Habits without Values

Habits ​ ​form​ ​a​ ​crucial​ ​component​ ​of​ ​behavior.​ ​In​ ​recent​ ​years,​ ​key​ ​computational​ ​models ​ ​have​ ​conceptualized​ ​habits ​ ​as behaviors ​ ​arising​ ​from​ ​model-free​ ​reinforcement​ ​learning​ ​(RL)​ ​mechanisms,​ ​which​ ​typically​ ​represent​ ​the​ ​expected​ ​value associated​ ​with​ ​the​ ​possible​ ​outcomes ​ ​of​ ​each​ ​action​ ​before​ ​one​ ​of​ ​those​ ​actions ​ ​is ​ ​chosen.​ ​Traditionally,​ ​however,​ ​habits have​ ​been​ ​understood​ ​as ​ ​arising​ ​from​ ​mechanisms ​ ​that​ ​are​ ​independent​ ​of​ ​outcomes.​ ​Here,​ ​we​ ​develop​ ​a​ ​computational model​ ​instantiating​ ​this ​ ​traditional​ ​view,​ ​in​ ​which​ ​habits ​ ​are​ ​acquired​ ​through​ ​the​ ​direct​ ​strengthening​ ​of​ ​recently​ ​taken actions,​ ​independent​ ​of​ ​outcome.​ ​We​ ​demonstrate​ ​how​ ​this ​ ​model​ ​accounts ​ ​for​ ​key​ ​behavioral​ ​manifestations ​ ​of​ ​habits, including​ ​insensitivity​ ​to​ ​outcome​ ​devaluation​ ​and​ ​contingency​ ​degradation,​ ​as ​ ​well​ ​as ​ ​the​ ​dependence​ ​of​ ​formation​ ​rates on​ ​the​ ​reinforcement​ ​schedule.​ ​The​ ​model​ ​also​ ​explains ​ ​the​ ​prevalent​ ​observation​ ​of​ ​perseveration​ ​in​ ​repeated​ ​choice tasks ​ ​as ​ ​an​ ​additional​ ​behavioral​ ​manifestation​ ​of​ ​the​ ​habit​ ​system.​ ​We​ ​suggest​ ​that​ ​mapping​ ​habitual​ ​behaviors ​ ​onto value-free​ ​mechanisms ​ ​provides ​ ​a​ ​parsimonious ​ ​account​ ​of​ ​existing​ ​behavioral​ ​and​ ​neural​ ​data.​ ​This ​ ​mapping​ ​may provide​ ​a​ ​new​ ​foundation​ ​for​ ​building​ ​robust​ ​and​ ​comprehensive​ ​models ​ ​of​ ​the​ ​interaction​ ​of​ ​habits ​ ​with​ ​other,​ ​more goal-directed​ ​types ​ ​of​ ​behaviors,​ ​and​ ​help​ ​to​ ​better​ ​guide​ ​research​ ​into​ ​the​ ​neural​ ​mechanisms ​ ​underlying​ ​control​ ​of instrumental​ ​behaviors ​ ​more​ ​generally. 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/067603 doi: bioRxiv preprint first posted online Aug. 3, 2016;