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Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

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  • Chris L. Baker

    (Massachusetts Institute of Technology)

  • Julian Jara-Ettinger

    (Massachusetts Institute of Technology)

  • Rebecca Saxe

    (Massachusetts Institute of Technology)

  • Joshua B. Tenenbaum

    (Massachusetts Institute of Technology)

Abstract

Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model.

Suggested Citation

  • Chris L. Baker & Julian Jara-Ettinger & Rebecca Saxe & Joshua B. Tenenbaum, 2017. "Rational quantitative attribution of beliefs, desires and percepts in human mentalizing," Nature Human Behaviour, Nature, vol. 1(4), pages 1-10, April.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:4:d:10.1038_s41562-017-0064
    DOI: 10.1038/s41562-017-0064
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    Cited by:

    1. Srishti Goel & Julian Jara-Ettinger & Desmond C. Ong & Maria Gendron, 2024. "Face and context integration in emotion inference is limited and variable across categories and individuals," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Jian-Qiao Zhu & Joshua C. Peterson & Benjamin Enke & Thomas L. Griffiths, 2024. "Capturing the Complexity of Human Strategic Decision-Making with Machine Learning," Papers 2408.07865, arXiv.org.
    3. Nihan TOMRİS KÜÇÜN & Sezen GÜNGÖR, 2020. "Victim Identification, Framing Heuristic And Stress Effects On The Donation Decision," Prizren Social Science Journal, SHIKS, vol. 4(2), pages 22-29, August.
    4. Andreas Hula & Iris Vilares & Terry Lohrenz & Peter Dayan & P Read Montague, 2018. "A model of risk and mental state shifts during social interaction," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-20, February.
    5. Amanda Royka & Annie Chen & Rosie Aboody & Tomas Huanca & Julian Jara-Ettinger, 2022. "People infer communicative action through an expectation for efficient communication," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Maciel, Marcelo V. & Martins, André C.R., 2020. "Ideologically motivated biases in a multiple issues opinion model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    7. Sam Ereira & Raymond J Dolan & Zeb Kurth-Nelson, 2018. "Agent-specific learning signals for self–other distinction during mentalising," PLOS Biology, Public Library of Science, vol. 16(4), pages 1-32, April.
    8. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
    9. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.

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