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Cited by 34Child and Animal Learning DevelopmentSocial Robot Interaction and HRIAction Observation and Synchronization

Inferring the intentional states of autonomous virtual agents

Pantelis, Peter C., Baker, Chris L., Cholewiak, Steven A., Sanik, Kevin, Weinstein, Ari, Wu, Chia-Chien, Tenenbaum, Joshua B., Feldman, Jacob·2014·Cognition
DOI 10.1016/j.cognition.2013.11.011
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Abstract

Inferring the mental states of other agents, including their goals and intentions, is a central problem in cognition. A critical aspect of this problem is that one cannot observe mental states directly, but must infer them from observable actions. To study the computational mechanisms underlying this inference, we created a two-dimensional virtual environment populated by autonomous agents with independent cognitive architectures. These agents navigate the environment, collecting "food" and interacting with one another. The agents' behavior is modulated by a small number of distinct goal states: attacking, exploring, fleeing, and gathering food. We studied subjects' ability to detect and classify the agents' continually changing goal states on the basis of their motions and interactions. Although the programmed ground truth goal state is not directly observable, subjects' responses showed both high validity (correlation with this ground truth) and high reliability (correlation with one another). We present a Bayesian model of the inference of goal states, and find that it accounts for subjects' responses better than alternative models. Although the model is fit to the actual programmed states of the agents, and not to subjects' responses, its output actually conforms better to subjects' responses than to the ground truth goal state of the agents.

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  • Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language.

    “Bayesian models of cognition have primarily flourished as explanations of the complex dependency of behavior on the environment in domains including intuitive physics, causal learning, perception, motor control, and language [1-10]”

    From Bayesian Brains without Probabilities (2016) · cites this paper as [3]

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Authors
Pantelis, P. C., Baker, C. L., Cholewiak, S. A., Sanik, K., Weinstein, A., Wu, C.-C., Tenenbaum, J. B., & Feldman, J.
Affiliations
  • Rutgers, The State University of New Jersey (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Pantelis, Peter C.
ORCID
Venue
Cognition
Publisher
Elsevier BV
Published
2014-03
Volume
130
Issue
3
Pages
360-379
ISSN
0010-0277 (ISSN-L)
DOI
10.1016/j.cognition.2013.11.011
OpenAlex
W2032255178
Cited by
34
References
56
Citations by year
2025: 3 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 1 · 2019: 4 · 2018: 8 · 2017: 5 · 2016: 1 · 2015: 2 · 2014: 2
FWCI
7.82
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Cognition (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
Keywords
goal inference, intention inference, mental state inference, Bayesian model, social cognition, agent navigation
MeSH terms
Cognition, Computer Simulation, Female, Humans, Male, Comprehension, Intention, Young Adult, Theory of Mind

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