directional hypothesis psychology definition

Research Hypothesis In Psychology: Types, & Examples

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.

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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.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Directional Hypothesis: Definition and 10 Examples

A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

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Statistics Made Easy

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“<“) or greater than (“>”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)
• H A : μ < 10 inches (the pesticide will cause mean plant growth to decrease)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “<” sign. The biologist believes that the pesticide will influence the mean plant growth in a negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

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psychologyrocks

Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, alternative to the null.

Directional hypothesis: A directional (or one tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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Aims And Hypotheses, Directional And Non-Directional

March 7, 2021 - paper 2 psychology in context | research methods.

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In Psychology, hypotheses are predictions made by the researcher about the outcome of a study. The research can chose to make a specific prediction about what they feel will happen in their research (a directional hypothesis) or they can make a ‘general,’ ‘less specific’ prediction about the outcome of their research (a non-directional hypothesis). The type of prediction that a researcher makes is usually dependent on whether or not any previous research has also investigated their research aim.

Variables Recap:

The  independent variable  (IV)  is the variable that psychologists  manipulate/change  to see if changing this variable has an effect on the  depen dent variable  (DV).

The  dependent variable (DV)  is the variable that the psychologists  measures  (to see if the IV has had an effect).

It is important that the only variable that is changed in research is the  independent variable (IV),   all other variables have to be kept constant across the control condition and the experimental conditions. Only then will researchers be able to observe the true effects of  just  the independent variable (IV) on the dependent variable (DV).

Research/Experimental Aim(S):

An aim is a clear and precise statement of the purpose of the study. It is a statement of why a research study is taking place. This should include what is being studied and what the study is trying to achieve. (e.g. “This study aims to investigate the effects of alcohol on reaction times”.

It is important that aims created in research are realistic and ethical.

Hypotheses:

This is a testable statement that predicts what the researcher expects to happen in their research. The research study itself is therefore a means of testing whether or not the hypothesis is supported by the findings. If the findings do support the hypothesis then the hypothesis can be retained (i.e., accepted), but if not, then it must be rejected.

Three Different Hypotheses:

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Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

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In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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How to Write a Directional Hypothesis: A Step-by-Step Guide

In research, hypotheses play a crucial role in guiding investigations and making predictions about relationships between variables.

One type of hypothesis that researchers often encounter is the directional hypothesis, also known as a one-tailed hypothesis.

In this blog post, we’ll explore what a directional hypothesis is, why it’s important, and provide a step-by-step guide on how to write one effectively.

Table of Contents

What is a Directional Hypothesis?

A directional hypothesis is a statement that predicts the direction of the relationship between two variables. Unlike non-directional hypotheses, which simply state that there is a relationship between variables without specifying the direction, directional hypotheses make a clear prediction about the expected outcome.

For example, a directional hypothesis might predict that an increase in one variable will lead to a decrease in another.

Examples of Directional Hypotheses

• Increasing the amount of sunlight exposure will lead to higher levels of vitamin D in the body.
• Decreasing the amount of sugar consumption will result in lower body weight among participants.
• Introducing mindfulness meditation techniques will reduce symptoms of anxiety in patients with generalized anxiety disorder.

Why to Write a Directional Hypothesis?

Directional hypotheses offer several advantages in research. They provide researchers with a more focused prediction, allowing them to test specific hypotheses rather than exploring all possible relationships between variables.

This can help streamline research efforts and increase the likelihood of finding meaningful results. Additionally, directional hypotheses are often used in experimental research, where researchers manipulate variables to observe their effects on outcomes.

Step 1: Identify the Variables

Start by identifying the independent variable (the variable you are manipulating) and the dependent variable (the variable you are measuring). Understanding the relationship between these variables is essential for writing a directional hypothesis.

Step 2: Predict the Direction

Based on your understanding of the relationship between the variables, predict the direction of the effect.

Will an increase in the independent variable lead to an increase or decrease in the dependent variable?

Be specific in your prediction.

Step 3: Use Clear Language

Write your directional hypothesis using clear and concise language. Avoid technical jargon or terms that may be difficult for readers to understand. Your hypothesis should be easily understood by both researchers and non-experts.

Step 4: Ensure Testability

Ensure that your hypothesis is testable by collecting data and conducting statistical analysis. You should be able to measure the variables and determine whether the observed results support or refute your hypothesis.

Step 5: Revise and Refine

Review your directional hypothesis to ensure that it accurately reflects your research question and predictions. Make any necessary revisions to improve clarity and specificity.

Writing a directional hypothesis is an essential skill for researchers conducting experiments and investigations.

By following the steps outlined in this guide, you can effectively formulate hypotheses that make clear predictions about the relationship between variables.

Whether you’re a researcher or just starting out in the field, mastering the art of writing directional hypotheses will enhance the quality and rigor of your research endeavors.

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DIRECTIONAL HYPOTHESIS

Directional Hypothesis: An Overview

The directional hypothesis is a statistical concept used to make predictions about the direction of the relationship between two variables. It is based on the idea that the relationship between two variables can be positive, negative, or nonexistent. By making a directional hypothesis, one can determine which direction the relationship between two variables is likely to take. This article provides an overview of directional hypotheses and their applications.

The concept of directional hypotheses dates back to the early 20th century. Karl Pearson, a British statistician, first proposed the idea in 1908. Since then, directional hypotheses have been used in a variety of contexts, including psychology, sociology, economics, and medicine.

