psychology, School of Public Administration, Guizhou University of Finance and Economics, Guiyang, 550025, People’s Republic of China

Shikang Gong

Jinliang guan.

This study innovatively investigated the potential selection bias involved in processing multiple subliminal stimuli during creative problem-solving (CPS). It addresses the existing gap in specialized research on how the handling of multiple unconscious stimuli influences higher-order cognitive processes, particularly creativity.

The study utilized a masked priming paradigm and a remote association task (RAT). Two experiments were conducted. Experiment 1 presented two stimuli simultaneously, with one being the correct answer, to examine whether there was a bias in the location of subliminal stimuli. In Experiment 2, two stimuli were presented sequentially, with one serving as the answer, to investigate whether there was a temporal bias in unconscious processing.

Our findings revealed that when solving easy RATs, subliminal stimuli presented on the left side had a negative priming effect compared to the right side. The results revealed that unconscious processing of subliminal stimuli enhanced performance on difficult CPS. Additionally, a temporal bias was observed, with more recent subliminal stimuli having a stronger effect than earlier stimuli.

Unconscious processing can improve CPS, especially for difficult tasks, and there is a bias towards processing stimuli on the left and more recently presented stimuli. These findings contribute to our understanding of unconscious processing, particularly the processing of multiple subliminal stimuli in CPS, and provide insights into the biases that exist in unconscious processing.

Numerous researchers have focused on understanding the role of unconscious processes in Creative problem-solving (CPS). Researchers have sought to elucidate the mechanisms through which unconscious cognition influences key CPS phases such as idea generation, incubation, and insight. 1–4 The exploration of the role of unconscious processes in CPS has been approached from diverse theoretical perspectives, including cognitive psychology, neuropsychology, and social psychology. Such investigations have contributed to the development of various models and frameworks that aim to explicate the nature of unconscious processing in CPS, such as the associative activation hypothesis, 5–7 the unconscious-thought theory, 8 and Wallas’ four-stage model.

Numerous empirical studies have demonstrated the positive incubation effect, wherein temporarily setting aside a creative problem that has reached an impasse leads to better creative results. 1 , 3 Based on these findings, researchers have proposed the unconscious work hypothesis, suggesting that the positive incubation effect occurs unconsciously during problem-solving. 3 , 9–11 Additionally, Dijksterhuis and Nordgren proposed the theory of unconscious thought (UT) 12 after conducting a series of experiments, which has since been widely discussed in the literature on decision-making and problem-solving. Dijksterhuis et al found that UT can be more effective than conscious thought in generating creative ideas, particularly for complex and novel problems. 13–15 The superiority of UT may be due to its ability to generate a wider range of ideas or its better associative search, which draws on a broader range of knowledge and experiences to generate creative solutions. 12

Despite extensive empirical research on CPS, the phenomenon of generating new and innovative ideas through unconscious processes remains an unresolved issue in the field. CPS involves finding semantic connections between seemingly unrelated concepts and combining them in novel and meaningful ways, which requires generating associations between mental elements to create new combinations of ideas. According to Campbell, 16 automatic spreading activation along associative connections in a semantic network can lead to remote and unusual associations without requiring conscious awareness. Additionally, subliminal activation, especially of unmet goals during incubation periods following unsuccessful conscious work, may also play a role in unconscious processing. Gilhooly conducted a series of experimental studies on the incubation effect, 2 , 17 which suggests that insight in CPS occurs when a semantic activation network system is triggered. The associated solution or relevant information is repeatedly activated until it reaches a conscious threshold level and generates a new solution. The associated solutions initially possess unique properties as subthreshold information or are the result of the integration and filtering of multiple subthreshold information. Therefore, the second question is how to select from a large amount of subthreshold information in the semantic network and whether certain features of the information associated with the answers make them more easily activated, such as unique positioning and appropriate timing.

Notably, the utilization of unconscious processes to generate novel and inventive ideas is posited to be a critical mechanism in the domain of creativity. The process involves the subconscious filtering of seemingly unrelated information, which may be accompanied by a selection bias towards certain choices. This stimulus selection bias refers to the tendency of individuals to favor certain stimuli over others when multiple subliminal stimuli are presented simultaneously. 18 Selective attention is defined as a cognitive process of attending to one or fewer sensory stimuli (i.e., external and internal) while ignoring or suppressing all other irrelevant sensory inputs. 19 , 20 It is a critical aspect of daily functioning as it allows individuals to selectively attend to certain stimuli while filtering out others. 19 , 20 Several different theories of selective attention have focused on the flow and filtering of information, 21 such as Broadbent’s filter theory, 22 the late selection theory of Deutsch and Deutsch, 23 and Treisman’s attenuation theory. 24 Traditional attention research suggests that selective attention is an adaptive mechanism that allows us to cope with rapidly changing environmental developments, facilitating the processing of relevant information and is typically automatic. 25 Thus, individuals might process certain subliminal stimuli more than others based on their relevance to the current problem-solving task. However, it is important to consider whether this bias towards selective processing mechanisms is also applicable to situations where multiple unconscious processing influences affect CPS. Therefore, further research is needed to clarify the role of unconscious processing selection bias in CPS. This experiment aims to investigate this issue by manipulating multiple unconscious stimuli using a masked priming paradigm.

Research has shown that unconscious priming can enhance individuals’ creative performance. Studies that have combined the masked priming paradigm with creativity experiments have discovered that unconsciously presented information can influence creative thinking. For example, Katz found that subliminal word materials could affect the structure and content of participants’ responses when completing creative tasks such as writing stories. 26 Similarly, Förster discovered that unconsciously reminding participants of cities they associated with creativity could increase their creativity. 27 However, this effect was only observed in participants who had already established a link between creativity and a specific city in their long-term memory. Another study by Chen et al 28 combined a revised masked prime paradigm with the RAT and found that unconscious priming and CPS contributions were distinct processes. While the aforementioned studies have demonstrated the influence of unconscious priming on CPS performance, the priming stimuli used were either directly related to the creative task or constituted a single, comprehensive unconscious stimulus. Therefore, how the unconscious processing selects multiple pieces of information in CPS remains unexplored.

In recent years, researchers have conducted experiments on unconscious cognition to examine how different subliminally perceived stimuli are processed. Many studies have shown that subliminally presented words can be integrated and filtered, including research by Van Gaal, Naccache & Meuwese, 29 Armstrong & Dienes, 30 and Sklar et al 31 For instance, Armstrong and Dienes investigated the subliminal processing of syntax, demonstrating that individuals can process the linguistic element “not” and derive meaning from word combinations unconsciously. 30 This indicated that unconscious cognition can filter out useful information from complex multiple stimuli at a semantic level. Sklar et al 31 utilized continuous flash suppression (CFS) to reveal that incongruent sentences, like “I ironed coffee”, break through inter-ocular suppression and enter consciousness faster than congruent sentences, such as “I ironed clothes”. These findings suggest that multiple words can be integrated unconsciously, and semantic violations can be detected, and it may be because the incoherent verbal stimuli are more surprising, so compared to coherent stimuli, they break through inhibition faster. Another study by van Gaal et al used behavioral priming and electroencephalography (EEG) to examine a specific rule-based linguistic operation (i.e the modifier–adjective pair on the processing of the subsequent target noun). 29 Their results indicated that multiple unconscious words can be rapidly integrated, and an unconscious negation can automatically reverse the meaning of an unconscious adjective. Based on their study’s finding of the subliminal negation effect, it can be inferred that there may be a biased processing of negating modifiers in unconscious integration.

Relevantly, several studies have examined the selection bias of unconscious stimuli through experiments involving multiple related stimuli, but the results have been mixed. Some studies have found no clear preferential processing bias for individuals with these unconscious stimuli, such as two fruit words 32 or two arrows, 33 indicating that individuals do not exhibit a selection bias towards a particular word or arrow. On the other hand, other experiments have demonstrated that certain unconscious stimuli out of a set of multiple stimuli are more likely to be preferentially processed by individuals, indicating a bias towards attention and selection. For instance, studies have found that the brain tends to process visual information and imagery when presented with unconscious stimuli with multiple attributes, such as words and pictures or visual and auditory information. 34 , 35 In Jiang et al’s experiment, 36 the presentation of unconscious nude and mosaic pictures side by side induced unconscious spatial attention, which affected behavioral responses. However, it is important to note that this particular experiment did not directly explore the selection bias between unconscious stimuli, as it employed mosaic pictures instead of meaningful ones and had a different experimental purpose. Taken together, the above-mentioned research indicates that the unconscious level is capable of processing various unconscious stimuli and may exhibit multiple selection biases in the process.

It is worth mentioning that the experimental tasks in these studies primarily focused on evaluating the valence (positive or negative) of given targets 30 or different tasks with the same stimuli. 37 There has been no direct investigation into the effects of processing multiple unconscious stimuli on CPS. Nevertheless, these selection biases at the unconscious level may have significant implications for our creative behavior, as biased processing of unconscious information can unconsciously influence our creative thinking. Consequently, more research is needed to explore the effects of processing multiple unconscious stimuli on CPS.

To better investigate the impact of unconscious stimuli on creative tasks, this study also controlled for the difficulty variables in the RATs. Chen et al discovered a clear facilitation effect of unconscious priming on CPS, 28 but this effect was only observed in the high-difficulty RAT condition, aligning with notion of Dijksterhuis and Meurs notion that difficult decision-making should be entrusted to unconscious thought. 8 Therefore, our study hypothesized that unconscious information would influence creative performance, especially in facilitating difficult problem solving. Furthermore, there are variations in multiple unconscious processing and unconscious selection biases when solving creative problems of different difficulties.

This study innovatively explored whether there is a selection bias in multiple unconscious processing in creative tasks, i.e., what kind of unconscious stimuli with specific spatial and temporal presentation features can be better processed to enhance creative performance. Experiment 1 simultaneously presented two stimuli, one of which is the correct answer, to test whether there is a bias in the location of subliminal stimuli. Experiment 1 may include our daily habit of reading from left to right. In Experiment 2, we presented two stimuli one after another, one of which is the answer, to test whether there is a time bias in unconscious processing. If biases in different positions and different time presentation orders are successfully observed, this will indicate that the mechanism of multiple unconscious information selections may be involved in CPS. Essentially, this experiment aims to reveal the mechanism of unconscious processing in CPS by manipulating multiple unconscious stimuli.

