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Related Experiment Video

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Introducing tomsup: Theory of mind simulations using Python.

Peter T Waade1,2, Kenneth C Enevoldsen3,4,5, Arnault-Quentin Vermillet6

  • 1School of Communication and Culture, Aarhus University, Aarhus, Denmark. peter@waade.net.

Behavior Research Methods
|August 11, 2022
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Summary
This summary is machine-generated.

This study introduces tomsup, a Python package for computational modeling of theory of mind (ToM) simulations. It enables researchers to investigate and compare ToM mechanisms, enhancing social cognition research.

Keywords:
Agent-based simulationComputational modelingGame theoryTheory of mindVariational Bayesian inference

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Psychology

Background:

  • Theory of Mind (ToM) is vital for social cognition, but verbal theories are often underspecified and incompatible.
  • Current ToM measures can be unreliable, with tasks sometimes failing to assess mental state representation.
  • Existing computational models of ToM lack broad accessibility for research applications.

Purpose of the Study:

  • To introduce tomsup, a Python package designed as a computational ecosystem for ToM simulations.
  • To facilitate the investigation and comparison of various computational models of ToM mechanisms.
  • To provide tools for generating experimental stimuli and analyzing data from ToM simulations.

Main Methods:

  • Development of the tomsup Python package, offering a computational framework for ToM research.
  • Implementation of the variational recursive Bayesian k-ToM model and simpler non-recursive decision models.
  • Provision of tutorials for simulating agents, generating stimuli, analyzing data, and specifying custom models.

Main Results:

  • The tomsup package provides accessible implementations of key ToM models, including k-ToM.
  • Users can simulate agents, generate experimental stimuli, and analyze resulting data within the package.
  • The package supports the specification of novel ToM and heuristic cognitive models.

Conclusions:

  • The tomsup package addresses challenges in ToM research by offering a unified computational environment.
  • It enables rigorous comparison of different ToM mechanisms and facilitates the development of new models.
  • This tool aims to advance the scientific understanding of social-cognitive abilities and impairments.