In essence, a directional hypothesis is a statement about the expected direction of the relationship between two variables. In a directional hypothesis, the direction is made explicit by stating which variable is expected to increase or decrease as the other variable changes. For example, a researcher may hypothesize that increasing the amount of time students spend studying will lead to higher grades.

When making a directional hypothesis, researchers must consider the relationship between the two variables. Generally speaking, there are three possible relationships: positive, negative, and no relationship. A positive relationship is one in which an increase in one variable leads to an increase in the other. A negative relationship is one in which an increase in one variable leads to a decrease in the other. A no relationship indicates that changes in one variable do not affect the other.

In addition to making directional hypotheses, researchers often use statistical tests to assess the strength of the relationship between two variables. Common tests include Pearson’s correlation, Spearman’s correlation, and multiple regression. Using such tests, researchers can determine whether the relationship between two variables is statistically significant.

Directional hypotheses can be used in a variety of contexts. For example, in medical research, directional hypotheses can be used to study the effects of treatments on patients. In economics, directional hypotheses can be used to examine the impact of economic policies on economic indicators. In psychology, directional hypotheses can be used to explore the causes and effects of various psychological phenomena.

In conclusion, directional hypotheses are an important tool for making predictions about the direction of the relationship between two variables. By making directional hypotheses, researchers can make more informed decisions and draw more accurate conclusions about the relationship between two variables.

Pearson, K. (1908). Contributions to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London, 197, 253–318.

Briggs, J., & Peacock, C. (2011). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 66(9), 784–800.

Friedman, M. (1953). The methodology of positive economics. In Essays in Positive Economics (pp. 3–43). Chicago, IL: University of Chicago Press.

Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, NY: The Guilford Press.

Related terms

Dyssomnia not, decerebration, defender strategy, decompensation, deep body temperature.

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

How to Perform a Partial F-Test in Excel

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DIRECTIONAL HYPOTHESIS

Prediction relating to the direction of experimental scores from one group will differ to another group.

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7 The Predictive Processing Hypothesis

Jakob Hohwy Cognition & Philosophy Lab, Department of Philosophy, Faculty of Arts, Monash University, Melbourne, Australia

• Published: 09 October 2018
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Prediction may be a central concept for understanding perceptual and cognitive processing. Contemporary theoretical neuroscience formalizes the role of prediction in terms of probabilistic inference. Perception, action, attention, and learning may then be unified as aspects of predictive processing in the brain. This chapter first explains the sense in which predictive processing is inferential and representational. Then follows an exploration of how the predictive processing framework relates to a series of considerations in favor of enactive, embedded, embodied, and extended cognition (4E cognition). The initial impression may be that predictive processing is too representational and inferential to fit well to 4E cognition. But, in fact, predictive processing encompasses many phenomena prevalent in 4E approaches, while remaining both inferential and representational.

Introduction

A millennium ago the great polymath Ibn al Haytham (Alhazen) (ca. 1030; 1989) developed the view that “many visible properties are perceived by judgment and inference” (II.3.16). He knew that there are optical distortions and omissions of the image hitting the eye, which without inference would make perception as we know it impossible ( Lindberg 1976 ; Hatfield 2002 ). Al Haytham was aware it is counterintuitive to say perception depends on typically intellectual activities of judgment and inference and so remarks that “the shape and size of a body . . . and such like properties of visible objects are in most cases perceived extremely quickly, and because of this speed one is not aware of having perceived them by inference and judgment” (II.3.26).

Since al Haytham, many in optics, psychology, neuroscience, and philosophy have advocated the role of inference in perception, and have insisted too that this inference is somehow unconscious (for review, see Hatfield 2002 ). With characteristic clarity, Hermann von Helmholtz coined the phrase unconscious perceptual inference and said that the “psychical activities” leading to perception:

are in general not conscious, but rather unconscious. In their outcomes they are like inferences insofar as we from the observed effect on our senses arrive at an idea of the cause of this effect. This is so even though we always in fact only have direct access to the events at the nerves, that is, we sense the effects, never the external objects. ( Helmholtz 1867 , p. 430)

The starting point for this inferential view is the conviction that perception can be explained only if a particular, fundamental problem of perception is solved, namely, how the brain can construct our familiar perceptual experience on the basis only of the imperfect data delivered to the senses, and without ever having unfettered access to the true hidden causes of that input. This type of problem is also at the heart of massive scientific endeavors in contemporary artificial intelligence and machine learning.

Recently, the notion of unconscious perceptual inference has been embedded in a vast probabilistic theoretical framework covering cognitive science, theoretical neurobiology, and machine learning. The basic idea is that unconscious perceptual inference is a matter of Bayesian inference, such that the brain in some manner follows Bayes’s rule and thereby can overcome the problem of perception. The most comprehensive, ambitious, and fascinating of these probabilistic theories build on the notion of prediction error minimization (PEM) (this notion arose in machine learning research, with versions of it going back to 1950s; for recent philosophical overviews, see Clark 2013 ; Hohwy 2013 ).