Experimental 1 examined the bias in unconscious processing towards different locations. Specifically, by examining whether individuals exhibit a selective bias towards unconscious stimuli presented on the left or right side. According to our habitual visual scanning order, the left-to-right sequence is the more natural reading condition. 38 , 39 Therefore, we hypothesized that in the resolution of CPS tasks, when multiple unconscious stimuli are presented, unconscious processing may exhibit a selection bias towards the left-sided information, prioritizing the processing of left-sided information over right-sided information.

Participants

This study was approved by the institutional review board at Hunan Normal University and participants provided written informed consent prior to the commencement of data collection. We conducted an a-priori power analysis with G*Power 3.1.9.7. 40 Based on the classic study by Kouider and Dupoux investigating the reference perceptual threshold, 41 the effect size of the subliminal repetition priming effect is η p 2 = 0.24 ( f = 0.56). For a 4×2 two-factor within-subjects experimental design, assuming an alpha of 0.05, power of 0.95, and a small effect size f = 0.56, the analyses suggested 7 participants. Taking into account the potential for participants to respond inattentively and the possibility of data loss, and to ensure the reliability of the data, we recruited a total of 25 participants for the study. Participants had a mean age of 20.42 ( SD = 0.67). All participants included in this study were right-handed, had a normal or corrected-to-normal vision, and had no history of, or current neurological or psychiatric conditions. Prior to participation, informed consent was obtained from each participant, following a detailed explanation of the study’s nature and purpose. In exchange for their participation, participants received either course credits or a modest amount of compensation.

The current experiment employed a 4x 2 within-subjects design, with two factors manipulated: prime condition (left-answer vs right-answer vs irrelevant word vs no-prime) and RAT difficulty (easy vs difficult RATs).

Apparatus and Materials

Chinese test materials were created based on Mednick’s Remote Associates Task (RAT). 5 The 108 experimental materials were taken from a research group focused on creativity at Southwest University and had been used in prior studies on creative performance. 28 The RAT problems involved finding a word related to three given words. The difficulty of the 96 RAT items was determined in a pre-experiment, with accuracy rate used as a reference index. The average accuracy ranged from 0.1 to 0.97, with RATs scoring between 0.1 to 0.45 considered difficult, and those between 0.60 to 0.95 deemed easy. Two sets of RATs (48 each for easy and difficult items) were selected from the 108 RAT items, and assigned randomly to four priming conditions. The priming material consisted of the RAT answer word and other words unrelated to the answer.

Threshold priming involves presenting two words simultaneously, with the left-prime referring to an answer word of the RAT on the left and an irrelevant word on the left, and the right-prime referring to an answer word presented on the right. The irrelevant word condition involves presenting two words that are both unrelated to the answer word. The no-prime condition involves not presenting any words. However, to maintain consistency between conditions, two identically timed fragmented pictures are presented in the same position.

The total set of 96 target stimuli was presented in eight different blocks of 16 trials each, and 48 items for the difficult group and 48 items for the easy group. The dependent variables in this study were the participants’ accuracy rate, response times (RTs), and insight (a sudden awareness of the RAT) in response to correct answers. The difference in difficulty between the easy set and the difficult set was highly significant ( F (1, 94) = 238.266, p < 0.05; M =0.305 vs M = 0.681). However, there were no significant differences in overall RAT difficulty between the three prime conditions without distinguishing difficulty ( F (2, 93) = 0.004, p > 0.05). Similarly, there were no significant differences in the overall difficulty between the three prime conditions of RATs for the easy set ( F (2, 45) = 0.066, p > 0.05), or for the difficult set ( F (2, 45) = 0.001, p > 0.05). Twelve RAT items from the remaining materials were selected as practice materials.

The stimuli were displayed on a 15-inch SVGA color computer screen with a gray background and black text, using E-prime 2.0, with a refresh rate of 60 Hz. The target words and prime words were presented in the center of the screen. The image stimuli masks had a visual angle ranged from 8.26 to 8.738 (height), and 7.56 to 8.438 (width), with a resolution of 221 *255 pixels.

Participants sat in a comfortable chair before a computer monitor in a semi-dark room. Before the experiment began, participants were informed that, in each trial, they might see a brief flash of a stimulus before or after they saw the target stimulus. Figure 1 presents the sequence of events in a trial. At the beginning of the experiment, a fixation cross appeared in the center of the screen for a randomly determined duration of 600 ms. Subsequently, the target task was displayed for 1000 ms. Then, a forward mask (a scrambled word), a prime, and a backward mask were presented sequentially for 100, 16, and 50 ms, respectively. The prime was presented as two words simultaneously, with one word on the left and the other on the right, in a parallel fashion ( Figure 2 ). After the backward mask, the target appeared again for 6000 ms or until the participant made a response, whichever occurred first. Participants were required to provide a common associate word as the answer to the target. Based on previous research indicating that activating regions associated with semantic processing during preparation can facilitate problem-solving, 42 we hypothesized that presenting unconscious stimuli after a creative task may have an even greater impact. Thus, in our experimental procedure, we presented the target stimulus for 1 second prior to the unconscious priming in order to enhance the ecological validity of the study.

Schematic illustration of the sequentially displayed stimuli of one trial. Between the two dashed lines is the masking phase of the prime stimuli. The prime stimuli were subliminally shown following the display of the target, with a duration of 1s. The procedures for the two experiments were the same except for differences in the priming phase involving the presentation of the prime stimuli, as shown in Figure 2 .

The specific presentation of stimuli during the priming phase differed between Experiment 1 and Experiment 2. In Experiment 1, two stimuli were presented simultaneously, while in Experiment 2, two stimuli were presented sequentially with a duration of 16 ms each.

Participants were instructed to provide their answer by pressing the “space” key as quickly and accurately as possible, while also verbally stating their response which was recorded by a separate recording device. The participants were asked to rate their sense of insight on a 4-point scale, by pressing a key corresponding to their level of feeling: (1) “no feeling at all”, (2) “a little bit of feeling” (feeling uncertain about the answer), (3) “almost clear feeling”, and (4) “absolutely clear experience”. They were provided with instructions beforehand that explained insight as a sudden awareness of the RAT. Immediately after giving their answer, the correct answer was displayed on the screen, and the participants had to indicate if their response was consistent with it by pressing “1” or “2”. This method was adapted from previous studies, 43–46 and Figure 1 illustrates the trial sequence. After the participant responded, a grey empty screen appeared for 1 second before the fixation point for the next trial was presented. Prior to the formal experiment, participants underwent a practice block of eight trials to become familiar with the procedure.

After the participants completed the preceding phase of the experiment, they were asked to report on whether they saw anything between the two masks to assess their ability to recognize the prime stimulus. Following this, participants engaged in a forced-choice task to assess their recognition of the masked word. The task began with a fixation cross displayed at the center of the screen for 600 ms. Subsequently, a forward mask appeared for 100 ms, followed by a word serving as a prime stimulus for 16 ms; this was followed by a backward mask for 50 ms. The prime stimuli used in this task were similar to those employed in the main experiment, where two words were simultaneously displayed in a parallel manner. Participants were instructed to identify whether the two priming words shared a common category, such as both being fruits or animals. They indicated their response by pressing “1” for affirmative or “2” for negative. Subsequently, participants rated the quality of their subjective experience regarding the visibility of the prime stimulus on a four-point scale using the Perceptual Awareness Scale (PAS): (1) “no experience” (2) “brief glimpse” (a feeling that something appeared but nothing more specific than that), (3) “almost clear experience”, and (4) “absolutely clear experience”. 47 , 48 The degree to which the participant perceived the prime stimulus was used as an important indicator to determine whether the prime was processed subliminally. 49 There were 40 discrimination trials, randomly presented across participants. All words used in the forced-choice task were randomly selected from the main experiment, consisting of 20 object names and 20 object usage words. Prior to performing this task, participants were informed that response accuracy, rather than speed, was the primary concern.

Previous research on unconsciousness has typically conducted the visibility test either after the priming experiment 50–52 or at the end of each trial. 48 , 53 However, administering the visibility test at the end of each trial may potentially influence the forced-choice response, particularly if the target judgment and forced-choice task require identical responses. Tu et al found that participants tend to avoid providing two consecutive identical responses. 54 Therefore, to avoid any possible carry-over effects from the target assessment, we administered the visibility test separately from the main experiment.

Prime Visibility Test results

All participants were included in further analyses. No participant selected “almost clear experience” more than thrice in the PAS test. Out of the total number of participants, data from 25 were used in subsequent analyses. The average percentage of correct recognition was 48.44%, which did not differ significantly from chance ( t (24) = −1.150, p = 0.261). Additionally, the mean d’ score ( M = −0.329, SE = 2.3123) did not differ significantly from zero ( t (24) = −0.774, p = 0.447).

Accuracy Analysis

Our analysis focused on participants’ accuracy rates, which were determined by whether they provided an associated word that was consistent with the correct answer. During the task, participants were instructed to press the “1” key as quickly and accurately as possible when they thought of a word that was consistent with the correct answer. Responses were considered incorrect if participants failed to generate any associated words, or if the words they thought of were inconsistent with the correct answer. The results of the study are summarized in Table 1 , which shows the accuracy rates for each condition. We conducted an analysis of variance (ANOVA) to investigate the influence of the prime condition and RAT difficulty on accuracy rates. Results showed a significant interaction between the prime condition and RAT difficulty, F (3, 72) =5.123, p = 0.003, η p 2 = 0.176. Additionally, the main effects of RAT difficulty ( F (1, 24) = 21.833, p < 0.001, η p 2 = 0.476) and the prime condition ( F (3, 72) = 4.112, p = 0.009, η p 2 = 0.146) were significant.

Means and Standard Error of Accuracy (%) for All the Conditions in Experiment 1

Simple effect analyses were conducted to further investigate the significant interaction between the prime condition and RAT difficulty ( Figure 3 ), and results indicated that: (1) for the difficult RAT items, the difference in accuracy between the left-prime condition (mean =60.96%) and the no-answer prime (mean =53.04%) condition was significant ( p = 0.031). But the difference in accuracy between the left-prime condition and the right-prime condition (mean =57.40%) was not significant ( p = 0.336). (2) for the easy RAT items, the difference in accuracy between three levels of the prime condition was significant, F (3, 22) = 4.828, p = 0.01. Specifically, accuracy for the left-answer prime condition (mean =68.48%), the irrelevant word prime condition (mean = 68.56%), and the no-prime condition (mean = 74.00%) were significantly higher than for the right-answer condition (mean = 58.16%) (all p < 0.05). However, the difference in accuracy between the no-prime condition and the left-answer prime condition was not significant ( p = 0.06).

Means of accuracy (%) for each Condition of Prime condition and RAT difficulty in Experiment 1. The error bars represent one standard error of the mean.

