Behavioural and brain activation differences in hierarchical bayesian inference across age

Abstract

Hierarchical causal relations underlie state changes in dynamic environments[. These relations could be encoded in the brain hierarchically and optimal inference occurs via Bayesian estimation of the context of incoming sensory evidence. The neural correlates of Hierarchical Bayesian Inference (HBI) and their changes across age are not well elucidated. Thus, we examined the neural encoding of HBI across age groups in a probabilistic task during an EEG recording.

Hierarchical Bayesian modelling revealed that older adults may experience lower than necessary levels of sensory prediction errors which resulted in comparatively higher variances in the estimation of contextual contingencies and thus higher variance in the estimation of context volatility, as compared to younger adults. Specific neural hierarchies that might be responsible for encoding HBI were found, and changes in these hierarchies may have resulted in different uncertainty estimates across ages. Posterior beta power in the prestimulus period increased with prediction error and estimated variance in predictions which is consistent with its proposed role in signalling predictive expectancies . This relationship was inverted in older adults which may explain why older adults experience lower sensory surprise. Alpha power is proposed to correlate both negatively and positively with stimulus uncertainty. These differences may be resolved by looking at uncertainty from a hierarchical perspective. We found that the correlations between poststimulus parietal alpha power correlated negatively with estimated precision and update of contextual rules in high surprise trials, while central prestimulus alpha power correlated positively with context volatility which is a higher level of uncertainty. This was inverted for older adults in high surprise trials which could suggest poorer inhibition control in the central region.