| Paper: | PS-1A.25 |
| Session: | Poster Session 1A |
| Location: | Symphony/Overture |
| Session Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation Time: | Thursday, September 6, 16:30 - 18:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Modeling the Intuitive Physics of Stability Judgments Using Deep Hierarchical Convolutional Neural Networks |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1206-0 |
| Authors: |
Colin Conwell, George Alvarez, Harvard University, United States |
| Abstract: |
To gauge whether a tower of objects will fall, are visual heuristics sufficient? In this study, we explore the potential of pattern recognition as a viable model of intuitive physical inference in a task that requires observers to estimate the stability of stacked building blocks, comparing the performance of a deep feedforward convolutional neural network to human performance using psychophysics. In analyzing human and machine behavior alike, we identify a pair of image-based visual features that strongly predict both human and machine performance and differ only in the summary statistic used to compute them. Our results suggest that a system trained only to recognize patterns in visual input and given no explicit physical knowledge (e.g. mass, gravity, friction or elasticity) is nonetheless capable of approximating human judgments in a paradigmatic intuitive physics task. |