Several aspects of unconscious perceptual inference are anathema to many versions of enactive, embedded, embodied, and extended (4E) cognition. If perception is a matter of Bayesian inference, then perception seems a very passive, intellectualist, neurocentric phenomenon of receiving sensory input and performing inferential operations on them in order to build internal representations. This process is divorced from action and active interaction with the environment; it appears insensitive to the situation in which the system is embedded; it leaves no foundational role for the body in cognitive and perceptual processes; and it makes perceptual processes a matter of what happens behind the sensory veil with no possibility of extension to mental states beyond the brain, let alone the body (4E cognition is now a vast and varied area of research; the types of approaches that stress anti-representational and anti-inferential elements are, for example, Varela et al. 1991 ; Clark 1997 ; Noë 2004; Gallagher 2005 ; Thompson 2007 ; Clark 2008 ; Hutto and Myin 2013 ).

The tension between perceptual inference and 4E cognition matters because both are influential attempts at explaining the same range of phenomena. Having noticed the initial tension between them, there are three main options: (1) perceptual inference and 4E cognition are incompatible as foundational accounts of perception and cognition, which means one must be false ( Anderson and Chemero 2013 ; Barrett 2015 ); this option appears unattractive because key aspects of both seem believable and important. The next two options are more discursive: (2) perceptual inference and 4E cognition should be considered compatible, but only because perceptual inference, rightly understood, is not a matter of neurocentric, representationalist inference but yields just the kinds of processes necessary for 4E cognition (Clark 2013 , 2015 , 2016 ). (3) Perceptual inference and 4E cognition should be considered compatible, but only because 4E cognition, rightly understood, is nothing but representation and inference ( Hohwy 2016b ). Options 2 and 3 deflate perceptual inference and 4E cognition, respectively, that is, they achieve reconciliation by recasting one of the sides of the debate in terms of the other.

This chapter aims to show that Option 3 is reasonable. Perceptual inference, in the shape of PEM, is tremendously resourceful and can therefore encompass phenomena highlighted in debates on 4E cognition. Reconciliation with somewhat deflated 4E notions is achieved without compromising PEM’s representationalist and inferentialist essence. This advances the debate about 4E cognition because, in the context of PEM, inference and representation are both shown to have several surprising aspects, such that, perhaps, 4E cognition need not abhor these notions altogether.

The chapter first explains PEM and lays out its specific notion of inference. Then action is subsumed under PEM’s inferential scheme, and the role of representation in perception and action is explained. Finally, select aspects of 4E cognition are incorporated into the PEM fold.

Predictive Processing and Inference

In many approaches to unconscious perceptual inference, the notion of inference is left unspecified; as Helmholtz says, our psychical activities are “like” inference. Here, the notion of inference captures the idea that the perceptual and cognitive systems need to draw conclusions about the true hidden causes of sensory input vicariously, working only from the incomplete information given in the sensory input.

On modern approaches, this is given shape in terms of Bayesian inference. This yields a concrete sense of “inference” where Bayes’s rule is used to update internal models of the causes of the input in the light of new evidence. A Bayesian system will arrive at new probabilistically optimal “conclusions” about the hidden causes by weighting its prior expectations about the causes against the likelihood that the current evidence was caused by those causes (there are useful textbook sources on machine learning such as Bishop 2007 ; philosophical reviews such as Rescorla 2015 ; see also recent treatments of hierarchical Bayes and volatility such as Payzan-LeNestour and Bossaerts 2011 ; Mathys et al. 2014 ).

Consider a series of sensory samples, for example, auditory inputs drawn from a sound source. The question for the perceiver is where the sound source is located (somewhere on a 180° space in front of the perceiver). Assume the samples are normally distributed and that the true source is 80°. Before any samples come in, the perceiver expects—predicts—samples to be distributed around 90°. The first sample comes in indicating 77°, and thereby suggests a prediction error of 13°. Which probabilistic inference should the perceiver make? Inferring that the source is at 77° would disregard prior knowledge and lead to a model overfitted to noise. Ignoring the prediction error would prevent perceptual learning altogether. So the right weight to assign to the prediction error in updating the prior belief of 90° ought to reflect an optimal, rational balance between the prior and the likelihood, and this is indeed what Bayes’s rule delivers. So probabilistic inference should be determined by Bayes’s rule. In other words, the learning rate in Bayesian inference is determined by how much is already known and how much is being learned by the current evidence, reflected in the likelihood. (In this toy example, I set aside the question how the perceiver knows not to add the weighted prediction error to 90°, moving toward 103° and away from 80°; notice that if the system does this, then prediction error will tend to grow over time).

The correct weights to give to the prior and the prediction error can be considered transparently through the variance of their probability distributions. The more the variance, the less the weight. A strong prior will have little variance and should be weighted highly, and a precise input, which fits well the expected values of the model in question, should be weighted highly. The inverse of the variance is called the precision , and it is a mathematically expedient convention to operate with precisions in discussions of inference: the learning rate in Bayesian inference therefore depends on the precisions of the priors and prediction errors. As will become apparent later, precisions are important to PEM and its ability to engage 4E-type issues.

So far, only one inferential step is described. For subsequent samples, Bayes’s rule should also be applied, but for the old inferred posterior as the new prior. Since there is an optimal mix of prior and likelihood, the model will converge on the true mean (80°) in the long run. Critically, in this process, the average prediction error is minimized over the long run. Even for quite noisy samples (imprecise distributions, or probability density functions), a Bayesian inference system will eventually settle on an expectation for the mean that keeps prediction error low. This can be turned around such that, subject to a number of assumptions about the shape of the probability distributions and the context in which they are considered, a system that minimizes prediction error in the long run will approximate Bayesian inference.

