{"pk":28467,"title":"Generating normative predictions with a variable-length rate code","subtitle":null,"abstract":"Cognitive science is an archipelago of concepts and models,with cross-pollination between topics of interest often prohib-ited by incompatible approaches. Despite this, behavioral per-formance universally depends on information transmission be-tween brain regions and is limited by physical and biologicalconstraints. These constraints can be formalized as informa-tion theoretic constraints on transmission, which provide nor-mative predictions across a surprising range of cognitive do-mains. To illustrate this, we describe a simple variable-lengthrate coding model built with Poisson processes, Bayesian in-ference, and an entropy-based decision threshold. This modelreplicates features of human task performance and provides aprincipled connection between a high-level normative frame-work and neural rate codes. We thereby integrate several dis-joint ideas in cognitive science by translating plausible con-straints into information theoretic terms. Such efforts to trans-late concepts, paradigms and models into common theoreti-cal languages are essential for synthesizing our rich but frag-mented understanding of cognitive systems.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"information theory; bayesian inference; rate cod-ing; response time; learning"}],"section":"Papers with Oral Presentations","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7v919486","frozenauthors":[{"first_name":"S. Thomas","middle_name":"","last_name":"Christie","name_suffix":"","institution":"University of Minnesota","department":""},{"first_name":"Paul","middle_name":"R.","last_name":"Schrater","name_suffix":"","institution":"University of Minnesota","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2019-01-02T02:00:00+08:00","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/28467/galley/18338/download/"}]}