Downloading Culture.zip: Social learning by program induction

Max Kleiman-Weiner, Felix Sosa, Bill Thompson, Bas van Opheusden, Thomas L. Griffiths, Samuel Gershman, Fiery Cushman

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

Cumulative culture depends on the fidelity of learning between successive generations, and the robustness with which the lessons of one generation apply to the problems of the next. How do humans accomplish these twin goals? We formalize social learning as a kind of program induction, and provide an experimental test of a key prediction. To do this, we exploit a key fact: When humans learn from others, in addition to observing inputs and outputs we often observe the process that led to that output. For instance, when preparing a meal, we don't just observe a pile of vegetables and then a ratatouille. Instead, we observe a causal process that transforms those ingredients into a finished food. Here, we use probabilistic programs to represent causal processes and show that the observation of an execution trace speeds up program induction, even when learning from only a single example. This model predicts that the inferences and behavior of people will be structured by these execution traces. In two behavioral experiments, we show that human judgments and behavior are affected by the execution trace in the systematic ways predicted by our formal model. These findings shed light on the mechanisms that underlie high fidelity social learning in humans, and unify the role of emulation and imitation in social learning.

Original languageEnglish (US)
Pages1667-1673
Number of pages7
StatePublished - 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: Jul 29 2020Aug 1 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period7/29/208/1/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • Bayesian modeling
  • imitation learning
  • program induction
  • social learning
  • theory of mind

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