Research graph
References from Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2018 · External reference
Holistic Reinforcement Learning: The Role of Structure and Attention
10.1016/j.tics.2019.01.010 · 2019 · External reference
Temporal difference models describe higher-order learning in humans
10.1038/nature02581 · 2004 · External reference
Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective
10.1016/j.cognition.2008.08.011 · 2009 · External reference
A framework for mesencephalic dopamine systems based on predictive Hebbian learning
10.1523/jneurosci.16-05-01936.1996 · 1996 · External reference
A neural substrate of prediction and reward
10.1126/science.275.5306.1593 · 1997 · External reference
Adaptive cost-benefit control fueled by striatal dopamine
10.1146/annurev-neuro-112723-025228 · 2025 · External reference
Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive
10.1037/a0037015 · 2014 · External reference
On the normative advantages of dopamine and striatal opponency for learning and choice
10.7554/elife.85107 · 2023 · External reference
Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia
10.1162/089976606775093909 · 2006 · External reference
The Role of Executive Function in Shaping Reinforcement Learning
10.1016/j.cobeha.2020.10.003 · 2021 · External reference
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis
10.1111/j.1460-9568.2011.07980.x · 2012 · External reference
Reinforcement learning in multidimensional environments relies on attention mechanisms
10.1523/jneurosci.2978-14.2015 · 2015 · External reference
Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal
10.1037/0033-295x.113.2.300 · 2006 · External reference
Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning
10.1073/pnas.0706111104 · 2007 · External reference
How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently
10.1037/xge0001817 · 2025 · External reference
Neural Index of Reinforcement Learning Predicts Improved Stimulus-Response Retention under High Working Memory Load
10.1523/jneurosci.1274-22.2023 · 2023 · External reference
Modeling the influence of working memory, reinforcement, and action uncertainty on reaction time and choice during instrumental learning
10.3758/s13423-020-01774-z · 2021 · External reference
Working memory contributions to reinforcement learning impairments in schizophrenia
10.1523/jneurosci.0989-14.2014 · 2014 · External reference
Age-related differences in prefrontal glutamate are associated with increased working memory decay that gives the appearance of learning deficits
10.7554/elife.85243 · 2023 · External reference
What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience
10.1016/j.cobeha.2021.06.004 · 2021 · External reference
How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective
10.1162/jocn_a_01808 · 2022 · External reference
Interactions among working memory, reinforcement learning, and effort in value-based choice: A new paradigm and selective deficits in schizophrenia
10.1016/j.biopsych.2017.05.017 · 2017 · External reference
On the Rate of Gain of Information
10.1080/17470215208416600 · 1952 · External reference
Jan Peters and Mark D’Esposito. The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controls
2020 · External reference
The drift diffusion model as the choice rule in reinforcement learning
10.3758/s13423-016-1199-y · 2017 · External reference
fMRI and EEG predictors of dynamic decision parameters during human reinforcement learning
10.1523/jneurosci.2036-14.2015 · 2015 · External reference
Chapter Five - Policy compression: An information bottleneck in action selection.
