A model for learning based on the joint estimation of stochasticity and volatility

[1]  Houeto Jean-Luc.  [Parkinson's disease]. , 2022, La Revue du praticien.

[2]  Eduardo A. Aponte,et al.  TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry , 2021, bioRxiv.

[3]  Katharina V. Wellstein,et al.  Hierarchical Bayesian models of social inference for probing persecutory delusional ideation. , 2020, Journal of abnormal psychology.

[4]  Payam Piray,et al.  A simple model for learning in volatile environments , 2020, PLoS Comput. Biol..

[5]  Guillermo Horga,et al.  Hallucinations and Delusions Relate to Distinct Hierarchical Alterations in Intrinsic Neural Timescales , 2020, Biological Psychiatry.

[6]  P. Corlett,et al.  Paranoia as a deficit in non-social belief updating , 2020, eLife.

[7]  A. Abi-Dargham,et al.  An integrative framework for perceptual disturbances in psychosis , 2019, Nature Reviews Neuroscience.

[8]  Eduardo A. Aponte,et al.  Subjective estimates of uncertainty during gambling and impulsivity after subthalamic deep brain stimulation for Parkinson’s disease , 2019, Scientific Reports.

[9]  Klaas E. Stephan,et al.  Atypical processing of uncertainty in individuals at risk for psychosis , 2019, NeuroImage: Clinical.

[10]  M. Browning,et al.  The Misestimation of Uncertainty in Affective Disorders , 2019, Trends in Cognitive Sciences.

[11]  Rebecca L. Bond,et al.  Altered learning under uncertainty in unmedicated mood and anxiety disorders , 2019, Nature Human Behaviour.

[12]  A. Soltani,et al.  Adaptive learning under expected and unexpected uncertainty , 2019, Nature Reviews Neuroscience.

[13]  Karl J. Friston,et al.  In the Body’s Eye: The computational anatomy of interoceptive inference , 2019, bioRxiv.

[14]  N. Daw,et al.  A distinct inferential mechanism for delusions in schizophrenia. , 2019, Brain : a journal of neurology.

[15]  S. Southwick,et al.  Neural computations of threat in the aftermath of combat trauma , 2019, Nature Neuroscience.

[16]  R. Cools,et al.  Emotionally Aversive Cues Suppress Neural Systems Underlying Optimal Learning in Socially Anxious Individuals , 2018, The Journal of Neuroscience.

[17]  P. Dayan,et al.  When planning to survive goes wrong: predicting the future and replaying the past in anxiety and PTSD , 2018, Current Opinion in Behavioral Sciences.

[18]  Norbert Kathmann,et al.  Modeling subjective relevance in schizophrenia and its relation to aberrant salience , 2018, PLoS Comput. Biol..

[19]  Massimo Silvetti,et al.  Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner , 2018, PLoS Comput. Biol..

[20]  Alexandre Zénon,et al.  Learning and forgetting using reinforced Bayesian change detection , 2018, bioRxiv.

[21]  J. Flanagan,et al.  The Integrative Review. , 2018, International journal of nursing knowledge.

[22]  C. Mathys,et al.  Volatility estimates increase choice switching and relate to prefrontal activity in schizophrenia , 2017, bioRxiv.

[23]  C. Mathys,et al.  Representational Uncertainty in the Brain During Threat Conditioning and the Link With Psychopathic Traits. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[24]  Katia M. Harlé,et al.  Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making , 2017, PloS one.

[25]  M. Browning,et al.  Affective bias as a rational response to the statistics of rewards and punishments , 2017, eLife.

[26]  M. Paulus,et al.  Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise , 2017, Biological Psychiatry.

[27]  Albert R. Powers,et al.  Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors , 2017, Science.

[28]  G. Rees,et al.  Adults with autism over-estimate the volatility of the sensory environment , 2017, Nature Neuroscience.

[29]  Alireza Soltani,et al.  Optimal structure of metaplasticity for adaptive learning , 2017, bioRxiv.

[30]  C. H. Donahue,et al.  Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty , 2017, Neuron.

[31]  Vincent D Costa,et al.  Motivational neural circuits underlying reinforcement learning , 2017, Nature Neuroscience.

[32]  D. Schwarzkopf,et al.  Unexpected arousal modulates the influence of sensory noise on confidence , 2016, eLife.

[33]  József Fiser,et al.  Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex , 2016, Neuron.

[34]  Vincent D Costa,et al.  Amygdala and Ventral Striatum Make Distinct Contributions to Reinforcement Learning , 2016, Neuron.

[35]  Christopher J. Mitchell,et al.  Attention and associative learning in humans: An integrative review. , 2016, Psychological bulletin.

[36]  Matthew R Nassar,et al.  Age differences in learning emerge from an insufficient representation of uncertainty in older adults , 2016, Nature Communications.

[37]  Peter C. Holland,et al.  Mini-review: Prediction errors, attention and associative learning , 2016, Neurobiology of Learning and Memory.

