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Computer Science Professor Honored for Cognitive Science Contributions

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Computer science professor Gary Cottrell is being recognized as a Fellow of the Cognitive Science Society. 

San Diego, Calif., July 26, 2017 -- Computer Science Professor Gary Cottrell is being honored for his work in cognitive science, including the 12 years he has been director of the UC San Diego-based Temporal Dynamics of Learning Center (TDLC), a National Science Foundation-funded Science of learning center that he heads with Professor Andrea Chiba in the Department of Cognitive Science. At UC San Diego, Cottrell also leads the Interdisciplinary Ph.D. program in cognitive science.

At the upcoming Cognitive Science Society annual meeting in London, Fellows in the Class of 2017 will be treated to a free dinner. Cottrell laments being elected this year, because his free dinner will be “British food.” However, always ready to see a silver lining, he notes that if he had been elected last year, when the conference was in Philadelphia, it would have probably been Philly cheesesteak (Cottrell is a vegetarian).

The annual meeting will run July 26  to 29, and this year's theme is “Computational Foundations of Cognition.” Cottrell has been a member of the meeting's program committee for decades.

Cottrell’s papers in this year’s conference include “Learning to See People Like People: Predicting Social Perceptions of Faces.”  Humans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc.) to subjective judgments (facial attractiveness, trustworthiness, sociability, friendliness, etc.). While the objective aspects of face perception have been extensively studied, relatively fewer computational models have been developed for the social impressions of faces. Bridging this gap, Cottrell’s team developed a method to predict human impressions of faces in 40 subjective social dimensions, using deep representations from state-of-the-art neural networks. Cottrell notes that these subjective impressions do not necessarily reflect objective truth, but could be useful in social robots, who will need to understand how people view each other. Cottrell's co-authors include first author Amanda Song, a Ph.D. student in cognitive science (co-advised by Cottrell); UC San Diego alumna Linjie Li (M.S. '16), who is now doing her Ph.D. at Purdue University; and computer science junior Chad Atalla, a machine learning undergraduate researcher in the Cottrell Lab, where he studies facial attractiveness predictors. The paper is available online from Cottrell’s publications page.

A second paper concerns the brain’s hemispheric asymmetries. The left hemisphere’s visual system tends to process fine detail (AKA “high spatial frequencies”), while the right hemisphere responds best to low-resolution features (AKA “low spatial frequencies”). Due to the strange way the human visual system is laid out, everything to the left of where people focus is directed to the right hemisphere and vice-versa, allowing visual psychophysicists to measure hemispheric differences by presenting stimuli to the left or right visual fields. Cottrell’s model (unlike previous ones) does not build in the fine detail/broad strokes difference between the hemispheres; rather, it falls out of building networks based on hypothesized connectivity differences between cortical patches within each hemisphere. In this paper, entitled “Categorical vs Coordinate Relationships Do Not Reduce to Spatial Frequency Differences,” Cottrell and colleagues employ his model to explain some classic data used by Stephen Kosslyn of Harvard to argue that the right hemisphere is better at metric tasks, while the left is better at categorical tasks, including data that does not fit Kosslyn’s theory.   Authors of the paper include first author computer science master’s student Vishaal Prasad (B.S. ’16, M.S. '17), and cognitive science Ph.D. alumnus and former postdoc Ben Cipollini (now at Classy.org). This paper is also available online.

Related Links

The Cottrell Lab  
TDLC

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