Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Smith, Ryan; Badcock, Paul; Friston, Karl J.

doi:10.1111/pcn.13138

Cited by 175Embodied and Extended CognitionNeural and Behavioral Psychology StudiesFunctional Brain Connectivity Studies

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Open access

Smith, Ryan, Badcock, Paul, Friston, Karl J.·2021·Psychiatry and Clinical Neurosciences

DOI 10.1111/pcn.13138

Open this paper in CoChat — free account Open PDF

Abstract

Research in clinical neuroscience is founded on the idea that a better understanding of brain (dys)function will improve our ability to diagnose and treat neurological and psychiatric disorders. In recent years, neuroscience has converged on the notion that the brain is a 'prediction machine,' in that it actively predicts the sensory input that it will receive if one or another course of action is chosen. These predictions are used to select actions that will (most often, and in the long run) maintain the body within the narrow range of physiological states consistent with survival. This insight has given rise to an area of clinical computational neuroscience research that focuses on characterizing neural circuit architectures that can accomplish these predictive functions, and on how the associated processes may break down or become aberrant within clinical conditions. Here, we provide a brief review of examples of recent work on the application of predictive processing models of brain function to study clinical (psychiatric) disorders, with the aim of highlighting current directions and their potential clinical utility. We offer examples of recent conceptual models, formal mathematical models, and applications of such models in empirical research in clinical populations, with a focus on making this material accessible to clinicians without expertise in computational neuroscience. In doing so, we aim to highlight the potential insights and opportunities that understanding the brain as a prediction machine may offer to clinical research and practice.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1111/pcn.13138