The heart of PEM is then the idea that a system need not explicitly know or calculate Bayes’s rule to approximate Bayesian inference. All the system needs is the ability to minimize prediction error in the long run. This is the sense in which unconscious perceptual inference is inference: internal models are refined through prediction error minimization such that Bayesian inference is approximated. The notion of inference is therefore nothing to do with propositional logic or deduction, nor with overly intellectual application of theorems of probability theory.

It would be misguided to withdraw the label “inference” from unconscious perceptual inference, or from PEM, just because it is an approximation to Bayes, or because the process is not an explicit application of a mathematical formalism by the brain. If the inferential aspect is not kept in focus, then it would appear to be a coincidence, or somehow an optional aspect of perceptual and cognitive processes that conform to what Bayes’s rule dictate. Put differently, anyone who subscribes to the notion of predictive processing must also accept the inferential aspect. If it is thrown out, then the “prediction error minimization” part becomes a meaningless, unconstrained notion.

PEM thus says that perceivers harbor internal models that give rise to precision-weighted predictions of what the sensory input should be, and that these predictions can be compared to the actual sensory input. The ensuing prediction error guides the updates of the internal model such that prediction error in the long run is minimized and Bayesian inference approximated.

However, this description of PEM is still too sparse. In any given situation, a PEM system will not know how much or how little to weight prediction error even if it can assess the precisions of the prior and of the current prediction error. In essence, a system that operates with only those precisions will be assuming the world is more simple and persistent than it really is. For example, different sensory modalities have different precisions in different contexts, and without prior knowledge of these precisions, the system can make no informed decisions about how to weight prediction error. For example, similarly sized prediction errors in the auditory and visual modalities should not be weighted the same, since the precisions of each should be expected to be different. Therefore a PEM system would need to have and shape expectations about the precisions as well as the means of probability distributions. The need for such expected precisions is also driven by the occurrence of multiple interacting causes of sensory input within and across sensory modalities. In the example of the location of the auditory source, variability in the sensory sampling might be due to a new cause interfering with the original sound source (e.g., a moving screen intermittently obscures the location of the sound). If the system does not have robust expectations for the precision of the sound source, then it will be unable to make the right inferences about the input (i.e., is it one cause with varying precisions or is it two interacting causes that gives rise to the nonlinear evolution in the auditory sensory input?).

A PEM system must model expectations of precisions, and this part of the PEM system itself needs to be Bayes-optimal. Models will harbor priors for precisions; they will predict precisions and generate precision prediction errors. Moreover, they will need to do this across all the hidden causes modeled such that their interactions can be taken into account. This calls for a hierarchical structure where the occurrence of various causes over many different time scales can impact on the predictions of the sensory input received at any given time. For example, the interaction of relatively slow time scale regularities (e.g., the trains driving past your house two or three times an hour) need to influence the predictions of faster time scale regularities (e.g., the words heard in a conversation in your lounge room), and vice versa.

A PEM system that operates in a complex environment, with levels of uncertainty that depend on the current state of the world and many interacting causes at many different time scales, will thus build up a vast internal model with many interacting, hierarchically ordered levels, which all pass messages to each other in an attempt to minimize average prediction error over the long term.

Consider finally what happens over time to the models harbored in the brain, on the basis of which predictions are made and prediction errors minimized. The parameters of these models will be shaped by the Bayesian inferential process to mirror the causes of the sensory input. In the example earlier, by minimizing prediction error over time for the location of the cause of auditory input, the model will revise its initial false belief that the location is at 90°, and come to expect it to be at its true position of 80°. Further, by minimizing precision prediction error, the model may be able to anticipate interacting causes, such as a moving screen intermittently blocking the sound. This means that, by approximating Bayesian inference, the models of a PEM system must represent its world.

Here, the notion of representation is not just a matter of receptor covariance, where the states of neural populations covary with the occurrence of certain environmental causes. The hierarchical model is highly structured, and performs operations over the parameters. For example, there will be model selection. In our example, the system might ask whether there is another cause interacting with the sound source, or if the signal itself is becoming noisier. In addition, there are convolutions of separate expected signals generated on the basis of the models; for example, when a cat and a fence are detected, the expected sensory signals from both hidden causes are convolved into one stream by the brain to take the occlusion of the cat by the fence into account. As will become clear, the representational aspects of PEM are critical when it comes to incorporating action, too.

The representational nature of a PEM system is not optional. The ability to minimize prediction error over time depends on building better and better representations of the causes of its sensory input. This is encapsulated in the very notion of model revision in Bayesian inference. (There is extensive discussion of what it takes for perception to be representational; for examples of relevance to Bayesian inference, see Ramsey 2007 ; Orlandi 2013 , 2014 ; Gładziejewski 2015; Ramsey 2015 .)

So far, it appears that predictive processing is inferential and representational in a specific Bayesian sense. Traditionally, 4E approaches have rejected both notions. Next, PEM will be shown to have explanatory reach into 4E cognition too.

PEM and Action

A representationalist and inferentialist account of cognition and perception may appear divorced from the concerns and activities of a real, embodied agent operating in its environment. Thus enactive and embodied accounts have de-emphasized classic representationalist understandings of cognition and perception and with it much semblance to inference (there are many versions and much discussion of embodiment; see, e.g., Brooks 1991 ; Noë 2004; Gallagher 2005 ; Alsmith and Vignemont 2012 ; Hutto and Myin 2013 ; Orlandi 2014 ).

