Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism
暂无分享,去创建一个
Stefan J. Kiebel | Dimitrije Markovic | Peter Bossaerts | John P. O'Doherty | Jan Gläscher | J. Gläscher | D. Marković | S. Kiebel | P. Bossaerts | J. O’Doherty
[1] Dennis Norris,et al. The Bayesian reader: explaining word recognition as an optimal Bayesian decision process. , 2006, Psychological review.
[2] Adam Kepecs,et al. A computational framework for the study of confidence in humans and animals , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.
[3] Lauren Sandler,et al. One and done. , 2010, Time.
[4] D. Knill,et al. The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.
[5] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[6] B. Milner. Effects of Different Brain Lesions on Card Sorting: The Role of the Frontal Lobes , 1963 .
[7] M. Platt,et al. Risky business: the neuroeconomics of decision making under uncertainty , 2008, Nature Neuroscience.
[8] Stefan J. Kiebel,et al. Online discrimination of nonlinear dynamics with switching differential equations , 2012, 1211.0947.
[9] E. Drewe,et al. The effect of type and area of brain lesion on Wisconsin card sorting test performance. , 1974, Cortex; a journal devoted to the study of the nervous system and behavior.
[10] Stanislas Dehaene,et al. Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[11] T. Poggio,et al. What and where: A Bayesian inference theory of attention , 2010, Vision Research.
[12] Konrad Paul Kording,et al. Review TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Bayesian decision theory in sensorimotor control , 2022 .
[13] Neslihan Serap Sengör,et al. A composite neural network model for perseveration and distractibility in the Wisconsin card sorting test , 2006, Neural Networks.
[14] Amy R. Bland,et al. Different Varieties of Uncertainty in Human Decision-Making , 2012, Front. Neurosci..
[15] S Dehaene,et al. A hierarchical neuronal network for planning behavior. , 1997, Proceedings of the National Academy of Sciences of the United States of America.
[16] Etienne Koechlin,et al. An evolutionary computational theory of prefrontal executive function in decision-making , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.
[17] Vinod Goel,et al. A computational model of frontal lobe dysfunction: working memory and the Tower of Hanoi task , 2001, Cogn. Sci..
[18] Karl J. Friston,et al. A Bayesian Foundation for Individual Learning Under Uncertainty , 2011, Front. Hum. Neurosci..
[19] Yan Meng,et al. Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model , 2011, IEEE Computational Intelligence Magazine.
[20] Christian Igel,et al. Evolution Strategies for Direct Policy Search , 2008, PPSN.
[21] Thomas L. Griffiths,et al. One and Done? Optimal Decisions From Very Few Samples , 2014, Cogn. Sci..
[22] T. Robbins. Dissociating executive functions of the prefrontal cortex. , 1996, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[23] E Guigon,et al. Neural correlates of learning in the prefrontal cortex of the monkey: a predictive model. , 1995, Cerebral cortex.
[24] Ron Sun,et al. The Cambridge Handbook of Computational Psychology , 2008 .
[25] R. Heaton,et al. The utility of the Wisconsin Card Sorting Test in detecting and localizing frontal lobe lesions. , 1980, Journal of consulting and clinical psychology.
[26] Nial Friel,et al. Estimating the evidence – a review , 2011, 1111.1957.
[27] D. Kersten,et al. Illusions, perception and Bayes , 2002, Nature Neuroscience.
[28] Karl J. Friston,et al. Bayesian model selection for group studies , 2009, NeuroImage.
[29] Samuel Kaski,et al. Winner-take-all networks for physiological models of competitive learning , 1994, Neural Networks.
[30] K. Zilles,et al. Hierarchical Processing of Tactile Shape in the Human Brain , 2001, Neuron.
[31] P. Dayan,et al. States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning , 2010, Neuron.
[32] Peter Dayan,et al. Inference, Attention, and Decision in a Bayesian Neural Architecture , 2004, NIPS.
