Cognitive Science ,  Experiments & Models in Cognition
Q270 ,  26657 ,  M. Jones

TuTh, 4:00PM- 5:15PM, RM. PY 230

Lab:
Q270/26658/
Wed:  1:25PM-2:15PM, RM. HP 154

The purpose of Q270 is to provide an intensive introduction to
research methods, data analysis, and formal modeling in cognitive
science. As we cannot directly observe cognition, it is essential
that a successful cognitive scientist have multiple tools at his/her
disposal to make inferences about unseen cognitive processes. The
first of these tools is the skill of experimental design: We will
focus on control and manipulation of variables to make logical
inferences about hypotheses. The second tool is statistical
inference—we need to determine quantitatively that the conclusions
drawn from our experiments are valid and are not simply due to
chance. The third tool we will cover is formal modeling—we take
conceptual theories and formalize them mathematically or as
computational models to generate predictions for particular
cognitive experiments. We may then quantitatively test competing
cognitive models by comparing them to human data to constrain
between theories.

The bulk of the course will be instructor-led lectures on
methodology or modeling, and these topics will be grounded with
examples and discussions from the cognitive science literature. The
laboratory component of the course will emphasize using computer
packages for experimental design, data analysis, and model fitting.
Since our discoveries as cognitive scientists are useless unless we
can adequately communicate them, the course will also contain an
emphasis on scientific writing.

Quite a bit of work is expected from students, which is true for all
of the cognitive science courses. However, this is my favorite type
of course to teach, because it integrates empiricism, statistics,
and theoretical modeling into one complete introduction to the
methods used in cognitive science. It will be worth it.

Goals:
•    A firm grasp of the theory behind research design and
inferential statistics
•    Hands-on experience with data analysis software
•    Ability to design and conduct simple experiments
•    Programming simple cognitive models and fitting models to data