Perhaps the basic sentiment could be summed up in the strong intuition that embodied action is not inference, and yet the body and its actions are crucial to gain any kind of understanding of perception and cognition. PEM can, however, easily cast action as a kind of inference—as active inference (Friston, Samothrakis, et al. 2012).

Recall that any system that minimizes prediction error over time will approximate Bayesian inference; that is, such a system will be inferential in the Bayesian sense that it increases the evidence for its internal model. Using the example from earlier again, by minimizing prediction error the system could accumulate evidence for the model that represents the sound source as located at 80°. In that case, the internal model is revised from the initial 90° to the new estimate of 80°.

It is trivial to observe that the perceiver could also have minimized prediction error by turning the head 10° to the left and thereby have accumulated evidence for the prediction that the sound source is located at 90°. Prediction error can be minimized both through passive updating of the internal model and through active changes to the sensory input. Action, such as turning one’s head, can therefore minimize prediction error. Since, as argued earlier, minimizing prediction error is inference, and action is inference. There is then no hindrance to incorporating action into an inferentialist framework.

In active inference, representations are central to guiding action. This is because action only occurs when a hypothesis—in this case a representation of a state that is yet to occur—has accumulated sufficient evidence relative to other hypotheses to become the target of PEM. This yields two aspects that are sometimes seen as hallmarks of representations: they are action-guiding and they are somehow detached from what they stand for (for discussion and review, see Orlandi 2014 ). Active inference therefore has a good claim to be both inferential and representational.

For perceptual inference, precisions were shown to be critical. Without precisions, the PEM system would not be able to minimize error in a world with state-dependent uncertainty and interacting causes. The same holds for active inference. Without any notion of how levels of prediction error tend to shift over many interacting time scales, the system would pick the action that minimizes most error here and now—for example, by entering and remaining in a dark room (for discussion, see Friston, Thornton, et al. 2012). This would be analogous to overfitting, and would come at the cost of increasing prediction error over the longer term. For example, even though the perceiver might minimize prediction error by forcing the sound to come at the 90° midline, this might make it difficult to ascertain the true source of a potentially moving cause such as the trajectory of a mosquito buzzing about (since direction detection is harder over the midline due to minimal interaural time difference). This calls for even more hierarchical model-building, namely, in terms of the precisions expected in the evolution of the prediction error landscape as a result of the agent’s active intervention in the world. These self-involving, modeled regularities are, however, not fundamentally different from the regularities involved in perceptual inference. They simply concern the sensory input the agent should expect to result from the interaction of one particular cause in the world—the agent itself—with all the other causes of sensory input (for discussion of self-models, see, e.g., Synofzik et al. 2008 ; Metzinger 2009 ).

There is thus room for a notion of action within PEM. But this possibility alone does not imply that a PEM system is likely to actually be an agent. If the system is endowed with a body such that it could act, then the imperative for minimization of prediction error will make actual action highly likely.

If the system has accumulated strong evidence for, say, an association between two sounds, it may still be unable to distinguish several hypotheses, for example, whether the sounds are related as cause and effect or if they are effects of some common cause. It is standard in the causal inference literature that intervention is required to acquire evidence for or against these hypotheses ( Pearl 2000 ; Woodward 2003 ). For example, if variation in one sound persists even if the other sound is actively switched off, then that is evidence the latter sound is not the cause of the first. The necessity of action is generalized in the observation from earlier that the system needs to learn differences in precisions and patterns of interactions among causes, such as occlusions and other causal relations that change the sensory input in nonlinear ways. Such learning thus requires action. The price of not engaging the body plant to intervene in the environment is that prediction error will tend to increase since predictions will be unable to distinguish between several different hypotheses. A PEM system that can act will therefore be best served to actually act.

This simple account of agency has profound consequences. It will be a learnable pattern in nature that inaction will tend to increase prediction error in the longer term (due to the inaccuracy of the hypotheses the system can accumulate evidence for by using only passive inference). Conversely, the system can learn that action tends to allow minimization of prediction error at reasonable time scales. Overall, this teaches the system that, on balance, its model will accumulate more precise evidence through action than through inaction. This will bias it to minimize prediction error through active inference. Of course, a system that only ever acts on the basis of unchanging models will never be able to learn new patterns, which is detrimental in a changing world. Therefore action must be interspersed with perceptual inference where models are updated, before new action takes place.

The mechanism by which this switching between perception and action takes place is best conceived in terms of precision optimization. Recall that the PEM system will build up expectations for precisions, which are crucial for dealing with state-dependent noise in a world with interacting causes. The role of expected precisions in inference is to optimally adjust weights for expected sensory input: input that is expected to be precise is favored in Bayesian inference whereas input that is expected to be imprecise is not favored. Mechanistically, this calls for a neuronal gating mechanism that inhibits or excites sensory inputs according to their expected precisions. This gating mechanism serves as a kind of probabilistic searchlight and thus plays the functional role of attention ( Feldman and Friston 2010 ; Brown et al. 2011 ; Hohwy 2012 , 2016a ).

