Satisficing in split-second decision making is characterized by strategic cue discounting
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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. It is thought that humans overcome these limitations through satisficing, fast but “good-enough” heuristic decision making that prioritizes some sources of information (cues) while ignoring others. However, the decision-making strategies we adopt under uncertainty and time pressure, for example during emergencies that demand split-second choices, are presently unknown. To characterize these decision strategies quantitatively, the present study examined how people solve a novel multi-cue probabilistic classification task under varying time pressure, by tracking shifts in decision strategies using variational Bayesian inference. We found that under low time pressure, participants correctly weighted and integrated all available cues to arrive at near-optimal decisions. With increasingly demanding, sub-second time pressures, however, participants systematically discounted a subset of the cue information by dropping the least informative cue(s) from their decision making process. Thus, the human cognitive apparatus copes with uncertainty and severe time pressure by adopting a “Drop-the-Worst” cue decision making strategy that minimizes cognitive time and effort investment while preserving the consideration of the most diagnostic cue information, thus maintaining “good-enough” accuracy. This advance in our understanding of satisficing strategies could form the basis of predicting human choices in high time pressure scenarios.
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Professor of Psychology and Neuroscience
My goal is to understand how humans produce purposeful, adaptive behavior. The main ingredient for adaptive behavior, in all animals, is memory: we understand the world around us by matching the flow of incoming sensory information to previous experience. Importantly, by retrieving past episodes that resemble our present situation, we can predict what is likely to happen next, thus anticipating forthcoming stimuli and advantageous responses learned from past outcomes. Hence, I am interested i
Adjunct Professor in the Department of Mechanical Engineering and Materials Science
Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning
W. H. Gardner, Jr. Associate Professor
We study circuits for cognition. Using a combination of neurophysiology and biomedical engineering, we focus on the interaction between brain areas during visual perception, decision-making, and motor planning. Specific projects include the role of frontal cortex in metacognition, the role of cerebellar-frontal circuits in action timing, the neural basis of "good enough" decision-making (satisficing), and the neural mechanisms of transcranial magnetic stimulation (TMS).
Alphabetical list of authors with Scholars@Duke profiles.