Matteo Colombo
Tilburg University
According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms
Keywords No keywords specified (fix it)
Categories (categorize this paper)
DOI 10.1093/bjps/axr043
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy

Upload a copy of this paper     Check publisher's policy     Papers currently archived: 68,975
Through your library

References found in this work BETA

Vision.David Marr - 1982 - W. H. Freeman.
Thinking About Mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.

View all 18 references / Add more references

Citations of this work BETA

A Deflationary Account of Mental Representation.Frances Egan - 2020 - In Joulia Smortchkova, Krzysztof Dolega & Tobias Schlicht (eds.), Mental Representations. New York, USA: Oxford University Press.
Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
Predictive Processing and the Representation Wars.Daniel Williams - 2018 - Minds and Machines 28 (1):141-172.

View all 31 citations / Add more citations

Similar books and articles

Bayesian Models and Simulations in Cognitive Science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
When Can Non‐Commutative Statistical Inference Be Bayesian?Miklós Rédei - 1992 - International Studies in the Philosophy of Science 6 (2):129-132.
Understanding Bayesian Procedures.Robert A. M. Gregson - 1998 - Behavioral and Brain Sciences 21 (2):201-202.
Bayes and Beyond.Geoffrey Hellman - 1997 - Philosophy of Science 64 (2):191-221.
Bayesian Probability.Patrick Maher - 2010 - Synthese 172 (1):119 - 127.
Ideal Observers, Real Observers, and the Return of Elvis.Ronald A. Rensink - 1996 - In David Knill & Whitman Richards (eds.), Perception as Bayesian Inference. Cambridge University Press. pp. 451-455.
Bayes, Hume, and Miracles.John Earman - 1993 - Faith and Philosophy 10 (3):293-310.
Hallucinations and Perceptual Inference.Karl J. Friston - 2005 - Behavioral and Brain Sciences 28 (6):764-766.


Added to PP index

Total views
387 ( #25,415 of 2,498,264 )

Recent downloads (6 months)
14 ( #56,069 of 2,498,264 )

How can I increase my downloads?


My notes