Cites

124 of 124 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Characteristic behaviours across disorders (such as avoidance in anxiety, compulsions in OCD, or social withdrawal in depression) can be understood as policies that initially reduce volatility or prediction error but become maladaptive when they reinforce overly precise priors or limit access to corrective evidence.
“This means that characteristic behaviours across disorder (such as avoidance in anxiety, compulsions in OCD, or social withdrawal in depression) can be understood as policies that initially reduce volatility or prediction error but become maladaptive when they reinforce overly precise priors or limit access to corrective evidence (51).”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [51]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 1× · see citing works
“Formally, and under certain simplifying assumptions, minimizing prediction error is equivalent to the minimization of a statistical quantity called variational free energy, which is often used to solve optimization problems in machine learning (12).”
Background
Untitled work
(1999) · Cited by 5,934 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Meeting of minds: the medial frontal cortex and social cognition
Amodio, D. M., & Frith, C. D. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1884 · Cited by 3,782 · see citing works
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 2× · see citing works
“6,7,12 Perceptual beliefs are then updated when sensory input differs from predicted input.”
Background
From stress to inflammation and major depressive disorder: A social signal transduction theory of depression.
Slavich, G. M., & Irwin, M. R. (2014) · Psychological Bulletin · doi:10.1037/a0035302 · Cited by 2,206 · cited 1× · see citing works
“…by early exposure to social stress (e.g., parental abuse or neglect), which promotes prior beliefs that social outcomes are uncontrollable and heightens the sensitivity of stress response systems to interpersonal stressors (e.g., in ﬂ ammatory immune responses; see Slavich and Irwin 59 ).”
Background
Structural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106 · see citing works
The Social Brain: Neural Basis of Social Knowledge
Adolphs, R. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.60.110707.163514 · Cited by 1,827 · see citing works
Interoceptive predictions in the brain
Barrett, L. F., & Simmons, W. K. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3950 · Cited by 1,766 · see citing works
Attention, uncertainty, and free-energy
Feldman, H., & Friston, K. J. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00215 · Cited by 1,365 · see citing works
Mindfulness and acceptance: expanding the cognitive-behavioral tradition
(2005) · Choice Reviews Online · doi:10.5860/choice.42-6797 · Cited by 1,273 · see citing works
Active inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156 · see citing works
Self-citation
Knowing what you’re feeling and knowing what to do about it: mapping the relation between emotion differentiation and emotion regulation
Barrett, L. F., Gross, J., Christensen, T. C., & Benvenuto, M. (2001) · Cognition & Emotion · doi:10.1080/02699930143000239 · Cited by 1,077 · cited 1× · see citing works
“87 and Kashdan et al .”
Computational psychiatry as a bridge from neuroscience to clinical applications
Huys, Q. J. M., Maia, T. V., & Frank, M. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4238 · Cited by 1,002 · see citing works
A Review and Clarification of the Terms “holistic,” “configural,” and “relational” in the Face Perception Literature
Piepers, D. W., & Robbins, R. A. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00559 · Cited by 922 · see citing works
Precise minds in uncertain worlds: Predictive coding in autism.
Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014) · Psychological Review · doi:10.1037/a0037665 · Cited by 885 · see citing works
The Computational Anatomy of Psychosis
Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00047 · Cited by 850 · cited 2× · see citing works
“65 This then leads the brain to treat random aspects of sensory observations as ‘ suspicious coincidences ’ in need of explanation, driving the development of overly complex models of the world with stranger and stranger (delusional) beliefs over time.”
Background
Computational psychiatry
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809 · see citing works
A Hierarchy of Time-Scales and the Brain
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000209 · Cited by 694 · see citing works
Self-citation
Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors
Powers, A. R., Mathys, C., & Corlett, P. R. (2017) · Science · doi:10.1126/science.aan3458 · Cited by 684 · see citing works
Active inference and learning
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O⿿Doherty, J., & Pezzulo, G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.022 · Cited by 674 · see citing works
An aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
Dopamine reward prediction error coding
Schultz, W. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.1/wschultz · Cited by 597 · see citing works
Untitled work
(1996) · PubMed · Cited by 564 · see citing works
An active inference theory of allostasis and interoception in depression
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0011 · Cited by 554 · see citing works
Toward a unified treatment for emotional disorders - republished Article
Barlow, D. H., Allen, L. B., & Choate, M. L. (2016) · Behavior Therapy · doi:10.1016/j.beth.2016.11.005 · Cited by 536 · see citing works
Hallucinations and strong priors
Corlett, P. R., Horga, G., Fletcher, P. C., Alderson-Day, B., Schmack, K., & Powers, A. R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.001 · Cited by 534 · see citing works
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works
Adults with autism overestimate the volatility of the sensory environment
Lawson, R. P., Mathys, C., & Rees, G. (2017) · Nature Neuroscience · doi:10.1038/nn.4615 · Cited by 470 · cited 1× · see citing works
“61,62,108 Lastly, a few recent studies have ﬁ t active inference models to data in transdiagnostic patient populations from tasks involving exploratory decision-making, approach-avoidance con ﬂ ict, and inter-oceptive awareness.”
Background
Uncertainty in perception and the Hierarchical Gaussian Filter
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465 · see citing works
Unpacking Emotion Differentiation: Transforming Unpleasant Experience by Perceiving Distinctions in Negativity
Kashdan, T. B., Barrett, L. F., & McKnight, P. E. (2015) · Current Directions in Psychological Science · doi:10.1177/0963721414550708 · Cited by 463 · cited 1× · see citing works
“88 ).”
Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00550 · Cited by 437 · cited 1× · see citing works
“66 presented a mathematical model to capture homeostatic and allostatic regulation of visceral states and how meta-cognitive estimates of the ef ﬁ cacy of allostasis could promote a depression-like state of fatigue.”
Background
The graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
Self-citation
Children acquire emotion categories gradually
Widen, S. C., & Russell, J. A. (2008) · Cognitive Development · doi:10.1016/j.cogdev.2008.01.002 · Cited by 388 · see citing works
What has serotonin to do with depression?
Cowen, P. J., & Browning, M. D. (2015) · World Psychiatry · doi:10.1002/wps.20229 · Cited by 374 · see citing works
Categorization in 3- and 4-Month-Old Infants: An Advantage of Words Over Tones
Ferry, A. L., Hespos, S. J., & Waxman, S. R. (2010) · Child Development · doi:10.1111/j.1467-8624.2009.01408.x · Cited by 368 · see citing works
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342 · see citing works
Emotional Valence and the Free-Energy Principle
Joffily, M., & Coricelli, G. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003094 · Cited by 322 · see citing works
A tutorial on the free-energy framework for modelling perception and learning
Bogacz, R. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2015.11.003 · Cited by 283 · see citing works
Do Theory of Mind and Executive Function Deficits Underlie the Adverse Outcomes Associated with Profound Early Deprivation?: Findings from the English and Romanian Adoptees Study
Colvert, E., Rutter, M., Kreppner, J., Beckett, C., Castle, J., Groothues, C., Hawkins, A., Stevens, S., & Sonuga-Barke, E. J. S. (2008) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-008-9232-x · Cited by 270 · see citing works
Uncertainty, epistemics and active inference
Parr, T., & Friston, K. J. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0376 · Cited by 252 · see citing works
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021) · Neural Computation · doi:10.1162/neco_a_01341 · Cited by 246 · see citing works
The neural basis of one's own conscious and unconscious emotional states
Smith, R. S., & Lane, R. D. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.003 · Cited by 240 · see citing works
Distorted Cognitive Processes in Major Depression: A Predictive Processing Perspective
Κube, T., Schwarting, R. K. W., Rozenkrantz, L., Glombiewski, J. A., & Rief, W. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.07.017 · Cited by 238 · see citing works
