A flexible framework for simulating and fitting generalized drift-diffusion models
Shinn M, Lam NH, Murray JD. A flexible framework for simulating and fitting generalized drift-diffusion models. ELife 2020, 9: e56938. PMID: 32749218, PMCID: PMC7462609, DOI: 10.7554/elife.56938.Peer-Reviewed Original ResearchConceptsDrift-diffusion modelArbitrary user-defined functionsImportant decision-making modelFokker-Planck equationEfficient numerical methodDecision-making mechanismUser-defined functionsDrift diffusion model frameworkFlexible frameworkSoftware packageGDDMHuman datasetsNumerical methodDecision-making modelResponse time distributionsDecision-making taskLatest methodologiesModel formGood accuracyFrameworkMaximum likelihoodModel innovationTime distributionDDM parametersModel frameworkConfluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics
Shinn M, Ehrlich D, Lee D, Murray JD, Seo H. Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics. Journal Of Neuroscience 2020, 40: 7326-7342. PMID: 32839233, PMCID: PMC7534922, DOI: 10.1523/jneurosci.0544-20.2020.Peer-Reviewed Original ResearchConceptsDecision-making taskReward biasesDecision-making strategiesPerceptual decision-making taskNovel decision-making taskGeneralized drift-diffusion modelsResponse time patternsTiming mechanismTemporal structureCognitive mechanismsSensory evidenceDecision-making dynamicsUrgency signalAsymmetric rewardsNeural mechanismsStimulus timingDrift-diffusion modelTemporal contextLeaky integrationRewardReward structureTemporal uncertaintyEvidence reliabilityEveryday lifeSuch regularities