{"id":305429,"date":"2016-10-06T00:00:49","date_gmt":"2016-10-06T07:00:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=305429"},"modified":"2016-10-13T12:24:53","modified_gmt":"2016-10-13T19:24:53","slug":"neurally-inspired-model-habit-empirical-implications","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/neurally-inspired-model-habit-empirical-implications\/","title":{"rendered":"A Neurally-Inspired Model of Habit and its Empirical Implications"},"content":{"rendered":"<p>The busy human brain creates fast, low-cost habits when choices are frequent and are providing stable rewards. Using evidence from animal learning and cognitive neuroscience, we model a two-controller system in which habit and model-based choice coexist. The key inputs are reward prediction error (RPE) and the absolute magnitude of RPE. As the RPEs from a choice move toward zero, habits form. When the magnitude of averaged RPE exceeds a threshold, habits are overridden by model-based choice. The model contrasts with long-standing approach in economics (which relies on complementarity of consumption choice) and has several interesting properties that can be tested with behavioral and cognitive data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The busy human brain creates fast, low-cost habits when choices are frequent and are providing stable rewards. Using evidence from animal learning and cognitive neuroscience, we model a two-controller system in which habit and model-based choice coexist. The key inputs are reward prediction error (RPE) and the absolute magnitude of RPE. As the RPEs from [&hellip;]<\/p>\n","protected":false},"featured_media":305498,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13548],"msr-video-type":[],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-305429","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-economics","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/AehDztKBEkc","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/305429"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/305429\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/305498"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=305429"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=305429"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=305429"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=305429"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=305429"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=305429"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}