Active Inference in OpenAI Gym: A Paradigm for Computational Investigations Into Psychiatric Illness.
暂无分享,去创建一个
[1] Karl J. Friston,et al. The graphical brain: Belief propagation and active inference , 2017, Network Neuroscience.
[2] Karl J. Friston,et al. Active Inference, Curiosity and Insight , 2017, Neural Computation.
[3] Karl J. Friston,et al. Computational Nosology and Precision Psychiatry , 2017, Computational Psychiatry.
[4] Albert R. Powers,et al. Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors , 2017, Science.
[5] M. Frank,et al. The drift diffusion model as the choice rule in reinforcement learning , 2017, Psychonomic bulletin & review.
[6] Karl J. Friston,et al. Active Inference: A Process Theory , 2017, Neural Computation.
[7] Andrea Vedaldi,et al. ResearchDoom and CocoDoom: Learning Computer Vision with Games , 2016, ArXiv.
[8] Karl J. Friston,et al. Neuroscience and Biobehavioral Reviews , 2022 .
[9] R. Moran,et al. Inputs to prefrontal cortex support visual recognition in the aging brain , 2016, Scientific Reports.
[10] Karl J. Friston,et al. The Functional Anatomy of Time: What and When in the Brain , 2016, Trends in Cognitive Sciences.
[11] Karl J. Friston,et al. Computational Phenotyping in Psychiatry: A Worked Example , 2016, eNeuro.
[12] Karl J. Friston,et al. Active Inference, epistemic value, and vicarious trial and error , 2016, Learning & memory.
[13] Wojciech Zaremba,et al. OpenAI Gym , 2016, ArXiv.
[14] Wojciech Jaskowski,et al. ViZDoom: A Doom-based AI research platform for visual reinforcement learning , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).
[15] Karl J. Friston,et al. A novel framework for improving psychiatric diagnostic nosology , 2016 .
[16] E. Hudlicka. Virtual Affective Agents and Therapeutic Games , 2016 .
[17] Karl J. Friston,et al. Disrupted effective connectivity of cortical systems supporting attention and interoception in melancholia. , 2015, JAMA psychiatry.
[18] Karl J. Friston,et al. The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes , 2014, Cerebral cortex.
[19] Raymond J. Dolan,et al. The anatomy of choice: dopamine and decision-making , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.
[20] Klaas E. Stephan,et al. A model-based analysis of impulsivity using a slot-machine gambling paradigm , 2014, Front. Hum. Neurosci..
[21] A. Mechelli,et al. Using Structural Neuroimaging to Make Quantitative Predictions of Symptom Progression in Individuals at Ultra-High Risk for Psychosis , 2014, Front. Psychiatry.
[22] Raymond J. Dolan,et al. The Brain Ages Optimally to Model Its Environment: Evidence from Sensory Learning over the Adult Lifespan , 2014, PLoS Comput. Biol..
[23] C. Mathys,et al. Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning , 2013, Neuron.
[24] Karl J. Friston,et al. Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation , 2013, The Journal of Neuroscience.
[25] P. Dayan,et al. Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis , 2013, Biology of Mood & Anxiety Disorders.
[26] Karl J. Friston,et al. Canonical Microcircuits for Predictive Coding , 2012, Neuron.
[27] Raymond J. Dolan,et al. Go and no-go learning in reward and punishment: Interactions between affect and effect , 2012, NeuroImage.
[28] S. Rosenman,et al. Psychiatric diagnoses are not mental processes: Wittgenstein on conceptual confusion , 2012, The Australian and New Zealand journal of psychiatry.
[29] Karl J. Friston,et al. Computational psychiatry , 2012, Trends in Cognitive Sciences.
[30] Woojae Kim,et al. A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation. , 2013, Journal of neuroscience, psychology, and economics.
[31] T. Sharot. The optimism bias , 2011, Current Biology.
[32] Joshua W. Brown,et al. A Model-Based fMRI Analysis with Hierarchical Bayesian Parameter Estimation , 2011 .
[33] Karl J. Friston,et al. Action understanding and active inference , 2011, Biological Cybernetics.
[34] D. Zald,et al. Reconsidering anhedonia in depression: Lessons from translational neuroscience , 2011, Neuroscience & Biobehavioral Reviews.
[35] W. Schultz,et al. Neural mechanisms of observational learning , 2010, Proceedings of the National Academy of Sciences.
[36] W. McIlroy,et al. Effectiveness of Virtual Reality Using Wii Gaming Technology in Stroke Rehabilitation: A Pilot Randomized Clinical Trial and Proof of Principle , 2010, Stroke.
[37] P. Dayan,et al. States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning , 2010, Neuron.
[38] Karl J. Friston,et al. Bayesian model selection for group studies , 2009, NeuroImage.
[39] Karl J. Friston,et al. Predictive coding under the free-energy principle , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.
[40] ChrisD . Frith,et al. Perceiving is believing: a Bayesian approach to explaining the positive symptoms of schizophrenia , 2009, Nature Reviews Neuroscience.
[41] Patrick D McGorry,et al. Early intervention in psychosis: concepts, evidence and future directions , 2008, World psychiatry : official journal of the World Psychiatric Association.
[42] Michael F. Green,et al. Anhedonia in schizophrenia: Distinctions between anticipatory and consummatory pleasure , 2007, Schizophrenia Research.
[43] Kenji Doya,et al. Reinforcement learning: Computational theory and biological mechanisms , 2007, HFSP journal.
[44] 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.
[45] P. Dayan,et al. Dopamine, learning, and impulsivity: a biological account of attention-deficit/hyperactivity disorder. , 2005, Journal of child and adolescent psychopharmacology.
[46] A. Cevasco,et al. Comparison of movement-to-music, rhythm activities, and competitive games on depression, stress, anxiety, and anger of females in substance abuse rehabilitation. , 2005, Journal of music therapy.
[47] A. Redish,et al. Addiction as a Computational Process Gone Awry , 2004, Science.
[48] Karl J. Friston,et al. Biophysical models of fMRI responses , 2004, Current Opinion in Neurobiology.
[49] E. Sonuga-Barke,et al. The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics , 2003, Neuroscience & Biobehavioral Reviews.
[50] T. Patterson,et al. Aging and outcome in schizophrenia , 2003, Acta psychiatrica Scandinavica.
[51] H. Gray,et al. On being sad and mistaken: mood effects on the accuracy of thin-slice judgments. , 2002, Journal of personality and social psychology.
[52] Sham M. Kakade,et al. Opponent interactions between serotonin and dopamine , 2002, Neural Networks.
[53] Jon Lee. Maximum entropy sampling , 2001 .
[54] R. Buxton,et al. Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.
[55] Lynn E. DeLisi,et al. Is schizophrenia a lifetime disorder of brain plasticity, growth and aging? , 1997, Schizophrenia Research.
[56] Christopher G. Harris,et al. A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.