In summary, the analysis of accuracy in the current study indicates that a positive priming effect was observed in resolving difficult RATs, but only when the subliminal answer was presented on the left side, with no significant difference observed between the left and right-prime conditions. On the other hand, a negative priming effect was observed in solving easy RAT items, with the left-prime condition significantly greater than the right-prime condition.

RT Analysis

A summary of participants’ reaction times (mean and standard error) for each condition is shown in Table 2 . A two-factor ANOVA was conducted to examine the effects. The results indicated that all effects were not statistically significant. Specifically, for the main effect of RAT difficulty, F (1, 24) =3.770, p = 0.064, η p 2 = 0.136. For the main effect of the prime condition, F (3, 72) = 0.017, p = 0.997, η p 2 = 0.001. Lastly, for the interaction between the prime condition and the RAT difficulty, F (3, 72) =2.059, p = 0.113, η p 2 = 0.079.

Means and Standard Error of Reaction Times (Ms) for All the Conditions in Experiment 1

Insight Analysis

Table 3 provides a summary of the participants’ insights for each condition. The same two-factor ANOVA used for analyzing the RT revealed a significant interaction between the prime condition and RAT difficulty, F (3, 72) =2.972, p = 0.037, η p 2 = 0.110. The main effect of RAT difficulty was not significant, F (1, 24) = 3.121, p = 0.09, η p 2 = 0.115, and the main effect of prime condition was also not significant, F (3, 72) = 1.992, p = 0.123, η p 2 = 0.077. However, simple effect analyses were conducted to further investigate the significant interaction between the prime condition and the RAT difficulty, and results indicated that both for the difficult RAT items and for the easy RAT items, there were no significant differences in insight between the four levels of the prime condition ( F (3, 22) = 2.095, p = 0.130; F (3, 22) = 2.038, p = 0.138; respectively).

Means and Standard Error of Insight for All the Conditions in Experiment 1

Overall, our study found that unconscious priming can facilitate CPS, as evidenced by a positive priming effect in resolving difficult creative problems and a negative priming effect in resolving easy creative problems. Specifically, the positive priming effect was observed only when the priming stimulus was presented on the left side during the resolution of difficult problems, while the left priming condition was superior to the right priming condition during the resolution of easy problems. In summary, the left-prime condition was more effective in facilitating creative problem-solving, and individuals may prioritize processing of stimuli presented on the left side in CPS.

Experiment 1 suggested that the left-prime condition is more effective in facilitating creative problem-solving, and individuals may prioritize processing of stimuli presented on the left side during CPS. The left-to-right reading order is actually a common reading habit for individuals. However, the issue of sequential effects needs to be clarified. Therefore, in Experiment 2, stimuli were presented in a sequential manner to investigate whether individuals exhibit a selective bias towards the order of presentation of two subthreshold stimuli. 47 In summary, Experiments 1 and 2 investigated the effects of location and presentation timing, respectively.

Twenty-seven participants (15 women and 12 men) from Hunan Normal University in China volunteered for this experiment. The participants had a mean age of 21.25 (SD = 0.27). All participants were right-handed, had either normal vision or vision that was corrected to normal using glasses or contact lenses, and had no prior history of neurological or psychiatric illnesses. They were offered either course credits or a small compensation as a token of gratitude for participating in the study.

The RAT items and the prime stimulus used in this experiment were the same as those used in Experiment 1.

Design and Procedure

Experiment 2 shared the same design and procedures with Experiment 1, except for the priming process of the subliminal stimuli (see Figure 2 ). In Experiment 1, two stimuli were presented simultaneously. However, in Experiment 2, the two subliminal words were presented in succession with a duration of 16ms each. This current experiment utilized a within-subjects design of 4 x 2, where two factors were manipulated: prime condition (the first-answer, the last-answer, the irrelevant word, and no-prime) and RAT difficulty (easy and difficult RATs). The first-answer prime condition refers to a sequence in which two subliminal stimuli are presented, with the first stimulus being the answer word. In contrast, the last-answer condition refers to a sequence in which the second word presented is the answer word.

Data for 27 participants were included in the following analyses. The mean percentage of correct recognition was 49.22%, which was not significantly different from chance, t (26) = −0.688, p = 0.497; nor was the mean d’ ( M = −0.2398, SE = 0.652) significantly different from zero, t (26) = −1.912, p = 0.067.

The method used to calculate accuracy for the analysis of variance was the same as that used in Experiment 1. A summary of participants’ accuracy (means and standard error) for each condition is shown in Table 4 . The ANOVA results revealed a significant interaction between the prime condition and RAT difficulty, F (3, 78) =15.889, p < 0.001, η p 2 = 0.379. The main effect of RAT difficulty was also significant, F (1, 26) = 81.425, p < 0.001, η p 2 = 0.758, while the main effect of prime condition was not significant, F (3, 78) = 0.655, p = 0.582, η p 2 = 0.025.

Means and Standard Error of Accuracy (%) for All the Conditions in Experiment 2

Simple effect analyses were conducted to further investigate the significant interaction between the prime condition and the RAT difficulty ( Figure 4 ), and results indicated that for the difficult RAT items, there was a significant difference in accuracy between the four levels of the prime condition ( F (3, 24) = 6.744, p = 0.002). Specifically, the accuracy for the first-answer condition (mean = 41.67%), the irrelevant word (mean = 37.00%), and no-prime (mean = 40.37%) were all significantly lower than for the last-answer prime condition (mean = 48.41%) ( p = 0.031, p < 0.001, and p = 0.024 respectively). However, there was no significant difference in accuracy between the first-answer condition and the irrelevant word condition ( p = 0.083), or between the first-answer condition and the no-prime condition ( p = 0.682). For the easy RAT items, there was also a significant difference in accuracy between the four levels of the prime condition ( F (3, 24) = 7.270, p < 0.001). Specifically, the accuracy for the no-prime condition (mean = 63.59%) was also significantly higher than the last-answer prime condition ( p < 0.001). The accuracy for the irrelevant word condition (mean = 65.41%) was significantly higher than the last-answer prime (mean = 50.93%) ( p < 0.001) and the first-answer prime (mean = 55.93%) ( p = 0.015), while the difference in accuracy between the last-answer prime and the first-answer prime was not significant ( p = 0.181).

Means of accuracy (%) for each condition of prime condition and RAT difficulty in Experiment 2. The error bar represents one standard error of the mean.

In summary, our analysis of variance revealed a positive priming effect on solving difficult RAT items when an answer word prime was included, as indicated by an increase in accuracy. Conversely, when attempting to solve easy RAT items under the same conditions, a negative priming effect was observed, resulting in a decrease in accuracy. The results of Experiment 2 were consistent with those of Experiment 1. Moreover, we observed a difference in priming order for difficult problems, with significantly higher accuracy under the last-prime condition compared to the first-prime condition. However, this difference was not observed in solving simple problems.

A summary of participants’ reaction times (RT) (means and standard error) for each condition is shown in Table 5 . A two-factor ANOVA showed a significant interaction between the prime condition and RAT difficulty, F (3, 78) =3.815, p = 0.013, η p 2 = 0.128. The main effect of RAT difficulty was also significant, F (1, 26) = 4.287, p = 0.048, η p 2 = 0.142, while the main effect of prime condition was not significant, F (3, 78) = 0.163, p = 0.921, η p 2 = 0.006.

Means and Standard Error of Reaction Times (Ms) for All the Conditions in Experiment 2

We conducted simple effect analyses to further investigate the significant interaction between the prime condition and the RAT difficulty ( Figure 5 ). The results indicated that for the difficult RAT items, there was a significant difference in RT between the four levels of the prime condition ( F (3, 24) = 3.784, p = 0.024). Specifically, the RT for the first-answer condition (mean = 2533) was significantly faster than the last-answer prime condition (mean = 2957) and the no-prime condition (mean = 3003) ( p = 0.044, p = 0.007, respectively). However, there was no significant difference in RT between the last-answer prime condition and the irrelevant word condition (mean = 3043) ( p = 0.771), or between the last-answer prime and the no-prime condition ( p = 0.855). For the easy RAT items, there was also a significant difference in RT between the four levels of the prime condition ( F (3, 24) = 3.824, p = 0.023). Specifically, the RT for the first-answer condition (mean = 2917) was significantly slower than the irrelevant word condition (mean = 2600) ( p = 0.007) and the no-prime condition (mean =2520) ( p = 0.004). However, the difference in RT between the last-answer prime and the first-answer prime was not significant ( p = 0.138).

Means of reaction times (ms) for each condition of prime condition and RAT difficulty in Experiment 2. The error bar represents one standard error of the mean.

In summary, our RT analysis showed consistent results with the accuracy analysis in that a positive priming effect was observed when solving difficult RAT items under the answer word prime condition, while a negative priming effect was observed when solving easy items. Additionally, the result showed that during the resolution of difficult RATs, the RT in the first-prime condition was significantly faster than the RT in the last-prime condition. This finding is contrary to the analysis of accuracy, as the accuracy in the first-prime condition was significantly lower than the accuracy in the last-prime condition.

Insight Analysis (INS)

A summary of participants’ insights (means and standard error) for each condition is shown in Table 6 . A two-factor ANOVA showed that only the main effect of difficulty was significant, F (1, 26) =31.507, p < 0.001, η p 2 = 0.548. A post hoc comparison showed that insight in the easy RAT condition (mean = 3.191) was significantly higher than in the difficult condition (mean = 2.876). However, the main effect of the prime condition was non-significant, F (3, 78) = 1.153, p = 0.333, η p 2 = 0.042. Additionally, the interaction between the prime condition and the RAT difficulty was also non-significant, F (3, 78) = 0.531, p = 0.662, η p 2 = 0.020.

Means and Standard Error of Insight for All the Conditions in Experiment 2

To summarize, the study revealed a positive priming effect in successfully solving challenging RAT items and a negative priming effect in attempting to solve simple RAT items. These effects were observed in both accuracy and response time, but no significant difference was found in terms of insight. Notably, when dealing with difficult problems, the accuracy of the last-prime condition was significantly higher than that of the first-prime condition, but the last-prime condition showed a significantly slower reaction time compared to the first-prime condition.

