Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration
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Real-life decision-making often involves combining multiple probabilistic sources of information under finite time and cognitive resources. 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 way in which the neural encoding of cue information changes when we satisfice under time pressure. Here, we combined human functional magnetic resonance imaging (fMRI) with a probabilistic classification task to characterize neural substrates of multi-cue decision-making under low (1500 ms) and high (500 ms) time pressure. Using variational Bayesian inference, we analyzed participants’ choices to track and quantify cue usage under each experimental condition, which was then applied to model the fMRI data. Under low time pressure, participants performed near-optimally, appropriately integrating all available cues to guide choices. Both cortical (prefrontal and parietal cortex) and subcortical (hippocampal and striatal) regions encoded individual cue weights, and activity linearly tracked trial-by-trial variations in amount of evidence and decision uncertainty. Under increased time pressure, participants adaptively shifted to using a satisficing strategy by discounting the least informative cue in their decision process. This strategic change in decision-making was associated with an increased involvement of the dopaminergic midbrain, striatum, thalamus, and cerebellum in representing and integrating cue values. We conclude that satisficing the probabilistic inference process under time pressure leads to a cortical-to-subcortical shift in the neural drivers of decisions.
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Associate 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).
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