Abstract
Determining others' motor competence is critical for action prediction and social decision making. One aspect of competence judgements involves assessing how costly a given action is for a particular agent (e.g., whether climbing 4 floors of stairs is a piece of cake or a tough physical exercise). Such information is not given away by the agents' physical appearance but can be inferred based on their behavior. Across two looking-time experiments, we show that 10-month-olds can infer and compare agent-specific costs of different actions. After being familiarized with agent A jumping over low obstacles and walking around high obstacles, and agent B jumping over both low and high obstacles, infants worked out that for B jumping bears little cost, while for A jumping high is more costly than detouring the obstacles by walking. Furthermore, they used this motor competence judgements to predict both agents' actions in a new environment. These findings suggest that basic building blocks competence evaluations are available in infancy and may be rooted in infants' action interpretation skills.
Original language | English |
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Pages (from-to) | 3339-3345 |
Number of pages | 7 |
Journal | Proceedings of the Annual Meeting of the Cognitive Science Society |
Volume | 44 |
Publication status | Published - 30 Jul 2022 |
Event | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada Duration: 27 Jul 2022 → 30 Jul 2022 |
Bibliographical note
Funding Information:We thank the families who participated in this research. We thank Dorottya Mészégető, Petra Kármán, Dorottya Kerschner, Maria Tóth and Ágnes Volein for research assistance. This research was funded by the European Research Council (Grant agreement #742231, PARTNERS).
Publisher Copyright:
© 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)
Keywords
- action interpretation
- competence
- infant cognition
- naïve utility calculus
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience