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
09/10 Jesse Prinz, University of North Carolina, Chapel Hill - Abstract
09/17 Evan Thompson, University of Toronto - Abstract
10/08 Jay McClelland, Stanford University - Abstract
10/22 Sue Becker, McMaster University - Abstract
10/29 Daniel Schwartz, Stanford University - Abstract
01/28 Brett Fajen, Rensselaer Polytechnic Institute - Abstract
03/17 Joe Halpern, Cornell University - Abstract
03/24 Mark Gluck, Rutgers University - Abstract
04/07 Susan Goldin-Meadow, University of Chicago - 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: TBA

4/9: Gad Saad, Concordia University (Montreal)
Title: TBA
Abstract: TBA

9/10: Jesse Prinz, University of North Carolina, Chapel Hill
Title: The Emotional Basis of Moral Values
Abstract: There is a long-standing philosophical view according to which moral values have an emotional basis. Some philosophers reject this view, however, and it has been the subject of considerable debate. Recent results from cognitive neuroscience, psychology, and psychopatholoy offer strong support for the emotional view. Recent research is also moving beyond old theories by revealing the specific nature of the emotions that undergird morality. In this talk, I review recent work in moral psychology and propose a theory that systematizes the findings. One implication of this theory is that there can be considerable variation in morality, because cultures can condition emotional responses in different ways. Some moral debates may result from incommensurable culturally conditioned emotional dispositions.

9/17: Evan Thompson, University of Toronto
Title: Meditation and the Neuroscience of Consciousness
Abstract: The main idea to be explored in this lecture is that neuroscience can advance the investigation of consciousness by employing introspective reports based on contemplative mental training of attention and awareness. The following topics will be discussed: (i) issues regarding introspection in cognitive science; (ii) the nature of contemplative mental training; (iii) contemplative introspection and the neurodynamics of consciousness; (iv) experience-dependent brain development.

10/8: Jay McClelland, Stanford University
Title: Graded Constraints in English Word Forms
Abstract: I will a describe graded constraint theory of English word forms that addresses the distribution of forms in the lexicon, the goodness judgments given by native speakers of nonwords as candidate wordforms, and the pattern of errors seen in language impaired individuals including dysfluent aphasics and individuals with specific language impairment. The theory is applied to the rhymes of English monosyllabic monomorphemes (items like 'cat', 'hold' and 'clamp'). Within a template specifying possible rhymes, a number of graded constraints are identified. For example, in rhymes containing at least one stop consonant, there is a graded constraint favoring short vowels, a graded constraint favoring unvoiced vs voiced obstruents, a constraint favoring coronal articulation, and a constraint against added embellishments such as a nasal, fricative, liquid, or second stop consonant (as in 'apt'). Each constraint affects the goodness of a rhyme type in a graded, cumulative fashion. Occurrence rates of different types of rhymes in the language conform closely to the predictions of both non-parametric and parametric versions of the theory. By adding a cut-off threshold, the theory can explain with good accuracy which types of rhymes occur at all and which do not occur, although both linear and interaction terms are necessary to give a complete account. The theory also accounts well for native speaker's judgments of the relative goodness of different rhyme types, although there are subtle differences between the patterns of occurrence and the patterns of judgments.

10/22: Sue Becker, McMaster University
Title: Hippocampal encoding of space and time
Abstract: The involvement of the hippocampus in space is widely acknowledged but remains poorly understood. In the first part of this talk, I will present a computational theory of the neural mechanisms in the parietal and temporal lobes that support spatial navigation, imagery, and episodic recall (Byrne, Becker and Burgess, Psych Review, in press). Predictions of the model are currently being tested in an fMRI study of spatial memory and imagery in virtual reality (with Neil Burgess and John King), and in a VR study of implicit learning of spatial layouts using the "yellowcab" game (with Mike Kahana). In the second part of the talk, I will consider how the hippocampus encodes spatio-temporal information. In recent work with Geoff Hinton, we show how a Restricted Boltzmann Machine model of the hippocampus can account for the encoding of episodic sequences. The model also postulates a role for theta oscillations and forward and reverse sequence replay in learning the spatio-temporal structure of events.

10/29: Daniel Schwartz, Stanford University
Title: TBA
Abstract: 0

1/28: Brett Fajen, Rensselaer Polytechnic Institute
Title: Learning novel mappings from optic flow to the control of action
Abstract: Over the course of a lifetime, people acquire numerous perceptual-motor skills, many of which involve a tight coupling between continuously available information in optic flow and continuously controlled movements of the body. People learn to steer bicycles, catch fly balls, drive automobiles, pilot aircraft, and so on. It is well established that behavior in these kinds of tasks can be characterized in terms of mappings (or laws of control) from information variables to movements of the body (or an input device, as in the case of vehicle control). Laws of control have been proposed and tested for tasks such as steering, braking, catching fly balls, and intercepting moving targets. However, little is known about how these mappings are acquired in the first place, and how they are updated with experience and changes in the body, environment, or task constraints. In this talk, I will present my research on how people flexibly adapt to changes in the dynamics of their bodies and the systems whose movements they control by learning novel mappings from optic flow variables to movement variables. This leads to a new view of visually guided action that emphasizes the importance of perceptual-motor learning.

3/17: Joe Halpern, Cornell University
Title: Causality, Responsibility, and Blame: A Structural-Model Approach
Abstract: I first review the basic definition of causality introduced by Halpern And Pearl. This definition (like most in the literature) treats causality as an all-or-nothing concept; either A is a cause of B or it is not. We show how it can be extended to take into account the degree of responsibility of A for B. For example, if someone wins an election 11--0, then each person who votes for him is less responsible for the victory than if he had won 6--5. I then define a notion of degree of blame, which takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent. I also briefly discuss the extent to which definitions reflect how people use notions like cause, blame, and responsibility in practice.

3/24: Mark Gluck, Rutgers University
Title: The Cognitive Neuroscience of Associative Learning and Generalization
Abstract: TBA

4/7: Susan Goldin-Meadow, University of Chicago
Title: TBA
Abstract: TBA