2021 · External reference
Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling
10.3758/s13415-019-00723-1 · 2019 · External reference
A reinforcement learning diffusion decision model for value-based decisions
10.3758/s13423-018-1554-2 · 2019 · External reference
Mutual benefits: Combining reinforcement learning with sequential sampling models
10.1016/j.neuropsychologia.2019.107261 · 2020 · External reference
Unresolved reference
2021 · External reference
A new model of decision processing in instrumental learning tasks
10.7554/elife.63055 · 2021 · External reference
A Rescorla-Wagner drift-diffusion model of conditioning and timing
10.1371/journal.pcbi.1005796 · 2017 · External reference
Combining error-driven models of associative learning with evidence accumulation models of decision-making
10.3758/s13423-019-01570-4 · 2019 · External reference
Improving out-of-sample predictions using response times and a model of the decision process
10.1016/j.jebo.2018.02.007 · 2018 · External reference
Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience
10.7554/elife.65074 · 2021 · External reference
Beyond drift diffusion models: Fitting a broad class of decision and reinforcement learning models with HDDM
10.1162/jocn_a_01902 · 2022 · External reference
Artificial neural networks for model identification and parameter estimation in computational cognitive models
10.1371/journal.pcbi.1012119 · 2024 · External reference
10.7554/elife.112168
10.7554/elife.112168 · External reference
The simplest complete model of choice response time: linear ballistic accumulation
10.1016/j.cogpsych.2007.12.002 · 2008 · External reference
Striatal dopamine can enhance both fast working memory, and slow reinforcement learning, while reducing implicit effort cost sensitivity
10.1038/s41467-025-61099-0 · 2025 · External reference
Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory
10.1073/pnas.1720963115 · 2018 · External reference
Model-Based Electroencephalography Phenotyping Uncovers Distinct Neurocomputational Mechanisms Underlying Learning Impairments Across Psychopathologies
2026 · External reference
Joint modeling of reaction times and choice improves parameter identifiability in reinforcement learning models
10.1016/j.jneumeth.2019.01.006 · 2019 · External reference
The diffusion decision model: theory and data for two-choice decision tasks
10.1162/neco.2008.12-06-420 · 2008 · External reference
Subthalamic nucleus stimulation reverses mediofrontal influence over decision threshold
10.1038/nn.2925 · 2011 · External reference
Basal ganglia components have distinct computational roles in decision-making dynamics under conflict and uncertainty
2025 · External reference
The variable nature of cognitive control: a dual mechanisms framework
10.1016/j.tics.2011.12.010 · 2012 · External reference
By carrot or by stick: cognitive reinforcement learning in parkinsonism
10.1126/science.1102941 · 2004 · External reference
Cognitive control deficits in schizophrenia: mechanisms and meaning
10.1038/npp.2010.156 · 2011 · External reference
The anterior cingulate as a conflict monitor: fMRI and ERP studies
10.1016/s0031-9384(02)00930-7 · 2002 · External reference
Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling
10.1371/journal.pcbi.1006803 · 2019 · External reference
Unresolved reference
2014 · External reference
Decomposing task-switching costs with the diffusion model
10.1037/a0026003 · 2012 · External reference
The Influence of Feedback on Task-Switching Performance: A Drift Diffusion Modeling Account
10.3389/fnint.2018.00001 · 2018 · External reference
Cognitive control and capacity for prospective memory in complex dynamic environments
10.1037/xge0000599 · 2019 · External reference
Proceeding with care for successful prospective memory: Do we delay ongoing responding or actively monitor for cues?
2018 · External reference
Racing to remember: A theory of decision control in event-based prospective memory
10.1037/rev0000113 · 2018 · External reference
Brace yourself: neural and computational insights into the experience of mental effort
10.1093/cercor/bhaf256 · 2025 · External reference
10.1002/9781118920497.ch31
10.1002/9781118920497.ch31 · 2017 · External reference
Proactive and reactive cognitive control and dorsolateral prefrontal cortex dysfunction in first episode schizophrenia
10.1016/j.nicl.2013.04.010 · 2013 · External reference
An Integrative Perspective on the Role of Dopamine in Schizophrenia
10.1016/j.biopsych.2016.05.021 · 2017 · External reference
Deficits in positive reinforcement learning and uncertainty-driven exploration are associated with distinct aspects of negative symptoms in schizophrenia
10.1016/j.biopsych.2010.10.015 · 2011 · External reference
Unresolved reference
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Towards end-to-end likelihood-free inference with convolutional neural networks
10.1111/bmsp.12159 · 2020 · External reference
Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making
10.3758/s13423-020-01719-6 · 2020 · External reference
The time course of perceptual choice: the leaky, competing accumulator model
10.1037/0033-295x.108.3.550 · 2001 · External reference
“Reliable organisms from unreliable components” revisited: the linear drift, linear infinitesimal variance model of decision making
10.3758/s13423-022-02237-3 · 2023 · External reference
Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method
10.1016/j.cogpsych.2009.12.001 · 2010 · External reference
Variational Inference: A Review for Statisticians
10.1080/01621459.2017.1285773 · 2017 · External reference
Unresolved reference
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Automatic Differentiation Variational Inference
2017 · External reference
PyMC: a modern, and comprehensive probabilistic programming framework in Python
10.7717/peerj-cs.1516 · 2023 · External reference
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Unresolved reference
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Absolute performance of reinforcement-learning models for the Iowa Gambling Task
10.1037/dec0000005 · 2014 · External reference