As the relationship between unconsciousness and creativity attracts increasing attention, it is crucial to investigate the mechanisms of unconscious processing involved in CPS. The current study controlled two subliminal words to examine the selection bias of the unconscious processing for multiple stimuli from a cognitive-behavioral perspective. This study innovatively examined the location and time bias of two unconscious stimuli during the process of CPS by using the masked priming paradigm and the Remote Associates Test. Experiments 1 and 2 focused on investigating the effects of location and presentation timing, respectively. Both experiments revealed an unconscious priming effect, suggesting that unconscious stimuli were processed and affected CPS. Specifically, a negative priming effect was observed in easy problems, whereas a positive priming effect was found in difficult problems, which is highly consistent with previous research in this area. 1 , 3 Experiment 1 found that when two unconscious words appear simultaneously, the stimulus on the left side produces better creative performance than the stimulus on the right side. Experiment 2 revealed that in a sequence of two subliminal words, presenting the second word resulted in higher accuracy but slower response times, compared to presenting the answer word as the first word. These results suggest that multiple unconscious processes can occur during CPS, and that there are location and timing biases, with left stimuli and the most recently presented stimulus being more effective in facilitating CPS.

Upon further analysis, we found that regardless of whether the unconscious stimuli were presented on the left or right, or whether they appeared first or second, the presence of a cue associated with the correct answer differed significantly from the presence of an irrelevant stimulus or no cue at all. This suggests that the mechanism underlying the effects of multiple unconscious processes may involve the simultaneous influence of multiple unconscious stimuli on behavioral responses. 34 , 37 , 55 However, it should be noted that specific responses are influenced by the bias in unconscious selection.

In Experiment 1, we observed a bias in location selection, with unconscious processing tending to favor the left stimulus when two stimuli were presented simultaneously. When solving easy RATs, the correct response rate was significantly higher when the cue associated with the answer was presented on the left rather than the right. When solving difficult RATs, we found a significant difference in the correct response rate only when the subliminal answer word was presented on the left compared to when it was on the right. These findings suggest that during the resolution of creative problems, multiple unconscious stimuli exhibit a clear bias towards the left location.

Most researchers in the field of cognitive research adopt the method of simultaneously presenting two or more subliminal stimuli to explore the influence of the relationship between subliminal stimuli on subsequent responses to a supraliminally presented target. 56–58 However, these studies did not directly investigate selection bias. Jiang et al simultaneously presented a nude image and its mosaic version on the left and right sides and found that the nude image can elicit unconscious spatial attention and affect subsequent decisions, 36 revealing the presence of an unconscious selection bias in processing. However, because of different experimental goals, mosaic images rather than meaningful images were used in their study. In our experiment, both sides were presented with meaningful words, which better revealed the priority of unconscious stimuli in two meaningful conditions, with stimuli on the left side receiving priority processing.

This selection bias towards the left side may be related to our habit of reading from left to right, 38 , 39 resulting in a preference for processing even subliminal stimuli on the left. Does stimulus processing have a temporal order effect, as left information is processed first due to our reading habits, followed by the right stimulus? Experiment 2 further explores processing bias in the temporal order of stimulus presentation. Our results showed that when solving difficult RATs, compared with the condition of presenting the answer word first, the accuracy of the condition of presenting the word later was higher, but the response time was slower. This suggests the possibility of a temporal order effect, which is reflected only in the speed of behavioral responses. Regarding CPS, we are more concerned about the ability to correctly solve problems, that is, accuracy. In Experiment 1, no significant difference was found in response times between the left and right sides. Combined with the higher accuracy of the word-later condition than the word-first condition in Experiment 2, we can conclude that the location bias found in Experiment 1 is not due to temporal order. This supports the view of Mudrik et al who found that presenting two prime stimuli simultaneously can be considered the ultimately shortest temporal separation. 58 The significant differences in reaction time in Experiment 2 may be attributed to a motion effect in behavioral response, which is caused by the activation of visual features. 33 , 56

According to the results of Experiment 2, we found that the most recently presented word was better for helping to correctly solve the creative problem, indicating that the stimulus processed first is not necessarily the preferred object of unconscious processing. Furthermore, the results of Experiment 2 indicate that when two stimuli are presented in succession, unconscious processing tends to favor the later stimulus. Until now, most researchers have utilized the sequential presentation of two or more subliminal words to investigate the integration of unconscious stimuli, 59–61 but have not explored the order processing bias of unconscious stimuli. Our study results revealed a selection bias in temporal processing, indicating an unconscious processing preference for the second stimulus when presented successively with another stimulus. This suggests that the second stimulus may further shorten the time window of semantic integration. Participants begin making semantic associations and diffusion after seeing the RAT task, and unconscious stimuli appearing at this time close the distance between the unconscious information and the target task, thereby facilitating the generation of correct answers.

Most importantly, while previous research on the integration of multiple unconscious processes suggests that multiple unconscious stimuli may jointly influence the target task through integration, our findings regarding the bias in stimulus selection suggest that the mechanism of multiple unconscious processing may involve each stimulus independently affecting creative performances and coexisting with the bias in stimulus selection. The discovery of unconscious selective processing can enhance our understanding of the creative processes such as Aha experience and incubation. It also contributes to a better understanding of the global workspace theory, various theories of consciousness, and broad cognitive processes such as resting state, sleep, and more. 47 Ultimately, a deeper understanding of the nature of unconscious processing and its role in creative cognition has the potential to inform a range of practical applications, from improving creativity training programs to enhancing problem-solving strategies in a variety of contexts.

The human mind is often engaged in a fascinating and complex battle, known as the unconscious tug-of-war. Within a vast amount of unconscious information, there exists such a tug-of-war that manifests as a bias in information selection. In the context of CPS, there is a tendency to favor certain information, particularly those with specific spatiotemporal features (such as being on the left side or recently encountered). These types of information are more likely to be associated with new ideas or activate related information, thus fostering the generation of creativity. This may be the key to solving creative problems.

The unconscious tug-of-war can be seen as a competitive mechanism for information processing, where different pieces of information compete for priority in our thinking. In this battle, information relevant to our current task or goal often gains an advantage, while information with certain distinctive features (such as being on the left side or recent in occurrence) is more likely to undergo processing. This processing advantage enables our brains to establish new associations more readily, leading to the creation of novel ideas and solutions. This process is crucial for the expression of creativity. By leveraging information with specific spatiotemporal features, we can more easily activate relevant knowledge and experiences, providing robust support for creative problem-solving. We can utilize this unconscious tug-of-war mechanism more flexibly, thus enhancing our creative abilities.

After controlling for the difficulty level of the RAT, we investigated the impact of subliminal stimuli processing at varying levels of difficulty on task performance. The results showed that unconscious priming can facilitate CPS, as evidenced by a positive priming effect in resolving difficult creative problems and a negative priming effect in resolving easy problems. These results support earlier research indicating that the unconscious mind can enhance problem-solving ability and confirm the effectiveness of unconscious processing for creative thinking. 28 , 62 These findings are consistent with prior research, providing evidence for the effectiveness of unconscious processing in creative thinking and supporting Dijksterhuis and Meurs’ theory of unconscious thought, 8 which suggests that unconscious thinking may have potential advantages over conscious thinking in solving complex problems requiring information integration and association.

This phenomenon may be due to the limited experiences or thought fixations of individuals when processing difficult creative problems, making it challenging for them to extract as many perceptual patterns as possible. Unconsciously activated information can assist in the acquisition of critical information, making it easier to search for answers in the meta-level space to facilitate problem-solving. Kaplan and Simon’s theory of information processing suggests that information processing can occur at both conscious and unconscious level and that unconscious processing can aid in the discovery of new connections and patterns. 63 Similarly, Knoblich et al argued that creative problem-solving often involves the integration of disparate pieces of information, 64 a task at which unconscious processing may excel. Furthermore, difficult RAT problems require more extensive information searching, and unconscious semantic processing of subliminal stimuli can aid in this search. Unconscious processing has a higher capacity and can search for more extensive and broader information than conscious thinking, which has a limited capacity. 8 , 65 In summary, our study supports the idea that unconscious processing can facilitate CPS by generating accurate problem representations and associations, contributing to our understanding of its potential advantages in complex problem-solving.

Incidentally, we did not find significant evidence of unconscious priming effects in our measurements of insight. Moreover, the differences observed in response time measures were not sensitive and did not align with the discrepancies observed in accuracy levels. This could be due to the fact that the RAT task requires longer processing time compared to consistency judgment tasks, and unconscious processing may not be sensitive to facilitating or inhibiting insight and reaction time in behavioral responses. This is consistent with previous research on the use of the RAT task. 28 In order to deepen our understanding of the role of unconscious processing in insight, future research could benefit from exploring potential physiological mechanisms using techniques such as electroencephalography (EEG).

Overall, we innovatively manipulated two unconscious stimuli to investigate the key mechanisms of multiple unconscious processing in CPS. Our findings revealed a bias towards processing stimuli on the left and recently presented stimuli during unconscious processing, which may be an important mechanism in CPS involving multiple unconscious processing. These results expand the scope of research on unconscious information processing through the utilization of RAT tasks, providing important theoretical implications for understanding multiple unconscious processing in higher cognitive functions such as CPS. In addition, we observed a positive priming effect in difficult creative problems and a negative priming effect in easy ones, consistent with the traditional perspective on this phenomenon. However, our use of multiple unconscious stimuli in the context of unconscious information selection provides a new explanation for the attentional selection of unconscious processing in CPS, potentially offering a significant mechanism for CPS. A definitive exploration to this question will require further research.

The current study offers behavioral evidence supporting the enhancement of creative performance through multiple unconscious processes, especially in challenging creative tasks. The observed biases and insights into the nature of unconscious cognitive operations deepen our understanding of the intricate dynamics of unconscious processing and its implications for creative problem-solving (CPS). Despite the relatively limited literature in this area, future research should conduct more extensive investigations, encompassing behavioral and neuroimaging methodologies. A comprehensive comprehension of the multifaceted mechanisms governing unconscious processing in CPS is crucial for advancing both the fields of unconsciousness and creativity. Therefore, this endeavor significantly contributes to our theoretical understanding of these domains.

Acknowledgments

We thank the students for their support and participation.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Declarations

We confirm that this study complies with the principles outlined in the Declaration of Helsinki.

The authors declare that they have no conflicts of interest/competing interests for this work.

Freud’s Theory of the Unconscious Mind

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sigmund Freud didn’t exactly invent the idea of the conscious versus unconscious mind, but he certainly was responsible for making it popular, and this was one of his main contributions to psychology.

Freud (1900, 1905) developed a topographical model of the mind, describing the features of the mind’s structure and function. Freud used the analogy of an iceberg to describe the three levels of the mind: conscious, preconscious, and unconscious.

This model divides the mind into three primary regions based on depth and accessibility of information:

Freud’s conception of consciousness can be compared to an iceberg because, much like an iceberg, the majority of an individual’s mind exists below the surface, hidden from immediate view.

