Point-estimating observer models for latent cause detection

Jennifer Laura Lee, Wei Ji Ma

Research output: Contribution to journalArticlepeer-review


The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several "point-estimating"observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by "committing"to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.

Original languageEnglish (US)
Article numbere1009159
JournalPLoS computational biology
Issue number10
StatePublished - Nov 2021


  • Adult
  • Algorithms
  • Bayes Theorem
  • Brain/physiology
  • Computational Biology/methods
  • Decision Making/physiology
  • Female
  • Humans
  • Male
  • Models, Neurological
  • Perception/physiology
  • Young Adult

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


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