| Paper: | PS-2B.27 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
A mathematical model of real-world object shape predicts human perceptual judgments |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1107-0 |
| Authors: |
Caterina Magri, Andrew Marantan, L Mahadevan, Talia Konkle, Harvard University, United States |
| Abstract: |
How do we represent the shape of different real-world objects? Modern approaches to explore this question with deep neural networks are highly efficient but as of yet not clearly interpretable. In this paper, we examined the Normalized Contour Curvature model (NCC), which represents a shape as an interpretable histogram over curvature values (from very concave, to straight, to very convex). To explore the shape-space produced by this model, we submitted the feature profile of thousands of objects to a principal component analysis, revealing that 4 axes summarized the space. To compare this model to behavior we tested both perceived curvature (E1) and overall shape similarity (E2). Behavioral judgments of the perceived curvature of an object were well predicted by this model, largely isolated to the second PC loading. Behavioral measures of overall shape similarity were also predicted reasonably well using the four PCs, and approached the performance of deep neural networks (E3). The success of this model implies that perceptual shape-space can be summarized with a relatively small number of dimensions through an interpretable feature space, where the major axes are meaningfully related to perceived shape and curvature of inanimate objects. |
