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Point-estimating observer models for latent cause detection
- Source :
- PLoS Computational Biology, Vol 17, Iss 10, p e1009159 (2021), PLoS Computational Biology, Vol 17, Iss 10 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- 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.<br />Author summary Perceptual systems are designed to make sense of fragmented sensory data by inferring common, latent causes. Seeing a cluster of insects might allow us to infer the presence of a common food source, whereas the same number of insects scattered over a larger area of land might not evoke the same suspicions. The ability to reliably make this inference based on statistical information about the environment is surprisingly non-trivial: making the best possible inference requires making full use of the probabilistic information provided by the sensory data, which would require considering a combinatorially explosive number of hypothetical world states. In this paper, we test human subjects on their ability to perform a causal detection task: subjects are asked to judge whether an underlying cause of clustering is present or absent, based on the spatial distribution of those items. We show that subjects do not reason optimally on this task, and that particular computational short cuts (“committing” to certain world states over others, rather than representing them all) might underlie perceptual decision-making in these causal detection schemes.
- Subjects :
- Male
Vision
Computer science
Social Sciences
Inference
computer.software_genre
Cognition
Psychology
Biology (General)
Ecology
Point (typography)
Applied Mathematics
Simulation and Modeling
Brain
Eukaryota
Generative model
Computational Theory and Mathematics
Common cause and special cause
Modeling and Simulation
Vertebrates
Physical Sciences
Pigeons
Female
Sensory Perception
Algorithms
Research Article
Adult
Observer (quantum physics)
QH301-705.5
Cognitive Neuroscience
Decision Making
Models, Neurological
Bayesian probability
Research and Analysis Methods
Bayesian inference
Machine learning
Birds
Young Adult
Clustering Algorithms
Cellular and Molecular Neuroscience
Reaction Time
Genetics
Humans
Animals
Point estimation
Molecular Biology
Ecology, Evolution, Behavior and Systematics
business.industry
Organisms
Cognitive Psychology
Computational Biology
Biology and Life Sciences
Bayes Theorem
Probability Theory
Probability Distribution
Amniotes
Cognitive Science
Perception
Artificial intelligence
business
Zoology
computer
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....f063024d719503385a3b61f94e978acf