Troubleshooting Bayesian cognitive models: A tutorial with matstanlib [PsyArXiv]
Baribault and Collins (2021)
PsyArXiv preprint
Plucking a string or playing a G? Degree of choice abstraction impacts human reinforcement learning [bioRxiv]
Rmus, Zou, Collins (2021)
bioRxiv preprint
The interpretation of computational model parameters depends on the context [PDF] [Data]
Eckstein, Master, Xia, Dahl, Wilbrecht, and Collins (2022)
eLife
Reinforcement Learning and Bayesian Inference Provide Complementary Models for the Unique Advantage of Adolescents in Stochastic Reversal [PDF]
Eckstein, Master, Dahl, Wilbrecht, and Collins (2022)
Developmental Cognitive Neuroscience
Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning [PDF]
Zou, Muñoz Lopez, Johnson, Collins (2022)
Frontiers in Psychiatry
Event segmentation reveals working memory forgetting rate [PDF]
Jafarpour, Buffalo, Knight, and Collins (2022)
iScience
How working memory and reinforcement learning are intertwined: a cognitive, neural, and computational perspective [PDF]
Yoo and Collins (2022)
Journal of Cognitive Neuroscience
How the Mind Creates Structure: Hierarchical Learning of Action Sequences [PDF]
Eckstein and Collins (2021)
Proceedings of Cog Sci 2021
Modeling Changes in Probabilistic Reinforcement Learning during Adolescence [PDF]
Xia, Master, Eckstein, Baribault, Dahl, Wilbrecht, Collins (2021)
PLoS Computational Biology
Context is key for learning motor skills [PDF]
Collins and McDougle (2021)
Nature
Executive Function Assigns Value to Novel Goal-Congruent Outcomes [PDF]
McDougle, Ballard, Baribault, Bishop, and Collins (2021)
Cerebral Cortex
What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience [PDF]
Eckstein, Wilbrecht, and Collins (2021)
Current Opinion in Behavioral Sciences
Proof-of-Mechanism Study of the PDE10 Inhibitor RG7203 in Patients with Schizophrenia and Negative Symptoms [PDF]
Umbricht D, Abt M, Tamburri P, Chatham C, Holiga Š, Frank MJ, Collins AGE, Walling DP, Mofsen R, Gruener D, Gertsik L, Sevigny J, Keswani S & Dukart J (2021)
Biological Psychiatry Global Open Science
Temporal and state abstractions for efficient learning, transfer and composition in humans [PDF] [SI] [Data]
Xia and Collins (2021)
Psychological Review
Beyond simple dichotomies in reinforcement learning. [PDF]
Collins and Cockburn (2020)
Nature Reviews Neuroscience
Modeling the influence of working memory, reinforcement, and action uncertainty on reaction time and choice during instrumental learning [PDF]
McDougle and Collins (2020)
Psychonomic Bulletin & Review
The Role of Executive Function in Shaping Reinforcement Learning [PDF]
Rmus, McDougle, and Collins (2020)
Current Opinion in Behavioral Sciences
Learning under uncertainty changes during adolescence [PDF]
Xia, Master, Eckstein, Wilbrecht, and Collins (2020)
CogSci 2020 Submission
What is a Choice in Reinforcement Learning? [PDF]
Rmus and Collins (2020)
CogSci 2020 Submission
Computational Evidence for Hierarchically-Structured Reinforcement Learning in Humans [PDF]
Eckstein and Collins (2020)
PNAS
Disentangling the systems contributing to changes in learning during adolescence [PDF]
Master, Eckstein, Gotlieb, Dahl, Wilbrecht, and Collins (2020)
Developmental Cognitive Neuroscience
Ten simple rules for the computational modeling of behavioral data [PDF] [example code]
Wilson and Collins (2019)
eLIFE
Reinforcement learning: bringing together computation and cognition [PDF]
Collins (2019)
Current Opinion in Behavioral Sciences
Chapter 5: Learning Structures Through Reinforcement [PDF]
Collins, AGE (2018)
Goal-Directed Decision Making
Sequential control underlies robust ramping dynamics in the rostrolateral prefrontal cortex [PDF]
Desrochers, Collins, and Badre (2018)
Journal of Neuroscience
The tortoise and the hare: interactions between reinforcement learning and working memory [PDF] [summary]
Collins, AGE (2018)
Journal of Cognitive Neuroscience
Within and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory [PDF]
Collins, AGE and Frank, MJ (2018)
PNAS
Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia [PDF]
Collins, AGE, Albrecht, MA, Waltz, JA, Gold, JM and Frank, MJ (2017)
Biological Psychiatry
Prefrontal Cortex in Control: Broadening the Scope to Identify Mechanisms [PDF]
Alexander, WH, Brown, JW, Collins, AGE, Hayden, BY, and Vassena, E (2017)
Journal of Cognitive Neuroscience
Working memory load strengthens reward prediction errors [PDF] [SI]
Collins, AGE, Ciullo, B, Frank, MJ, and Badre, D. (2017)
Journal of Neuroscience
The cost of structure learning [PDF]
Collins, AGE (2017)
Journal of Cognitive Neuroscience
