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

Thomas H.B Fitzgerald,Rosalyn J Moran,Karl J Friston,Raymond J Dolan

doi:10.1016/j.neuroimage.2014.12.015

Thomas H.B Fitzgerald, Rosalyn J Moran + Show 2 more

Open Access

https://doi.org/10.1016/j.neuroimage.2014.12.015

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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.

Highlights

Perceptual judgements can be made rapidly, many involve integrating information over an extended period of time
Based on the Multiple Sparse Priors (MSP) source reconstruction, which was highly consistent with our a priori predictions based on the existing literature (Gold and Shadlen, 2007), we modelled evoked responses between 0 and 500 ms post-motion onset with six sources, left and right visual cortex (VC) ([− 19/19 − 86 − 14], MNI coordinates), left and right middle temporal area (MT) ([− 46/46 − 70 − 6], MNI coordinates), left and right posterior parietal cortex (PPC) ([− 33/33 − 48 40], MNI coordinates) using 8 spatial modes
A key role is played by the expected precision or uncertainty associated with bottom-up sensory information, relative to the precision of top-down predictions

Summary

Introduction

Perceptual judgements can be made rapidly, many involve integrating information over an extended period of time. We consider evidence accumulation as perceptual inference in the context of predictive coding and ask whether we can understand the neuronal correlates in this light. Predictive coding is an influential theory of perception (Rao and Ballard, 1999; Friston, 2008; Summerfield and Egner, 2009) and brain function (Friston, 2010) in which inference is realised in message. Ascending prediction errors are accumulated by high-level units encoding posterior expectations. In order to approximate Bayes-optimal inference, the brain needs to represent the estimated precision (inverse variance) of ascending sensory signals (prediction errors) (Feldman and Friston, 2010). The expected precision is proposed to play a key role in weighting sensory evidence against prior beliefs and optimises the rate of evidence (prediction error) accumulation

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