ArticleNeuron2020
A Neuro-computational Account of Arbitration between Choice Imitation and Goal Emulation during Human Observational Learning.
Article in Neuron, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed, 131 citations in OpenAlex.
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- Deciding When to Align: Computational and Neural Mechanisms of Goal-Directed Social Alignment.Brain sciences · 2025Review
- Neurocomputational basis of learning when choices simultaneously affect both oneself and others.Nature communications · 2025Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
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3 authors at 1 institution in 1 country.
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Abstract
When individuals learn from observing the behavior of others, they deploy at least two distinct strategies. Choice imitation involves repeating other agents' previous actions, whereas emulation proceeds from inferring their goals and intentions. Despite the prevalence of observational learning in humans and other social animals, a fundamental question remains unaddressed: how does the brain decide which strategy to use in a given situation? In two fMRI studies (the second a pre-registered replication of the first), we identify a neuro-computational mechanism underlying arbitration between choice imitation and goal emulation. Computational modeling, combined with a behavioral task that dissociated the two strategies, revealed that control over behavior was adaptively and dynamically weighted toward the most reliable strategy. Emulation reliability, the model's arbitration signal, was represented in the ventrolateral prefrontal cortex, temporoparietal junction, and rostral cingulate cortex. Our replicated findings illuminate the computations by which the brain decides to imitate or emulate others.
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