Hippocampal Processing of Levels of Uncertainty and Environmental Volatility. OPM-MEG Correlates of Learning Through Levels of Uncertainty by a Hierarchical Gaussian Filter

Successful interaction with complex, changing environments requires discriminating between levels (or sources) of uncertainty to tune the balance between reliance on previous beliefs and incoming sensory information. A critical aspect of cognition is adaptive learning, whereby learning rate increases in volatile scenarios to promote adaptation to the new context. Evidence indicates that the hippocampus plays a central role in processing uncertainty and predictability. This study employs recordings with optically pumped magnetometer magnetoencephalography to track the involvement of the hippocampus, as well as sensory and prefrontal areas on a probabilistic task with volatile contingencies. Learning through different levels of uncertainty was modelled with a hierarchical Gaussian filter, from which trial-by-trial estimates of inference and uncertainty could be obtained. Increased gamma activity was observed in relation to phasic and tonic volatility in hippocampus and other regions of interest (with differences in pattern), suggesting enhancement of bottom-up processing to promote learning in dynamic contexts. Conversely, estimation uncertainty drove gamma suppression in several areas, whereas conditional entropy (largely determining expected uncertainty about the environment's probabilistic structure) showed no modulatory effect except in the anterior cingulate cortex. A possible role of pre-stimulus hippocampal ripples was suggested in increasing bottom-up sensory transmission of stimuli with high informational gain. All in all, results are consistent with predictive processing accounts of adaptation to different levels of uncertainty.