Elsevier

NeuroImage

Volume 107, 15 February 2015, Pages 219-228
NeuroImage

Precision and neuronal dynamics in the human posterior parietal cortex during evidence accumulation

https://doi.org/10.1016/j.neuroimage.2014.12.015Get rights and content
Under a Creative Commons license
open access

Highlights

  • MEG and DCM used to characterise neuronal dynamics during decision making.

  • DCM suggested plausible hierarchical network architecture.

  • Rate of accumulation best explained by pyramidal cell gain.

  • Results support predictive coding models of evidence accumulation.

Abstract

Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy. Here, we tested a key prediction of such hierarchical inference, namely that the estimated precision (reliability) of information ascending the cortical hierarchy plays a key role in determining both the speed of decision-making and the rate of increase of PPC activity. Using dynamic causal modelling of magnetoencephalographic (MEG) evoked responses, recorded during a simple perceptual decision-making task, we recover ramping-activity from an anatomically and functionally plausible network of regions, including early visual cortex, the middle temporal area (MT) and PPC. Precision, as reflected by the gain on pyramidal cell activity, was strongly correlated with both the speed of decision making and the slope of PPC ramping activity. Our findings indicate that the dynamics of neuronal activity in the human PPC during perceptual decision-making recapitulate those observed in the macaque, and in so doing we link observations from primate electrophysiology and human choice behaviour. Moreover, the synaptic gain control modulating these dynamics is consistent with predictive coding formulations of evidence accumulation.

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