[38]  R. Dolan,et al.  Computations of uncertainty mediate acute stress responses in humans , 2016, Nature Communications.

[39]  M. Frank,et al.  Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.

[40]  K. Stephan,et al.  Translational Perspectives for Computational Neuroimaging , 2015, Neuron.

[41]  Wolfram Schultz,et al.  Scaling prediction errors to reward variability benefits error-driven learning in humans , 2015, Journal of neurophysiology.

[42]  P. Dayan,et al.  Depression: a decision-theoretic analysis. , 2015, Annual review of neuroscience.

[43]  A. Hamm,et al.  UPDATED META‐ANALYSIS OF CLASSICAL FEAR CONDITIONING IN THE ANXIETY DISORDERS , 2015, Depression and anxiety.

[44]  Timothy E. J. Behrens,et al.  Anxious individuals have difficulty learning the causal statistics of aversive environments , 2015, Nature Neuroscience.

[45]  Joseph T. McGuire,et al.  Functionally Dissociable Influences on Learning Rate in a Dynamic Environment , 2014, Neuron.

[46]  Klaas E. Stephan,et al.  Inferring on the Intentions of Others by Hierarchical Bayesian Learning , 2014, PLoS Comput. Biol..

[47]  Karolina M. Lempert,et al.  Emotion and decision making: multiple modulatory neural circuits. , 2014, Annual review of neuroscience.

[48]  Robert C. Wilson,et al.  Orbitofrontal Cortex as a Cognitive Map of Task Space , 2014, Neuron.

[49]  D. Gamerman,et al.  A NON‐GAUSSIAN FAMILY OF STATE‐SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD , 2013 .

[50]  C. Mathys,et al.  Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning , 2013, Neuron.

[51]  Robert C. Wilson,et al.  A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems , 2013, PLoS Comput. Biol..

[52]  Angela J. Yu,et al.  Emotion and decision-making: affect-driven belief systems in anxiety and depression , 2012, Trends in Cognitive Sciences.

[53]  Catherine A. Hartley,et al.  Anxiety and Decision-Making , 2012, Biological Psychiatry.

[54]  Robert C. Wilson,et al.  Rational regulation of learning dynamics by pupil–linked arousal systems , 2012, Nature Neuroscience.

[55]  Guillem R. Esber,et al.  Surprise! Neural correlates of Pearce–Hall and Rescorla–Wagner coexist within the brain , 2012, The European journal of neuroscience.

[56]  Karl J. Friston,et al.  Computational psychiatry , 2012, Trends in Cognitive Sciences.

[57]  N. Daw,et al.  Differential roles of human striatum and amygdala in associative learning , 2011, Nature Neuroscience.

[58]  Greg Hajcak,et al.  Intolerance of uncertainty and decisions about delayed, probabilistic rewards. , 2011, Behavior therapy.

[59]  Karl J. Friston,et al.  Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making , 2010, PloS one.

[60]  Robert C. Wilson,et al.  An Approximately Bayesian Delta-Rule Model Explains the Dynamics of Belief Updating in a Changing Environment , 2010, The Journal of Neuroscience.

[61]  Joshua I. Gold,et al.  Bayesian Online Learning of the Hazard Rate in Change-Point Problems , 2010, Neural Computation.

[62]  Michael N. Shadlen,et al.  Temporal context calibrates interval timing , 2010, Nature Neuroscience.

[63]  P. Berkes,et al.  Statistically optimal perception and learning: from behavior to neural representations , 2010, Trends in Cognitive Sciences.

[64]  Karl J. Friston.  The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[65]  Peter M. Jones,et al.  Two kinds of attention in Pavlovian conditioning: evidence for a hybrid model of learning. , 2010, Journal of experimental psychology. Animal behavior processes.

[66]  D. Wolpert,et al.  Changing your mind: a computational mechanism of vacillation , 2009, Nature.

[67]  M. Shadlen,et al.  Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex , 2009, Science.

[68]  Scott D. Brown,et al.  Detecting and predicting changes , 2009, Cognitive Psychology.

[69]  Mark W Woolrich,et al.  Associative learning of social value , 2008, Nature.

[70]  Konrad Paul Kording,et al.  Causal Inference in Multisensory Perception , 2007, PloS one.

[71]  Karl J. Friston,et al.  Free-energy and the brain , 2007, Synthese.

[72]  Timothy E. J. Behrens,et al.  Learning the value of information in an uncertain world , 2007, Nature Neuroscience.

[73]  J. O'Doherty,et al.  Contributions of the Amygdala to Reward Expectancy and Choice Signals in Human Prefrontal Cortex , 2007, Neuron.

[74]  Synichi Yamamoto,et al.  Space , 2007, SIGGRAPH '07.

[75]  D. Wolpert.  Probabilistic models in human sensorimotor control. , 2007, Human movement science.

