A Statistical Referential Theory of Content: Using Information Theory to Account for Misrepresentation

Abstract

A naturalistic scheme of primitive conceptual representations is proposed using the statistical measure of mutual information. It is argued that a concept represents, not the class of objects that caused its tokening, but the class of objects that is most likely to have caused it (had it been tokened), as specified by the statistical measure of mutual information. This solves the problem of misrepresentation which plagues causal accounts, by taking the representation relation to be determined via ordinal relationships between conditional probabilities. The scheme can deal with statistical biases and does not rely on arbitrary criteria. Implications for the theory of meaning and semantic content are addressed.

Cite this paper

@inproceedings{Usher2001ASR, title={A Statistical Referential Theory of Content: Using Information Theory to Account for Misrepresentation}, author={Marius Usher and Madeline Usher}, year={2001} }