| Paper: | PS-2A.19 |
| Session: | Poster Session 2A |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 17:15 - 19:15 |
| Presentation Time: | Friday, September 7, 17:15 - 19:15 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Combining Convolutional Neural Networks and Cognitive Models to Predict Novel Object Recognition in Humans |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1062-0 |
| Authors: |
Jeffrey Annis, Thomas Palmeri, Vanderbilt University, United States |
| Abstract: |
Cognitive models have become ubiquitous in cognitive science and cognitive neuroscience, playing a key role in understanding visual cognition, providing insights into how we recognize, remember, and categorize objects. Cognitive models are often relatively abstract, instantiating high-level aspects of visual cognition, such as how visual evidence is represented, how it is accumulated, and how response bias and caution combine to predict errors and response times in perceptual decisions. Many such models instantiate mechanisms flowing from an object representation to a perceptual decision but do not specify how an object representation is created from the visual image of an object. Convolutional Neural Networks (CNNs) have become successful at visual tasks like classifying objects in real-world images. We explore if CNN object representations, built up over a network hierarchy from object images, can be used as input to a cognitive model to predict human recognition performance. We specifically use CNN representations to drive a cognitive model of decision making, the Linear Ballistic Accumulator (LBA), to predict a range of performance in a visual matching task with novel objects. |