[33] Claudius Gros,et al. Cognitive Computation with Autonomously Active Neural Networks: An Emerging Field , 2009, Cognitive Computation.
[34] Yourong Li,et al. Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy , 2009, Expert Syst. Appl..
[35] Marco K. Wittmann,et al. Multiple Neural Mechanisms of Decision Making and Their Competition under Changing Risk Pressure , 2014, Neuron.
[36] S Dehaene,et al. Hierarchical neuronal modeling of cognitive functions: from synaptic transmission to the Tower of London. , 1998, Comptes rendus de l'Academie des sciences. Serie III, Sciences de la vie.
[37] C. Mathys,et al. Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning , 2013, Neuron.
[38] T. Robbins,et al. The prefrontal cortex: Executive and cognitive functions. , 1998 .
[39] George Houghton,et al. Connectionist models in cognitive psychology , 2004 .
[40] Bing J. Sheu,et al. A high-precision VLSI winner-take-all circuit for self-organizing neural networks , 1993 .
[41] L. Allan,et al. The widespread influence of the Rescorla-Wagner model , 1996, Psychonomic bulletin & review.
[42] J. N. Lyness,et al. Van der Monde systems and numerical differentiation , 1966 .
[43] Melvin J. Hinich,et al. Time Series Analysis by State Space Methods , 2001 .
[44] L. Vaina. Matters of Intelligence , 1987 .
[45] Thomas P. Trappenberg,et al. Modelling divided visual attention with a winner-take-all network , 2005, Neural Networks.
[46] Jonathan D. Cohen,et al. Prefrontal cortex and flexible cognitive control: rules without symbols. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[47] Stephan F Taylor,et al. Updating Beliefs for a Decision: Neural Correlates of Uncertainty and Underconfidence , 2010, The Journal of Neuroscience.
[48] Karl J. Friston,et al. Attention, Uncertainty, and Free-Energy , 2010, Front. Hum. Neurosci..
[49] Gustavo Deco,et al. Neurons and the synaptic basis of the fMRI signal associated with cognitive flexibility , 2005, NeuroImage.
[50] Konrad Paul Kording,et al. Causal Inference in Multisensory Perception , 2007, PloS one.
[51] C. Koch,et al. Attention activates winner-take-all competition among visual filters , 1999, Nature Neuroscience.
[52] Matthew J. Beal. Variational algorithms for approximate Bayesian inference , 2003 .
[53] Edward H. Adelson,et al. Motion illusions as optimal percepts , 2002, Nature Neuroscience.
[54] Élise Payzan Le Nestour. Bayesian Learning in UnstableSettings: Experimental Evidence Based on the Bandit Problem , 2010 .
[55] Nicolas P. Rougier,et al. Learning representations in a gated prefrontal cortex model of dynamic task switching , 2002, Cogn. Sci..
[56] Karl J. Friston,et al. Comparing Families of Dynamic Causal Models , 2010, PLoS Comput. Biol..
[57] Karl J. Friston,et al. Spatial Attention, Precision, and Bayesian Inference: A Study of Saccadic Response Speed , 2013, Cerebral cortex.
[58] Karl J. Friston,et al. Bayesian model selection for group studies — Revisited , 2014, NeuroImage.
[59] Luigi Acerbi,et al. On the Origins of Suboptimality in Human Probabilistic Inference , 2014, PLoS Comput. Biol..
[60] Yuguang Fang,et al. Dynamics of a Winner-Take-All Neural Network , 1996, Neural Networks.
[61] J. J. Hopfield,et al. “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.
[62] James L. McClelland,et al. The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.
[63] Karl J. Friston,et al. Computational modeling of perceptual inference: A hierarchical Bayesian approach that allows for individual and contextual differences in weighting of input , 2012 .
[64] James L. McClelland,et al. Connectionist models of cognition. , 2008 .
[65] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[66] J. Changeux,et al. The Wisconsin Card Sorting Test: theoretical analysis and modeling in a neuronal network. , 1991, Cerebral cortex.
