Date of Award

8-2022

Degree Type

Thesis

Degree Name

Master of Arts (MA)

Department

Philosophy

First Advisor

Daniel Weiskopf

Second Advisor

Andrea Scarantino

Abstract

In this thesis, I defend the explanatory force of algorithmic information processing models in cognitive neuroscience. I describe the algorithmic approach to cognitive explanation, its relation to Shea’s theory of cognitive representation, and challenges stemming from neuronal population analysis and dimensionality reduction. I then consider competing interpretations of some neuroscientific data that have been central to the debate. I argue in favor of a sequenced computational explanation of the phenomenon, contra Burnston. Finally, I argue that insights from theoretical neuroscience allow us to understand why dimensionality reduction does not militate against localizing distinct contents to distinct components of functioning brain systems.

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