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Colloquia occur: Selected Mondays at 4:00 pm - 5:00 pm - Room PY 101.
Colloquia titles will be posted as they become available.
Also see: vislab.psych.indiana.edu/~jgold/q733
- 01/29 Marlene Behrman, University of Toronto - Abstract
- 02/12 Tom Mitchell, CMU - Abstract
- 03/26 David Heeger, NYU - Abstract
- 04/09 Gad Saad, Concordia University (Montreal) - Abstract
Abstract 1/29: Marlene Behrman, University of Toronto
Title: Let's Face It: Psychological and Neural Mechanisms Underlying Face Processing
Abstract: The extent to which faces engage different, perhaps, dedicated psychological and neural mechanisms from those engaged by non-face objects remains highly debated. While some claim that faces are 'special', others suggest that more general-purpose visual processes are used for all visual stimuli but that faces place additional demands on these common
systems. I will examine behavioral and MRI (structural and functional) evidence from three different neuropsychological populations as well as data from a developmental study to address this controversy, and will demonstrate that faces are not special per se but that they invoke configural processing to a greater degree than any other object class because of the need for individual level identification. I will also present data to show that face (and other object) processing engages a distributed neural network and that the 'fusiform face area' is not sufficient for face processing. These findings favor an interactive and dynamic set of neural and behavioral processes which come to be optimized for stimuli which are highly frequent and of evolutionary significance for the observer.2/12: Tom Mitchell, CMU
Title: Machine Learning and Analyzing Human Brain Activity
Abstract: In recent years there has been a breakthrough in instruments for observing human brain activity, and even more recently machine learning methods have emerged as a valuable new approach to analyzing this data.This talk will present our recent research exploring the patterns of human brain activity associated with the meanings of different words and pictures. For example, machine learning methods can be used to train classifiers to decode whether a person is reading a word about tools or buildings from the fMRI image of their brain activation. The same trained classifier can decode the semantic category of the stimulus whether it is an English word, a Portuguese word, or a line drawing of the object. We will describe efforts to use machine learning to study the neural representations of meaning in the human brain, including the challenge of dealing with this very hight dimensional, very sparse training data sets. 3/26: David Heeger, NYU
Title: TBA
Abstract: TBA4/9: Gad Saad, Concordia University (Montreal)
Title: TBA
Abstract: TBA |
