Palmer, CE;
Auksztulewicz, R;
Ondobaka, S;
Kilner, JM;
(2019)
Sensorimotor beta power reflects the precision-weighting afforded to sensory prediction errors.
Neuroimage
, 200
pp. 59-71.
10.1016/j.neuroimage.2019.06.034.
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Palmer - Sensorimotor beta power reflects the precision-weighting afforded to sensory prediction errors - Neuroimage ACCEPTED.pdf - Accepted Version Download (6MB) | Preview |
Abstract
It has been proposed that accurate motor control relies on Bayesian inference that integrates sensory input with prior contextual knowledge (Bays and Wolpert, 2007; Körding and Wolpert, 2004; Wolpert et al., 1995). Recent evidence has suggested that modulations in beta power (∼12–30 Hz) measured over sensorimotor cortices using electroencephalography (EEG) may represent parameters of Bayesian inference. While the well characterised post-movement beta synchronisation has been shown to correlate with prediction error (H. Tan, Jenkinson, & Brown, 2014; Huiling Tan, Wade, & Brown, 2016), recent evidence suggests that beta power may also represent uncertainty measures (Tan et al., 2016; Tzagarakis et al., 2015). The current study aimed to measure the neurophysiological correlates of uncertainty mediating Bayesian updating during a visuomotor adaptation paradigm in healthy human participants. In particular, sensory uncertainty was directly modulated to measure its effect on sensorimotor beta power. Participant's behaviour was modelled using the Hierarchical Gaussian Filter (HGF) in order to extract the latent variables involved in learning actions required by the task and correlate these with the measured EEG. We found that sensorimotor beta power correlated with inverse uncertainty afforded to sensory prediction errors both prior to and following a movement. This suggests that sensorimotor beta oscillations may more readily represent relative uncertainty within the sensorimotor system rather than error. Neurophysiological models describing the generation of beta oscillations offer a potential mechanism by which this neural signature may encode latent uncertainty parameters. This is essential for understanding how the brain controls behaviour.
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