As the system gates its sensory input according to where it expects the most precise sensory input will occur, across several time scales, it may switch between perception and action. For example, if more precision is expected by the agent having its hand at the position of the coffee cup rather than at the current position at the laptop, then it will begin gating the current sensory input, which suggests the hand is at the laptop. This in turn allows the coffee hypothesis to gain relative weight over the laptop hypothesis, and the prediction error generated by that hypothesis can easily by minimized by moving the hand. Since the gain is high on this prediction error, the new hypothesis quickly accumulates evidence for its truth, and the hand will find itself at the coffee cup (for more on the dynamics of action and perception in relation to temporal phenomenology, see Hohwy et al. 2015 ; for the formal background, see Friston, Trujillo-Barreto et al. 2008).

Embodied, Embedded, and Inferential and Representational

When all the elements described in the last section are combined, a wholly inferential conception of agency begins to take shape. If action and agency are moments of PEM, then desires are just beliefs (or priors) about states that happen to be future, with a focus on their anticipated levels of prediction error, and where reward is the absence of prediction error. This suggests a neat continuity with perceptual inference, which also relies on priors and the imperative to minimize prediction error.

The idea that action is driven by PEM relative to a model does raise a question about the content of the model relative to which error is minimized. This model is what defines what we would normally describe as the agent’s desires. In the wider PEM framework—which, as shall be described later, relies on notions of free energy minimization —the expected states that anchor active inference relate to set points in terms of the organism’s homeostasis. This immediately evokes an evolutionary perspective, where expected bodily states are central to behavior. Apart from the specific evolutionary aspects, this suggests an embodiment perspective, because all aspects of perception and cognition then have a foundation in bodily states, and movement and purposeful behavior have a foundation in the environment. This element of embodiment makes it more likely that contact can be made between probabilistic theories of perception and action and embodied cognition approaches (such as, e.g., Varela et al. 1991 ; Gallagher 2005 ; Thompson 2007 ; for recent treatments that relate to PEM, see Bruineberg and Rietveld 2014 ; Fazelpour and Thompson 2015 ).

However, even this foundational embodiment is conceived probabilistically in PEM. A set of expectations for bodily states (relating to homeostasis) is essentially a model. In probabilistic terms, this model gives the probability of finding the organism in some subset of the overall set of states it could be in. The model is specified in terms of internal states, as signaled in interoception, but is tied to the overall setting of the organism in a subset of environmental states. The expected states defined in interoceptive terms would, in real organisms traversing actual environments, be mirrored in the expected states described in environmental terms, or in terms of their sensory input or exteroception. For example, fish are most likely to find their sensory organs impinged upon from watery states and this is associated strongly with the homeostatic needs specified in their model. In general, within this probabilistic reading of the foundational embodiment of a PEM organism, there is thus a tight coupling between the interoceptive and exteroceptive prediction error landscapes for any PEM system.

Not only does PEM provide a notion of embodiment, it also speaks to elements of embedded or situated cognition (see van Gelder 1995 ; Clark 1997 ; Aydede and Robbins 2009 ). With the tight coupling of the organism’s expected states in terms of interoception and exteroception, perception and cognition cannot be separated from bodily or environmental aspects of the PEM system.

Crucially, this reading of embodiment and embedding leads directly to inferential processing and PEM. The model specifies the probability of finding the organism in any one of all the possible states. To know this model directly would require the agent averaging over all possible states and ascertaining the occurrence of itself in them. This is not possible for a finite organism to learn directly. Instead, the organism must essentially guess what its expected states are and minimize the ensuing error through perceptual and active inference. In slightly more formal terms, the organism needs to minimize surprise; that is, it needs to avoid finding itself in states that are surprising given its model. The sum of prediction error is always equal to or larger than the surprise, so minimizing prediction error will implicitly minimize surprise. This bound on surprise is also known in probabilistic terms as the free energy, and so this challenging idea is enshrined in the so-called free energy principle ( Friston 2010 ).

When viewed in this larger context of the free energy principle, promising notions of embodied and embedded cognition present themselves. More research is needed on the extent to which they capture facets of the wide-ranging and heterogeneous 4E body of research. However, for the conception of embodiment and embedding mooted here, an inferential conception is inescapable.

Hierarchical Inference for a Changing World

In much 4E research there is a focus on fluid interactions with the world, characterized by non-inferential, nonrepresentational, “quick and dirty” processing. This picture is set up to contrast with inferential, representational, “slow and clean” processing (Clark 1997 , 2013 , 2015 ). Often, this kind of quick and dirty, situated cognition is discussed in terms of affordances : salient elements of the environment that are in some sense perceived directly and are immediately action-guiding. Affordances in quick and dirty processing are thought to evade the computational bottleneck that a traditional representational system would have trying to passively encode the entire sensory input presented at any given time. For some types of action and at some stages of learning, performance is rather plodding and sluggish, but there is an important insight in how the notion of situated cognition highlights the fluid swiftness with which organisms can perform some complex actions in their environment.

In a PEM system there is no bottleneck problem in the first place, however. There is never an issue of starting from scratch and encoding an entire natural scene in order to be able to perceive it. Hierarchical Bayesian inference is based on prior learning, which over time has shaped priors at many levels. Given priors, the sensory input is no longer something that needs to be encoded here and now. Instead the sensory input is, functionally speaking, the feedback to the forward predictive signal generated by the brain’s internal model ( Friston 2005 ). The model predicts what will happen and gets confirmation or disconfirmation on these predictions from the sensory input. There is thus no encoding of the entire sensory input in each perceptual instance. This means the PEM system has no need to resort to quick and dirty processing tricks to overcome a computational bottleneck. Instead, the system relies on slow and clean learning in order to facilitate swift and fluid perception of and interaction with the world. This learning is “slow” because is relies on meticulous accumulation of evidence for hypotheses at multiple time scales. It is “clean” because the learning slots into a hierarchy with clearly defined, general functional roles for time scales, for predictions of values, and for predictions of precisions.