Stimulus discriminability may bias value-based probabilistic learning [PDF]
Schutte, I, Slagter, HA, Collins, AGE, Frank, MJ, and Kenemans, JL (2017)
PLoS One
Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants [PDF]
Werchan, DM, Collins, AGE, Frank, MJ, and Amso, D (2016)
The Journal of Neuroscience
Probabilistic Reinforcement Learning in Patients with Schizophrenia: Relationships to Anhedonia and Avolition [PDF]
Dowd, EC, Frank, MJ, Collins, AGE, Gold, JM, and Barch, DM (2016)
Biological Psychiatry
Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning [PDF]
Collins, AGE, Frank, MJ (2016)
Cognition
Motor demands constrain cognitive rule structures [PDF]
Collins, AGE, and Frank, MJ (2016)
Plos Computational Biology
Surprise! dopamine signals mix action, value and error [PDF]
Collins, AGE, and Frank, MJ (2015)
Nature Neuroscience
8-Month-Old infants spontaneously learn and generalize hierarchical rules [PDF]
Werchan, DM, Collins, AGE, Frank, MJ, and Amso, D (2015)
Psychological Science
Working memory contributions to reinforcement learning impairments in schizophrenia [PDF]
Collins, AGE, Brown, J, Gold, J, Waltz, J, and Frank, MJ (2014)
Journal of Neuroscience
A reinforcement learning mechanism responsible for the valuation of free choice [PDF]
Cockburn, J, Collins, AGE, and Frank, MJ (2014)
Neuron
Opponent Actor Learning (OpAL): modeling interactive effect of striatal dopamine on reinforcement learning and choice incentive [PDF]
Collins, AGE and Frank, MJ. (2014)
Psychological Review
Foundations of human reasoning in the prefrontal cortex [PDF]
Donoso, M, Collins, AGE, Koechlin, E. (2014)
Science
Human EEG uncovers latent generalizable rule structure during learning [PDF]
Collins, AGE, Cavanagh, JF, and Frank, MJ (2014)
Journal of Neuroscience
Cognitive control over learning: creating, clustering and generalizing task-set structure [PDF]
Collins, AGE and Frank, MJ. (2013)
Psychological Review
How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational and neurogenetic analysis [PDF]
Collins, AGE and Frank, MJ. (2012)
European Journal of Neuroscience
Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence [PDF]
Gold, JM; Waltz, JA; Matveeva, TM; Kasanova, Z; Strauss, G; Herbener, E; Collins, AGE; Frank, MJ (2012)
Arch Gen Psychiatry
Reasoning, learning, and creativity: frontal lobe function and human decision-making [PDF]
Collins A, Koechlin E (2012)
PLoS Biology
A computational theory of prefrontal executive control
Collins, A. and Koechlin, E. (2009)
Frontiers in Systems Neuroscience
This is a link to the neural network model simulated in Collins & Frank, 2013, Psychological Review. It is a model of task-set structure learning in hierarchical corticostriatal circuits. It runs on the emergent neural simulator (Aisa et al., 2008). Details about emergent and previous neural networks on which this network builds can be found on Michael Frank's basal ganglia projects webpage, here.
This network includes a two-stage cascaded basal ganglia loop circuit enabling hierarchical control of action selection and learning by generating task-set structure, generalizable to novel situations. The model selects among four different motor actions, and at the higher level, three possible task-sets, and simultaneously learns to create (or re-use) abstract task-sets while also learning the particular response mappings given the selected task-set, using pure reinforcement learning.
This matlab script can be used for more detailed analysis of model output showing transfer, and here is an example mat file. Similarly, for more detailed analysis of a case in which there is incentive to clustering task-sets around context during initial learning, please use this matlab script.
The computations of this model were linked to those of a higher level "C-TS" (context task-set) model based on a non-parametric Bayesian approach to clustering task-sets using a Chinese Restaurant Process. Here is a single zip file including simulations from the C-TS model in matlab.
Data for the paper "The interpretation of computational model parameters depends on the context" (Eckstein et al., 2022).
Data for the paper "Temporal and state abstractions for efficient learning, transfer, and composition in humans." (Xia and Collins, 2021).
Data for the paper "Modeling Changes in Probabilistic Reinforcement Learning during Adolescence" (Xia and Collins, 2021).
Data for the paper "Understanding the Unique Advantage of Adolescents in Stochastic, Volatile Environments: Combining Reinforcement Learning and Bayesian Inference" (Eckstein et al., 2020).
CCN Lab