Chapter

Linking Neuronal Variability to Perceptual Decision Making via Neuroimaging

Paul Sajda, Marios G. Philiastides, Hauke Heekeren and Roger Ratcliff

in The Dynamic Brain

Published in print January 2011 | ISBN: 9780195393798
Published online September 2011 | e-ISBN: 9780199897049 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780195393798.003.0010
Linking Neuronal Variability to Perceptual Decision Making via Neuroimaging

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Perceptual decision making is the simplest example of a cognitive process by which a stimulus is evaluated, evidence is accumulated, an action is planned and a response is executed. A defining characteristic of perceptual decision making is that the behavioral responses that are generated are variable in their accuracy and response time, even given nominally identical stimuli. What is the source of this underlying behavioral variability? One possibility is that the variability is due to noise in the system—i.e. underlying neuronal noise. Alternatively the variability may reflect latent fluctuations in neuronal activity that are functionally significant. This chapter reviews our efforts to use neuroimaging to link electrophysiological fluctuations to behavioral variability as a way to identify functionally significant neuronal variability. Specifically the chapter describes how trial-to-trial fluctuations in the electroencephalogram (EEG) can be related to behavioral variability using signal detection theory, used to estimate decision variables via analysis of this activity within the context of diffusion models, and used to infer latent cognitive processes via coupling to function magnetic resonance imaging (fMRI). The chapter concludes that in many cases the neuronal variability is functionally significant and can be related to specific decision variables and cortical networks.

Keywords: perceptual decision making; behavioral variability; neuronal variability; function magnetic resonance imaging; trial-to-trial variability; cortical networks

Chapter.  6487 words.  Illustrated.

Subjects: Neuroscience

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