Working memory, attention, and salience in active inference
Parr, T., & Friston, K. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-15249-0 · Cited by 226 · see citing works
Active inference on discrete state-spaces: A synthesis
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102447 · Cited by 226 · cited 1× · see citing works
“In decision-making and action selection, a related class of biologically plausible algorithms comes from the ‘active inference’ framework.(2,16,17,19) Active inference models go a step beyond predictive coding to emphasize that the brain does not simply predict sensory input passively.”
Background
Evidence for social working memory from a parametric functional MRI study
Meyer, M. L., Spunt, R. P., Berkman, E. T., Taylor, S. E., & Lieberman, M. D. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1121077109 · Cited by 220 · see citing works
The dopaminergic midbrain encodes the expected certainty about desired outcomes
Schwartenbeck, P., FitzGerald, T. H. B., Mathys, C., Dolan, R., & Friston, K. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu159 · Cited by 219 · see citing works
The hierarchically mechanistic mind: A free-energy formulation of the human psyche
Badcock, P. B., Friston, K. J., & Ramstead, M. J. D. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2018.10.002 · Cited by 217 · see citing works
Emotion-Focused Therapy
Greenberg, L. S. (2010) · PsycEXTRA Dataset · doi:10.1037/e602962010-001 · Cited by 211 · see citing works
Affective agnosia: Expansion of the alexithymia construct and a new opportunity to integrate and extend Freud's legacy
Lane, R. D., Weihs, K. L., Herring, A. M., Hishaw, A., & Smith, R. S. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.06.007 · Cited by 205 · see citing works
What is mood? A computational perspective
Clark, J. E., Watson, S., & Friston, K. J. (2018) · Psychological Medicine · doi:10.1017/s0033291718000430 · Cited by 204 · see citing works
Active inference on discrete state-spaces: A synthesis
Costa, L. D., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. J. (2020) · UCL Discovery (University College London) · Cited by 204 · see citing works
Planning and navigation as active inference
Kaplan, R., & Friston, K. J. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0753-2 · Cited by 203 · see citing works
The Anatomy of Inference: Generative Models and Brain Structure
Parr, T., & Friston, K. J. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00090 · Cited by 198 · see citing works
A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders
Smith, R. S., Kuplicki, R., Feinstein, J. S., Forthman, K. L., Stewart, J. L., Paulus, M. P., Tulsa 1000 investigators, & Khalsa, S. S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008484 · Cited by 195 · see citing works
The depressed brain: an evolutionary systems theory
Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.005 · Cited by 190 · see citing works
Computational models of motivated action selection in corticostriatal circuits
Frank, M. J. (2011) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2011.02.013 · Cited by 175 · see citing works
Attention or salience?
Parr, T., & Friston, K. J. (2018) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2018.10.006 · Cited by 174 · see citing works
Emotion understanding in postinstitutionalized Eastern European children
Fries, A. B. W., & Pollak, S. D. (2004) · Development and Psychopathology · doi:10.1017/s0954579404044554 · Cited by 170 · see citing works
Reconciling cognitive and affective neuroscience perspectives on the brain basis of emotional experience
Panksepp, J., Lane, R. D., Solms, M., & Smith, R. S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.09.010 · Cited by 167 · see citing works
The hierarchically mechanistic mind: an evolutionary systems theory of the human brain, cognition, and behavior
Badcock, P. B., Friston, K. J., Ramstead, M. J. D., Ploeger, A., & Hohwy, J. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00721-3 · Cited by 166 · see citing works
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
Hierarchical prediction errors in midbrain and septum during social learning
Diaconescu, A. O., Mathys, C. D., Weber, L. A. E., Kasper, L., Mauer, J., & Stephan, K. E. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw171 · Cited by 130 · see citing works
Simulating Emotions: An Active Inference Model of Emotional State Inference and Emotion Concept Learning
Smith, R. S., Parr, T., & Friston, K. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02844 · Cited by 128 · see citing works
Computational Phenotyping in Psychiatry: A Worked Example
Schwartenbeck, P., & Friston, K. J. (2016) · eNeuro · doi:10.1523/eneuro.0049-16.2016 · Cited by 124 · see citing works
From conflict management to reward-based decision making: Actors and critics in primate medial frontal cortex
Silvetti, M., Alexander, W., Verguts, T., & Brown, J. W. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.11.003 · Cited by 114 · see citing works
Unconscious emotion: A cognitive neuroscientific perspective
Smith, R. S., & Lane, R. D. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.013 · Cited by 113 · see citing works
The Structure of Emotional Experience and Its Relation to Trait Emotional Awareness: A Theoretical Review
Smith, R., Killgore, W. D., & Lane, R. (2017) · Emotion · doi:10.1037/emo0000376 · Cited by 106 · see citing works
The effects of a novel psychological attribution and emotional awareness and expression therapy for chronic musculoskeletal pain: A preliminary, uncontrolled trial
Burger, A. J., Lumley, M. A., Carty, J. N., Latsch, D. V., Thakur, E. R., Hyde‐Nolan, M. E., Hijazi, A. M., & Schubiner, H. H. (2015) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2015.12.003 · Cited by 101 · see citing works
A mega-analysis of fixed-dose trials reveals dose-dependency and a rapid onset of action for the antidepressant effect of three selective serotonin reuptake inhibitors
Hieronymus, F., Nilsson, S., & Eriksson, E. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.104 · Cited by 100 · see citing works
Social Working Memory: Neurocognitive Networks and Directions for Future Research
Meyer, M. L., & Lieberman, M. D. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00571 · Cited by 93 · see citing works
Emotional awareness and expression training improves irritable bowel syndrome: A randomized controlled trial
Thakur, E. R., Holmes, H. J., Lockhart, N. A., Carty, J. N., Ziadni, M. S., Doherty, H. K., Lackner, J. M., Schubiner, H. H., & Lumley, M. A. (2017) · Neurogastroenterology & Motility · doi:10.1111/nmo.13143 · Cited by 92 · see citing works
Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia
Adams, R. A., Perrinet, L. U., & Friston, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047502 · Cited by 91 · see citing works
Neurocomputational mechanisms underlying emotional awareness: Insights afforded by deep active inference and their potential clinical relevance
Smith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.002 · Cited by 89 · see citing works
Dramatherapy: Theory and Practice 2
Jennings, S. E. (2013) · Routledge eBooks · doi:10.4324/9780203713747 · Cited by 85 · see citing works
Social working memory and its distinctive link to social cognitive ability: an fMRI study
Meyer, M. L., Taylor, S. E., & Lieberman, M. D. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv065 · Cited by 84 · see citing works
The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness
Whyte, C. J., & Smith, R. (2020) · doi:10.1101/2020.02.11.944611 · Cited by 84 · see citing works
Self-citation
The predictive global neuronal workspace: a formal active inference model of visual consciousness
Whyte, C. J., & Smith, R. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101918 · Cited by 84 · see citing works
Introducing a Bayesian model of selective attention based on active inference
Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83 · see citing works
Volatility Estimates Increase Choice Switching and Relate to Prefrontal Activity in Schizophrenia
Deserno, L., Boehme, R., Mathys, C. D., Katthagen, T., Kaminski, J. A., Stephan, K. E., Heinz, A., & Schlagenhauf, F. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.10.007 · Cited by 80 · see citing works
Imprecise action selection in substance use disorder: Evidence for active learning impairments when solving the explore-exploit dilemma
Smith, R. S., Schwartenbeck, P., Stewart, J. L., Kuplicki, R., Ekhtiari, H., T Investigators, & Paulus, M. P. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.108208 · Cited by 78 · see citing works
Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
Smith, R. S., Kirlić, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Feinstein, J. S., Paulus, M. P., & Aupperle, R. L. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200032 · Cited by 78 · see citing works
Atypical processing of uncertainty in individuals at risk for psychosis
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., Brodersen, K. H., Mathys, C. D., Julkowski, D., Ruhrmann, S., Schilbach, L., Tittgemeyer, M., Vogeley, K. T., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102239 · Cited by 72 · see citing works
Erratum: Computational psychiatry
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.04.003 · Cited by 65 · see citing works
