Probabilistic inference under time pressure leads to a cortical-to-subcortical shift in decision evidence integration
Abstract
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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Jeffrey Beck
Assistant Professor of Neurobiology
We study neural coding and computation from a theoretical perspective with particular
emphasis on probabilistic reasoning and decision making under uncertainty, complex
behavioral modeling, computational models of cortical circuits and circuit function,
dynamics of spiking neural networks, and statistical analysis of neural and behavioral
data. Previous work has been largely concerned with sensory-motor transformations
and neural representations of complex stimuli such as odors. More
Tobias Egner
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
Silvia Ferrari
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
Marc A. Sommer
Professor of Biomedical Engineering
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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