NEURAL MECHANISMS ONLINE

21 February 2020
15-17 Greenwhich Mean Time
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Marcin MIŁKOWSKI* & Piotr LITWIN*,**

* Polish Academy of Science, ** University of Warsav)

Testing Predictive Processing: lessons from the mechanistic perspective

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Abstract

The predictive processing theory of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. But it is somewhat difficult to say how to test this theory empirically and whether it is even possible at all. In this paper, we argue that principles, general theories and particular models driven by PP can be only partially tested or falsified. In some cases, there is no sufficient detail to make falsification possible, and when details are present, the models, theories and principles seem often implausible or outright false. Moreover, if one assumes that proper explanations should be mechanistic, several important lessons for PP can be drawn. As we will argue, current research practice is nonetheless far from the normative ideals for mechanistic explanations and cognitive
modeling in general.

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7 February, Ruth MILLIKAN [WEBINAR]

1/31/2020

7 February 2020
15-17 Greenwhich Mean Time
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Ruth MILLIKAN

(University of Connecticut)

Mental Representation made simple

Abstract

Contrary to current rumors that there is something suspicious about the notion of mental representation, I am persuaded that the description of “intentional icons” and of “representations” first presented in my Language, Thought and Other Biological Categories (1984) captures a central and also a remarkably simple causal-explanatory principle that is involved in the workings of perception, cognition and language. So I am going to return to this description, highlighting its outlines to bring out its simplicity and also, I hope, the obviousness and innocuous nature of this principle. I will add a few words about "intensionality" and why it is irrelevant to the naturalization of mental representation.

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24 January, Frances EGAN [WEBINAR]

1/13/2020

24 January 2020
15-17 Greenwhich Mean Time
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Frances EGAN

(Rutgers University)

The role of Representation in Computational Cognitive Models

Abstract

Much of cognitive neuroscience construes cognitive capacities as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the world. Neurons are often said to represent elements of their receptive fields. Despite the widespread use of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. Is representational talk to be taken literally? Is it just a useful fiction? In this talk I sketch an account of the nature and function of representation in computational cognitive models that rejects both of these views. I call it a deflationary account.

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10 January, Manolo MARTINEZ [WEBINAR]

12/30/2019

10 January 2020
15-17 Greenwhich Mean Time
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Manolo MARTINEZ

(University of Barcelona, Spain)

Information Theory is a Formal Theory of Representation

Abstract

Information theory is a formal theory of representation. All signals in an information-processing pipeline are, in a minimal sense, representations. Core cases of representation are those for which, furthermore, a description in information-theoretic terms is particularly illuminating---that is, those that undergo substantial source- or channel-coding.

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12 July, Beate KRICKEL & Matej KOHAR [WEBINAR]

7/4/2019

12 July 2019
15-17 - Greenwhich Mean Time
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Beate KRICKEL & Matej KOHAR

(Ruhr University Bochum)

Compare and Contrast:
how to assess the completeness of mechanistic explanation