When: Thursday 13th November, 12-1pm
Where: Keats Reading Room, Psychology Building
What: Nathan Evan's PhD Confirmation Seminar
Evaluating the applicability of collapsing
thresholds in computational models of perceptual decision making
Evidence
accumulation models have successfully been fit to human decision data for
decades, modeling decisions from simple perceptual choice to consumer choice.
Traditionally, these models have assumed that decision thresholds (i.e., the
amount of evidence required to trigger a decision) remain constant over the
course of the decision (fixed thresholds). However, recent evidence has
suggested that decision thresholds may in fact decrease as decision time
unfolds (collapsing thresholds), though the experimental evidence associated
with collapsing thresholds has been the target of criticism. This thesis aims
to explore how applicable collapsing thresholds are to the decision process,
and if they are applicable, under what conditions. The first experiment aims to
further explore the difference in decision strategy found between humans and
non-human primates (fixed thresholds and collapsing thresholds, respectively),
and the idea that this is caused by a difference in experimental procedure. The
second experiment aims to directly measure decision thresholds through an adjusted
version of a standard perceptual choice paradigm, by allowing the stimulus
evidence level to change over the course of a trial. The third experiment aims
to examine a potential condition under which collapsing thresholds may be most
applicable to decision making, with this condition being one that informs
participants of reward rate optimality (a goal that collapsing thresholds
better achieves than fixed thresholds). The fourth experiment aims to contrast
fixed thresholds, and evidence accumulation models in general, to the urgency
gating model; a variant of the collapsing thresholds model that suggests that
there is no evidence accumulation involved in the decision making process. These
experiments, and this thesis as a whole, will provide further insight into
which decision thresholds best represent the decision process, and help to
insure the accuracy of evidence accumulation models used in the past, present,
and future.