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In the last decade, great progress has been made in characterizing the accumulation of neural information during simple unitary perceptual decisions. However, much less is known about how sequentially presented evidence is integrated over time for successful decision making. The aim of this study was to study the mechanisms of sequential decision making in humans. In a magnetoencephalography (MEG) study, we presented healthy volunteers with sequences of centrally presented arrows. Sequence length varied between one and five arrows, and the accumulated directions of the arrows informed the subject about which hand to use for a button press at the end of the sequence (e.g., LRLRR should result in a right-hand press). Mathematical modeling suggested that nonlinear accumulation was the rational strategy for performing this task in the presence of no or little noise, whereas quasilinear accumulation was optimal in the presence of substantial noise. MEG recordings showed a correlate of evidence integration over parietal and central cortex that was inversely related to the amount of accumulated evidence (i.e., when more evidence was accumulated, neural activity for new stimuli was attenuated). This modulation of activity likely reflects a top-down influence on sensory processing, effectively constraining the influence of sensory information on the decision variable over time. The results indicate that, when making decisions on the basis of sequential information, the human nervous system integrates evidence in a nonlinear manner, using the amount of previously accumulated information to constrain the accumulation of additional evidence.

Original publication

DOI

10.1523/JNEUROSCI.4080-09.2010

Type

Journal article

Journal

J Neurosci

Publication Date

13/01/2010

Volume

30

Pages

731 - 738

Keywords

Adult, Brain Mapping, Decision Making, Electronic Data Processing, Female, Humans, Magnetoencephalography, Male, Models, Biological, Models, Theoretical, Neuronal Plasticity, Pattern Recognition, Visual, Photic Stimulation, Psychomotor Performance, Reaction Time, Serial Learning, Time Factors, Young Adult