Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making
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.
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