“Theory of Mind” (ToM; people’s ability to infer and use information about others’ mental states) varies across cultures. In four studies ( N = 881), including two preregistered replications, we show that social class predicts performance on ToM tasks. In Studies 1A and 1B, we provide new evidence for a relationship between social class and emotion perception: Higher-class individuals performed more poorly than their lower-class counterparts on the Reading the Mind in the Eyes Test, which has participants infer the emotional states of targets from images of their eyes. In Studies 2A and 2B, we provide the first evidence that social class predicts visual perspective taking: Higher-class individuals made more errors than lower-class individuals in the Director Task, which requires participants to assume the visual perspective of another person. Potential mechanisms linking social class to performance in different ToM domains, as well as implications for deficiency-centered perspectives on low social class, are discussed.
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Too Many Cooks: Bayesian Inference for Coordinating Multi‐Agent Collaboration
Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi‐agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high‐level plans (e.g., what sub‐task they should work on) and their low‐level actions (e.g., avoiding getting in each other's way). When matched with partners that act using the same algorithm, Bayesian Delegation outperforms alternatives. Bayesian Delegation is also a capable ad hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results argue for the centrality of ToM for successful decentralized multi‐agent collaboration.
more » « less- NSF-PAR ID:
- 10220850
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Topics in Cognitive Science
- Volume:
- 13
- Issue:
- 2
- ISSN:
- 1756-8757
- Page Range / eLocation ID:
- p. 414-432
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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