| Paper: | PS-1B.42 | ||
| Session: | Poster Session 1B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation Time: | Saturday, September 14, 16:30 - 19:30 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Extreme Translation Tolerance in Humans and Machines | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
||
| DOI: | https://doi.org/10.32470/CCN.2019.1091-0 | ||
| Authors: | Ryan Blything, University of Bristol, United Kingdom; Ivan Vankov, New Bulgarian University, Bulgaria; Casimir Ludwig, Jeffrey Bowers, University of Bristol, United Kingdom | ||
| Abstract: | What mechanism supports our ability to recognize objects over a wide range of different retinal locations? Most research in psychology and neuroscience suggests that learning to identify a novel object at one retinal location only supports the ability to identify that object at nearby retinal locations, and to date, neural network models of object identification show a similar restriction in generalization. As a consequence, it is widely assumed that objects need to be learned at multiple locations. We challenge this view and show the capacity to generalize across retinal locations (what we call on-line translation tolerance) has been underestimated in humans and artificial neural networks. Two eye tracking studies demonstrate that novel objects can be recognized following translations of 9° and even 18°. Additionally, computational studies showed that convolutional neural networks can achieve similarly robust generalization when a mechanism (Global Average Pooling) was built in to generate larger receptive fields. | ||