Strategic adaptation to non-reward prediction error qualities and irreducible uncertainty in fMRI

Prediction errors are deemed necessary for the updating of internal models of the environment, prompting us to stop or assert current action plans and helping us to adapt to environmental features. The aim of the present study was twofold: First, we sought to determine the neural underpinnings of qualitatively different abstract prediction errors in a serial pattern detection task. Distinct frontoparietal components were found for sequential terminations (inferior frontal gyrus - IFG) and extensions (superior frontal sulcus, posterior cingulate cortex, and angular gyrus), respectively. These findings provide a novel approach of distinguishing non-reward prediction error signals with regard to behavioural consequences they entail. Second, we investigated predictive processing as a function of statistical context (irreducible uncertainty). We hypothesised that the prospective scope of model-based expectancies is adapted to the stability of respective contexts in that unstable environments call for more frequent comparisons of expectancies with sensory input, resulting in stepwise predictions. Changes in environmental stability were reflected in activation of the angular gyrus and IFG for the highly uncertain context at potential points of prediction violation (checkpoints). Notably, this effect was not due to local fluctuations in stimulus improbability (surprise). Although further behavioural support is needed, data point towards a context-dependent adaptation of predictive strategies. Conceivably, enhanced neural responses at sequential checkpoints could reflect stepwise rather than full-length prediction. This strategic adjustment presumably relies on the iterant evaluation of model information retrieved from working memory, as suggested by strengthened functional connectivity of the parahippocampal area during epochs of high uncertainty.