Reading list

(Might need to categorize better).

• Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. arXiv preprint arXiv:1604.00289. Post.

Quality and resolution of mental simulations

• Crespi, S., Robino, C., Silva, O., & de'Sperati, C. (2012). Spotting expertise in the eyes: Billiards knowledge as revealed by gaze shifts in a dynamic visual prediction task. Journal of vision, 12(11), 30-30.
• Hamrick, J. B., & Griffiths, T. L. (2014). What to simulate? Inferring the right direction for mental rotation. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society.
• Hamrick, J. B., Smith, K. A., Griffiths, T. L., & Vul, E. (2015). Think again? The amount of mental simulation tracks uncertainty in the outcome. In Proceedings of the thirtyseventh annual conference of the cognitive science society.
• Schwartz, D. L., & Black, J. B. (1996). Analog imagery in mental model reasoning: Depictive models. Cognitive Psychology, 30(2), 154-219.
• Smith, K., Dechter, E., Tenenbaum, J., & Vul, E. (2013). Physical predictions over time. In Proceedings of the 35th annual meeting of the cognitive science society (pp. 1-6).
• Saxe, R. (2005). Against simulation: the argument from error. Trends in cognitive sciences, 9(4), 174-179.
• Lieder, F., & Griffiths, T. L. (2015). When to use which heuristic: A rational solution to the strategy selection problem. In CogSci.
• Smith, K. A., Battaglia, P., & Vul, E. (2013, July). Consistent physics underlying ballistic motion prediction. In CogSci.
• Schwartz, D. L., & Black, T. (1999). Inferences through imagined actions: Knowing by simulated doing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(1), 116.
• Runeson, S., Juslin, P., & Olsson, H. (2000). Visual perception of dynamic properties: cue heuristics versus direct-perceptual competence. Psychological review, 107(3), 525.

Intuitive physics

• Spelke, E. S. (1990). Cognitive capacities of human infants: Conceptions of object motion. Signal and sense: Local and global order in the nervous system. NY: Wiley. Post.
• Spelke, E. S., Gutheil, G., & Van de Walle, G. (1995). The development of object perception.
• Bates, C. J., Yildirim, I., Tenenbaum, J. B., & Battaglia, P. W. (2015). Humans predict liquid dynamics using probabilistic simulation. In Proceedings of the 37th annual conference of the cognitive science society.
• Battaglia, P. W., Hamrick, J. B., & Tenenbaum, J. B. (2013). Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences, 110(45), 18327-18332. [IPE]
• Gerstenberg, T., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (2015). How, whether, why: Causal judgments as counterfactual contrasts. In Proceedings of the 37th annual conference of the cognitive science society (pp. 782-787).
• Sanborn, A. N., Mansinghka, V. K., & Griffiths, T. L. (2013). Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological review, 120(2), 411. [noisy Newtonian model]
• Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., & Tenenbaum, J. B. (2016). Inferring mass in complex scenes by mental simulation. Cognition, 157, 61-76. Finish notes / second reading.
• Smith, K. A., & Vul, E. (2013). Sources of uncertainty in intuitive physics. Topics in cognitive science, 5(1), 185-199.
• Mason, R., & Just, M. (2016). Neural representations of physics concepts. Psychological Science 27 (6), 904-913.
• Fischer, J., Mikhael, J. G., Tenenbaum, J. B., & Kanwisher, N. (2016). Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences, 201610344. Post.
• Yildirim, I., Siegel, M. H., & Tenenbaum, J. B. Perceiving Fully Occluded Objects via Physical Simulation. Quick glance summary.
• Kozhevnikov, M., & Hegarty, M. (2001). Impetus beliefs as default heuristics: Dissociation between explicit and implicit knowledge about motion. Psychonomic Bulletin & Review, 8(3), 439-453.
• Yildirim, I., Wu, J., Du, Y., & Tenenbaum, J. B. Interpreting Dynamic Scenes by a Physics Engine and Bottom-Up Visual Cues.

Innate aspects of intuitive physics

• Baillargeon, R. (2002). The acquisition of physical knowledge in infancy: A summary in eight lessons. Blackwell handbook of childhood cognitive development, 47–83. Post.

Static learning of intuitive physics

• Kemp, C., Tenenbaum, J. B., Niyogi, S., & Griffiths, T. L. (2010). A probabilistic model of theory formation. Cognition, 114(2), 165-196.

Dynamic learning of intuitive physics

• Ullman, T., Stuhlmüller, A., Goodman, N., & Tenenbaum, J. B. (2014). Learning physics from dynamical scenes. In Proceedings of the 36th Annual Conference of the Cognitive Science society (pp. 1640-1645). Finish notes / second reading.

