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A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human–machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’.
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This content will become publicly available on February 11, 2026
Computational Thought Experiments for a More Rigorous Philosophy and Science of the Mind
We offer philosophical motivations for a method we call Vir- tual World Cognitive Science (VW CogSci), in which re- searchers use virtual embodied agents that are embedded in virtual worlds to explore questions in the field of Cognitive Science. We focus on questions about mental and linguistic representation and the ways that such computational modeling can add rigor to philosophical thought experiments, as well as the terminology used in the scientific study of such represen- tations. We find that this method forces researchers to take a god’s-eye view when describing dynamical relationships be- tween entities in minds and entities in an environment in a way that eliminates the need for problematic talk of belief and con- cept types, such as the belief that cats are silly, and the concept CAT, while preserving belief and concept tokens in individual cognizers’ minds. We conclude with some further key advan- tages of VW CogSci for the scientific study of mental and lin- guistic representation and for Cognitive Science more broadly.
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- Award ID(s):
- 2436103
- PAR ID:
- 10571349
- Publisher / Repository:
- Proceedings of the Cognitive Science Society
- Date Published:
- Format(s):
- Medium: X
- Location:
- San Francisco, CA
- Sponsoring Org:
- National Science Foundation
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