Task Learnability Modulates Surprise but Not Valence Processing for Reinforcement Learning in Probabilistic Choice Tasks
暂无分享,去创建一个
Gilles Pourtois | Marco Steinhauser | Mario Carlo Severo | Wioleta Walentowska | Benjamin Ernst | Franz Wurm | G. Pourtois | M. Steinhauser | Benjamin Ernst | Franz Wurm | W. Walentowska
[1] John P O'Doherty,et al. Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. , 2010, Wiley interdisciplinary reviews. Cognitive science.
[2] R. Baker,et al. When is an error not a prediction error? An electrophysiological investigation , 2009, Cognitive, affective & behavioral neuroscience.
[3] Sander Nieuwenhuis,et al. Noradrenergic and Cholinergic Modulation of Belief Updating , 2018, Journal of Cognitive Neuroscience.
[4] Samuel M. McClure,et al. BOLD Responses Reflecting Dopaminergic Signals in the Human Ventral Tegmental Area , 2008, Science.
[5] B. Balleine,et al. The Role of Learning in the Operation of Motivational Systems , 2002 .
[6] E. Wagenmakers,et al. Absolute performance of reinforcement-learning models for the Iowa Gambling Task , 2014 .
[7] Nick Yeung,et al. Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning , 2017, NeuroImage.
[8] Clay B. Holroyd,et al. The feedback correct-related positivity: sensitivity of the event-related brain potential to unexpected positive feedback. , 2008, Psychophysiology.
[9] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[10] Matthew R Nassar,et al. Taming the beast: extracting generalizable knowledge from computational models of cognition , 2016, Current Opinion in Behavioral Sciences.
[11] R. Oostenveld,et al. Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.
[12] Edward M Bernat,et al. Time-frequency theta and delta measures index separable components of feedback processing in a gambling task. , 2015, Psychophysiology.
[13] N. Daw,et al. Multiple Systems for Value Learning , 2014 .
[14] Robert C. Wilson,et al. Is Model Fitting Necessary for Model-Based fMRI? , 2015, PLoS Comput. Biol..
[15] Roshan Cools,et al. Feedback-related Negativity Codes Prediction Error but Not Behavioral Adjustment during Probabilistic Reversal Learning , 2011, Journal of Cognitive Neuroscience.
[16] Nathaniel D. Daw,et al. Selective impairment of prediction error signaling in human dorsolateral but not ventral striatum in Parkinson's disease patients: evidence from a model-based fMRI study , 2010, NeuroImage.
[17] P. Dayan,et al. A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[18] M. Steinhauser,et al. Top-down control over feedback processing: The probability of valid feedback affects feedback-related brain activity , 2017, Brain and Cognition.
[19] J. Polich. 50+ years of P300: Where are we now? , 2020, Psychophysiology.
[20] Dylan A. Simon,et al. Model-based choices involve prospective neural activity , 2015, Nature Neuroscience.
[21] Thomas D. Sambrook,et al. A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages. , 2015, Psychological bulletin.
[22] David M. Groppe,et al. Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.
[23] Robert C. Wilson,et al. Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms , 2015, The Journal of Neuroscience.
[24] Karl J. Friston,et al. Bayesian model selection for group studies — Revisited , 2014, NeuroImage.
[25] Charles H Hillman,et al. Age, physical fitness, and attention: P3a and P3b. , 2009, Psychophysiology.
[26] G. Pourtois,et al. Goal relevance influences performance monitoring at the level of the FRN and P3 components. , 2016, Psychophysiology.
[27] Kara D. Federmeier,et al. Timed Action and Object Naming , 2005, Cortex.
[28] M. Frank,et al. Dopaminergic Genes Predict Individual Differences in Susceptibility to Confirmation Bias , 2011, The Journal of Neuroscience.
[29] Y. Niv. Reinforcement learning in the brain , 2009 .
[30] Wouter Kool,et al. Planning Complexity Registers as a Cost in Metacontrol , 2018, Journal of Cognitive Neuroscience.
[31] E. Bernat,et al. Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task , 2014, Clinical Neurophysiology.
[32] K. Branson,et al. Behavioral Variability through Stochastic Choice and Its Gating by Anterior Cingulate Cortex , 2014, Cell.
[33] Joshua W. Brown,et al. Medial prefrontal cortex as an action-outcome predictor , 2011, Nature Neuroscience.
[34] B. Balleine. Neural bases of food-seeking: Affect, arousal and reward in corticostriatolimbic circuits , 2005, Physiology & Behavior.
[35] Tim Fingscheidt,et al. A computational analysis of the neural bases of Bayesian inference , 2015, NeuroImage.
[36] J. O'Doherty,et al. Model‐Based fMRI and Its Application to Reward Learning and Decision Making , 2007, Annals of the New York Academy of Sciences.
[37] Clay B. Holroyd,et al. It's worse than you thought: the feedback negativity and violations of reward prediction in gambling tasks. , 2007, Psychophysiology.
[38] Greg H. Proudfit,et al. Anterior cingulate activity to monetary loss and basal ganglia activity to monetary gain uniquely contribute to the feedback negativity , 2015, Clinical Neurophysiology.
[39] H. Seo,et al. Neural basis of reinforcement learning and decision making. , 2012, Annual review of neuroscience.
[40] M. Steinhauser,et al. The influence of internal models on feedback-related brain activity , 2020, Cognitive, Affective, & Behavioral Neuroscience.
[41] M. Frank,et al. Instructional control of reinforcement learning: A behavioral and neurocomputational investigation , 2009, Brain Research.
[42] Dejan Draschkow,et al. Cluster-based permutation tests of MEG/EEG data do not establish significance of effect latency or location. , 2019, Psychophysiology.
