| dc.description.abstract |
<p>Much of our real-life decision making is bounded by uncertain information, limitations
in cognitive resources, and a lack of time to allocate to the decision process. To
mitigate these pressures, people satisfice, foregoing a full evaluation of all available
evidence to focus on a subset of cues that allow for fast and “good-enough” decisions.
Although this form of decision-making likely mediates many of our everyday choices,
very little is known about the manner in which satisficing is spontaneously triggered
and accomplished. The aim of this dissertation, therefore, is to characterize cognitive
and neural mechanisms underlying human satisficing behavior via tasks that closely
model real-life challenges in decision making. Specifically, the empirical studies
presented here examine how people solve a novel multi-cue probabilistic classification
task under various external and internal pressures, using a set of strategy analyses
based on variational Bayesian inference, which can track and quantify shifts in strategies.
Results from these behavioral and computational approaches are then applied to model
human functional magnetic resonance imaging (fMRI) data to investigate neural correlates
of satisficing. The findings indicate that the human cognitive apparatus copes with
uncertainty and various pressures by adaptively employing a “Drop-the-Worst” heuristic
that minimizes cognitive time and effort investment while preserving the consideration
of the most diagnostic cue information.</p>
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