{"pk":25765,"title":"Generating Functions in Neural Learning of Sequential Structures","subtitle":null,"abstract":"A cornerstone of human statistical learning is the ability to\nextract abstract regularities from sequential events. Here we\npresent a unique method to derive the generating functions for\nthe waiting time of sequential patterns, then compare these\nfunctions with the neural mechanisms for learning sequential\nstructures. We show that the way the neocortex integrates information\nover time bears a striking resemblance to the way these\nnormative functions operate. They both operate by organizing\ncombinatorial objects into meaningful groups then compressing\nthe representations by discarding irrelevant information. As a\nresult, discrete-time signals are converted into frequency signals,\nand similarity-based structures are converted into abstract\nrelational structures. Our analyses not only reveal surprisingly\nrich statistical structures embedded in the seemingly random\nsequences, but also offer an explanation for how higher-order\ncognitive biases may have emerged as a consequence of temporal\nintegration.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"generating function; waiting time; statistical learning;\ntemporal integration; compressed representation"}],"section":"Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/8xz1v34k","frozenauthors":[{"first_name":"Yanlong","middle_name":"","last_name":"Sun","name_suffix":"","institution":"Texas A&M University Health Science Center","department":""},{"first_name":"Hongbin","middle_name":"","last_name":"Wang","name_suffix":"","institution":"Texas A&M University Health Science Center","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2015-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/25765/galley/15389/download/"}]}