4.7 Article

Animal thought exceeds language-of-thought

Journal

BEHAVIORAL AND BRAIN SCIENCES
Volume 46, Issue -, Pages -

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S0140525X23002017

Keywords

animal cognition; automaticity; cognitivearchitecture; deep learning; dual-process theories; implicit attitudes; infant cognition; language-of-thought; object files; visual cognition

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This paper surveys evidence from various fields of psychology and concludes that the language-of-thought (LoT) is a common representational format in biological cognition. The LoT format is characterized by properties such as discrete constituents, role-filler independence, predicate-argument structure, logical operators, inferential promiscuity, and abstract content. Researchers should take the explanatory power of LoT-based architectures seriously.
Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought(LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures,including iconic and associative structures as well as deep-neural-network-like architectures.However, as computational/representational approaches to the mind continue to advance,classical compositional symbolic structures-that is, LoTs-only prove more flexible andwell-supported over time.

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