{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:18:28Z","timestamp":1773656308411,"version":"3.50.1"},"reference-count":42,"publisher":"MIT Press - Journals","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2011,8]]},"abstract":"<jats:p> An important class of psychological models of decision making assumes that evidence is accumulated by a diffusion process to a response criterion. These models have successfully accounted for reaction time (RT) distributions and choice probabilities from a wide variety of experimental tasks. An outstanding theoretical problem is how the integration process that underlies diffusive evidence accumulation can be realized neurally. Wang ( 2001 , 2002 ) has suggested that long timescale neural integration may be implemented by persistent activity in reverberation loops. We analyze a simple recurrent decision making architecture and show that it leads to a diffusive accumulation process. The process has the form of a time-inhomogeneous Ornstein-Uhlenbeck velocity process with linearly increasing drift and diffusion coefficients. The resulting model predicts RT distributions and choice probabilities that closely approximate those found in behavioral data. <\/jats:p>","DOI":"10.1162\/neco_a_00150","type":"journal-article","created":{"date-parts":[[2011,4,26]],"date-time":"2011-04-26T20:21:23Z","timestamp":1303849283000},"page":"2000-2031","source":"Crossref","is-referenced-by-count":23,"title":["Diffusive Information Accumulation by Minimal Recurrent Neural Models of Decision Making"],"prefix":"10.1162","volume":"23","author":[{"given":"Philip L.","family":"Smith","sequence":"first","affiliation":[{"name":"Psychological Sciences, University of Melbourne, Victoria, 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cameron R. 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