Iceberg Theory

Freud’s iceberg theory metaphorically represents the mind’s three levels: the conscious (visible tip of the iceberg), the preconscious (just below the surface), and the unconscious (vast submerged portion).

While we’re aware of the conscious, the preconscious contains easily accessible memories, and the unconscious houses deep-seated desires and memories, influencing behavior despite being largely inaccessible.

Freud (1915) described the conscious mind, which consists of all the mental processes of which we are aware, and this is seen as the tip of the iceberg. For example, you may be feeling thirsty at this moment and decide to get a drink.

The preconscious contains thoughts and feelings that a person is not currently aware of, but which can easily be brought to consciousness (1924). It exists just below the level of consciousness, before the unconscious mind.

The preconscious is like a mental waiting room, in which thoughts remain until they “succeed in attracting the eye of the conscious” (Freud, 1924, p. 306).

This is what we mean in our everyday usage of the word available memory. For example, you are presently not thinking about your mobile telephone number, but now it is mentioned you can recall it with ease.

Mild emotional experiences may be in the preconscious, but sometimes traumatic and powerful negative emotions are repressed, hence not available in the preconscious.

In common language, “subconscious” is often used more generally to describe thoughts or feelings operating below the level of conscious awareness, without the nuanced distinctions of Freudian theory. However, within the context of Freud’s model, “preconscious” (German translation: Unterbewusstsein) has a more specific and distinct meaning.

According to Freud (1915), the unconscious mind is the primary source of human behavior. Like an iceberg, the most important part of the mind is the part you cannot see.

While we are fully aware of what is happening in the conscious mind, we have no idea what information is stored in the unconscious mind.

The unconscious mind acts as a repository, a ‘cauldron’ of primitive wishes and impulses kept at bay and mediated by the preconscious area.

Our feelings, motives, and decisions are powerfully influenced by our past experiences, and stored in the unconscious.

Unconscious Mind

In psychoanalysis, the unconscious mind refers to that part of the psyche that contains repressed ideas and images, as well as primitive desires and impulses that have never been allowed to enter the conscious mind.

Freud viewed the unconscious mind as a vital part of the individual. It is irrational, emotional, and has no concept of reality, so its attempts to leak out must be inhibited.

Content contained in the unconscious mind is generally deemed too anxiety-provoking to be allowed in consciousness. It is maintained at an unconscious level where, according to Freud, it still influences our behavior.

The unconscious mind comprises mental processes inaccessible to consciousness but that influence judgments, feelings, or behavior (Wilson, 2002).

Sigmund Freud emphasized the importance of the unconscious mind, and a primary assumption of Freudian theory is that the unconscious mind governs behavior to a greater degree than people suspect. Indeed, the goal of  psychoanalysis is to make the unconscious conscious.

The unconscious contains all sorts of significant and disturbing material which we need to keep out of awareness because they are too threatening to acknowledge fully.

Much of our behavior, according to Freud, is a product of factors outside our conscious awareness. People use a range of defense mechanisms (such as repression or denial) to avoid knowing their unconscious motives and feelings.

For example, Freud (1915) found that some events and desires were often too frightening or painful for his patients to acknowledge and believed such information was locked away in the unconscious mind. This can happen through the process of repression.

Freud recognized that some physical symptoms may have psychological causes. Hysteria (sometimes known as conversion hysteria) is a physical symptom with no physical cause. However, the ailment is just as real as if it had but is caused by some underlying unconscious problem.

Psychosomatic disorders are a milder version of this. The unconscious is seen as a vital part of the individual; it is irrational, emotional, and has no concept of reality, so its attempts to leak out must be inhibited.

The unconscious mind contains our biologically based instincts (eros and Thanatos) for the primitive urges for sex and aggression (Freud, 1915). Freud argued that our primitive urges often do not reach consciousness because they are unacceptable to our rational, conscious selves.

Freud believed that the influences of the unconscious reveal themselves in various ways, including dreams , and slips of the tongue, now popularly known as Freudian slips.

Freud (1920) gave an example of such a slip when a British Member of Parliament referred to a colleague with whom he was irritated as “the honorable member from Hell” instead of from Hull.

Critical Evaluation

Initially, psychology was skeptical regarding the idea of mental processes operating at an unconscious level. To other psychologists determined to be scientific in their approach (e.g. behaviorists ), the concept of the unconscious mind has proved a source of considerable frustration because it defies objective description, and is extremely difficult to test or measure objectively.

However, the gap between psychology and psychoanalysis has narrowed, and the notion of the unconscious is now an important focus of psychology.

For example, cognitive psychology has identified unconscious processes, such as procedural memory (Tulving, 1972), automatic processing (Bargh & Chartrand, 1999; Stroop, 1935), and social psychology has shown the importance of implicit processing (Greenwald & Banaji, 1995). Such empirical findings have demonstrated the role of unconscious processes in human behavior.

However, empirical research in psychology has revealed the limits of the Freudian theory of the unconscious mind, and the modern notion of an “adaptive unconscious” (Wilson, 2004) is not the same as the psychoanalytic one.

Indeed, Freud (1915) underestimated the importance of the unconscious, and in terms of the iceberg analogy, there is a much larger portion of the mind under the water. The mind operates most efficiently by relegating a significant degree of high-level, sophisticated processing to the unconscious.

Whereas Freud (1915) viewed the unconscious as a single entity, psychology now understands the mind to comprise a collection of modules that have evolved over time and operate outside of consciousness.

For example, universal grammar (Chomsky, 1972) is an unconscious language processor that lets us decide whether a sentence is correctly formed. Separate from this module is our ability to recognize faces quickly and efficiently, thus illustrating how unconscious modules operate independently.

Finally, while Freud believed that primitive urges remained unconscious to protect individuals from experiencing anxiety, the modern view of the adaptive unconscious is that most information processing resides outside of consciousness for reasons of efficiency, rather than repression (Wilson, 2004).

Bargh, J. A., & Chartrand, T. L. (1999). The unbearable automaticity of being . American psychologist, 54(7) , 462.

Chomsky, N. (1972). Language and mind . New York: Harcourt Brace Jovanovich.

Freud, S. (1915). The unconscious . SE, 14: 159-204.

Freud, S. (1924). A general introduction to psychoanalysis , trans. Joan Riviere.

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychological review , 102(1), 4.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of experimental psychology , 18(6), 643.

Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of Memory , (pp. 381–403). New York: Academic Press.

Wilson, T. D. (2004). Strangers to ourselves . Harvard University Press.

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Solve problems by tapping into your unconscious mind

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How many times have you sweated over a complex problem late into the night, and believed you’d only solve it by working longer and harder? What if you knew that if you dropped your labours and turned to a completely different task, like running, gardening or solving a Rubix cube for 15 minutes, the solution would likely occur to you when you returned to the task?

Leadership experts are now exploring the science of the mind. One important finding – and there’s scientific research to prove it – is that the unconscious can be better at insight-based problem-solving than conscious deliberation.

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The Science Behind Why You Should Switch Off Your Thinking Brain to Spark Creativity and Smart Decisions

Not always.

Eurekas are hardly ever discovered that deliberately. If a solution is outside of your brain’s familiar experience — which is shaped by your beliefs, culture, and biases — your conscious mind will most likely never find it. An analytical search for a solution can comb through the entire content of your mind’s “known” but not outside of it. Novel answers reside outside of your mind’s known box.

When you allow your brain to integrate new information with existing knowledge on a subconscious level, it can establish new connections and see patterns not obvious to your conscious mind. Creative solutions and ideas are more likely to bubble up from a brain that applies unconscious thought to a problem, rather than going at it in a deliberate approach with your frontal lobe. When your thinking brain is active and inundated with information, it doesn’t have the opportunity to connect concepts or make creative leaps.

Science shows that your brain’s resting-state circuitry, called the default mode network (DMN) — which is activated when you stop thinking about something specific and just veg out — is the best place to park a problem. In the DMN, your brain does some of its best, wisest, and most creative work.

More Information Is Not Always Better

There is an implicit belief in our society that more information is better.  According to economic theory, more information is always better unless the cost of acquiring further information exceeds the anticipated gain from it. Economists do concede and make the exception that more is not always better when the information isn’t free. This rule may work for economics, but in your brain, more information and thinking is not always better, for several reasons.

Your brain doesn’t like too much information.

Research  indicates that people like to have choices when faced with making a decision. However, if they are given too many choices, they feel less happy about their decision and are less satisfied with the decision-making process itself. One study showed that as people received more information, activity increased in the region of the brain responsible for decision-making, problem-solving,  and control of emotions, the prefrontal cortex. However, when the load became too much it was as though a breaker in the brain was tripped and the prefrontal cortex just shut down.

Your working memory is limited.  

Even though the brain can store virtually limitless amounts of information in long-term memory, you can only keep a limited amount of information in short-term (STM) memory at one time. Research shows that the average span is 7.3 for letters and  9.3 for numbers. Information stays in STM  between 15 and 30 seconds.   Then, it is either attended to by working memory or discarded. 

You learn better with spaced sessions than with contiguous practice.

You probably know from experience and science  confirms that your brain performs better if you take in information in chunks with regular breaks rather than trying to cram everything into one long session. Your brain needs downtime to consolidate the incoming information before you can use it effectively. Studies show that napping can improve memory and creative problem-solving .

Why Your Unconscious Brain May Have The Answer

Research suggests that thinking about an issue too methodically is often a detriment to problem-solving because your brain actually blocks potential solutions from registering into consciousness through a phenomenon known as cognitive inhibition .  Basically, your mind tunes out any information it deems not relevant to the issue you are focusing on. But, the answer may reside in that extraneous info.

Mark Beeman Ph.D., a cognitive neuroscientist at Northwestern University, and John Kounios Ph.D.,  a professor of psychology at Drexel University, have been studying problem-solving anbd “Aha moments”. In the report,  The Aha! Moment, The Cognitive Neuroscience of Insight, they write:

A sudden comprehension that solves a problem, reinterprets a situation, explains a joke, or resolves an ambiguous percept is called an insight (i.e., the ‘‘Aha! moment’’). Psychologists have studied insight using behavioral methods for nearly a century. Recently, the tools of cognitive neuroscience have been applied to this phenomenon. A series of studies have used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the neural correlates of the ‘‘Aha! moment’’ and its antecedents. Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales. Elucidation of these precursors suggests interventional opportunities for the facilitation of insight.”

