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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: http://www.indiana.edu/~clcl/Q733_WWW/
Organizer: Mike Jones
Office: PY 357
Phone: 856-1490
Email: jonesmn@indiana.edu
- 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 1/28: 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: TBA4/7: Susan Goldin-Meadow, University of Chicago
Title: TBA
Abstract: TBA
Previous Q733 Colloquia
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