Learning physical parameters from dynamic scenes

[1]  Noah D. Goodman,et al.  Concepts in a Probabilistic Language of Thought , 2014 .

[2]  Susan J. Hespos,et al.  Divisions of the physical world: Concepts of objects and substances. , 2015, Psychological bulletin.

[3]  R. Baillargeon The Acquisition of Physical Knowledge in Infancy: A Summary in Eight Lessons , 2007 .

[4]  Noah D. Goodman,et al.  Learning a theory of causality. , 2011, Psychological review.

[5]  David M. Sobel,et al.  Detecting blickets: how young children use information about novel causal powers in categorization and induction. , 2000, Child development.

[6]  J. Tenenbaum,et al.  Mind Games: Game Engines as an Architecture for Intuitive Physics , 2017, Trends in Cognitive Sciences.

[7]  Amy Needham,et al.  Intuitions about support in 4.5-month-old infants , 1993, Cognition.

[8]  Katherine D. Kinzler,et al.  Core knowledge. , 2007, Developmental science.

[9]  S Ullman,et al.  Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. , 1995, Cerebral cortex.

[10]  S. Runeson,et al.  Realism of Confidence, Modes of Apprehension, and Variable-Use in Visual Discrimination of Relative Mass , 2008 .

[11]  Jessica B. Hamrick,et al.  Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.

[12]  Henrik Olsson,et al.  Visual perception of dynamic properties: cue heuristics versus direct-perceptual competence. , 2000, Psychological review.

[13]  Charles Kemp,et al.  How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.

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

[15]  Noah D. Goodman,et al.  Knowledge and implicature: Modeling language understanding as social cognition , 2012, CogSci.

[16]  K. Stanovich,et al.  Advancing the rationality debate , 2000, Behavioral and Brain Sciences.

[17]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[18]  Edward Vul,et al.  Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference , 2011, Science.

[19]  David Wingate,et al.  Automated Variational Inference in Probabilistic Programming , 2013, ArXiv.

[20]  Sang Ah Lee,et al.  Two systems of spatial representation underlying navigation , 2010, Experimental Brain Research.

[21]  H. Wellman,et al.  Cognitive development: foundational theories of core domains. , 1992, Annual review of psychology.

[22]  F. Lacquaniti,et al.  Does the brain model Newton's laws? , 2001, Nature Neuroscience.

[23]  E. Davis,et al.  How Robust Are Probabilistic Models of Higher-Level Cognition? , 2013, Psychological science.

[24]  Kenneth D. Forbus Qualitative physics: past present and future , 1988 .

[25]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[26]  Jiajun Wu,et al.  Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning , 2015, NIPS.

[27]  J T Todd,et al.  Visual Perception of Relative Mass in Dynamic Events , 1982, Perception.

[28]  R. Stickgold,et al.  Replaying the game: hypnagogic images in normals and amnesics. , 2000, Science.

[29]  Noah D. Goodman,et al.  The anchoring bias reflects rational use of cognitive resources , 2018, Psychonomic bulletin & review.

[30]  Noah D. Goodman,et al.  Theory learning as stochastic search in the language of thought , 2012 .

[31]  E S Spelke,et al.  Infants' sensitivity to effects of gravity on visible object motion. , 1992, Journal of experimental psychology. Human perception and performance.

[32]  Joshua B. Tenenbaum,et al.  Noisy Newtons: Unifying process and dependency accounts of causal attribution , 2012, CogSci.

[33]  S. Carey Bootstrapping & the origin of concepts , 2004, Daedalus.

[34]  D. Proffitt,et al.  Heuristic judgment of mass ratio in two-body collisions , 1994, Perception & psychophysics.

[35]  Christopher G. Lucas,et al.  When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships , 2014, Cognition.

[36]  Tomaso Poggio,et al.  From Understanding Computation to Understanding Neural Circuitry , 1976 .

[37]  Joshua B. Tenenbaum,et al.  A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.

[38]  Kevin A. Smith,et al.  Sources of uncertainty in intuitive physics , 2012, CogSci.

[39]  Charles Kemp,et al.  Evaluating the inverse reasoning account of object discovery , 2015, Cognition.

[40]  Noah D. Goodman,et al.  Deep Amortized Inference for Probabilistic Programs , 2016, ArXiv.

[41]  Susan J. Hespos,et al.  PSYCHOLOGICAL SCIENCE Research Article Five-Month-Old Infants Have Different Expectations for Solids and Liquids , 2022 .

[42]  Noah D. Goodman,et al.  Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs , 2014, Cognitive Systems Research.

[43]  R. Baillargeon Innate Ideas Revisited: For a Principle of Persistence in Infants' Physical Reasoning , 2008, Perspectives on psychological science : a journal of the Association for Psychological Science.

[44]  Adam N. Sanborn,et al.  Bridging Levels of Analysis for Probabilistic Models of Cognition , 2012 .

[45]  S. Denison,et al.  Probabilistic models, learning algorithms, and response variability: sampling in cognitive development , 2014, Trends in Cognitive Sciences.

[46]  Razvan Pascanu,et al.  Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.

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

[48]  Thomas L. Griffiths,et al.  Inferring mass in complex physical scenes via probabilistic simulation , 2013, CogSci.

[49]  S. Sisson,et al.  A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.

[50]  J. Tenenbaum,et al.  A probabilistic model of theory formation , 2010, Cognition.

[51]  Joshua B. Tenenbaum,et al.  Church: a language for generative models , 2008, UAI.

[52]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[53]  Tejas D. Kulkarni,et al.  Deep Generative Vision as Approximate Bayesian Computation , 2014 .

[54]  Thomas L. Griffiths,et al.  "Burn-in, bias, and the rationality of anchoring" , 2012, NIPS.

[55]  Andrew Gelman,et al.  Automatic Variational Inference in Stan , 2015, NIPS.

[56]  Vikash K. Mansinghka,et al.  Reconciling intuitive physics and Newtonian mechanics for colliding objects. , 2013, Psychological review.

[57]  D R Proffitt,et al.  Understanding collision dynamics. , 1989, Journal of experimental psychology. Human perception and performance.

[58]  A. Gopnik,et al.  Mechanisms of theory formation in young children , 2004, Trends in Cognitive Sciences.

[59]  Michael C. Frank,et al.  Relevant and Robust , 2015, Psychological science.

[60]  Nick Chater,et al.  A rational analysis of the selection task as optimal data selection. , 1994 .

[61]  Rineke Verbrugge,et al.  Proceedings of the 34th Annual Conference of the Cognitive Science Society , 2012 .

[62]  J. Tenenbaum,et al.  Using Physical Theories to Infer Hidden Causal Structure , 2004 .

[63]  Joshua B. Tenenbaum,et al.  Multistability and Perceptual Inference , 2012, Neural Computation.

[64]  A. Gopnik,et al.  Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory. , 2012, Psychological bulletin.