The role of medial prefrontal cortex in the working memory maintenance of one’s own emotional responses
Smith, R. S., Lane, R. D., Alkozei, A., Bao, J., Smith, C., Sanova, A., Nettles, M., & Killgore, W. D. S. (2018) · Scientific Reports · doi:10.1038/s41598-018-21896-8 · Cited by 61 · see citing works
Maintaining the feelings of others in working memory is associated with activation of the left anterior insula and left frontal-parietal control network
Smith, R. S., Lane, R. D., Alkozei, A., Bao, J., Smith, C., Sanova, A., Nettles, M., & Killgore, W. D. S. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx011 · Cited by 59 · see citing works
A neuro-cognitive process model of emotional intelligence
Smith, R. S., Killgore, W. D. S., Alkozei, A., & Lane, R. D. (2018) · Biological Psychology · doi:10.1016/j.biopsycho.2018.10.012 · Cited by 59 · see citing works
Unifying treatments for depression: an application of the free energy principle
Chekroud, A. M. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00153 · Cited by 55 · see citing works
Spontaneous Prediction Error Generation in Schizophrenia
Yamashita, Y., & Tani, J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037843 · Cited by 53 · see citing works
Biased Competition Favoring Physical Over Emotional Pain: A Possible Explanation for the Link Between Early Adversity and Chronic Pain
Lane, R. D., Anderson, F. S., & Smith, R. S. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000640 · Cited by 49 · see citing works
Deeply Felt Affect: The Emergence of Valence in Deep Active InferencePreprint
Hesp, C., Smith, R. S., Parr, T., et al. (2019) · doi:10.31234/osf.io/62pfd · Cited by 49 · see citing works
Self-citation
Prefrontal Computation as Active Inference
Parr, T., Rikhye, R. V., Halassa, M. M., & Friston, K. J. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz118 · Cited by 48 · see citing works
Resting state functional connectivity correlates of emotional awareness
Smith, R. S., Alkozei, A., Bao, J., Smith, C., Lane, R. D., & Killgore, W. D. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.044 · Cited by 46 · see citing works
The evolution and development of the uniquely human capacity for emotional awareness: A synthesis of comparative anatomical, cognitive, neurocomputational, and evolutionary psychological perspectives
Smith, R. S., Steklis, H. D., Steklis, N. G., Weihs, K. L., & Lane, R. D. (2020) · Biological Psychology · doi:10.1016/j.biopsycho.2020.107925 · Cited by 46 · see citing works
Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: insights from active inference
Smith, R. S., Moutoussis, M., & Bilek, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-89047-0 · Cited by 46 · see citing works
Precision Psychiatry
Friston, K. J. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.08.007 · Cited by 46 · see citing works
Nested positive feedback loops in the maintenance of major depression: An integration and extension of previous models
Smith, R. S., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2017) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2017.09.011 · Cited by 38 · see citing works
A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disordersPreprint
Smith, R. S., Kuplicki, R., Feinstein, J. S., et al. (2020) · medRxiv · doi:10.1101/2020.06.03.20121343 · Cited by 38 · see citing works
Self-citation
A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision
Idei, H., Murata, S., Chen, Y., Yamashita, Y., Tani, J., & Ogata, T. (2018) · Computational Psychiatry · doi:10.1162/cpsy_a_00019 · Cited by 37 · see citing works
Role of medial prefrontal cortex in representing one's own subjective emotional responses: a preliminary study.
Smith, R., Fass, H., & Lane, R. (2014) · Consciousness and Cognition · doi:10.1016/j.concog.2014.08.002 · Cited by 34 · see citing works
An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease
Smith, R. S., Weihs, K. L., Alkozei, A., Killgore, W. D. S., & Lane, R. D. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000661 · Cited by 33 · see citing works
Neuroscience of Enduring Change: Implications for Psychotherapy
Kramer, U. (2021) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.20200052 · Cited by 28 · see citing works
Higher levels of trait emotional awareness are associated with more efficient global information integration throughout the brain: a graph-theoretic analysis of resting state functional connectivity
Smith, R. S., Sanova, A., Alkozei, A., Lane, R. D., & Killgore, W. D. S. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy047 · Cited by 27 · see citing works
An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case
Smith, R., Schwartenbeck, P., Parr, T., & Friston, K. J. (2020) · UCL Discovery (University College London) · Cited by 26 · see citing works
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Idei, H., Murata, S., Yamashita, Y., & Ogata, T. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00762 · Cited by 24 · see citing works
The importance of identifying underlying process abnormalities in alexithymia: Implications of the three-process model and a single case study illustration
Smith, R. S., Kaszniak, A. W., Katsanis, J., Lane, R. D., & Nielsen, L. (2018) · Consciousness and Cognition · doi:10.1016/j.concog.2018.12.004 · Cited by 23 · see citing works
An Active Inference Approach to Dissecting Reasons for Non-Adherence to AntidepressantsPreprint
Smith, R., Khalsa, S., & Paulus, M. (2019) · bioRxiv · doi:10.1101/743542 · Cited by 23 · see citing works
Self-citation
Confirmatory Evidence that Healthy Individuals Can Adaptively Adjust Prior Expectations and Interoceptive Precision Estimates
Smith, R. S., Kuplicki, R., Teed, A. R., Upshaw, V., & Khalsa, S. S. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-64919-7_16 · Cited by 23 · see citing works
An Active Inference Approach to Dissecting Reasons for Nonadherence to Antidepressants
Smith, R. S., Khalsa, S. S., & Paulus, M. P. (2019) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2019.11.012 · Cited by 21 · see citing works
Two facets of being bothered by bodily sensations: anxiety sensitivity and alexithymia in psychosomatic patients
Mueller, J., & Alpers, G. W. (2006) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2006.03.001 · Cited by 20 · see citing works
Greater cortical thickness within the limbic visceromotor network predicts higher levels of trait emotional awareness
Smith, R. S., Bajaj, S., Dailey, N. S., Alkozei, A., Smith, C., Sanova, A., Lane, R. D., & Killgore, W. D. S. (2017) · Consciousness and Cognition · doi:10.1016/j.concog.2017.11.004 · Cited by 20 · see citing works
The neural basis of attaining conscious awareness of sad mood
Smith, R. S., Braden, B. B., Chen, K., Ponce, F. A., Lane, R. D., & Baxter, L. C. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-014-9318-8 · Cited by 19 · see citing works
Hebbian Plasticity
Brown, T. H., Zhao, Y., & Leung, V. C. (2009) · Elsevier eBooks · doi:10.1016/b978-008045046-9.00796-8 · Cited by 19 · see citing works
Common and Unique Neural Systems Underlying the Working Memory Maintenance of Emotional vs. Bodily Reactions to Affective Stimuli: The Moderating Role of Trait Emotional Awareness
Smith, R. S., Lane, R. D., Sanova, A., Alkozei, A., Smith, C., & Killgore, W. D. S. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00370 · Cited by 17 · see citing works
Atypical processing of uncertainty in individuals at risk for psychosisPreprint
Cole, D. M., Diaconescu, A. O., Pfeiffer, U. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/796300 · Cited by 17 · see citing works
Unwanted reminders: The effects of emotional memory suppression on subsequent neuro-cognitive processing
Smith, R. S., Alkozei, A., Lane, R. D., & Killgore, W. D. S. (2016) · Consciousness and Cognition · doi:10.1016/j.concog.2016.07.008 · Cited by 10 · see citing works
Confirmatory evidence that healthy individuals can adaptively adjust prior expectations and interoceptive precision estimatesPreprint
Smith, R. S., Kuplicki, R., Teed, A. R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.31.275594 · Cited by 9 · see citing works
Self-citation
Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modeling approachPreprint
Smith, R. S., Kirlić, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Paulus, M. P., T Investigators, & Aupperle, R. L. (2020) · doi:10.31234/osf.io/t2dhn · Cited by 9 · see citing works
An active inference approach to modeling structure learning: concept learning as an example casePreprint
Smith, R., Schwartenbeck, P., Parr, T., & Friston, K. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/633677 · Cited by 8 · see citing works
Simulating the computational mechanisms of cognitive and behavioral psychotherapeutic interventions: Insights from active inferencePreprint
Smith, R. S., Moutoussis, M., & Bilek, E. (2020) · doi:10.31234/osf.io/8m62p · Cited by 7 · see citing works
Self-citation
Homogeneous intrinsic neuronal excitability induces overfitting to sensory noise: A robot model of neurodevelopmental disorderPreprint
Idei, H., Murata, S., Yamashita, Y., et al. (2020) · doi:10.31234/osf.io/ah89z · Cited by 4 · see citing works
How Do Emotions Work?
Smith, R. A., Alkozei, A., & Killgore, W. D. S. (2017) · Frontiers for Young Minds · doi:10.3389/frym.2017.00069 · Cited by 3 · see citing works

