Expecting and perceiving: (how) does the Bayesian brain hypothesis stand?

Bayesian cognitive scientists, in general, have no doctrinal commitment to the classical computational theory of mind implemented by a modular architecture. On a modular architecture, symbolic representations are computed in modules (Turing 1950, Newell and Simon 1972; Fodor 1983, Fodor and Pylyshyn 1988, Marr 1982). Very few scientists and philosophers today endorse the old Fodorian modularity, so today modular senses live on in hierarchical modules in neural networks. Recently, however, some arguments are construed to show that classic and non-classic cognitive models need not always be incompatible with each other. Strikingly it has been suggested that some of the modelling strategies used by Bayesians bear a resemblance to classic modularity in both computational and algorithm levels (Jenkin and Siegel; 2015; Piantadosi and Jacobs, 2016; Newen et al. 2017; Samuels 2019; Hohwy 2020).

The view of the brain as literally a Bayesian organ is now increasingly dominating the cognitive sciences. This special issue scrutinises whether Bayesian approaches to brain and cognition, most notably process theories, such as predictive coding/processing and the Bayesian brain hypothesis, support a view of the brain and cognitive function as modular. In so far as the Bayesian brain supports modularity, which notion of modularity does it capture? The classical, Fodorian notion, i.e. the mind as composed by a set of modular input systems and a central cognitive system, all systems with limited communication among them? Or do Bayesian approaches suggest a revised and perhaps weaker notion of modularity, as for example modular networks? Conversely, do these Bayesian frameworks speak against the idea that brain and cognitive function is modular and, if so, what might the consequences of this be for work in the sciences of brain and cognition, in general, and for perception as an expectation in particular.

In addition to addressing these broad scope issues and their implications, this special issue also seeks to examine specific and outstanding questions in the field for the notion of modularity in light of the Bayesian brain, which includes but are not limited to, the following: can one explain visual illusions in frameworks that draw a sharp distinction between cognition and perception? Is perception distinct from other forms of cognition, such as imagination or hallucination? Does perception communicate freely with higher forms of cognition, and what does ‘communication’ mean? What is the nature of this ‘communication’, is it informationally based where information refers to the way we use the concept in our labs? Is there a plausible argument to take a realist standpoint about inferential computations in the brain? Do Bayesian views of brain and cognition imply that there is no distinction at all between perception, cognition and action? Or might these views be entirely compatible with ‘standard’ modular accounts of perception?

This special issue aims to contribute to the philosophical programme of cognitive and computational models. We invite contributions addressing these questions about the mechanisms, architectures and dynamics of the brain and cognition, from interdisciplinary fields of philosophy, neuroscience, cognitive anthropology and theoretical biology.

We especially welcome proposals from scholars with an emphasis on empirical research and with backgrounds that are underrepresented in publishing and academia (including women, ethnic minority scientists, scientists with disabilities, researchers of the global south, and other underrepresented groups).


Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. San Francisco, CA: W.H. Freeman.

Fodor, J. A. (1983). The modularity of mind. MIT press.

Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition28(1-2), 3-71.

Hohwy, J. (2020). Self-supervision, normativity and the free energy principle. Synthese, 1-25.

Jenkin, Z., & Siegel, S. (2015). Cognitive penetrability: Modularity, epistemology, and ethics. Review of Philosophy and Psychology6(4), 531-545.

Newell, A., & Simon, H. A. (1972). Human problem solving (Vol. 104, No. 9). Englewood Cliffs, NJ: Prentice-Hall.

Newen, A., Marchi, F., & Brössel, P. (2017). Introduction—Cognitive penetration and predictive coding. Pushing the debate forward with the recent achievements of cognitive science. Consciousness and Cognition: An International Journal.

Piantadosi, S. T., & Jacobs, R. A. (2016). Four problems solved by the probabilistic language of thought. Current Directions in Psychological Science25(1), 54-59.

Samuels, R. (2019) Reductive explanation between psychology and neuroscience computational mind. In Sprevak, C. & Colombo M. The Routledge Handbook of the Computational Mind.

Turing, I. B. A. (1950). Computing machinery and intelligence-AM Turing. Mind59(236), 433.

Guest Editors:

Dr. Inês Hipólito, Humboldt-Universität zu Berlin﻿.

Dr. Michael Kirchhoff, University of Wollongong.


Submission portal open from: December 20, 2020

Submission deadline: April 20, 2021

Acceptance deadline: July 2021

Expected publication: 2021

Submission Instructions:

Authors to select “VSI: Expecting and perceiving” as the article type when submitting special issue paper in EM.

Kindly refer Guide for Authors for detailed guidelines:


As per the publication model, article collection („special issue“) papers once accepted will be included in the next available issue and get published. In ScienceDirect, they will be grouped under the special issue.