[67] Jerome R Busemeyer,et al. Sequential Learning Models for the Wisconsin Card Sort Task: Assessing Processes in Substance Dependent Individuals. , 2010, Journal of mathematical psychology.
[68] Timothy E. J. Behrens,et al. Learning the value of information in an uncertain world , 2007, Nature Neuroscience.
[69] Petros Koumoutsakos,et al. Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.
[70] Richard N Aslin,et al. Bayesian learning of visual chunks by human observers , 2008, Proceedings of the National Academy of Sciences.
[71] Wolfgang M. Pauli,et al. Computational models of cognitive control , 2010, Current Opinion in Neurobiology.
[72] Sylvain Arlot,et al. A survey of cross-validation procedures for model selection , 2009, 0907.4728.
[73] Y. Munakata,et al. Active versus latent representations: a neural network model of perseveration, dissociation, and decalage. , 2002, Developmental psychobiology.
[74] Joan L. Walker,et al. Risk, uncertainty and discrete choice models , 2008 .
[75] Karl J. Friston,et al. Observing the Observer (II): Deciding When to Decide , 2010, PloS one.
[76] S Berdia,et al. An artificial neural network stimulating performance of normal subjects and schizophrenics on the Wisconsin card sorting test , 1998, Artif. Intell. Medicine.
[77] Simon M. Lucas,et al. Parallel Problem Solving from Nature - PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings , 2008, PPSN.
[78] J. Rauschecker,et al. Hierarchical Organization of the Human Auditory Cortex Revealed by Functional Magnetic Resonance Imaging , 2001, Journal of Cognitive Neuroscience.
[79] R. R. Miller,et al. Assessment of the Rescorla-Wagner model. , 1995, Psychological bulletin.
[80] Richard Granger,et al. A cortical model of winner-take-all competition via lateral inhibition , 1992, Neural Networks.
[81] Bard Ermentrout,et al. Complex dynamics in winner-take-all neural nets with slow inhibition , 1992, Neural Networks.
[82] Karl J. Friston,et al. A Hierarchy of Time-Scales and the Brain , 2008, PLoS Comput. Biol..
[83] Maneesh Sahani,et al. Attention in a Bayesian Framework , 2012, Front. Hum. Neurosci..
[84] H. Pashler,et al. Measuring the Crowd Within , 2008, Psychological science.
[85] Robert C. Wilson,et al. Inferring Relevance in a Changing World , 2012, Front. Hum. Neurosci..
[86] Rajesh P. N. Rao,et al. Bayesian Inference and Attentional Modulation in the Visual Cortex Correspondence and Requests for Reprints to Rajesh , 2005 .
[87] Karl J. Friston,et al. Action and behavior: a free-energy formulation , 2010, Biological Cybernetics.
[88] Nikolaus Hansen,et al. Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.
[89] H. Nelson. A Modified Card Sorting Test Sensitive to Frontal Lobe Defects , 1976, Cortex.
[90] Verena Heidrich-Meisner,et al. Neuroevolution strategies for episodic reinforcement learning , 2009, J. Algorithms.
[91] A. Yuille,et al. Object perception as Bayesian inference. , 2004, Annual review of psychology.
[92] Adam Binch,et al. Perception as Bayesian Inference , 2014 .
[93] J. Changeux,et al. Neuronal Models of Prefrontal Cortical Functions , 1995, Annals of the New York Academy of Sciences.
[94] Karl J. Friston,et al. Observing the Observer (I): Meta-Bayesian Models of Learning and Decision-Making , 2010, PloS one.
[95] S. Gerhand. THE PREFRONTAL CORTEX—EXECUTIVE AND COGNITIVE FUNCTIONS. , 1999 .
[96] Wolfgang Maass,et al. On the Computational Power of Winner-Take-All , 2000, Neural Computation.
[97] Timothy E. J. Behrens,et al. Perceptual Classification in a Rapidly Changing Environment , 2011, Neuron.