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Programs as causal models: speculations on mental programs and mental representation.

Nick Chater1, Mike Oaksford

  • 1Behavioural Sciences Group, Warwick Business School, University of Warwick, UK. nick.chater@wbs.ac.uk

Cognitive Science
|July 17, 2013
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Summary
This summary is machine-generated.

This study explores how counterfactual reasoning and causality are fundamental to intelligence. It proposes that mental programs possess a causal structure, offering insights into mental representations and external world modeling in cognitive science.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Philosophy of Mind

Background:

  • Judea Pearl's work emphasizes causality and counterfactuals as key to intelligence.
  • Current cognitive science explores mental programs, algorithms, data structures, and representation.

Purpose of the Study:

  • To connect Judea Pearl's framework of causality with cognitive science.
  • To investigate the causal structure of mental programs and representations.
  • To explore the role of causal models in perception, cognition, and language.

Main Methods:

  • Conceptual analysis linking Judea Pearl's causality framework to cognitive science.
  • Argumentation based on the counterfactual-supporting nature of computational programs.
  • Theoretical exploration of causal models in mental processes.

Main Results:

  • Mental programs, comprising algorithms and data structures, exhibit a causal (counterfactual-supporting) structure.
  • Counterfactuals derived from these programs can elucidate the nature of mental representations.
  • Computational programs can serve as causal models of the external world.

Conclusions:

  • Causality and counterfactuals are integral to understanding natural and artificial intelligence.
  • Mental representations and external world models in cognition are underpinned by causal structures.
  • The proposed framework offers a new perspective on perception, cognition, and language processing.