Flagged first, then by citations

Research works printed but not linked: 1

Smith R, Schwartenbeck P, Parr T, Friston KJ. An active inference approach to modeling concept learning. bioRxiv. 2019:633677.

Sorted by citations

A step-by-step tutorial on active inference and its application to empirical data
Smith, R., Friston, K. J., & Whyte, C. J. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2021.102632 · Cited by 246 · see citing works
Individual differences in computational psychiatry: A review of current challenges
Karvelis, P., Paulus, M. P., & Diaconescu, A. O. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105137 · Cited by 113 · see citing works
Correspondence of functional connectivity gradients across human isocortex, cerebellum, and hippocampus
(2023) · doi:10.1038/s42003-023-04796-0 · Cited by 89 · see citing works
Allostasis as a core feature of hierarchical gradients in the human brain
Katsumi, Y., Theriault, J. E., Quigley, K. S., & Barrett, L. F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00240 · Cited by 88 · see citing works
The Human Affectome
Schiller, D., Yu, A. N. C., Alia‐Klein, N., Becker, S., Cromwell, H. C., Dolcos, F., Eslinger, P. J., Frewen, P., Kemp, A. H., Pace-Schott, E. F., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105450 · Cited by 86 · see citing works
The empirical status of predictive coding and active inference
Hodson, R., Mehta, M., & Smith, R. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105473 · Cited by 69 · see citing works
The Embodied-Enactive-Interactive Brain: Bridging Neuroscience and Creative Arts Therapies
Vaisvaser, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.634079 · Cited by 76 · see citing works
Interpersonal attunement in social interactions: from collective psychophysiology to inter- personalized psychiatry and beyond
Bolis, D., Dumas, G., & Schilbach, L. (2022) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2021.0365 · Cited by 81 · see citing works
Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations
Brændholt, M., Kluger, D. S., Varga, S., Heck, D. H., Groß, J., & Allen, M. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105262 · Cited by 79 · see citing works
Toward recovery in schizophrenia: Current concepts, findings, and future research directions
Onitsuka, T., Hirano, Y., Nakazawa, T., Ichihashi, K., Miura, K., Inada, K., Mitoma, R., Yasui‐Furukori, N., & Hashimoto, R. (2022) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13342 · Cited by 70 · see citing works
Rethinking delusions: A selective review of delusion research through a computational lens
Ashinoff, B. K., Singletary, N. M., Baker, S. C., & Horga, G. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.01.023 · Cited by 56 · see citing works
An Introduction to Predictive Processing Models of Perception and Decision‐Making
Sprevak, M. D., & Smith, R. S. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12704 · Cited by 57 · see citing works
Osteopathic Care as (En)active Inference: A Theoretical Framework for Developing an Integrative Hypothesis in Osteopathy
Esteves, J. E., Cerritelli, F., Kim, J., & Friston, K. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.812926 · Cited by 58 · see citing works
The future of psychological treatments: The Marburg Declaration
Rief, W., Asmundson, G. J. G., Bryant, R. A., Clark, D. M., Ehlers, A., Holmes, E. A., McNally, R. J., Neufeld, C. B., Wilhelm, S., Jaroszewski, A. C., et al. (2024) · Clinical Psychology Review · doi:10.1016/j.cpr.2024.102417 · Cited by 54 · see citing works
Collective behavior from surprise minimization
Heins, C., Millidge, B., Da Costa, L., Mann, R. P., Friston, K. J., & Couzin, I. D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2320239121 · Cited by 55 · see citing works
A Step-by-Step Tutorial on Active Inference and its Application to Empirical DataPreprint
Smith, R. S., Friston, K. J., & Whyte, C. J. (2021) · doi:10.31234/osf.io/b4jm6 · Cited by 51 · see citing works
Trends in big data analyses by multicenter collaborative translational research in psychiatry
Onitsuka, T., Hirano, Y., Nemoto, K., Hashimoto, N., Kushima, I., Koshiyama, D., Koeda, M., Takahashi, T., Noda, Y., Matsumoto, J., et al. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13311 · Cited by 49 · see citing works
Predictive processing models and affective neuroscience
Lee, K. M., Ferreira‐Santos, F., & Satpute, A. B. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.09.009 · Cited by 46 · see citing works
The Brain Is Adaptive Not Triune: How the Brain Responds to Threat, Challenge, and Change
Steffen, P. R., Hedges, D., & Matheson, R. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.802606 · Cited by 48 · see citing works
Clinical Manifestations of Body Memories: The Impact of Past Bodily Experiences on Mental Health
Gentsch, A., & Kuehn, E. (2022) · Brain Sciences · doi:10.3390/brainsci12050594 · Cited by 55 · see citing works
A new predictive coding model for a more comprehensive account of delusions
Harding, J. N., Wolpe, N., Brugger, S. P., Navarro, V., Teufel, C., & Fletcher, P. C. (2024) · The Lancet Psychiatry · doi:10.1016/s2215-0366(23)00411-x · Cited by 45 · see citing works
Regulative development as a model for origin of life and artificial life studies
Fields, C., & Levin, M. G. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104927 · Cited by 42 · see citing works
Therapeutic Alliance as Active Inference: The Role of Therapeutic Touch and Biobehavioural Synchrony in Musculoskeletal Care
McParlin, Z., Cerritelli, F., Rossettini, G., Friston, K. J., & Esteves, J. E. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.897247 · Cited by 42 · see citing works
Metaverse in Mental Health: The Beginning of a Long History
Cerasa, A., Gaggioli, A., Pioggia, G., & Riva, G. (2024) · Current Psychiatry Reports · doi:10.1007/s11920-024-01501-8 · Cited by 37 · see citing works
Intrinsic timescales and predictive allostatic interoception in brain health and disease
Ibanez, A., & Northoff, G. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105510 · Cited by 36 · see citing works
Auditory event-related potentials in separating patients with depressive disorders and non-depressed controls: A narrative review
Kangas, E. S., Vuoriainen, E., Lindeman, S. M., & Astikainen, P. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2022.07.003 · Cited by 34 · see citing works
Theories and models of negative symptoms in schizophrenia and clinical implications
Chan, R. C. K., Wang, L., & Lui, S. S. Y. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00065-9 · Cited by 34 · see citing works
Signal processing in the vagus nerve: Hypotheses based on new genetic and anatomical evidence
Shaffer, C., Barrett, L. F., & Quigley, K. S. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108626 · Cited by 33 · see citing works
Reconceptualizing complex posttraumatic stress disorder: A predictive processing framework for mechanisms and intervention
Putica, A., & Agathos, J. A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105836 · Cited by 32 · see citing works
An Enactive–Ecological Model to Guide Patient-Centered Osteopathic Care
Cerritelli, F., & Esteves, J. E. (2022) · Healthcare · doi:10.3390/healthcare10061092 · Cited by 32 · see citing works
Language Dysfunction in Schizophrenia: Assessing Neural Tracking to Characterize the Underlying Disorder(s)?
Meyer, L., Lakatos, P., & He, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.640502 · Cited by 34 · see citing works
Disconnection from prediction: A systematic review on the role of right temporoparietal junction in aberrant predictive processing
Masina, F., Pezzetta, R., Lago, S., Mantini, D., Scarpazza, C., & Arcara, G. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104713 · Cited by 30 · see citing works
Neurodynamics of Relational Aesthetic Engagement in Creative Arts Therapies
Vaisvaser, S., King, J. L., Orkibi, H., & Aleem, H. (2024) · Review of General Psychology · doi:10.1177/10892680241260840 · Cited by 29 · see citing works
