Computational Psychiatry for Computers

[1]  P. Dayan,et al.  The first steps on long marches: The costs of active observation , 2020 .

[2]  Nick Bostrom,et al.  Ethical Issues in Advanced Artificial Intelligence , 2020 .

[3]  Jessica B. Hamrick,et al.  Levels of Analysis for Machine Learning , 2020, ArXiv.

[4]  Richard Nock,et al.  Adversarial manipulation of human decision-making , 2020, bioRxiv.

[5]  M. Mendl,et al.  Towards a comparative science of emotion: Affect and consciousness in humans and animals , 2019, Neuroscience & Biobehavioral Reviews.

[6]  Samuel J. Gershman,et al.  Analyzing machine-learned representations: A natural language case study , 2019, Cogn. Sci..

[7]  Yonatan Loewenstein,et al.  From choice architecture to choice engineering , 2019, Nature Communications.

[8]  Jeff Clune,et al.  AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence , 2019, ArXiv.

[9]  Cynthia Breazeal,et al.  Machine behaviour , 2019, Nature.

[10]  Falk Lieder,et al.  Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources , 2019, Behavioral and Brain Sciences.

[11]  M. Walter,et al.  Concepts and Dysfunctions of Emotion in Neuropsychiatric Research. , 2019, Advances in experimental medicine and biology.

[12]  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.

[13]  S. Tamang,et al.  Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data , 2018, JAMA internal medicine.

[14]  Sharad Goel,et al.  The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.

[15]  S. Bishop,et al.  Anxiety, Depression, and Decision Making: A Computational Perspective. , 2018, Annual review of neuroscience.

[16]  Marcelo G Mattar,et al.  Prioritized memory access explains planning and hippocampal replay , 2017, Nature Neuroscience.

[17]  S. Gershman,et al.  Where do hypotheses come from? , 2017, Cognitive Psychology.

[18]  P. Dayan,et al.  Algorithms for survival: a comparative perspective on emotions , 2017, Nature Reviews Neuroscience.

[19]  A. Reiter,et al.  Model-Based Control in Dimensional Psychiatry , 2017, Biological Psychiatry.

[20]  Samuel J. Gershman,et al.  Compositional inductive biases in function learning , 2016, Cognitive Psychology.

[21]  N. Daw,et al.  Taking Psychiatry Research Online , 2016, Neuron.

[22]  M. Seligman,et al.  Learned helplessness at fifty: Insights from neuroscience. , 2016, Psychological review.

[23]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

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

[25]  R. Dolan,et al.  Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis , 2016, Trends in Neurosciences.

[26]  Philipp Hennig,et al.  Dual Control for Approximate Bayesian Reinforcement Learning , 2015, J. Mach. Learn. Res..

[27]  Karl J. Friston,et al.  Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis. , 2016, The lancet. Psychiatry.

[28]  Aaron Klein,et al.  Efficient and Robust Automated Machine Learning , 2015, NIPS.

[29]  Samuel J. Gershman,et al.  Computational rationality: A converging paradigm for intelligence in brains, minds, and machines , 2015, Science.

[30]  Peter Dayan,et al.  Decision-Theoretic Psychiatry , 2015 .

[31]  Antoine Cully,et al.  Robots that can adapt like animals , 2014, Nature.

[32]  G. Arbanas.  Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2015 .

[33]  Jenifer Z. Siegel,et al.  Harm to others outweighs harm to self in moral decision making , 2014, Proceedings of the National Academy of Sciences.

[34]  P. Dayan,et al.  Action versus valence in decision making , 2014, Trends in Cognitive Sciences.

[35]  Joseph E LeDoux.  Coming to terms with fear , 2014, Proceedings of the National Academy of Sciences.

[36]  Thomas L. Griffiths,et al.  One and Done? Optimal Decisions From Very Few Samples , 2014, Cogn. Sci..

[37]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[38]  R. Aslin,et al.  Rational snacking: Young children’s decision-making on the marshmallow task is moderated by beliefs about environmental reliability , 2013, Cognition.

[39]  Bradley C. Love,et al.  Thirty years of Marr's Vision: Levels of Analysis in Cognitive Science , 2015, CogSci.

[40]  P. Dayan.  Twenty-Five Lessons from Computational Neuromodulation , 2012, Neuron.

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

[42]  A. Goldman.  To Appear in: , 2008 .

[43]  Toniann Pitassi,et al.  Fairness through awareness , 2011, ITCS '12.

[44]  Mary-Anne Williams,et al.  Social Robotics , 2012, Lecture Notes in Computer Science.

[45]  Maureen O'Hara,et al.  The Microstructure of the “Flash Crash”: Flow Toxicity, Liquidity Crashes, and the Probability of Informed Trading , 2011, The Journal of Portfolio Management.

[46]  Adam Johnson,et al.  Computing motivation: Incentive salience boosts of drug or appetite states , 2008, Behavioral and Brain Sciences.

[47]  Peter Dayan,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[48]  Hubert L. Dreyfus,et al.  What artificial experts can and cannot do , 1992, AI & SOCIETY.

[49]  P. Dayan,et al.  Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.

[50]  Uta Frith,et al.  Theory of mind , 2001, Current Biology.

[51]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[52]  R. Selten,et al.  Bounded rationality: The adaptive toolbox , 2000 .

[53]  Sebastian Thrun,et al.  Learning to Learn: Introduction and Overview , 1998, Learning to Learn.

[54]  E. Fehr.  A Theory of Fairness, Competition and Cooperation , 1998 .

[55]  Clifford Nass,et al.  The media equation - how people treat computers, television, and new media like real people and places , 1996 .

[56]  J. Cacioppo,et al.  Emotional Contagion , 1993 .

[57]  Richard S. Sutton,et al.  Dyna, an integrated architecture for learning, planning, and reacting , 1990, SGAR.

[58]  R. Hinde,et al.  Cooperation and prosocial behaviour , 1991 .

[59]  W. Hershberger.  An approach through the looking-glass , 1986 .

[60]  James O. Berger,et al.  STATISTICAL DECISION THEORY: FOUNDATIONS, CONCEPTS, AND METHODS , 1984 .

[61]  David Marr,et al.  Vision: A computational investigation into the human representation , 1983 .

[62]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[63]  K. Breland,et al.  The misbehavior of organisms. , 1961 .