Hosted by the Generalisation in Mind & Machine research group
Garrison Cottrell, University of California, San Diego, USA
- Join via Zoom: https://bristol-ac-uk.zoom.us/j/97470581070?pwd=M0hFZE9rc0lBRm9nR2JQVERJTVVDZz09
- Meeting ID: 974 7058 1070
- Passcode: 666911
Over the last thirty or so years, my lab has used variants of a relatively simple biologically-inspired neurocomputational model of face and object recognition (The Model™) to explain a number of behavioral, developmental, and neurophysiological phenomena. These results include, for example, fits to data supporting both the categorical and continuous theories of facial expression perception (“one model to rule them all”), a novel explanation of hemispheric asymmetries (local and global perception of hierarchical stimuli), and my favorite result, why the fusiform face area is recruited for other domains of visual expertise. Here, I report on some results of The Model 2.0, a deep version of The Model that includes a foveated retina, the log-polar mapping from the visual field to V1, sampling from the image via a salience map, and dual pathways from V1, central and peripheral. First, I describe some previously reported results on how The Model 2.0 can explain behavioral data in human scene perception under scotoma and tunnel vision conditions (Wang & Cottrell, 2017). Second, I provide a novel explanation of the face inversion effect. Contrary to the generally accepted wisdom that this occurs deep in the visual stream, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features in face recognition.