Too Rational: How Predictive Coding’s Success Risks Harming the Mentally Disordered and Ill


  • Lee Elkin Erasmus University Rotterdam
  • Karolina Wiśniowska Institute of Philosophy/INCET


The so-called predictive coding or predictive processing theory of mind has attracted significant attention in the brain and behavioral sciences over the past couple of decades. We aim to discuss an important ethical implication of the theory’s success. As predictive coding has become influential in the study of mental disorder and illness, particularly on autism spectrum disorder (ASD) and schizophrenia, we point out a significant risk of further harming an already stigmatized population. Specifically, because predictive coding is undergirded by Bayesian inference, and Bayesian inference is often thought to imply ‘rationality’, the cognitive framework engenders a risk of strengthening existing negative attitudes towards individuals having mental disorders and illnesses by associating such individuals with also having ‘irrational brains.’


predictive coding, Bayesian brain, ASD, schizophrenia, ethics


Download data is not yet available.


Metrics Loading ...

Author Biographies

Lee Elkin, Erasmus University Rotterdam

Erasmus School of Philosophy/EIPE, Erasmus University Rotterdam, Burg. Oudlaan 50 (Bayle 5-69), 3062 PA Rotterdam, The Netherlands

Karolina Wiśniowska, Institute of Philosophy/INCET

Institute of Philosophy/INCET, Jagiellonian University, ul. Grodzka 52 31-044 Kraków, Poland


Andersen, ML. The many and varied social constructions of intelligence. In TR Sarbin and JI Kitsuse (eds.), Constructing the Social. Sage Publications, 1994: 119-38.

Baetu I, Barberia I, Murphy RA and Baker AG. (2011). Maybe this old dinosaur isn’t extinct: What does Bayesian modeling add to associationism?. Behavioral and Brain Sciences 2011; 34: 190-191.

Baron-Cohen S, Ring HA, Bullmore ET, Wheelwright S, Ashwin C and Williams SCR. The amygdala theory of autism. Neuroscience & Biobehavioral Reviews 2000; 24: 355-364.

Bharadwaj P, Pai MM and Suziedelyte A. Mental health stigma. Economics Letters 2017; 159: 57-60.

Chase A. The Legacy of Malthus: The Social Costs of the New Scientific Racism. Knopf, 1977.

Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences 2013; 36: 181-204.

Clark A. Neuroethics, the predictive brain, and hallucinating neural networks. The Neuroethics Blog, 2017. <>

Colombo M, Elkin L and Hartmann S. Being realist about Bayes, and the predictive processing theory of mind. The British Journal for the Philosophy of Science 2021; 72: 185-220.

Colombo M and Wright C. Explanatory pluralism: An unrewarding prediction error for free energy theorists. Brain and Cognition 2017; 112: 3-12.

Corrigan PW, Markowitz FE and Watson AC. Structural levels of mental illness stigma and discrimination. Schizophrenia Bulletin 2004; 30: 481-491.

De Finetti B. Theory of Probability: A Critical Introductory Treatment. Wiley, 1974.

Fancher RE. The Intelligence Men: Makers of the IQ Controversy. W.W. Norton and Company, 1985.

Friston K. The free-energy principle: a rough guide to the brain? Trends in Cognitive Sciences 2009; 13: 293-301.

Friston K. The free-energy principle: a unified brain theory? Nature Reviews Neuroscience 2010; 11: 127-138.

Glymour C. Osiander’s psychology. Behavioral and Brain Sciences 2011; 34: 199-200.

Hohwy J. The Predictive Mind. Oxford University Press, 2013.

Hohwy J, Roepstorff A and Friston, K. Predictive coding explains binocular rivalry: An epistemological review. Cognition 2008; 108: 687-701.

Holmes OW and Supreme Court of The United States. U.S. Reports: Buck v. Bell, 274 U.S. 200, 1927. [Periodical] Retrieved from the Library of Congress.

Horga G, Schatz KC, Abi-Dargham A and Peterson BS. Deficits in predictive coding underlie hallucinations in schizophrenia. Journal of Neuroscience, 2014; 34: 8072-82.

Hunt E. Human Intelligence. Cambridge University Press, 2010.

Kahneman D. Thinking, Fast and Slow. Macmillan, 2011.

Knill DC and Pouget A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences 2004; 27: 712-719.

Kröner HP. From eugenics to genetic screening. In The Ethics of Genetic Screening. Springer, 1999: 131-145.

Lombardo PA. Three generations, no imbeciles: New light on Buck v. Bell. New York University Law Review 1985; 60: 30-62.

Mensh E and Mensh H. The IQ Mythology: Class, Race, Gender, and Inequality. SIU Press, 1991.

Mill JS. On Liberty. John W. Parker and Son, 1859.

National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. U.S. Department of Health and Human Services, 1978.

Oaksford M and Chater N. Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Oxford University Press, 2007.

Parcesepe AM and Cabassa LJ. Public stigma of mental illness in the United States: a systematic literature review. Administration and Policy in Mental Health and Mental Health Services Research 2013; 40: 384–399.

Pellicano E and Burr D. When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends in Cognitive Sciences 2012; 16: 504-510.

Powers AR, Mathys C and Corlett PR. Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science 2017; 357: 596-600.

Ramsey FP. Truth and probability. In The Foundations of Mathematics and Other Logical Essays. Routledge, 1926.

Rescorla M. A realist perspective on Bayesian cognitive science. In A Nes and T Chan (eds.), Inference and Consciousness. Routledge, 2020.

Scarr S. From evolution to Larry P., or what shall we do about IQ tests? Intelligence 1978; 2: 325-342.

Sterzer P, Adams RA, Fletcher P, Frith C, Lawrie SM, Muckli L and Corlett PR. The predictive coding account of psychosis. Biological Psychiatry 2018; 84: 634-643.

Tenenbaum JB, Griffiths TL and Kemp C. Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 2006; 10: 309-318.

Van de Cruys S, Evers K, Van der Hallen R, Van Eylen L, Boets B, de-Wit L and Wagemans J.Precise minds in uncertain worlds: predictive coding in autism. Psychological Review 2014; 121: 649-675.

Yon D, Heyes C and Press C. Beliefs and desires in the predictive brain. Nature Communications 2020; 11: 1-4.

Zagorsky JL. Do you have to be smart to be rich? The impact of IQ on wealth, income and financial distress. Intelligence 2007; 35: 489-501.



How to Cite

Elkin, L., & Wiśniowska, K. (2022). Too Rational: How Predictive Coding’s Success Risks Harming the Mentally Disordered and Ill. Journal of NeuroPhilosophy, 1(1). Retrieved from



Opinion and Perspectives