{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/62097"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/62097","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Sampling in human cognition","abstract":"Bayesian Decision Theory describes optimal methods for combining sparse, noisy data with prior knowledge to build models of an uncertain world and to use those models to plan actions and make novel decisions. Bayesian computational models correctly predict aspects of human behavior in cognitive domains ranging from perception to motor control and language. However the predictive success of Bayesian models of cognition has highlighted long-standing challenges in bridging the computational and process levels of cognition. First, the computations required for exact Bayesian inference are incommensurate with the limited resources available to cognition (e.g., computational speed; and memory). Second, Bayesian models describe computations but not the processes that carry out these computations and fail to accurately predict human behavior under conditions of cognitive load or deficits. I suggest a resolution to both challenges: The mind approximates Bayesian inference by sampling. Experiments across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. When sampling algorithms are treated as process models of human cognition, the computational and process levels can be modeled jointly to shed light on new and old cognitive phenomena..","abstract_html":"Bayesian Decision Theory describes optimal methods for combining sparse, noisy data with prior knowledge to build models of an uncertain world and to use those models to plan actions and make novel decisions. Bayesian computational models correctly predict aspects of human behavior in cognitive domains ranging from perception to motor control and language. However the predictive success of Bayesian models of cognition has highlighted long-standing challenges in bridging the computational and process levels of cognition. First, the computations required for exact Bayesian inference are incommensurate with the limited resources available to cognition (e.g., computational speed; and memory). Second, Bayesian models describe computations but not the processes that carry out these computations and fail to accurately predict human behavior under conditions of cognitive load or deficits. I suggest a resolution to both challenges: The mind approximates Bayesian inference by sampling. Experiments across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. When sampling algorithms are treated as process models of human cognition, the computational and process levels can be modeled jointly to shed light on new and old cognitive phenomena..","abstract_has_math":false,"creators":["Vul, Edward"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences.","school":null,"contributors":[],"advisors":["Nancy G. Kanwisher."],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-22T22:22:26Z","subjects":["Brain and Cognitive Sciences."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/62097","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nancy G. 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Second, Bayesian models describe computations but not the processes that carry out these computations and fail to accurately predict human behavior under conditions of cognitive load or deficits. I suggest a resolution to both challenges: The mind approximates Bayesian inference by sampling. Experiments across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. When sampling algorithms are treated as process models of human cognition, the computational and process levels can be modeled jointly to shed light on new and old cognitive phenomena.."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Sampling in human cognition"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nancy G. 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However the predictive success of Bayesian models of cognition has highlighted long-standing challenges in bridging the computational and process levels of cognition. First, the computations required for exact Bayesian inference are incommensurate with the limited resources available to cognition (e.g., computational speed; and memory). Second, Bayesian models describe computations but not the processes that carry out these computations and fail to accurately predict human behavior under conditions of cognitive load or deficits. I suggest a resolution to both challenges: The mind approximates Bayesian inference by sampling. Experiments across a wide range of cognition demonstrate Monte-Carlo-like behavior by human observers; moreover, models of cognition based on specific Monte Carlo algorithms can describe previously elusive cognitive phenomena such as perceptual bistability and probability matching. 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