Collision dynamics

• Gilden, D. L., & Proffitt, D. R. (1989). Understanding collision dynamics. Journal of Experimental Psychology: Human Perception and Performance, 15(2), 372.

Perception versus prediction: which systems do physical tasks use or invoke?

• White, P. A. (2012). The experience of force: the role of haptic experience of forces in visual perception of object motion and interactions, mental simulation, and motion-related judgments. Psychological bulletin, 138(4), 589.
• Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N., & Griffiths, T. (2014). Algorithm selection by rational metareasoning as a model of human strategy selection. In Advances in Neural Information Processing Systems (pp. 2870-2878).

Simple heuristics modelling

• Proffitt, D. R., & Gilden, D. L. (1989). Understanding natural dynamics. Journal of Experimental Psychology: Human Perception and Performance, 15(2), 384.
• Todd, J. T., & Warren, W. H. (1982). Visual perception of relative mass in dynamic events. Perception, 11(3), 325-335.

Using neural networks to predict physics outcomes

• Lerer, A., Gross, S., & Fergus, R. (2016). Learning Physical Intuition of Block Towers by Example. arXiv preprint arXiv:1603.01312.
• Denil, M., Agrawal, P., Kulkarni, T. D., Erez, T., Battaglia, P., & de Freitas, N. (2016). Learning to Perform Physics Experiments via Deep Reinforcement Learning. arXiv preprint arXiv:1611.01843.
• Zhang, R., Wu, J., Zhang, C., Freeman, W. T., & Tenenbaum, J. B. (2016). A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding. arXiv preprint arXiv:1605.01138.
• Fragkiadaki, K., Agrawal, P., Levine, S., & Malik, J. (2015). Learning Visual Predictive Models of Physics for Playing Billiards. arXiv preprint arXiv:1511.07404. [Implements intuitive physics in RNN]
• Finn, C., Goodfellow, I., & Levine, S. (2016). Unsupervised learning for physical interaction through video prediction. In Advances In Neural Information Processing Systems (pp. 64-72).
• Mottaghi, R., Bagherinezhad, H., Rastegari, M., & Farhadi, A. (2016). Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3521-3529).
• Chang, M. B., Ullman, T., Torralba, A., & Tenenbaum, J. B. (2016). A compositional object-based approach to learning physical dynamics. arXiv preprint arXiv:1612.00341.
• Freer, C. E., Roy, D. M., & Tenenbaum, J. B. (2012). Towards common-sense reasoning via conditional simulation: Legacies of Turing in artificial intelligence. arXiv preprint arXiv:1212.4799.
• Fragkiadaki, K., Agrawal, P., Levine, S., & Malik, J. (2015). Learning visual predictive models of physics for playing billiards. arXiv preprint arXiv:1511.07404.
• Battaglia, P., Pascanu, R., Lai, M., & Rezende, D. J. (2016). Interaction networks for learning about objects, relations and physics. In Advances in Neural Information Processing Systems (pp. 4502-4510).
• Schulz, E., Tenenbaum, J., Duvenaud, D. K., Speekenbrink, M., & Gershman, S. J. (2016). Probing the compositionality of intuitive functions. In Advances In Neural Information Processing Systems (pp. 3729-3737).
• Degrave, J., Hermans, M., & Dambre, J. (2016). A Differentiable Physics Engine for Deep Learning in Robotics. arXiv preprint arXiv:1611.01652.

Overview of Gaussian naive Bayes classifier cross-validation as applied to fMRI data

• Just, M. A., Cherkassky, V. L., Aryal, S., & Mitchell, T. M. (2010). A neurosemantic theory of concrete noun representation based on the underlying brain codes. PloS one, 5(1), e8622.

Bayesian models of cognition

• Griffiths, T.L., Kemp, C., & Tenenbaum, J.B. (2008). Bayesian models of cognition. In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press. Finish notes / second reading.
• Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011). A Tutorial Introduction to Bayesian Models of Cognitive Development. Cognition, 120, 302-321.
• Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331, 1279-1285.
• Gershman, S. J., Horvitz, E. J., & Tenenbaum, J. B. (2015). Computational Rationality: A Converging Paradigm for Intelligence in Brains, Minds, and Machines. Science, 349 (6245), 273-278.
• Teglas, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., & Bonatti, L. L. (2011). Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference. Science, 332, 1054-1059. Optional: Supplementary material for the Teglas et al. article.
• Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory Learning as Stochastic Search in the Language of Thought. Cognitive Development, 27(4), 455-480.
• Petzschner, F.H., Glasauer, S., & Stephan, K.E. (2015). A Bayesian perspective on magnitude estimation. Trends in cognitive sciences 19 (5), 285-293.
• Tenenbaum, J.B., Griffiths, T.L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in cognitive sciences, 10(7), 309-318.
• Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J. (2016). Toward the neural implementation of structure learning. Current opinion in neurobiology, 37, 99-105.
• Gershman, S. J., & Beck, J. M. (2016). Complex probabilistic inference: from cognition to neural computation. Computational Models of Brain and Behavior, ed A. Moustafa (Hoboken, NJ: Wiley-Blackwell).
• Tran, D., Hoffman, M. D., Saurous, R. A., Brevdo, E., Murphy, K., & Blei, D. M. (2017). Deep Probabilistic Programming. arXiv preprint arXiv:1701.03757.
• Sanborn, A. N. (2015). Types of approximation for probabilistic cognition: Sampling and variational. Brain and cognition.