The article, The Meaning (and Science)  Behind Those Life Changing, Transformation Aha Moments , explained it this way:

‘It’s a bit like trying to look at a dim star,’ Beeman says. ‘You have to turn your head and spy it out of the corner of your eye; if you look at it directly, it disappears.’ In lab experiments, subjects who are given a brainteaser and sleep on the problem or otherwise back away from it are usually more likely to solve it than if they just keep pounding away.”

Timing is critical when it comes to putting your problem-solving subconscious mind to work for you. If you stay in the deliberate mode of thinking too long, you can inhibit possible solutions from emerging.  However, if you back off of a problem too soon, before you have all the puzzle pieces, your brain doesn’t have the information it needs to come up with an answer. The trick is to know how much time to spend concentrating on a problem and when to ease off and let your subconscious brain do the heavy lifting.

The Default Mode Network

Research  shows that there’s a predictable pattern of neurological activity that’s your brain’s go-to state when it’s at rest, not focused on anything in particular, or actively engaging with its environment. This resting state of your brain is called the  default mode network (DMN). Ruminating and worrying take place in the DMN.

Science discovered the DMN using fMRI studies where people were asked to lay in the scanner with no specific thinking assignment. The DMN refers to the “internal mode of cognition,” which is a very abstract concept.  One  study  provided empirical support that the DMN is one of the most abstract networks in your brain.

Research shows that the harder and more cognitively demanding a particular task is, the less the DMN is activated. Decreased activation of the DMN can also be brought about by mindfulness practices such as yoga and meditation . Specifically, researchers who examined brain activation during meditation using functional MRI, found decreased activation of regions related to the DMN . The researchers also suggested that meditation training can increase the synchronization of activation between DMN regions that are related to the awareness of the ‘self’.

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So interesting Debbie. I remember reading that Eddison used to take frequent naps so that his subconscious would help him reach the solutions he needed.

And Napoleon Hill, when he couldn’t come up with the title for his Book…Think and Grow Rich…went to bed each night instructing his subconscious to come up with the ideal title…and one night…it did exactly that. And the rest is history! 🙂

Neat examples, Elle. I’ll do that a lot too. I’ll tell my brain a question or situation to ponder over night. It works sometimes! 🙂

Being an analytical person, it took me a while to learn the lessons from this article. In the past, I used to dwell on ‘pro’s and con’s’ lists and the like and usually didn’t make the best decisions. Now, I gather as much information as I can, sit with the question in meditation and see what my body or heart tells me, then sleep on it, asking my subconscious to work on it. Then I would have an ‘aha!’ moment sometime later, just like you’ve described.

We’ll never fully understand the mysteries of how our brains work. The more we understand our inability to control the process and work with what is, the better our decisions and lives will be.

Thanks for the insight, Paige. I too have learned not to try to hammer a decision to death and just let it come to me. Works much better! 🙂

This is fascinating, Debbie. It validates the importance of paying attention to our gut, our instinct, our intuition.

Yes. Indeed, it does, Sandra. 🙂

Apparently Vitamin B12 helps the subconscious mind with lucid dreaming. Just something to think about as far as creativity goes. 😛

Good to know! Thanks. 🙂

Thanks, Debbie! Just came on your blog today looking for resources for K-12 teachers who’re offering mindfulness in schools, and was very glad to read your cogent writing. Thank you, I will follow you from now on!

Thanks, Betsy. I’m glad you found my website helpful! 🙂

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Consciousness

The hard problem of...psychology, how psychology was the first science of consciousness..

Posted July 20, 2024 | Reviewed by Tyler Woods

The hard problem of consciousness was made famous by the philosopher David Chalmers. He presented it at the first Toward a Science of Consciousness conference in 1994. He defined it as the problem of “how and why” physical processes in the brain give rise to subjective conscious experiences (i.e., the taste of wine or seeing the colors of a rainbow). He further elaborated on the analysis a few years later in his influential book, The Conscious Mind: In Search of a Fundamental Theory .

Part of what made his analysis helpful was that Chalmers differentiated what he called the “easy” problems from the “hard” ones. The easy problems deal with brain functions and behavior. For example, to explain why a frog zapped a fly with its tongue, we can think of its brain as a kind of neuro-information processing center that governs the frog’s body. That system has a template that picks up visual stimuli that behave the way flying insects behave. And there are motor reflex programs that shoot the tongue out.

But does the frog actually see the fly? Does success or failure at snatching the fly result in feelings of pleasure or frustration? Is there anything that it is like to be a frog from the inside? Or, are frogs “zombies” (i.e., the philosophical term for there being no subjective conscious experience at all)? As lively as frogs are “from the outside,” it is an open scientific question if they have subjective conscious experiences.

In The Conscious Mind , Chalmers labeled the easy problems as being “psychological” in nature. These are problems having to do with behavioral outputs and related neurocognitive functions, but have no direct relation to subjective experience. In contrast, he labeled subjective experiences the “phenomenal” aspects of the mind. The difference between the two is clear in the case of the frog. We know a lot about the frog's neurocognitive activity, defined in terms of the functional relations between the frog’s brain and its behavior in its environmental context. However, we know little about its phenomenal experience, including the question of whether it has any.

UTOK, the Unified Theory of Knowledge 1 , aligns with Chalmer’s analysis in many regards. However, it comes at the problem from an entirely new angle. Chalmers was first trained as a physicist, and then he became a philosopher. As such, he comes at the question from the vantage point of its philosophical nature. This can be framed by the question: What is consciousness, and how does it fit inside the physical universe?

I am trained as a clinical psychologist. My journey toward a unified theory of psychology started at about the time as the new science of consciousness was being born. At that time, I was learning to become a psychotherapist and wanted a coherent scientific framework in which to ground my approach. It turns out there is not one. Why? Because, as I have detailed in many blogs, journal articles, and two books 1,2,3,4,5 , psychology is not a coherent discipline. It lacks a coherent identity and subject matter. It is something I have labeled "the problem of psychology."

Why and how did the problem of psychology emerge? Psychology started out as the science of consciousness. We can see this in two of its earliest established lines. First there were the psychophysicists in the middle of the 19 th century. They looked at the relationship between physical stimuli and sensation and developed the “psychophysical laws” that continue to impact research today (e.g., absolute threshold and just noticeable difference). Then came Wilhelm Wundt, who officially founded the science of psychology in 1879. He framed it as the science of human consciousness and trained folks in the methods of introspection. Wundt’s methods and findings came under attack by both functionalists and behaviorists, and his approach, which came to be known as structuralism, died.

The reason it died was because there was “gap” when it came to subjective experience. The gap was both epistemological and ontological. The epistemological gap was the fact that the nature of science is that it is based on behaviors that can be measured and verified via intersubjective agreement. The ontological gap is what Chalmers points to as the hard problem.

Behaviorists like John Watson thought of the brain as being like a set of wires on a switchboard, and behavior was produced by how electrical impulses cause reflexes. But consciousness was a mystery and was banned from behavioral psychology. Decades later, the cognitive revolution happened and cognitive psychology embraced the concept of the brain as a kind of neuro-information processing system. The result has been that psychology became aligned with functional analyses of behavior and mental processes, analyzed through the methods of science.

However, as I have made clear in my writings on the problem of psychology, this means psychology completely failed to solve the ontological problem. That is, the science of psychology has been defined as a methods-based discipline—psychologists do not have a clear, consensually agreed-upon framework for what they mean by “the mind.”

The Current Science of Consciousness Is Round 2

The 30 years since the first Toward a Science of Consciousness conference have seen an explosion of interest in consciousness. However, despite all the activity, serious problems are emerging. A recent, massive review on the “landscape” of theories of consciousness identified almost 85 different angles and approaches! There is debate about whether progress is being made on the hard problem. Consider that in 1998, the neuroscientist Christoph Koch bet Chalmers a fine case of wine that progress in the field would be made in the next 25 years at answering the question of what consciousness is. Koch lost the bet and paid up in 2023 . There has also been serious infighting between different approaches . For example, last year Integrated Information Theory, which is one of the more popular and heavily researched approaches, came under attack in the form of an open letter of over 100 scholars accusing it of being “pseudoscience.” This attack was reminiscent of the attack the behaviorists launched against Wundt and the structuralists.

My father is an emeritus professor of history. A common phrase in our household growing up was one of the great adage of historians: Those who fail to understand history are doomed to repeat it.

Given that scientific psychology started out being defined as the science of consciousness but failed, one might think that the history of psychology and the problem that emerged would be relevant to the new science of consciousness. However, as far as I could tell, not a single approach in the massive review on the landscape of consciousness addresses the problem of psychology. Instead, the ideas were mostly from philosophers, neuroscientists, and physicists grappling with how consciousness fits in the universe as defined by physics. As a theoretical psychologist who knows the history of psychology, I am here to say, “We have been here before!”

The hard problem of consciousness surfaced more than a century ago, and it ended up breaking psychology. As such, maybe we need a new frame on this problem. Perhaps we should return to the problem of psychology, and see if we can grip the problem from that angle. When you do this from the perspective of UTOK a whole new solution to the difficulties becomes apparent 1 .

1. Henriques, G. (2022). A new synthesis for solving the problem of psychology: Addressing the Enlightenment Gap . Palgrave MacMillan. (see Appendix C for the specific chapter references).

2. Henriques, G. R. (2011). A new unified theory of psychology . New York: Springer.

3. Henriques, G. (2008). The problem of psychology and the integration of human knowledge: Contrasting Wilson’s Consilience with the Tree of Knowledge System. Theory and Psychology, 18, 731-755.

4. Henriques, G. R. (2004). Psychology defined. Journal of Clinical Psychology, 60, 1207-1221.

5. Henriques, G. R. (2003). The tree of knowledge system and the theoretical unification of psychology. Review of General Psychology, 7, 150-182.

Note that the above references provide a systematic argument for the clarifying the nature of problem of psychology and its solution. The Unified Theory of Knowledge is a new philosophical system that coherently aligns the natural sciences, the human psyche, and the collective wisdom traditions. The result transforms our understanding of the hard problem into a resolution.

Gregg Henriques, Ph.D. , is a professor of psychology at James Madison University.

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More From Forbes

Intuitive intelligence and 8 common blocks that impact it.

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Hulan Hagen is the founder and CEO of the Business Intuition Institute , an international executive coach, and strategy consultant.

What is intuitive intelligence, and how can it help you solve today’s business challenges? According to experts , there are several problems of emerging importance in the business world. A few key issues are hiring for mindset, staying focused, adapting to consumer preferences, navigating change and integrating artificial intelligence. All of these demand foresight and creativity, which are elements of intuitive intelligence.

How can business leaders develop the intuitive intelligence necessary to flourish in the face of relentless technological, economic and social change? They must better comprehend this skill set and recognize common barriers that can negatively impact it.

