Commonsense psychology in human infants and machines

Gala Stojnić, Kanishk Gandhi, Shannon Yasuda, Brenden M. Lake, Moira R. Dillon

Research output: Contribution to journalArticlepeer-review


Human infants are fascinated by other people. They bring to this fascination a constellation of rich and flexible expectations about the intentions motivating people's actions. Here we test 11-month-old infants and state-of-the-art learning-driven neural-network models on the “Baby Intuitions Benchmark (BIB),” a suite of tasks challenging both infants and machines to make high-level predictions about the underlying causes of agents' actions. Infants expected agents' actions to be directed towards objects, not locations, and infants demonstrated default expectations about agents' rationally efficient actions towards goals. The neural-network models failed to capture infants' knowledge. Our work provides a comprehensive framework in which to characterize infants' commonsense psychology and takes the first step in testing whether human knowledge and human-like artificial intelligence can be built from the foundations cognitive and developmental theories postulate.

Original languageEnglish (US)
Article number105406
StatePublished - Jun 2023


  • Action understanding
  • Artificial intelligence
  • Commonsense psychology
  • Infancy
  • Intuitive psychology
  • Machine common sense

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Language and Linguistics
  • Developmental and Educational Psychology
  • Linguistics and Language
  • Cognitive Neuroscience


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