Neural Computations Underlying Causal Structure Learning
暂无分享,去创建一个
Momchil S. Tomov | Samuel J Gershman | Hayley M. Dorfman | Momchil S Tomov | Hayley M Dorfman | S. Gershman
[1] Jonathan W. Pillow,et al. A Bayesian method for reducing bias in neural representational similarity analysis , 2016, bioRxiv.
[2] Jan Gläscher,et al. Visualization of Group Inference Data in Functional Neuroimaging , 2009, Neuroinformatics.
[3] Denis Cousineau,et al. Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method , 2005 .
[4] Robert C. Wilson,et al. Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms , 2015, The Journal of Neuroscience.
[5] R. Rescorla,et al. Role of context in autoshaping. , 1984 .
[6] J. Kruschke. Bayesian approaches to associative learning: From passive to active learning , 2008, Learning & behavior.
[7] Russell A. Poldrack,et al. Orthogonalization of Regressors in fMRI Models , 2015, PloS one.
[8] Anne G E Collins,et al. Cognitive control over learning: creating, clustering, and generalizing task-set structure. , 2013, Psychological review.
[9] N. Mackintosh,et al. Context specificity of conditioning, extinction, and latent inhibition. , 1984 .
[10] Peter Dayan,et al. Explaining Away in Weight Space , 2000, NIPS.
[11] R. Rescorla,et al. A theory of Pavlovian conditioning : Variations in the effectiveness of reinforcement and nonreinforcement , 1972 .
[12] Jonathan W. Peirce,et al. PsychoPy—Psychophysics software in Python , 2007, Journal of Neuroscience Methods.
[13] Steen Moeller,et al. Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.
[14] Mark E. Bouton,et al. Context effects on conditioning, extinction, and reinstatement in an appetitive conditioning preparation , 1989 .
[15] Timothy E. J. Behrens,et al. Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration , 2013, PLoS biology.
[16] Anders M. Dale,et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.
[17] Fabian A. Soto,et al. Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization. , 2014, Psychological review.
[18] Justin L. Vincent,et al. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. , 2008, Journal of neurophysiology.
[19] A. Pouget,et al. Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.
[20] Karl J. Friston,et al. Comparing the similarity and spatial structure of neural representations: A pattern-component model , 2011, NeuroImage.
[21] Edmund T. Rolls,et al. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas , 2015, NeuroImage.
[22] S. Kakade,et al. Acquisition and extinction in autoshaping. , 2002, Psychological review.
[23] M. Seghier. The Angular Gyrus , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[24] André J. W. van der Kouwe,et al. Brain morphometry with multiecho MPRAGE , 2008, NeuroImage.
[25] Simon B. Eickhoff,et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.
[26] P. Dayan,et al. States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning , 2010, Neuron.
[27] Rainer Goebel,et al. Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[28] Ralph R. Miller,et al. Assessment of the Rescorla-Wagner model. , 1995 .
[29] József Fiser,et al. Perceptual Decision-Making as Probabilistic Inference by Neural Sampling , 2014, Neuron.
[30] S. Gershman. Context-dependent learning and causal structure , 2017, Psychonomic bulletin & review.
[31] N. Daw,et al. Rethinking Extinction , 2015, Neuron.
[32] M. D’Esposito,et al. Frontal Cortex and the Discovery of Abstract Action Rules , 2010, Neuron.
[33] M. Frank,et al. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning , 2016, Cognition.
[34] Karl J. Friston,et al. Bayesian model selection for group studies — Revisited , 2014, NeuroImage.
[35] P. Dayan,et al. Cortical substrates for exploratory decisions in humans , 2006, Nature.
[36] Robert C. Wilson,et al. Orbitofrontal Cortex as a Cognitive Map of Task Space , 2014, Neuron.
[37] R. Bolles,et al. Contextual control of the extinction of conditioned fear , 1979 .
[38] N. Daw,et al. Human Reinforcement Learning Subdivides Structured Action Spaces by Learning Effector-Specific Values , 2009, The Journal of Neuroscience.
[39] Francisco Pereira,et al. Information mapping with pattern classifiers: A comparative study , 2011, NeuroImage.