Understanding Intuitive Intelligence

Intuitive intelligence is the ability to make decisions based on insights that aren't obvious through logical reasoning. It synthesizes experience, instinct and subconscious information. In their 1958 book Organizations , James March and Herbert Simon showed how decision-making models that depend on complete knowledge of parameters, like return on investment and probabilities of outcomes, are unrealistic. Rationality is bounded or limited, and economists who claim that firms optimize profit assume too much. Bounded rationality necessitates satisficing, or just-good-enough, solutions .

But from the standpoint of intuitive intelligence, optimizing profit may amount to very little. Breakthrough innovation—the leap from zero to one, as entrepreneur Peter Thiel puts it —creates returns that blow past improvements possible through optimization. In Thiel’s terminology, optimizing can take a firm from one to n , with n being the optimal iteration of a product or service. But the blowoff profits are made from monopolizing innovations, or taking them from "zero to one" by creating something new.

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The recognition that rationality is bounded can expand, rather than limit, outcomes because innovations that result from intuitive intelligence in the face of ambiguity can create new industries and monopoly profits. The best business decision-makers develop their intuitive intelligence to broaden their perceptual scope, jettisoning biases like stereotyping, thinking in one language and selective perception.

Masters Of Intuitive Intelligence

Beyond Peter Thiel, there are many leaders and entrepreneurs who saw opportunities for innovation and ran with them. For example, Richard Branson's decision to start Virgin Atlantic—despite having no experience in the airline industry—was driven by the belief that he could offer better service than existing airlines.

Spanx's founder Sara Blakely was getting ready for a party when she decided to cut the feet off some pantyhose and wear them under a pair of white pants to ensure her outfit had clean lines. Her intuitive thinking and firm understanding of her target market’s needs were key to her company's success.

Howard Schultz, the former CEO of Starbucks, relied on his intuition to transform a dry-coffee store into a global coffeehouse chain by recreating the Italian coffeehouse experience in the United States. Starbucks, which was named after a character from Melville’s Moby Dick , was able to grow because Schultz intuited that customers would value a place to relax and socialize over a cup of coffee.

8 Common Blocks To Intuitive Intelligence

At the Business Intuition Institute, we've identified eight blocks to intuitive intelligence.

1. Lack Of Self-Awareness: Understanding your own thought processes and emotional responses is crucial. Intuitive intelligence requires reflection and pattern recognition.

2. Lack Of Experience And Knowledge: The more varied your experiences, the richer your pool of subconscious knowledge. By engaging in activities outside your usual scope, you can broaden your perspective and enhance your intuitive abilities.

3. Bias Against Intuition: Many professionals believe that only left-brained thinking, which is associated with logic, can result in profitable solutions. However, some of the greatest, revenue-generating innovations can come from right-brained, or creative and qualitative, thinking.

4. Thinking In One Language: A major barrier to intuitive intelligence is being locked into the process of deductive reasoning. Many of us think only in words or numbers. But intuitive insights arrive when we tap into the flow of our subconscious.

5. Selective Perception: Because of habits or unconscious blockages, we often instinctively restrict the kind of information we receive. To build up our intuition, we must overcome things like perceptual distortion and biases, artificial constraints or thinking inside the box.

6. Failing To See The Forest For The Trees: It's common for decision-makers to get lost in detail and suffer from analysis paralysis . Being so caught up in the granular can keep people from big-picture, intuitive insights.

7. Conformity To Organizational Norms: When organizations have cultures that are overly uniform, they tend to block creativity and intuition.

8. Gresham’s Law: When ways of operating and thinking become the default process, the possibility of breakthrough intuitive thinking is hindered. Sir Thomas Greshman said that " bad money drives out good ," meaning that overuse of poor currency (or thinking, in this case) keeps people from making better currency. This phenomenon causes us to rely on second-rate forms of problem-solving because they're familiar, which drives away creative, breakthrough solutions.

Business leaders who can overcome these barriers to intuitive intelligence will help open the doors for breakthrough innovation.

To harness the power of intuitive intelligence, we need to tap into our inner wisdom. Used alongside analytical thinking, this skill can help us generate innovative, original solutions that transform our businesses and create a bold future.

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Incubation in Problem Solving and Creativity

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Can problems be solved by setting them aside or by sleeping on them? Incubation, the process of stopping conscious work on problems for a set period of time, is an integral part of the creative problem solving process.

Providing an overview of the main issues, findings and implications of cognitive research on incubation effects in problem solving and creativity, this book argues that incubation is an effective strategy for tackling problems that do not yield to initial solution attempts. Gilhooly reasons that unconscious work is automatic and explores the underlying processes involved in incubation, providing evidence to showcase the major role of unconscious processing in problem solving. Incubation in Problem Solving and Creativity concludes with a discussion of the implications of unconscious work theory for enhanced problem solving, positioning incubation as an effective and important stage in creative problem solving.

This book is an invaluable resource for students and researchers of problem solving, creativity and thinking and reasoning as well as for students from all disciplines taking problem solving modules.

TABLE OF CONTENTS

Chapter 1 | 24  pages, problems, problem solving and creativity, chapter 2 | 20  pages, historical background to the “incubation” concept, chapter 3 | 8  pages, early laboratory based studies of incubation, chapter 4 | 14  pages, broad theoretical approaches to incubation, chapter 5 | 17  pages, unconscious work, chapter 6 | 22  pages, sleep on it, chapter 7 | 6  pages, overview and conclusions.

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New research helps unlock the secrets of flow, an important tool for creative and problem solving tasks

Does it seem like everyone's talking about 'flow', all of a sudden?

Maybe because many people are. Flow has been shown to help those experiencing it become effortlessly absorbed in a creative or problem-solving task, and more resistant to distraction, whether that task be writing, playing sport, conducting surgery or making music.

New research is routinely emerging extolling the virtues of the seemingly-elusive mental state, and its enormous potential for creativity and performance.

A recent study out of Drexel University's Creative Research Lab in Philadelphia, led by Dr. John Kounios, sought to examine the 'neural and psychological correlates of flow' in a sample of jazz guitarists. 

Some guitarists were very experienced and some less so, with the study looking at what their brains were up to while they improvised.

Study participants were fitted with EEG (electroencephalogram) electrode caps and their brain activity was monitored while performing an improvisation to a pre-determined chord progression, or jazz 'lead'.

They were then told to self-report their experience of flow. Their performances were subsequently assessed for quality by a panel of musical experts.

According to the study, the participants with the most experience found their flow most easily and also gave the best-rated performances. This was found to be from a combination of established skills and their capacity to 'let go.'

Similarly, the EEGs of the best-performing improvisers showed reduced activity in the superior frontal gyri of their brains. This region is associated with executive control, or conscious decision-making.

Letting go, in this instance, means a relinquishing of conscious control.

What is flow, and how can it help us

Mihalyi Csikszentmihalyi was the psychologist who first identified flow: "a state in which people are so involved in an activity that nothing else seems to matter; the experience is so enjoyable that people will continue to do it even at great cost, for the sheer sake of doing it."

ABC Classic presenter and registered psychologist Greta Bradman breaks down the flow state further: 

"Being in flow feels good. You might not even have a sense of time when doing a task, be it washing the dishes or getting into a gnarly work task. 

"There's this real sense of having focus or meeting the world in flow."

For people working in highly competitive fields where optimised performance is vital, like music performance, being able to tap into tools like flow can make a huge difference for success.

Dr. Steffen Herff, leader of the Sydney Music, Mind and Body Lab at Sydney University, suggests one way flow might help musicians find that cutting edge.

"One component that makes flow so interesting from a cognitive neuroscience and psychology perspective, is that it comes with a 'loss of self-consciousness'," he says. 

The fears and insecurities that come with performing to an audience are pushed from the forefront of the mind.

"In other words, gone are all these pesky thoughts of self-doubt."

The benefits of flow for peak creativity

Herff and his team are continually exploring ways to best support musicians both mentally and physically, with techniques such as biofeedback and mental imagery.

Herff says improvising requires a lot of split-second decisions, alongside high-level creative judgements. 

By introducing flow into this process, "all the fears, desires, and anxieties that hold you back are gone, whilst at the same time [you're] able to draw more efficiently from all the hours of practise and experience you have accumulated over the years."

Pianist, composer and improviser Nat Bartsch first heard about flow in her Honours year at the Victorian College of the Arts. 

As an artist with autism and ADHD, Bartsch has learnt to deliberately foster ways of creating time and space to find that flow state.

"What I love about this study is that it dispels the myth that artists must always wait for 'inspiration to strike' – to be a professional artist is to be able to switch your creativity on and off, on any given day."

She agrees that experience makes all the difference, particularly when it comes to letting go.

"If you know who you are on the stage, or at your instrument, it's easier to let go and trust in what you'll come up with."

Finding flow by letting go

The study's authors explain that flow requires three conditions: "a balance between challenge and skill, clear, proximate goals, and immediate feedback about progress and performance."

It makes sense, then, that a more experienced player would be able to access these conditions more readily. They've had more time to develop skills, set directions for themselves, and form the capacity to critically analyse their own work. And then, let that go.

While flow is not the only way to develop one's musical improvisation skills, Herff acknowledges that this new research is exciting in showing great potential in helping to clarify the brain processes that determine whether a flow state is achieved.

Kounios is clear that practice makes perfect, but flow is about letting go, leaving those looking to find it with one last piece of advice taken from jazz great Charlie Parker:

"You've got to learn your instrument. Then, you practise, practise, practise. And then, when you finally get up there on the bandstand, forget all that and just wail."

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• The University of Sydney

No, Kamala Harris Didn't Say 'the Problem of Solving a Problem Is Not a Problem'

The purported quote continued, "but when a problem solves a problem without any problem, then the problem is not at all a problem.", jordan liles, published july 24, 2024.

About this rating

According to memes and social media posts, U.S. Vice President Kamala Harris is rumored to have once opined about dealing with problems, problems and more problems. The purported quote reads, "The problem of solving a problem is not a problem, but when a problem solves a problem without any problem, then the problem is not at all a problem."

For example, a reverse-image search found numerous postings of the quote in meme and text form from 2023 and 2024 on America's Best Pics and Videos , Facebook , iFunny , Imgflip and X . Some users also shared the quote after President Joe Biden dropped out of the 2024 election and endorsed Harris.

In a more prominent example, two-time, Oscar-nominated actor James Woods posted one of the most engaged-with shares of the quote meme with Harris' name. Woods captioned his June 15, 2024, post ( archived ) with the words "DEI VP" — a reference to some right-wing disagreements with efforts toward promoting initiatives regarding diversity, equity and inclusion.

