Population activity in the human dorsal pathway predicts the accuracy of visual motion detection.
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SourceJournal of Neurophysiology, 98, 1, (2007), pp. 345-359
Article / Letter to editor
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Donders Centre for Cognitive Neuroimaging
F.C. Donders Centre for Cognitive Neuroimaging
Medical Physics and Biophysics
Journal of Neurophysiology
Subject120 000 Neuronal Coherence; 120 004 Integrating distributed brain processes; Biophysics; DCN 1: Perception and Action; UMCN 3.2: Cognitive neurosciences
A person's ability to detect a weak visual target stimulus varies from one viewing to the next. We tested whether the trial-to-trial fluctuations of neural population activity in the human brain are related to the fluctuations of behavioral performance in a "yes-no" visual motion-detection task. We recorded neural population activity with whole head magnetoencephalography (MEG) while subjects searched for a weak coherent motion signal embedded in spatiotemporal noise. We found that, during motion viewing, MEG activity in the 12- to 24-Hz ("beta") frequency range is higher, on average, before correct behavioral choices than before errors and that it predicts correct choices on a trial-by-trial basis. This performance-predictive activity is not evident in the prestimulus baseline and builds up slowly after stimulus onset. Source reconstruction revealed that the performance-predictive activity is expressed in the posterior parietal and dorsolateral prefrontal cortices and, less strongly, in the visual motion-sensitive area MT+. The 12- to 24-Hz activity in these key stages of the human dorsal visual pathway is correlated with behavioral choice in both target-present and target-absent conditions. Importantly, in the absence of the target, 12- to 24-Hz activity tends to be higher before "no" choices ("correct rejects") than before "yes" choices ("false alarms"). It thus predicts the accuracy, and not the content, of subjects' upcoming perceptual reports. We conclude that beta band activity in the human dorsal visual pathway indexes, and potentially controls, the efficiency of neural computations underlying simple perceptual decisions.
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