[40] Michael J Frank,et al. Human EEG Uncovers Latent Generalizable Rule Structure during Learning , 2014, The Journal of Neuroscience.
[41] David Badre,et al. Functional Magnetic Resonance Imaging Evidence for a Hierarchical Organization of the Prefrontal Cortex , 2007, Journal of Cognitive Neuroscience.
[42] Steen Moeller,et al. Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.
[43] Nikolaus Kriegeskorte,et al. Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift , 2015, bioRxiv.
[44] M. Frank,et al. Mechanisms of hierarchical reinforcement learning in corticostriatal circuits 1: computational analysis. , 2012, Cerebral cortex.
[45] N. Mackintosh,et al. Contextual Conditional Discriminations , 1986 .
[46] József Fiser,et al. Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex , 2016, Neuron.
[47] Konrad Paul Kording,et al. Causal Inference in Multisensory Perception , 2007, PloS one.
[48] Joshua B Tenenbaum,et al. Toward the neural implementation of structure learning , 2016, Current Opinion in Neurobiology.
[49] Nikolaus Kriegeskorte,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[50] Yael Niv,et al. A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex , 2016, The Journal of Neuroscience.
[51] D. A. King,et al. Contextual control of the extinction of conditioned fear: tests for the associative value of the context. , 1983, Journal of experimental psychology. Animal behavior processes.
[52] Ralph R. Miller,et al. Context as an occasion setter following either CS acquisition and extinction or CS acquisition alone , 1990 .
[53] C. Summerfield,et al. An information theoretical approach to prefrontal executive function , 2007, Trends in Cognitive Sciences.
[54] Nicolas W. Schuck,et al. Human Orbitofrontal Cortex Represents a Cognitive Map of State Space , 2016, Neuron.
[55] Etienne Koechlin,et al. Foundations of human reasoning in the prefrontal cortex , 2014, Science.
[56] Y. Niv,et al. Discovering latent causes in reinforcement learning , 2015, Current Opinion in Behavioral Sciences.
[57] Samuel J. Gershman,et al. A Tutorial on Bayesian Nonparametric Models , 2011, 1106.2697.
[58] Samuel Gershman,et al. A Unifying Probabilistic View of Associative Learning , 2015, PLoS Comput. Biol..
[59] Stephen M. Smith,et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.
[60] Björn Meder,et al. Structure induction in diagnostic causal reasoning. , 2014, Psychological review.
[61] D. Swartzentruber,et al. Modulatory mechanisms in Pavlovian conditioning , 1995 .
[62] J. Tenenbaum,et al. Structure and strength in causal induction , 2005, Cognitive Psychology.
[63] E. Koechlin,et al. The Architecture of Cognitive Control in the Human Prefrontal Cortex , 2003, Science.
[64] Robert A. Legenstein,et al. Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment , 2014, PLoS Comput. Biol..
[65] N. Mackintosh. A Theory of Attention: Variations in the Associability of Stimuli with Reinforcement , 1975 .
[66] M. Botvinick,et al. Neural representations of events arise from temporal community structure , 2013, Nature Neuroscience.
[67] Edgar A Ycu,et al. Evaluation of ambiguous associations in the amygdala by learning the structure of the environment , 2016, Nature Neuroscience.
[68] Timothy Edward John Behrens,et al. How Green Is the Grass on the Other Side? Frontopolar Cortex and the Evidence in Favor of Alternative Courses of Action , 2009, Neuron.
[69] N. Mackintosh,et al. Context Specificity of Conditioning and Latent Inhibition: Evidence for a Dissociation of Latent Inhibition and Associative Interference , 1987 .
[70] M. Bouton,et al. Analysis of the associative and occasion-setting properties of contexts participating in a Pavlovian discrimination. , 1986 .
[71] Alexander Borst,et al. How does Nature Program Neuron Types? , 2008, Front. Neurosci..
[72] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[73] J. Pearce,et al. Theories of associative learning in animals. , 2001, Annual review of psychology.
[74] Ralph R. Miller,et al. Contextual potentiation of acquired behavior after devaluing direct context-US associations , 1981 .
[75] F. J. Odling-Smee. The Overshadowing of Background Stimuli: Some Effects of Varying Amounts of Training and UCS Intensity , 1978, The Quarterly journal of experimental psychology.