http:\/\/dx.doi.org\/10.1101\/064287<\/a><\/p>\nReinforcement learning and decision-making (RLDM) provide a quantitative framework, which allows us to specify psychiatric conditions with basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and potentially diagnose psychiatric patients, and there is growing enthusiasm on RLDM and Computational Psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes as exemplified by model-based functional magnetic resonance imaging (fMRI) or electroencephalogram (EEG). However, many researchers often find the approach too technical and have difficulty adopting it for their research. Thus, there remains a critical need to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling on an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, where both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, it is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons – each with a single line of coding. Users can also extract trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI\/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational modeling approaches and investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, it is our expectation that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within their populations.<\/p>\n","protected":false},"excerpt":{"rendered":"
A\u00a0 new version of the hBayesDM package is available: http:\/\/dx.doi.org\/10.1101\/064287 Reinforcement learning and decision-making (RLDM) provide a quantitative framework, which allows us to specify psychiatric conditions with basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"categories":[16],"tags":[],"_links":{"self":[{"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/posts\/558"}],"collection":[{"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/comments?post=558"}],"version-history":[{"count":1,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/posts\/558\/revisions"}],"predecessor-version":[{"id":559,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/posts\/558\/revisions\/559"}],"wp:attachment":[{"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/media?parent=558"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/categories?post=558"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.quentinhuys.com\/tcpw\/wp-json\/wp\/v2\/tags?post=558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}