David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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British Journal for the Philosophy of Science 63 (3):697-723 (2012)
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 (  ). 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
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References found in this work BETA
József Fiser, Pietro Berkes, Gergő Orbán & Máté Lengyel (2010). Statistically Optimal Perception and Learning: From Behavior to Neural Representations: Perceptual Learning, Motor Learning, and Automaticity. Trends in Cognitive Sciences 14 (3):119.
Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
Christopher Hitchcock & Elliott Sober (2004). Prediction Versus Accommodation and the Risk of Overfitting. British Journal for the Philosophy of Science 55 (1):1-34.
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Citations of this work BETA
Kenneth Williford (2013). Husserl's Hyletic Data and Phenomenal Consciousness. Phenomenology and the Cognitive Sciences 12 (3):501-519.
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