Challenges of probabilistic models

Ventral visual pathway used not only for object recognition but also physical scene understanding

• Gallivan, J. P., Cant, J. S., Goodale, M. A., & Flanagan, J. R. (2014). Representation of object weight in human ventral visual cortex. Current Biology, 24(16), 1866-1873.
• Vaziri, S., & Connor, C. E. (2016). Representation of Gravity-Aligned Scene Structure in Ventral Pathway Visual Cortex. Current Biology, 26(6), 766-774. Quick glance summary.

Working memory

• Brady, T. F., Konkle, T., & Alvarez, G. A. (2009). Compression in visual working memory: using statistical regularities to form more efficient memory representations. Journal of Experimental Psychology: General, 138(4), 487.

Event-type representations in adults

• Strickland, B., & Scholl, B. J. (2015). Visual perception involves event-type representations: The case of containment versus occlusion. Journal of Experimental Psychology: General, 144(3), 570. Post.

Other

• Peters, M. A., Ma, W. J., & Shams, L. (2016). The Size-Weight Illusion is not anti-Bayesian after all: a unifying Bayesian account. PeerJ, 4, e2124. Quick glance summary.
• Funamizu, A., Kuhn, B., & Doya, K. (2016). Neural substrate of dynamic Bayesian inference in the cerebral cortex. Nature Neuroscience, 19(12), 1682-1689.

Ensemble Perception

• Haberman, J., & Whitney, D. (2012). Ensemble perception: Summarizing the scene and broadening the limits of visual processing. From perception to consciousness: Searching with Anne Treisman, 339-349.
• Alvarez, G. A., & Oliva, A. (2009). Spatial ensemble statistics are efficient codes that can be represented with reduced attention. Proceedings of the National Academy of Sciences, 106(18), 7345-7350.
• Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in cognitive sciences, 15(3), 122-131.
• Zhao, J., Ngo, N., McKendrick, R., & Turk-Browne, N. B. (2011). Mutual interference between statistical summary perception and statistical learning. Psychological Science.
• Emmanouil, T. A., & Treisman, A. (2008). Dividing attention across feature dimensions in statistical processing of perceptual groups. Perception & psychophysics, 70(6), 946-954.
• Albrecht, A. R., & Scholl, B. J. (2010). Perceptually averaging in a continuous visual world extracting statistical summary representations over time. Psychological Science, 21(4), 560-567.

Orientation

• McCloskey, M., Valtonen, J., & Cohen Sherman, J. (2006). Representing orientation: A coordinate-system hypothesis and evidence from developmental deficits. Cognitive Neuropsychology, 23(5), 680-713.

Sequence learning journal club

• Luft, C. D., Meeson, A., Welchman, A. E., & Kourtzi, Z. (2015). Decoding the future from past experience: learning shapes predictions in early visual cortex. Journal of neurophysiology, 113(9), 3159-3171. Post.
• Fiser, J., & Aslin, R. N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 458.

General

• Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2016). Discovering event structure in continuous narrative perception and memory. bioRxiv, 081018. Quick glance summary.
• Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., & Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. Nature Neuroscience, 20(1), 115-125.
• Cravo, A. M., Rohenkohl, G., Santos, K. M., & Nobre, A. C. (2016). Temporal anticipation based on memory. bioRxiv, 090555.

Vision seminar

• Otten, M., Pinto, Y., Paffen, C. L., Seth, A. K., & Kanai, R. (2016). The uniformity illusion: central stimuli can determine peripheral perception. Psychological Science.

Sensorimotor journal club

• Suvrathan, A., Payne, H. L., & Raymond, J. L. (2016). Timing Rules for Synaptic Plasticity Matched to Behavioral Function. Neuron, 92(5), 959-967.

McCloskey lab meetings

• Rapp, B., & Caramazza, A. (1997). From graphemes to abstract letter shapes: levels of representation in written spelling. Journal of experimental psychology: human perception and performance, 23(4), 1130.