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Abstract 9/5: Jerome Busemeyer
Quantum probability viewed as a generalization of classic probability.
What are the critical but hidden assumptions upon which our
traditional cognitive theories rely? Quantum theory provides a
fundamentally different approach to logic, reasoning, and
probabilistic inference. For example, quantum logic does not always
follow the distributive axiom of Boolean logic; quantum probabilities
do not always obey the law of total probability; quantum reasoning
does not always obey the principle of monotonic reasoning. For this
talk, I will present a tutorial of the basic assumptions of classic
versus quantum probability theories. These basic assumptions will be
examined, side by side, in a parallel and elementary manner. Classic
theory will emerge as a possibly overly restrictive case of the more
general quantum theory. The fundamental implications of these
contrasting assumptions will be examined closely with concrete
examples and applications to cognition.9/12: Pat Shaftho
A Bayesian model of communicative inference
How do people communicate concepts which themselves are not observed?
Why is it that people learn more quickly from a teacher? Traditional
approaches to learning assume that observed data are sampled by some
random process. These approaches cannot explain intuitive effects in
learning which result from the purposeful transmission of information,
such as the importance of negative evidence in marking boundaries. I
will present a Bayesian model that formalizes two complementary
components of communicative inference: how speakers (or teachers)
generate communicative acts given a hypothesis that they intend to
communicate, and how listeners (or learners) invert this process,
inferring communicative intent given some set of communicative acts.
This formalizes the notion of communicative relevance, and suggest how
learners may exploit communicative/pedagogical situations to learn
quickly from relatively limited data. I will present preliminary
evidence from a novel concept learning experiment which investigates
predictions about how people choose examples to communicate concepts,
and how learners use examples to make inferences about the intended
concept. I will conclude by suggesting a number of interesting (but
yet unexplored) directions, and end with fanciful speculation.9/19: Eric Dimperio
This presentation will outline some work done in collaboration with Air
Force Research Labs to create cognitive models of pilot behaviors for
training purposes. Models have been created within the ACT-R 6.0
framework to fly a Predator UAV through simulated reconnaissance
missions over a variety of scenarios introducing different levels of
difficulty. Underlying strategies of the are based on pilot verbal
reports and eye-tracking data collecting while performing the same
reconnaissance task. Although the model is still early in its
development, initial analyses show it can recreate certain flight
strategies quite well.9/26: Jean-Philippe Thivierge
10/3: Linda Smith
10/10: WooYoung Ahn
10/17: Woojae Kim
10/24: Jesse Spencer-Smith
10/31: Angela Nelson
11/7: Joe Anderson
11/14: Mike Brady
11/28: Soren Kyllingsbaek
12/5: Mike Roberts
Previous Cognitive Lunch
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