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


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.







Tobias Egner

Professor of Psychology and Neuroscience

My research focuses on the computational and neural mechanisms of cognitive control, the use of internal goals to guide behavior. This involves understanding how people configure and focus on a current task, and how they switch from one task to another. We study these processes using behavioral experiments as well as computational modeling, neuroimaging, and neurostimulation techniques.


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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