Browsing by Subject "probabilistic inference"
Results Per Page
Sort Options
Item Open Access Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making(2017) Oh, HannaMuch 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.
Item Open Access Probabilistic inferential decision-making under time pressure in rhesus macaques (Macaca mulatta)(Journal of Comparative Psychology) Toader, Andrew; Rao, Hrishikesh; Ryoo, Minyoung; Bohlen, Martin; Cruger, Jessi; Oh-Descher, Hanna; Ferrari, Silvia; Egner, Tobias; Beck, Jeffrey; Sommer, MarcDecisions often involve the consideration of multiple cues, each of which may inform selection on the basis of learned probabilities. Our ability to use probabilistic inference for decisions is bounded by uncertainty and constraints such as time pressure. Previous work showed that when humans choose between visual objects in a multiple-cue, probabilistic task, they cope with time pressure by discounting the least informative cues, an example of satisficing or “good enough” decision-making. We tested two rhesus macaques (Macaca mulatta) on a similar task to assess their capacity for probabilistic inference and satisficing in comparison with humans. On each trial, a monkey viewed two compound stimuli consisting of four cue dimensions. Each dimension (e.g., color) had two possible states (e.g., red or blue) with different probabilistic weights. Selecting the stimulus with highest total weight yielded higher odds of receiving reward. Both monkeys learned the assigned weights at high accuracy. Under time pressure, both monkeys were less accurate as a result of decreased use of cue information. One monkey adopted the same satisficing strategy used by humans, ignoring the least informative cue dimension. Both monkeys, however, exhibited a strategy not reported for humans, a “group-the-best” strategy in which the top two cues were used similarly despite their different assigned weights. The results validate macaques as an animal model of probabilistic decision-making, establishing their capacity to discriminate between objects using at least four visual dimensions simultaneously. The time pressure data suggest caution, however, in using macaques as models of human satisficing.