[43] Joshua I. Gold,et al. A Healthy Fear of the Unknown: Perspectives on the Interpretation of Parameter Fits from Computational Models in Neuroscience , 2013, PLoS Comput. Biol..
[44] M. Botvinick,et al. Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective , 2009, Cognition.
[45] S. Gershman. Empirical priors for reinforcement learning models , 2016 .
[46] John R. Anderson,et al. Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice , 2012, Neuroscience & Biobehavioral Reviews.
[47] Matthew R. Nassar,et al. Catecholaminergic Regulation of Learning Rate in a Dynamic Environment , 2016, PLoS Comput. Biol..
[48] M. Steinhauser,et al. Effects of feedback reliability on feedback-related brain activity: A feedback valuation account , 2018, Cognitive, Affective, & Behavioral Neuroscience.
[49] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[50] Margot J. Taylor,et al. Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. , 2000, Psychophysiology.
[51] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[52] B. Kopp,et al. Prior probabilities modulate cortical surprise responses: A study of event-related potentials , 2016, Brain and Cognition.
[53] R. Dolan,et al. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans , 2006, Nature.
[54] Thomas D. Sambrook,et al. Mediofrontal event-related potentials in response to positive, negative and unsigned prediction errors , 2014, Neuropsychologia.
[55] H. Hoijtink,et al. P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study , 2016, Cognitive, affective & behavioral neuroscience.
[56] N. Daw,et al. Model-based learning protects against forming habits , 2015, Cognitive, Affective, & Behavioral Neuroscience.
[57] E. Koechlin,et al. The Importance of Falsification in Computational Cognitive Modeling , 2017, Trends in Cognitive Sciences.
[58] Michael X. Cohen,et al. Behavioral / Systems / Cognitive Reinforcement Learning Signals Predict Future Decisions , 2007 .
[59] William H. Alexander,et al. Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex , 2015, Neural Computation.
[60] John R. Anderson,et al. Modulation of the feedback-related negativity by instruction and experience , 2011, Proceedings of the National Academy of Sciences.
[61] W. Schultz. Dopamine reward prediction error coding , 2016, Dialogues in clinical neuroscience.
[62] Amir Dezfouli,et al. Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes , 2011, PLoS Comput. Biol..
[63] Darrell A. Worthy,et al. Heterogeneity of strategy use in the Iowa gambling task: A comparison of win-stay/lose-shift and reinforcement learning models , 2013, Psychonomic bulletin & review.
[64] Karl J. Friston,et al. Variational free energy and the Laplace approximation , 2007, NeuroImage.
[65] Wouter Kool,et al. Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems , 2017, Psychological science.
[66] Olave E Krigolson,et al. Event-related brain potentials and the study of reward processing: Methodological considerations. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[67] Darrell A. Worthy,et al. A Comparison Model of Reinforcement-Learning and Win-Stay-Lose-Shift Decision-Making Processes: A Tribute to W.K. Estes. , 2014, Journal of mathematical psychology.
[68] U. Sailer,et al. Effects of learning on feedback-related brain potentials in a decision-making task , 2010, Brain Research.
[69] Yuan Chang Leong,et al. Dynamic Interaction between Reinforcement Learning and Attention in Multidimensional Environments , 2017, Neuron.
[70] Michael J. Frank,et al. Statistical context dictates the relationship between feedback-related EEG signals and learning , 2019, bioRxiv.
[71] P. Dayan,et al. Reinforcement learning: The Good, The Bad and The Ugly , 2008, Current Opinion in Neurobiology.
[72] S. Debener,et al. Trial-by-Trial Fluctuations in the Event-Related Electroencephalogram Reflect Dynamic Changes in the Degree of Surprise , 2008, The Journal of Neuroscience.
[73] Jeff T. Larsen,et al. The good, the bad and the neutral: Electrophysiological responses to feedback stimuli , 2006, Brain Research.
[74] Karl J. Friston,et al. Bayesian model selection for group studies , 2009, NeuroImage.
[75] Markus Ullsperger,et al. Real and Fictive Outcomes Are Processed Differently but Converge on a Common Adaptive Mechanism , 2013, Neuron.
[76] Andy J. Wills,et al. Model-free and model-based reward prediction errors in EEG , 2018, NeuroImage.
[77] Stefano Palminteri,et al. Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing , 2016, PLoS Comput. Biol..
[78] Nathaniel D. Daw,et al. Trial-by-trial data analysis using computational models , 2011 .
[79] P. Dayan,et al. Cortical substrates for exploratory decisions in humans , 2006, Nature.
[80] C. Braun,et al. Event-Related Brain Potentials Following Incorrect Feedback in a Time-Estimation Task: Evidence for a Generic Neural System for Error Detection , 1997, Journal of Cognitive Neuroscience.
[81] Shinsuke Shimojo,et al. Neural Computations Underlying Arbitration between Model-Based and Model-free Learning , 2013, Neuron.
[82] M. Rushworth,et al. Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control , 2011, Quarterly journal of experimental psychology.
[83] P. Glimcher,et al. JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR 2005, 84, 555–579 NUMBER 3(NOVEMBER) DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS , 2022 .
[84] P. Dayan,et al. Model-based influences on humans’ choices and striatal prediction errors , 2011, Neuron.
[85] Gilles Pourtois,et al. Neurophysiological evidence for evaluative feedback processing depending on goal relevance , 2020, NeuroImage.
[86] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[87] P. Dayan,et al. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.
[88] Clay B. Holroyd,et al. The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. , 2002, Psychological review.
[89] Adrian G. Fischer,et al. The feedback-related negativity indexes prediction error in active but not observational learning. , 2019, Psychophysiology.
[90] M. Steinhauser,et al. Differential effects of instructed and objective feedback reliability on feedback-related brain activity. , 2019, Psychophysiology.