The difference between swift and fluid processing and plodding and sluggish processing can easily be accommodated within a PEM system. Affordances are just causes of sensory input that, on the basis of prior learning, are strongly expected to give rise to high precision prediction error. To maintain Bayes optimality, the system gates sensory input accordingly, and strongly focuses both perceptual and active inference on these affordances. In this setting, PEM happens quickly, since highly precise distributions are easier to deal with computationally than imprecise ones. This means that the agent in question will obtain its expected states swiftly and fluidly.

Typically, the 4E preference for quick and dirty processing and affordances comes with a rejection of rich representational states (Clark 2008 , 2015 ). The point is that such representations cannot come about due to the bottleneck problem. Moreover, the appeal to affordance-based quick and dirty processing is thought to obviate the need for rich internal representations altogether as the world’s affordances in some sense are its own representation ( Brooks 1991 ).

On the PEM-based account of swift and fluid processing, internal representations are, however, necessary. Over time, multilayered representations are constructed and shaped, and Bayesian model selection picks the model with the best evidence as the representation of the world relative to which prediction error is minimized in active inference (this kind of approach is developed in more detail for PEM in Seth 2014 , 2015 ). Again, we get the result that PEM has the resources to speak to typical 4E discussions, but that it happens on the basis of representation and inference.

It could be that the brain builds rich representations as it learns about the world, and then gradually substitutes these much sparser and representation-poor, purpose-made representations that more directly tie in with and engage the environment. One argument here derives from Occam’s razor, in the sense that there are simplicity gains from opting for a simple over a complex, rich model ( Clark 2015 ). However, simplicity is not something additional to inference. Complex models are to be avoided because they are overfitted and thereby incur a prediction error cost in the longer run. How rich or simple a model should be is thus fully given by PEM in the first place.

In fact, there is reason to think the PEM account is preferable to the affordance-based account. It is true that swift and fluid processing is a salient and impressive aspect of human cognition. But so is the flexible way we shift between contexts, projects, beliefs, and actions. We might engage in attentive, fluid, and swift interaction for a period of time, but other beliefs and concerns always creep in and make it imperative to shift to another behavior. On the affordance-based account, it is not readily explained how the agent might disengage from a given set of affordances; the focus is at best on how representation-rich learning is needed before swift and fluid processing is possible, rather than the role of rich representation during swift and fluid processing. The agent seems tightly knitted to its environment, and it is not clear how the agent can step back and reconsider its current course of action.

In contrast, flexible cognition is a central motivation for adopting PEM’s hierarchical Bayesian inference in the first place. Active inference is driven by the most probable hypothesis at any given time. The system will have built up expectations not just for what the most likely causes of sensory input might be but also for the typical evolution of prediction error precision. In particular, there will be accumulated evidence that any given hypothesis under which prediction error is minimized at a certain time will have a limited life span—in essence, the system will know that it lives in a changing world where precise evidence for any given hypothesis will soon begin to be hard to find. For example, as the agent fluidly and swiftly catches baseballs, it will know that the sun will soon set and make the visual input imprecise. It will therefore begin accumulating evidence for the next hypothesis (e.g., “I am eating dinner”) under which evidence will soon begin to be accumulated and prediction error minimized.

This speaks to a crucial balance, which a PEM system must obtain. As prediction error is minimized in active inference, the hypothesis relative to which error is minimized is held stable. This means that, as prediction error is minimized, the world can in fact change “behind the scenes” to such an extent that it would eventually be better to abandon the current hypothesis and adopt a new one. Anticipating such change in the environment matters greatly to the agent because it should never engage in any behavior, no matter how swift and fluid, for so long that when it ceases the behavior, the world has changed in other respects and predictive error will be very large. A PEM agent therefore will be inclined to believe that the current state of affairs will change, and therefore the agent will intersperse active inference with perceptual inference, where the internal model is checked and the size of the overall prediction error is adjusted and tightened up before a new hypothesis is selected for active inference (see Hohwy 2013 ; Hohwy et al. 2015 ).

A hierarchical system operating with slow and clean processing can thus economically explain both swift and fluid, affordance-based cognition as well as flexible cognition. This is an important point to make in the context of PEM’s affinity to 4E cognition. The motivation for PEM is, in the end, the simple observation that we live in a changing world. Our world presents many different causes of our sensory input, and these causes interact with each other to create nonlinearities in the input; moreover, these interactions happen concurrently at many different time scales (e.g., “The setting sun makes the balls hard to see, but this time of the year the janitor often turns on the floodlights at the far pitch”). This complexity is what creates the need for hierarchical Bayesian inference in the first place: a rich internal model that keeps track of all these contingencies and can mix the various causes in the right way to anticipate the sensory input. This has a 4E-type ring to it: the cognitive system is the way it is because the agent’s world and body are the way they are. In particular, PEM is not the best solution for non-ecological, lab-style model environments where typically context and interactions between hidden causes are kept to a minimum. In other words, a machine learning researcher who never tests their system against the real world will have little impetus to build a PEM system. On 4E approaches, there is also a strong focus on real-world settings, but the response is typically to tie the agent very closely to its environment. This, however, makes it harder to see how the real world, and also that fact that the real world is a changing place, can be taken into consideration. PEM, in contrast, makes room for the changing world by retracting farther away from the world, into a vast internal model that seeks to represent the full richness of the world and the way it changes over many time scales. On the PEM conception of the agent’s place in the world, cognition is not a matter of being closely in tune with and driven by the sensory input. Rather, cognition is a matter of having richly represented expectations for the world and the body and seeking confirming feedback on those expectations through the senses.

