Combining convolutional neural networks and cognitive models to predict novel object recognition in humans.
Jeffrey Annis, L. Gauthier, Thomas J. Palmeri
Abstract
that have been used in a number of previous object recognition studies. We specifically investigated whether a model combining high-level CNN representations of these novel objects could be used to drive an LBA model of decision making to account for errors and RTs in a same-different matching task (from Richler et al., 2019). Combining linearly transformed CNN object representations with the LBA provided reasonable accounts of performance not only on average, but at the individual-participant level and the item level as well. We frame the findings in the context of growing interest in using CNN models to understand visual object representations and the promise of using CNN representations to extend cognitive models to explain more complex aspects of human behavior. (PsycInfo Database Record (c) 2021 APA, all rights reserved).