Neural Substrates and Models of Omission Responses and Predictive Processes
Braga, A., & Schönwiesner, M. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.799581 · Cited by 29 · see citing works
Foundational AI in Insurance and Real Estate: A Survey of Applications, Challenges, and Future Directions
Kuppan, K., Acharya, D. B., & Divya, B. (2024) · IEEE Access · doi:10.1109/access.2024.3509918 · Cited by 35 · see citing works
Hierarchical predictive coding in distributed pain circuits
Chen, Z. S. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1073537 · Cited by 34 · see citing works
Structurally informed models of directed brain connectivity
Greaves, M. D., Novelli, L., Sina Mansour, L., Zalesky, A., & Razi, A. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00881-3 · Cited by 27 · see citing works
Toward a Multidimensional Understanding of Misophonia Using Cluster-Based Phenotyping
Norris, J. E., Kimball, S. H., Nemri, D. C., & Ethridge, L. E. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.832516 · Cited by 25 · see citing works
Recent findings on neurofeedback training for auditory hallucinations in schizophrenia
Hirano, Y., & Tamura, S. (2021) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000693 · Cited by 18 · see citing works
Disembodiment and Affective Resonances in Esketamine Treatment of Depersonalized Depression Subtype: Two Case Studies
Sarasso, P., Billeci, M., Ronga, I., Raffone, F., Martiadis, V., & Petta, G. D. (2024) · Psychopathology · doi:10.1159/000539714 · Cited by 24 · see citing works
Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review
Gkintoni, E., Panagioti, M., Vassilopoulos, S. P., Νικολάου, Γ., Boutsinas, B., & Vantarakis, A. (2025) · Healthcare · doi:10.3390/healthcare13151776 · Cited by 26 · see citing works
Mental health meets computational neuroscience: A predictive Bayesian account of the relationship between interoception and multisensory bodily illusions in anorexia nervosa
Lernia, D. D., Serino, S., Tuena, C., Cacciatore, C., Polli, N., & Riva, G. (2023) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2023.100383 · Cited by 22 · see citing works
The Potential Role of Dopamine in Mediating Motor Function and Interpersonal Synchrony
Probolovski, H. Z. G., & Dahan, A. (2021) · Biomedicines · doi:10.3390/biomedicines9040382 · Cited by 15 · see citing works
Functional connectivity gradients as a common neural architecture for predictive processing in the human brain.
Katsumi, Y., Kamona, N., Zhang, J., Bunce, J. G., Hutchinson, J. B., Yarossi, M., Tunik, E., Quigley, K. S., Dickerson, B. C., & Barrett, L. F. (2021) · doi:10.1101/2021.09.01.456844 · Cited by 89 · see citing works
Beauty and Uncertainty as Transformative Factors: A Free Energy Principle Account of Aesthetic Diagnosis and Intervention in Gestalt Psychotherapy
Sarasso, P., Francesetti, G., Roubal, J., Gecele, M., Ronga, I., Neppi-Mòdona, M., & Sacco, K. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.906188 · Cited by 20 · see citing works
Understanding the sense of self through robotics
Prescott, T. J., Vogeley, K. T., & Wykowska, A. (2024) · Science Robotics · doi:10.1126/scirobotics.adn2733 · Cited by 18 · see citing works
Active Inference in Psychology and Psychiatry: Progress to Date?
Badcock, P. B., & Davey, C. G. (2024) · Entropy · doi:10.3390/e26100833 · Cited by 20 · see citing works
Repeating patterns: Predictive processing suggests an aesthetic learning role of the basal ganglia in repetitive stereotyped behaviors
Spee, B. T. M., Sladky, R., Fingerhut, J., Laciny, A., Kraus, C., Carls‐Diamante, S., Brücke, C., Pelowski, M. J., & Treven, M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.930293 · Cited by 17 · see citing works
A Computational Model of Hopelessness and Active-Escape Bias in Suicidality
Karvelis, P., & Diaconescu, A. O. (2022) · Computational Psychiatry · doi:10.5334/cpsy.80 · Cited by 21 · see citing works
Cognition Without Neural Representation: Dynamics of a Complex System
Hipólito, I. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.643276 · Cited by 15 · see citing works
Therapeutic touch and therapeutic alliance in pediatric care and neonatology: An active inference framework
McParlin, Z., Cerritelli, F., Manzotti, A., Friston, K. J., & Esteves, J. E. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.961075 · Cited by 17 · see citing works
Anticipating and enacting worlds: moods, illness and psychobehavioral adaptation
Crippen, M. (2023) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-023-09934-x · Cited by 14 · see citing works
Training the embodied self in its impermanence: meditators evidence neurophysiological markers of death acceptance
Dor‐Ziderman, Y., Schweitzer, Y., Nave, O., Trautwein, F., Fulder, S. J., Antoine Lutz, A., Goldstein, A., & Berkovich‐Ohana, A. (2025) · Neuroscience of Consciousness · doi:10.1093/nc/niaf002 · Cited by 16 · see citing works
The Human AffectomePreprint
Schiller, D., Yu, A. N. C., Alia‐Klein, N., Becker, S., Cromwell, H. C., Dolcos, F., Eslinger, P. J., Frewen, P., Kemp, A. H., Pace-Schott, E. F., et al. (2022) · doi:10.31234/osf.io/9nu32 · Cited by 14 · see citing works
Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings
Pan, Y., Wen, Y., Wang, Y., Schilbach, L., & Ji, C. (2023) · Psychoradiology · doi:10.1093/psyrad/kkad002 · Cited by 13 · see citing works
The Therapeutic Alliance as Active Inference: The Role of Trust and Self-Efficacy
Krupnik, V. (2022) · Journal of Contemporary Psychotherapy · doi:10.1007/s10879-022-09576-1 · Cited by 14 · see citing works
A Theoretical Framework for Studying the Phenomenon of Gaslighting
Klein, W., Wood, S. C., & Bartz, J. A. (2025) · Personality and Social Psychology Review · doi:10.1177/10888683251342291 · Cited by 12 · see citing works
Inference on homeostatic belief precision
Unal, O., Eren, O. C., Alkan, G., Petzschner, F. H., Yao, Y., & Stephan, K. E. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108190 · Cited by 16 · see citing works
Nature heals: An informational entropy account of self-organization and change in field psychotherapy
Sarasso, P., Tschacher, W., Schoeller, F., Francesetti, G., Roubal, J., Gecele, M., Sacco, K., & Ronga, I. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.09.005 · Cited by 12 · see citing works
Thoughts and thinkers: On the complementarity between objects and processes
Fields, C., & Levin, M. G. (2025) · Physics of Life Reviews · doi:10.1016/j.plrev.2025.01.008 · Cited by 11 · see citing works
An active inference approach to interpersonal differences in depression
Kirchner, L., Eckert, A.-L., Berg, M., Endres, D., Straube, B., & Rief, W. (2024) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2024.101092 · Cited by 9 · see citing works
Further N-Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI
Edwards, D. J. (2025) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2025.1551960 · Cited by 10 · see citing works
Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders
Shaw, A. D., Sumner, R. L., & Berndt, L. C. S. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1713833 · Cited by 9 · see citing works
Attachment as Prediction: Insights From Cognitive and Developmental Neuroscience
Tottenham, N., & Vannucci, A. (2025) · Current Directions in Psychological Science · doi:10.1177/09637214251313714 · Cited by 9 · see citing works
Predictive processing accounts of psychosis: bottom-up or top-down disruptions
Goodwin, I., Diederen, K. M. J., Hird, E., Weilnhammer, V. A., Garrido, M. I., & Knolle, F. (2026) · Nature Mental Health · doi:10.1038/s44220-025-00558-5 · Cited by 9 · see citing works
Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models
Idei, H., & Yamashita, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.005 · Cited by 9 · see citing works
Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools
Koike, S., Uematsu, A., Sasabayashi, D., Maikusa, N., Takahashi, T., Ohi, K., Nakajima, S., Noda, Y., & Hirano, Y. (2021) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2021-0050 · Cited by 9 · see citing works