The Mind and Things Without It

Both perception and action are inferential and representational. The PEM system’s process of minimizing prediction error implies that the sensory input is explained away on the basis of the evolving hypotheses of an internal model. The more the system can minimize its prediction error, the more it will accumulate evidence for its own model. This is a trivial observation: if I can minimize prediction error for my theory that my hamster has escaped, the more evidence I have for that theory. If we consider the PEM system an agent, then it acquires evidence for its own existence through its activities ( Friston 2010 ). Borrowing a term from philosophy of science, the PEM system can thus be said to be self-evidencing ( Hempel 1965 ; Hohwy 2016b ).

A self-evidencing system creates a sensory boundary between itself (i.e., the model) and the causes of its sensory input. This again is a trivial consequence of self-evidencing: there is something that garners evidence and then there is what the evidence is evidence of. Or again, in both perceptual and active inference there is something doing the inference and something being inferred. This boundary can also be described in terms of causal nets, where a set of inner states (i.e., brain states) can be said to have a “Markov blanket” ( Pearl 1988 ) consisting of the inner states’ parents (i.e., the sensory states) and their children and other parents of the children (i.e., the active states driving active inference) ( Friston 2013 ; Hohwy 2015 , 2017 ; causal Bayes nets must be acyclic, but brains have recurrent (cyclic) states; there are technical ways, such as dynamical Bayes nets, to deal with such problems). The activity of the states within a Markov blanket is wholly determined by the states of the blanket. In principle, nothing about the environmental states beyond the blanket need be known to know what the system is doing. By extension, in principle, only the states of the sensory organs need be known to know everything the mind does.

PEM then comes with a principled way of drawing a boundary between the mind and the outside world. If a particular state is part of what is doing the inference, then it must be within the sensory boundary, as a part of what approximates inference about outside causes of sensory input. This may relate to the vigorous debate about extended cognition ( Clark and Chalmers 1998 ; Clark 2008 ), which is the last member of 4E cognition to discuss.

Extended cognition is the idea that some objects, such as notebooks and smartphones, play such an integrated, memory-like function in the mental economy of some agents that, by parity of reasoning, they should be considered part of the agent’s mental states even though they reside outside the central nervous system. There is much discussion of this idea (see, e.g., Menary 2007 ; Adams and Aizawa 2008 ; Anderson et al. 2012 ; Spaulding 2012 ). PEM brings with it a new way of thinking about the role of such external objects. On the one hand, these objects are inferred (e.g., on the basis of the sensory input from the notebook) and as such they are outside the mental states of the system. On the other hand, if the extended cognition hypothesis is correct, they are within the sensory boundary, forming part of the inner states behind a Markov blanket inferring the hidden causes beyond it.

Interpreting purported cases of extended cognition according to PEM thus leaves two main options. There might be contradiction, since something cannot be both within and beyond the same boundary at the same time. Or, there might be multiple coexisting sensory boundaries. The second option is very interesting and very likely to be true, since Markov blankets occur easily. There is an associated cost, however: we have identified the inner states (or the model) with the agent, and if there are multiple Markov blankets then there are multiple agents coexisting at the same time. Though this may be true in a weak sense of agent, it is explanatorily messy. When asking which agent is acting, there would then be a multitude of correct answers, depending on how many nested Markov blankets are involved in the same action. This speaks in favor of using inference to the best explanation to identify the agent whose relatively invariant involvement accounts for most of observed behavior over time. It seems likely this more pragmatically identified agent would be the agent as specified by the model harbored just in the nervous system. This is the agent relative to which prediction error is minimized over the longer time scale, which as we saw is central to understanding predictive processing accounts in the first place (for discussion, see Hohwy 2016b ). Bringing this discussion back to extended cognition, the pragmatic method of identifying the agent suggests that there is no extended cognition, since the special objects in question are beyond the one Markov blanket. The more lax way of identifying agents suggests that extended cognition ambiguous, since the special objects are beyond some blankets and within others.

The existence of the sensory boundary or Markov blanket implies that perception and agency are confined to the inner states of the PEM system (wherever the boundary or boundaries of the system are located). Those inner states will mirror the states outside the boundary: the inner states will, through PEM, come to represent the worldly causes of the sensory input impinging at the system’s periphery. Conversely, through active inference, the outside states will come to conform to the expectations harbored in the internal states.

There is then an intriguing duality to this sensory boundary between mind and world. On the one hand, the boundary is epistemic (cf. self-evidencing): the worldly causes can only be known vicariously, through inference on sensory input. On the other hand, the boundary is characterized in causal terms (cf. Markov blanket): there is a dynamic coupling between mind and world, enabled through both perception and action.

This duality summarizes well why PEM is a good fit for many of the issues in 4E debates: PEM is able to throw light on embodied agents dynamically interacting with the environment in which they are embedded. This good fit with 4E cognition is, however, made possible precisely because PEM is inferential and representational.

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”