Call for Papers: Predictive Coding and Psychopathology
Deadline: March 23, 2018
- Philip Corlett
- Aprajita Mohanty
- Angus MacDonald III
Purpose and Goals
We invite papers on the application of predictive processing to abnormal psychology.
Responsive proposals include theoretical and empirical accounts of key features of perception, action, belief and affect that are pertinent to abnormal psychology.
This can include phenomena in which context or prior knowledge are inaccurately used to draw inferences about internal states or external stimuli or prediction errors are not used effectively to modify priors; for example, compartmentalization of beliefs, multisensory integration, embodiment, corollary discharge, intention, agency and bodily ownership.
We are particularly interested in treatments of more than one illness or symptom (comparing and contrasting aspects of predictive processing that have been implicated in each).
Please send an abstract (200 words or fewer) of a proposed submission by March 23, 2018 via email. Full submissions will be due September 30, 2018.
Do not send a completed manuscript without approval of the abstract.
All submissions for the special section will go through the normal peer-review process, with no guarantee of acceptance.
All submissions must comply with APA policies, including certification of compliance with APA ethical principles for research, the prohibition of multiple submissions and duplicate publication, authors' obligation to retain raw data, and other requirements for submission to the Journal of Abnormal Psychology® as noted on the submission site.
Predictive coding has emerged as a popular and powerful account of how psychopathological symptoms might relate to underlying psychology and neurobiology (Friston, 2005). As a formal account, predictive coding is perhaps uniquely placed to provide insights into abnormal psychology.
Examples include the aberrant prediction error theory of delusions (Corlett, Honey, & Fletcher, 2007), the weak prior account of autism (Pellicano & Burr, 2012) and predictive coding explanations of depression (Chekroud, 2015) and anxiety (Sussman et al., 2016).
In brief, predictive processing theory suggests that the brain is an inference machine continuously updating its models of the world, and of itself (Apps & Tsakiris, 2014) distinct from other agents (Friston & Frith, 2015; Kilner, Friston, & Frith, 2007).
Within this framework, we arrive at decisions about the external world based on both current sensory information (likelihood) and prior knowledge (priors) concerning the environment.
Aberrant precision of sensory data or priors have psychopathological consequences (Adams, Stephan, Brown, Frith, & Friston, 2013), for example errors in predictive inferences may cause enhanced sensory precision and thus agency misattributions such as hallucinations and passivity symptoms (Thakkar, Diwadkar, & Rolfs, 2017).
Over time, some of the same mechanisms have been proposed for somewhat disparate, perhaps orthogonal, illnesses.
For example, weak priors, and relatively enhanced sensory precision have been suggested as a mechanism for delusion formation in schizophrenia and for the symptoms of autism.
These conditions share commonalities but also key differences which render identical mechanisms unlikely (van Schalkwyk, Volkmar, & Corlett, 2017).
Likewise, data have suggested perpendicular mechanisms for symptoms that commonly co-occur, for example hallucinations have been related to strong priors (Powers, Mathys, & Corlett, 2017) and delusions to weak (Schmack et al., 2013; Schmack, Rothkirch, Priller, & Sterzer, 2017; Stuke, Stuke, Weilnhammer, & Schmack, 2017), yet people often experience both symptoms simultaneously.
Finally, some of the cardinal aspects of psychotic symptoms — like delusion fixity, delusion contents and affective salience have yet to be pulled completely into this explanatory framework. Predictive processing theory has also inspired work on affective symptoms.
This special section on predictive coding theory is designed to take stock of its empirical base, its explanatory shortcomings and the features that warrant further scrutiny.
- Adams, R. A., Stephan, K. E., Brown, H. R., Frith, C. D., & Friston, K. J. (2013). The computational anatomy of psychosis. Frontiers in psychiatry, 4, 47. doi: 10.3389/fpsyt.2013.00047
- Apps, M. A., & Tsakiris, M. (2014). The free-energy self: a predictive coding account of self-recognition. [Research Support, Non-U.S. Gov't Review]. Neurosci Biobehav Rev, 41, 85-97. doi: 10.1016/j.neubiorev.2013.01.029
- Chekroud, A. M. (2015). Unifying treatments for depression: an application of the Free Energy Principle. Front Psychol, 6, 153. doi: 10.3389/fpsyg.2015.00153
- Corlett, P. R., Honey, G. D., & Fletcher, P. C. (2007). From prediction error to psychosis: ketamine as a pharmacological model of delusions. J Psychopharmacol, 21(3), 238-252.
- Friston, K. (2005). A theory of cortical responses. Philos Trans R Soc Lond B Biol Sci, 360(1456), 815-836.
- Friston, K., & Frith, C. (2015). A Duet for one. [Research Support, Non-U.S. Gov't]. Conscious Cogn, 36, 390-405. doi: 10.1016/j.concog.2014.12.003
- Kilner, J. M., Friston, K. J., & Frith, C. D. (2007). The mirror-neuron system: a Bayesian perspective. Neuroreport, 18(6), 619-623.
- Pellicano, E., & Burr, D. (2012). When the world becomes 'too real': a Bayesian explanation of autistic perception. [Research Support, Non-U.S. Gov't]. Trends Cogn Sci, 16(10), 504-510. doi: 10.1016/j.tics.2012.08.009
- Powers, A. R., Mathys, C., & Corlett, P. R. (2017). Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors. Science, 357(6351), 596-600. doi: 10.1126/science.aan3458
- Schmack, K., Gomez-Carrillo de Castro, A., Rothkirch, M., Sekutowicz, M., Rossler, H., Haynes, J. D., . . . Sterzer, P. (2013). Delusions and the role of beliefs in perceptual inference. J Neurosci, 33(34), 13701-13712. doi: 10.1523/JNEUROSCI.1778-13.2013
- Schmack, K., Rothkirch, M., Priller, J., & Sterzer, P. (2017). Enhanced predictive signalling in schizophrenia. Hum Brain Mapp, 38(4), 1767-1779. doi: 10.1002/hbm.23480
- Stuke, H., Stuke, H., Weilnhammer, V. A., & Schmack, K. (2017). Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning. PLoS Comput Biol, 13(1), e1005328. doi: 10.1371/journal.pcbi.1005328
- Sussman, T.J., *Szekely, A., Proudfit, G.H., Mohanty, A. (2015). It's all in the anticipation: How perception of threat is enhanced in anxiety. Emotion, doi: 10.1037/emo0000098.
- Thakkar, K. N., Diwadkar, V. A., & Rolfs, M. (2017). Oculomotor Prediction: A Window into the Psychotic Mind. Trends Cogn Sci, 21(5), 344-356. doi: 10.1016/j.tics.2017.02.001
- van Schalkwyk, G. I., Volkmar, F. R., & Corlett, P. R. (2017). A Predictive Coding Account of Psychotic Symptoms in Autism Spectrum Disorder. J Autism Dev Disord, 47(5), 1323-1340. doi: 10.1007/s10803-017-3065-9