[76]  Jadin C. Jackson,et al.  Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. , 2007, Psychological review.

[77]  J. Rieser,et al.  Bayesian integration of spatial information. , 2007, Psychological bulletin.

[78]  Konrad Paul Kording,et al.  The dynamics of memory as a consequence of optimal adaptation to a changing body , 2007, Nature Neuroscience.

[79]  P. Dayan,et al.  Cortical substrates for exploratory decisions in humans , 2006, Nature.

[80]  Karl J. Friston,et al.  Synaptic Plasticity and Dysconnection in Schizophrenia , 2006, Biological Psychiatry.

[81]  C. Grillon,et al.  Classical fear conditioning in the anxiety disorders: a meta-analysis. , 2005, Behaviour research and therapy.

[82]  Angela J. Yu,et al.  Uncertainty, Neuromodulation, and Attention , 2005, Neuron.

[83]  M. Bouton.  Context and behavioral processes in extinction. , 2004, Learning & memory.

[84]  J. Pearce,et al.  A partial reinforcement extinction effect despite equal rates of reinforcement during Pavlovian conditioning. , 2004, Journal of experimental psychology. Animal behavior processes.

[85]  M. Pelley.  The Role of Associative History in Models of Associative Learning: A Selective Review and a Hybrid Model: , 2004 .

[86]  M. L. Le Pelley.  The Role of Associative History in Models of Associative Learning: A Selective Review and a Hybrid Model , 2004, The Quarterly journal of experimental psychology. B, Comparative and physiological psychology.

[87]  Konrad Paul Kording,et al.  Bayesian integration in sensorimotor learning , 2004, Nature.

[88]  R C Miall,et al.  System Identification Applied to a Visuomotor Task: Near-Optimal Human Performance in a Noisy Changing Task , 2003, The Journal of Neuroscience.

[89]  Peter Dayan,et al.  Uncertainty and Learning , 2003 .

[90]  S. Kakade,et al.  Acquisition and extinction in autoshaping , 2002 .

[91]  S. Kakade,et al.  Learning and selective attention , 2000, Nature Neuroscience.

[92]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[93]  R. Ladouceur,et al.  Experimental manipulation of intolerance of uncertainty: a study of a theoretical model of worry. , 2000, Behaviour research and therapy.

[94]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[95]  C. Gallistel,et al.  Time, rate, and conditioning. , 2000, Psychological review.

[96]  R. Rescorla.  Within-subject Partial Reinforcement Extinction Effect in Autoshaping , 1999 .

[97]  P. Holland,et al.  Amygdala circuitry in attentional and representational processes , 1999, Trends in Cognitive Sciences.

[98]  D. Wolpert,et al.  Signal-dependent noise determines motor planning , 1998, Nature.

[99]  M. Freeston,et al.  Generalized anxiety disorder: a preliminary test of a conceptual model. , 1998, Behaviour research and therapy.

[100]  Peter Dayan,et al.  Statistical Models of Conditioning , 1997, NIPS.

[101]  M. Gallagher,et al.  Amygdala central nucleus lesions disrupt increments, but not decrements, in conditioned stimulus processing. , 1993, Behavioral neuroscience.

[102]  M. West.  On scale mixtures of normal distributions , 1987 .

[103]  D. Marr.  Vision: A Computational Investigation into the Human Representation and Processing of Visual Information , 1983 .

[104]  J. Pearce,et al.  Restoring the Associability of a Pre-Exposed CS by a Surprising Event , 1982 .

[105]  J. Pearce,et al.  A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. , 1980, Psychological review.

[106]  H. Terrace,et al.  Partial reinforcement in autoshaping with pigeons , 1980 .

[107]  N. Mackintosh.  A Theory of Attention: Variations in the Associability of Stimuli with Reinforcement , 1975 .

[108]  R. Kálmán.  A new approach to linear filtering and prediction problems" transaction of the asme~journal of basic , 1960 .

[109]  J. Gold,et al.  The human as delta-rule learner. , 2020 .

[110]  D. Blei,et al.  Context, learning, and extinction. , 2010, Psychological review.

[111]  N. Mackintosh,et al.  Two theories of attention: a review and a possible integration , 2010 .

[112]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[113]  Aaron C. Courville,et al.  The rat as particle filter , 2007, NIPS.

[114]  C. Jansen,et al.  Severity: A meta-analysis , 2006 .

[115]  Thomas L. Griffiths,et al.  A more rational model of categorization , 2006 .

[116]  J. Gittins.  Bandit processes and dynamic allocation indices , 1979 .

[117]  A. Beck,et al.  Depression: Causes and Treatment , 1967 .

[118]  D. Touretzky,et al.  Bayesian theories of conditioning in a changing world , 2006, Trends in Cognitive Sciences.

[119]  J. Daunizeau,et al.  A Bayesian Foundation for Individual Learning Under Uncertainty , 2011, Front. Hum. Neurosci..