Probabilistic inferential decision-making under time pressure in rhesus macaques (Macaca mulatta)
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Decisions 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.
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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
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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