However, we uncovered no video, audio or other type of documentary evidence suggesting Harris ever spoke or wrote the words in the quote.

In our research of the quote, we examined a Reddit thread on the r/Conservative subreddit featuring a meme of Harris with the quote. One user replying to the Jan. 20, 2024, thread commented , "The fact that I can't tell if this is something she actually said because it sounds like something she would say is funny."

However, all that user – or any user in the thread – needed to do to research the quote was quickly and simply search online.

For example, we performed a Google search for the first few words of the quote: "the problem of solving a problem is not." Surrounding the words with quotation marks instructed Google to only display results for an exact match of our search.

As of July 2024, Google only displayed 14 links on two pages of search results. None of the results brought forth any information suggesting video, audio or other evidence existed of Harris saying or writing the quote.

If the quote truly originated from Harris, political websites — primarily right-wing blogs — would likely have widely covered the quote. However, we located no such coverage.

We will update this report if further details come to light.

By Jordan Liles

Jordan Liles is a Senior Reporter who has been with Snopes since 2016.

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A.I. Can Write Poetry, but It Struggles With Math

A.I.’s math problem reflects how much the new technology is a break with computing’s past.

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By Steve Lohr

In the school year that ended recently, one class of learners stood out as a seeming puzzle. They are hard-working, improving and remarkably articulate. But curiously, these learners — artificially intelligent chatbots — often struggle with math.

Chatbots like Open AI’s ChatGPT can write poetry, summarize books and answer questions, often with human-level fluency. These systems can do math, based on what they have learned, but the results can vary and be wrong. They are fine-tuned for determining probabilities, not doing rules-based calculations. Likelihood is not accuracy , and language is more flexible, and forgiving, than math.

“The A.I. chatbots have difficulty with math because they were never designed to do it,” said Kristian Hammond, a computer science professor and artificial intelligence researcher at Northwestern University.

The world’s smartest computer scientists, it seems, have created artificial intelligence that is more liberal arts major than numbers whiz.

That, on the face of it, is a sharp break with computing’s past. Since the early computers appeared in the 1940s, a good summary definition of computing has been “math on steroids.” Computers have been tireless, fast, accurate calculating machines. Crunching numbers has long been what computers are really good at, far exceeding human performance.

Traditionally, computers have been programmed to follow step-by-step rules and retrieve information in structured databases. They were powerful but brittle. So past efforts at A.I. hit a wall.

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Intuitively, sophisticated problem solving requires deliberately, consciously considering the problem before rendering a conclusion. This wisdom is tied up in the aphorisms that we inculcate in ...

Historically, incubation effects refer to the idea that setting a problem aside for a while helps creative thought and problem solving as unconscious processes are working on the problem while the individual is not consciously thinking about the problem (see Wallas, 1926, as well as, e.g., Hadamard, 1945; Kris, 1952; Rugg, 1963; Kubie, 1985).

Unconscious cognition is the processing of perception, memory, learning, thought, and language without being aware of it. ... But new insight revealing that the unconscious brain might also be an active player in decision making, problem solving, creative writing and critical thinking have revolutionized the predominant view of the importance ...

Abstract. Creative problem solving, in which novel solutions are required, has often been seen as involving a special role for unconscious processes (Unconscious Work) which can lead to sudden intuitive solutions (insights) when a problem is set aside during incubation periods. This notion of Unconscious Work during incubation periods is ...

Abstract. We present a theory about human thought named the unconscious-thought theory (UTT). The theory is applicable to decision making, impression formation, attitude formation and change, problem solving, and creativity. It distinguishes between two modes of thought: unconscious and conscious. Unconscious thought and conscious thought have ...

1 Department of Psychology, Heidelberg University, Heidelberg, Germany; 2 Independent Researcher, Berlin, Germany; Unconscious Thought Theory (Dijksterhuis, 2004) states that thinking about a complex problem unconsciously can result in better solutions than conscious deliberation.We take a fresh look at the cognitive processes underlying "unconscious" thought by analyzing data of 822 ...

Incubation in Problem Solving and Creativity concludes with a discussion of the implications of unconscious work theory for enhanced problem solving, positioning incubation as an effective and important stage in creative problem solving. This book is an invaluable resource for students and researchers of problem solving, creativity and thinking ...

Last Updated on Sat, 23 Dec 2023 | Stimulate Thinking. This method relies on the unconscious mind to be continually processing the various sensory inputs stored in short-term and long-term memory. Using your unconscious to solve problems is a process of listening and a readiness to record ideas as they percolate into your conscious mind.

"A problem arises when a living creature has a goal but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to thinking" (Duncker 1945, p. 1).Given that any situation that involves thought processes can be considered a problem, solving or attempting to solve problems is the ...

How much of our problem-solving abilities are founded on unconscious processes? Researchers disagree widely over the importance, and even the existence, of implicit cognition as an issue in human ...

Our data showed that creative problem solving can benefit from unconscious cues, although not as much as from conscious cues. More importantly, we found that there are crucial ERP components associated with unconscious processing of cues in solving divergent problems. Similar to the processing of conscious cues, processing unconscious cues in ...

The conscious mind is what we are aware of and includes our thoughts, perceptions, and feelings. Right below the surface sits the preconscious mind, which comprises memories, knowledge, and ...

The unconscious is a quick study and it gets lots of crucial things right. That saves time and energy for problems the conscious mind is uniquely equipped to solve. SHARE

These explanations ascribe to the unconscious a merely passive role, whereas the term 'incubation' itself suggests that the unconscious also actively contributes to solving a problem (e.g., Claxton, 1997, Koestler, 1964). Therefore, these explanations may not be the only benefit of an incubation period, and the question rises as to whether ...

If unconscious problem-solving—such as by expanding the search of the problem space via spreading activation—occurs only when individuals are in a diffuse attentional state, then this state is likely influenced by the subliminal stimuli. Thus, the unconscious stimuli should be controlled. In Experiment 2 of this study, we innovatively ...

Unconscious thought theory (UTT) suggests that creativity benefits more from unconscious thought than conscious thought. However, previous studies have only focused on creative problem solving. This study aims to explore the effect of unconscious thought and conscious thought in creative science problem finding (CSPF).

Introduction. Numerous researchers have focused on understanding the role of unconscious processes in Creative problem-solving (CPS). Researchers have sought to elucidate the mechanisms through which unconscious cognition influences key CPS phases such as idea generation, incubation, and insight. 1-4 The exploration of the role of unconscious processes in CPS has been approached from diverse ...

Freud used the analogy of an iceberg to describe the three levels of the mind: conscious, preconscious, and unconscious. This model divides the mind into three primary regions based on depth and accessibility of information: Freud's conception of consciousness can be compared to an iceberg because, much like an iceberg, the majority of an ...

One important finding - and there's scientific research to prove it - is that the unconscious can be better at insight-based problem-solving than conscious deliberation.

Incubation is related to intuition and insight in that it is the unconscious part of a process whereby an intuition may become validated as an insight. Incubation substantially increases the odds of solving a problem, and benefits from long incubation periods with low cognitive workloads.

Studies show that napping can improve memory and creative problem-solving. Why Your Unconscious Brain May Have The Answer. Research suggests that thinking about an issue too methodically is often a detriment to problem-solving because your brain actually blocks potential solutions from registering into consciousness through a phenomenon known ...

A new synthesis for solving the problem of psychology: Addressing the Enlightenment Gap. Palgrave MacMillan. (see Appendix C for the specific chapter references). 2. Henriques, G. R. (2011).

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Flow can help you be more absorbed and resistant to distraction during creative or problem-solving tasks, but it isn't always easy. New research unlocks some of the secrets.

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This study examined the relationship between students' SRL behaviours and problem-solving efficiency in the context of TREs. Methods. Eighty-two medical students accomplished a diagnostic task in a computer-simulated environment, and they were classified into the efficient or less efficient group according to diagnostic performance and time-on ...

The purported quote continued, "but when a problem solves a problem without any problem, then the problem is not at all a problem." Jordan Liles Published July 24, 2024

A.I.'s math problem reflects how much the new technology is a break with computing's past. By Steve Lohr In the school year that ended recently, one class of learners stood out as a seeming ...

The first puzzle box presented raccoons with a single-solution type for all compartments, and we predicted that multiple wild raccoons would demonstrate innovative problem-solving by finding the solution. We also predicted that, similar to other studies of innovation, exploratory diversity in the first trial would predict problem-solving success.

• Readings on Kerala

Kuttippuram Bridge | Readings on Kerala

• It portrays the narrator's pride in the newly constructed bridge, which represents human achievement but also symbolizes the transformation and encroachment of urbanization on natural landscapes.
• The poem highlights the loss of the river's natural power and beauty, reducing it to a subdued presence crawling beneath the bridge, which symbolizes the dominance of human-made structures over nature.
• The descriptions of walls springing up everywhere, the bustling noises, and the loss of neighborhood connections depict the negative consequences of unchecked urbanization, such as the erosion of community bonds and the intrusion of noise and pollution.
• Edasseri criticizes the rapid pace of development, emphasizing the destruction of traditional landscapes, customs, and natural beauty in favor of a mechanized and concrete environment.
• Sustainable development that balances economic growth with environmental conservation and community well-being. This model aims to minimize the negative impacts of urbanization on nature and promote eco-friendly practices.
• Smart and inclusive urban planning that prioritizes green spaces, preserves cultural heritage, and fosters a sense of community and connection among residents.
• Mixed-use development that integrates residential, commercial, and recreational spaces within the same locality, reducing the need for extensive commuting and promoting walkability.
• Emphasis on preserving and revitalizing traditional villages and small towns, supporting local economies and cultural practices, while also providing necessary infrastructure and amenities.
• Adoption of renewable energy sources, green building practices, and sustainable transportation options to mitigate the environmental impact of urbanization.
• Expressing a sense of melancholy and nostalgia for the disappearing natural landscapes, traditional customs, and harmonious community life.
• Picturing a future where humanity turns into machines, losing their playfulness, emotions, and laughter, thus sacrificing their essential human qualities.
• Symbolically questioning the fate of the river, Perar, if humanity becomes mechanized, suggesting that it might also lose its natural vitality and turn into a polluted drain.
• Portraying the rapid urbanization and mechanization as a threat to the spiritual and emotional well-being of individuals, as reflected in the fading rustic beauty and the loss of genuine connections among people.
• Through these reflections, Edasseri serves as a warning voice, urging society to reflect on the consequences of mindless development and to preserve the essence of humanity in the face of modernization.

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