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Causal generative models are just a start

Published online by Cambridge University Press:  10 November 2017

Ernest Davis  and
Gary Marcus
Ernest Davis
Affiliation:
Department of Computer Science, New York University, New York, NY 10012. davise@cs.nyu.eduhttp://www.cs.nyu.edu/faculty/davise
Gary Marcus
Affiliation:
Uber AI Labs, San Francisco, CA 94103 Department of Psychology, New York University, New York, NY 10012. Gary.marcus@nyu.eduhttp://garymarcus.com/
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Abstract

Human reasoning is richer than Lake et al. acknowledge, and the emphasis on theories of how images and scenes are synthesized is misleading. For example, the world knowledge used in vision presumably involves a combination of geometric, physical, and other knowledge, rather than just a causal theory of how the image was produced. In physical reasoning, a model can be a set of constraints rather than a physics engine. In intuitive psychology, many inferences proceed without detailed causal generative models. How humans reliably perform such inferences, often in the face of radically incomplete information, remains a mystery.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e262
Copyright
Copyright © Cambridge University Press 2017 

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References

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 of the United States of America 110(45):18327–32.CrossRefGoogle ScholarPubMed
Davis, E. & Marcus, G. (2014) The scope and limits of simulation in cognition. arXiv preprint 1506.04956. Available at: arXiv: http://arxiv.org/abs/1506.04956.Google Scholar
Davis, E. & Marcus, G. (2016) The scope and limits of simulation in automated reasoning. Artificial Intelligence 233:60–72.Google Scholar
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, Pasadena, CA, July 22–25, 2015, ed. Noelle, D. C., Dale, R., Warlaumont, A. S., Yoshimi, J., Matlock, T., Jennings, C. D. & Maglio, P. P., pp. 782–787. Cognitive Science Society.Google Scholar
Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. (2015a) Human-level concept learning through probabilistic program induction. Science 350(6266):1332–38.CrossRefGoogle ScholarPubMed
Sanborn, A. N., Mansingkha, V. K. & Griffiths, T. L. (2013) Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review 120(2):411–37.Google Scholar
Siegler, R. (1976) Three aspects of cognitive development. Cognitive Psychology 8(4):481–520.Google Scholar
Tan, L. H., Spinks, J. A., Eden, G. F., Perfetti, C. A. & Siok, W. T. (2005) Reading depends on writing, in Chinese. Proceedings of the National Academy of Sciences of the United States of America 102(24):8781–85.CrossRefGoogle ScholarPubMed