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Greatest surprise reduction semantics: an information theoretic solution to misrepresentation and disjunction

  • D. E. WeissglassEmail author
Article

Abstract

Causal theories of content, a popular family of approaches to defining the content of mental states, commonly run afoul of two related and serious problems that prevent them from providing an adequate theory of mental content—the misrepresentation problem and the disjunction problem. In this paper, I present a causal theory of content, built on information theoretic tools, that solves these problems and provides a viable model of mental content. This is the greatest surprise reduction theory of content, which identifies the content of a signal as the event the surprisal of which is most reduced by that signal. Conceptually, this amounts to the claim that the content of a signal is the event the probability of which has increased by the largest proportion, or the event that the signal makes the most less surprising to us. I develop the greatest surprise reduction theory of content in four stages. First, I introduce the general project of causal theories of content, and the challenges presented to this project by the misrepresentation and disjunction problems. Next, I review two recent and prominent causal theories of content and demonstrate the serious challenges faced by these approaches, both clarifying the need for a solution to the misrepresentation and disjunction problems and providing a conceptual background for the greatest surprise reduction theory. Then, I develop the greatest surprise reduction theory of content, demonstrate its ability to resolve the misrepresentation and disjunction problems, and explore some additional applications it may have. Finally, I conclude with a discussion of a particularly difficult challenge that remains to be addressed—the partition problem—and sketch a path to a potential solution.

Keywords

Intentionality Information semantics Psychosemantics Disjunction problem Misrepresentation Representational content 

Notes

Acknowledgements

This paper began as part of my dissertation, and I owe a great debt to the comments provided to me by my advisor—Jesse Prinz—and my committee members—Barbara Montero and David Papineau. I would also like to thank an anonymous reviewer at this journal whose comments enabled me to strengthen the paper a great deal.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.The Graduate Center, City University of New YorkNew YorkUnited States of America

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