Piloting the Update: The Use of Therapeutic Relationship for Change – A Free Energy Account
Hauke, G., & Lohr, C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.842488 · Cited by 9 · see citing works
Do eye movement abnormalities in schizophrenia cause Praecox Gefühl ?
Hashimoto, R. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13197 · Cited by 10 · see citing works
State and capacity in neural models of cognition and consciousness
Wang, P., Liu, H., Ilievski, F., Ganushchak, L., Zou, L., Laureys, S., & Paas, F. (2026) · Trends Open · doi:10.1016/j.treopn.2026.04.004 · Cited by 8 · see citing works
RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain
Banaraki, A. K., Toghi, A., & Mohammadzadeh, A. (2024) · Computational Psychiatry · doi:10.5334/cpsy.119 · Cited by 9 · see citing works
Towards an expanded neurocognitive account of ketamine’s rapid antidepressant effects
Dai, Y., Harrison, B. J., Davey, C. G., & Steward, T. (2025) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyaf010 · Cited by 10 · see citing works
The habenula clock influences response to a stressor
Basnakova, A., Cheng, R., Chia, J. S. M., D’Agostino, G. A., Suryadi, Tan, G. J. H., Langley, S. R., & Jesuthasan, S. J. (2021) · Neurobiology of Stress · doi:10.1016/j.ynstr.2021.100403 · Cited by 8 · see citing works
Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks
Pak, V., & Hashmi, J. A. (2023) · Network Neuroscience · doi:10.1162/netn_a_00328 · Cited by 7 · see citing works
Digital Being: social media and the predictive mind
White, B., Clark, A., & Miller, M. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae008 · Cited by 7 · see citing works
Object-Centric Scene Representations Using Active Inference
Van de Maele, T., Verbelen, T., Mazzaglia, P., Ferraro, S., & Dhoedt, B. (2024) · Neural Computation · doi:10.1162/neco_a_01637 · Cited by 5 · see citing works
“…planning, and action in terms of probabilistic inference (Parr et al., 2022), which is applied in various domains ranging from neuroscience (Smith et al., 2020), neuropsychology (Parr et al., 2018), biology (Pio-Lopez et al., 2022) to robotics and artiﬁcial intelligence (Lanil-los et al.,…”
Background
To be or not to be: The active inference of suicide
Krupnik, V., & Danilova, N. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105531 · Cited by 6 · see citing works
From broken models to treatment selection: Active inference as a tool to guide clinical research and practice
Kirchner, L., Eckert, A.-L., & Berg, M. (2022) · Clinical Psychology in Europe · doi:10.32872/cpe.9697 · Cited by 6 · see citing works
Editorial: Visual mismatch negativity (vMMN): A unique tool in investigating automatic processing
Astikainen, P., Kreegipuu, K., & Czigler, I. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.1056208 · Cited by 6 · see citing works
Intensity dependence of auditory evoked potentials distinguish participants with unmedicated depression from non‐depressed controls
Kangas, E. S., Li, X., Vuoriainen, E., Lindeman, S. M., & Astikainen, P. (2024) · European Journal of Neuroscience · doi:10.1111/ejn.16569 · Cited by 5 · see citing works
A systematic empirical comparison of active inference and reinforcement learning models in accounting for decision-making under uncertainty
Chou, K.-P., Hakimi, N., Hsu, T., & Smith, R. (2026) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2026.103011 · Cited by 5 · see citing works
Perceptual Awareness and Its Relationship with Consciousness: Hints from Perceptual Multistability
Saracini, C. (2022) · NeuroSci · doi:10.3390/neurosci3040039 · Cited by 5 · see citing works
Predictive processing and body representation
Gadsby, S., & Hohwy, J. (2022) · Routledge eBooks · doi:10.4324/9780429321542-16 · Cited by 5 · see citing works
Preserved use of prior information under time constraints: an EEG study of action anticipation in expert athletes
Lu, Y., Huang, Y., Wang, D., Li, D., & Luan, M. (2025) · Cognitive Research Principles and Implications · doi:10.1186/s41235-025-00685-8 · Cited by 4 · see citing works
Individual prediction tendencies do not generalize across modalities
Schubert, J., Suess, N., & Weisz, N. (2023) · Psychophysiology · doi:10.1111/psyp.14435 · Cited by 3 · see citing works
Reduced stereotypicality and spared use of facial expression predictions for social evaluation in autism
Robles, M., Ramos-Grille, I., Amaia Hervas, G., Duran‐Tauleria, E., Galiano-Landeira, J., Wormwood, J. B., Falter‐Wagner, C. M., & Chanes, L. (2024) · International Journal of Clinical and Health Psychology · doi:10.1016/j.ijchp.2024.100440 · Cited by 4 · see citing works
Distinct alterations in probabilistic reversal learning across at-risk mental state, first episode psychosis and persistent schizophrenia
Griffin, J. D., Diederen, K. M. J., Haarsma, J., Barnham, I. J., Cook, B., Fernández-Egea, E., Williamson, S., van Sprang, E. D., Raphaël Gaillard, B., Vinckier, F., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-68004-7 · Cited by 4 · see citing works
Atypical pupil-linked arousal induced by low-risk probabilistic choices, and intolerance of uncertainty in adults with ASD
Pultsina, K. I., Stroganova, T. A., Kozunova, G. L., Prokofyev, A. O., Miasnikova, A. S., Rytikova, A. M., & Chernyshev, B. V. (2024) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-024-01227-3 · Cited by 4 · see citing works
Relationships and representations of brain structures, connectivity, dynamics and functions
Schmitt, O. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111332 · Cited by 4 · see citing works
A Hitchhiker’s Guide to Translation – Ideas for Fostering and Disseminating (Clinical) Translational Psychology
Berg, M., Suchotzki, K., Zimmermann, J., Merz, C. J., Szota, K., Brandt, H., Hartwigsen, G., Lincoln, T. M., Mokros, A., Gade, M., et al. (2025) · European Psychologist · doi:10.1027/1016-9040/a000555 · Cited by 4 · see citing works
Why Human Prejudice is so Persistent: A Predictive Coding Analysis
Hung, T.-W. (2023) · Social Epistemology · doi:10.1080/02691728.2023.2237942 · Cited by 4 · see citing works
Application of neuroscience and cognitive studies in psychology
Jamalpour, Z., Khodarahmi, P., Jamalpour, H., & Kuzhilnaya, E. (2024) · Cadernos de Educação Tecnologia e Sociedade · doi:10.14571/brajets.v17.n1.351-359 · Cited by 4 · see citing works
A dual-receptor model of serotonergic psychedelicsPreprint
Juliani, A., Chelu, V., Graesser, L., & Safron, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.12.589282 · Cited by 4 · see citing works
Qualia as query act, the phenomenology of predictive error coding
Harris, H. W. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1531269 · Cited by 3 · see citing works
Are mental dysfunctions autonomous from brain dysfunctions? A perspective from the personal/subpersonal distinction
Jurjako, M. (2024) · Discover Mental Health · doi:10.1007/s44192-024-00117-x · Cited by 4 · see citing works
Computational Approaches for Uncovering Interoceptive Mechanisms in Psychiatric Disorders and Their Biological Basis
Mehta, M., Paulus, M. P., & Smith, R. S. (2025) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_572 · Cited by 3 · see citing works
Active inference as a model of collision avoidance behavior in human drivers
Schumann, J. F., Engström, J., Johnson, L., O’Kelly, M., Messias, J. V., Kober, J., & Zgonnikov, A. (2026) · Nature Communications · doi:10.1038/s41467-026-73345-0 · Cited by 5 · see citing works
Perceptual Dysfunction in Eating Disorders
Reilly, E. E., Brown, T. A., & Frank, G. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_470 · Cited by 3 · see citing works
Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models
Nehrer, S. W., Laursen, J. E., Heins, C., Friston, K. J., Mathys, C. D., & Waade, P. T. (2025) · Entropy · doi:10.3390/e27010062 · Cited by 3 · see citing works
Towards naturalized phenomenology: Dynamics of space–time clouds and power law of working memory
Lubashevsky, I., & Lubashevskiy, V. (2025) · Cognitive Systems Research · doi:10.1016/j.cogsys.2025.101374 · Cited by 3 · see citing works

Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders
Shaw, A. D., Sumner, R. L., & Berndt, L. C. S. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1713833 · Cited by 9 · see citing works
Predictions, perception, and a sense of self
Picard, F., & Friston, K. J. (2014) · Neurology · doi:10.1212/wnl.0000000000000798 · Cited by 118 · see citing works
Predictive coding in psychopathology: mechanistic model or metaphorical re-description?
Alotaibi, A. S. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1743028 · Cited by 3 · see citing works
Towards an Empirically Grounded Predictive Coding Account of Action Understanding
Urgen, B. A., & Miller, L. E. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0144-15.2015 · Cited by 20 · see citing works
Computational psychiatry: the brain as a phantastic organ
Friston, K. J., Stephan, K. E., Montague, R., & Dolan, R. J. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70275-5 · Cited by 531 · see citing works

Authors: Smith, R., Badcock, P., & Friston, K. J.
Affiliations: Laureate Institute for Brain Research (US)
The University of Melbourne (AU)
Wellcome Centre for Human Neuroimaging (GB)
Corresponding author: Smith, Ryan
ORCID: Smith, Ryan
Badcock, Paul
Friston, Karl J.
Venue: Psychiatry and Clinical Neurosciences
Publisher: Wiley
Published: 2020-09-25
Volume: 75
Issue: 1
Pages: 3-13
ISSN: 1323-1316 (ISSN-L)
1440-1819
DOI: 10.1111/pcn.13138
OpenAlex: W3082730045
Cited by: 174
References: 116
Citations by year: 2026: 26 · 2025: 29 · 2024: 34 · 2023: 39 · 2022: 33 · 2021: 13
FWCI: 2.87
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Psychiatry and Clinical Neurosciences (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Minerva Access (University of Melbourne) (submittedVersion)
Funders: William K. Warren Foundation
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: predictive processing, active inference, predictive coding, computational psychiatry, neural circuit architecture, mental illness, neurological disorders
MeSH terms: Mental Disorders, Humans, Models, Theoretical, Neurosciences, Computational Biology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Recent advances in the application of predictive coding and active inference models within clinical neuroscience

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks