Open AccessProceedings Article
Quantification and the language of thought
Charles Kemp
- Vol. 22, pp 943-951
TLDR
To support this proposal, behavioral results from a concept learning study inspired by the work of Shepard, Hovland and Jenkins are presented and it is shown that the language of thought allows first order quantification more readily than second-order quantification.Abstract:
Many researchers have suggested that the psychological complexity of a concept is related to the length of its representation in a language of thought. As yet, however, there are few concrete proposals about the nature of this language. This paper makes one such proposal: the language of thought allows first order quantification (quantification over objects) more readily than second-order quantification (quantification over features). To support this proposal we present behavioral results from a concept learning study inspired by the work of Shepard, Hovland and Jenkins.read more
Citations
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Journal ArticleDOI
The logical primitives of thought: Empirical foundations for compositional cognitive models.
TL;DR: This work shows how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments, and shows how specific LOT theories can be distinguished empirically.
Dissertation
Learning and the language of thought
TL;DR: An inductive statistical model is presented over a compositionally structured representation system, a language of thought (LOT) (Fodor, 1975), that formalizes an optimal Bayesian trade-off between representational complexity and fit to the observed data.
Journal Article
Review: George Boole, An Investigation of the Laws of Thought, on Which are Founded the Mathematical Theories of Logic and Probabilities
Journal Article
Inferring priors in compositional cognitive models.
Eric Bigelow,Steven Piantadosi +1 more
TL;DR: A data analysis technique is developed for a family of compositional “Language of Thought” (LOT) models which permits discovery of subjects’ prior probability of mental operations in this domain and reveals high correlations between model mean predictions and subject generalizations.
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Proceedings Article
Learning and using relational theories
TL;DR: It is proposed that intuitive theories are mentally represented in a logical language, and that the subjective complexity of a theory is determined by the length of its representation in this language.