Technical Program

Paper Detail

Paper: PS-2B.37
Session: Poster Session 2B
Location: H Fläche 1.OG
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: A representation-level algorithm for detecting spatial coincidences
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Jennifer Lee, Weiji Ma, NYU, United States
Abstract: Spatial coincidences can lead to causal discoveries. We might expect to find a few ants on the sidewalk, but an unusually large cluster tips us off about the presence of a nearby food source. The leading cognitive explanation for this class of reasoning is Bayesian, but Bayesian models remain notoriously agnostic about the inner workings of the cognitive “black box.” In this cluster detection paradigm, we ask what algorithms the brain might actually implement to detect spatial coincidences in an “approximately Bayesian” way. We find evidence that the brain represents the two variables of the generative model: 1) the location of a hypothesized causal source and 2) the location of the points to which it gave rise. However, we propose that the brain is limited to representing probability distributions over one but not both of these variables, resulting in strong deviations from Bayes-optimality. We find, counterintuitively, that subjects become more prone to false alarms as the amount of information increases, and our proposed cognitive algorithm accounts for this pattern. Our representation-level algorithm elucidates the cognitive processes underlying coincidence detection, and helps explain our tendency to perceive causal patterns where none exist.