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As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision-making intended to capture processes jointly giving rise to RT distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of SSMs. In addition, recent work has motivated the combination of SSMs with reinforcement learning models, which had historically been considered in separate literatures. Here, we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user-extensible) wide variety of SSMs and how they can be combined with reinforcement learning models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.<\/jats:p>","DOI":"10.1162\/jocn_a_01902","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T18:34:28Z","timestamp":1659983668000},"page":"1780-1805","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":41,"title":["Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM"],"prefix":"10.1162","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0104-3905","authenticated-orcid":true,"given":"Alexander","family":"Fengler","sequence":"first","affiliation":[{"name":"Brown University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krishn","family":"Bera","sequence":"additional","affiliation":[{"name":"Brown University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mads L.","family":"Pedersen","sequence":"additional","affiliation":[{"name":"Brown University"},{"name":"University of Oslo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Frank","sequence":"additional","affiliation":[{"name":"Brown University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2022,9,1]]},"reference":[{"key":"2022090203363944800_bib1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.jneumeth.2019.01.006","article-title":"Joint modeling of reaction times and choice improves parameter identifiability in reinforcement learning models","volume":"317","author":"Ballard","year":"2019","journal-title":"Journal of Neuroscience Methods"},{"key":"2022090203363944800_bib2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/MCSE.2010.118","article-title":"Cython: The best of both worlds","volume":"13","author":"Behnel","year":"2010","journal-title":"Computing in Science & Engineering"},{"key":"2022090203363944800_bib3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.0906","article-title":"Hamiltonian Monte Carlo for hierarchical models","author":"Betancourt","year":"2013","journal-title":"arXiv:1312.0906"},{"key":"2022090203363944800_bib4","doi-asserted-by":"publisher","first-page":"102613","DOI":"10.1016\/j.jmp.2021.102613","article-title":"Fast solutions for the first-passage distribution of diffusion models with space\u2013time-dependent drift functions and time-dependent boundaries","volume":"105","author":"Boehm","year":"2021","journal-title":"Journal of Mathematical Psychology"},{"key":"2022090203363944800_bib5","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1080\/10618600.1998.10474787","article-title":"General methods for monitoring convergence of iterative simulations","volume":"7","author":"Brooks","year":"1998","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"2022090203363944800_bib6","doi-asserted-by":"publisher","first-page":"11560","DOI":"10.1523\/JNEUROSCI.1844-09.2009","article-title":"Decisions in changing conditions: The urgency-gating model","volume":"29","author":"Cisek","year":"2009","journal-title":"Journal of Neuroscience"},{"key":"2022090203363944800_bib7","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41386-021-01126-y","article-title":"Advances in modeling learning and decision-making in neuroscience","volume":"47","author":"Collins","year":"2022","journal-title":"Neuropsychopharmacology"},{"key":"2022090203363944800_bib8","doi-asserted-by":"publisher","first-page":"e56694","DOI":"10.7554\/eLife.56694","article-title":"The caudate nucleus contributes causally to decisions that balance reward and uncertain visual information","volume":"9","author":"Doi","year":"2020","journal-title":"eLife"},{"key":"2022090203363944800_bib9","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.bpsc.2016.05.005","article-title":"Probabilistic reinforcement learning in patients with schizophrenia: Relationships to anhedonia and avolition","volume":"1","author":"Dowd","year":"2016","journal-title":"Biological Psychiatry: Cognitive Neuroscience and Neuroimaging"},{"key":"2022090203363944800_bib10","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.cobeha.2021.06.004","article-title":"What do reinforcement learning models measure? 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