Drawing conclusions from spatial coincidences: a cumulative clustering account

AbstractSpatial coincidences allow us to infer the presence of latent causes in the world. For instance, an unusually large cluster of ants allows us to infer the presence of a food source. The leading cognitive model for such inferences is Bayesian, but the Bayesian algorithm is computationally taxing. Humans likely employ a more efficient, approximative algorithm. To characterize the cognitive algorithms used, we had subjects judge whether a set of dots was drawn from a uniform distribution or from a mixture of a uniform and a gaussian “source” (tending to produce clusters). Responses systematically deviate from Bayesian optimality: as the number of dots increase, subjects more often report a latent cause where none exists. The bias is accounted for by a Bayesian clustering algorithm that cumulatively considers the next-nearest dot to a putative source. This finding helps characterize our tendency to perceive causal patterns where none exist.

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