Fluctuations in alpha and beta power provide neural states favourable for contextually relevant anticipatory processes.

Cued sensory input occasionally fails to immediately ensue its respective trigger. Given that our environments are rich in sensory cues, we often end up processing other contextually relevant information in the meantime. The experimental design of the present study allowed us to investigate how such temporal delays and visual interferences may impact anticipatory processes.

Early alpha/beta oscillations reflect the formation of face-related expectations in the brain.

Although statistical regularities in the environment often go explicitly unnoticed, traces of implicit learning are evident in our neural activity. Recent perspectives have offered evidence that both pre-stimulus oscillations and peri-stimulus event-related potentials are reliable biomarkers of implicit expectations arising from statistical learning. What remains ambiguous, however, is the origination and development of these implicit expectations.

Positive and negative prediction error signals to violated expectations of face and place stimuli distinctively activate FFA and PPA.

Surprising scenarios can have different behavioural and neuronal consequences depending on the violation of the expectation. On the one hand, previous research has shown that the omission of a visual stimulus results in a robust cortical response representing that missing stimulus, a so-called negative prediction error. On the other hand, a large amount of studies revealed positive prediction error signals, entailing an increased neural response that can be attributed to the experience of a surprising, unexpected stimulus.

Exploitation of local and global information in predictive processing.

While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, probabilistic information at critical points in time (so-called checkpoints) has been suggested to be sampled in order to evaluate the internal model, particularly in uncertain contexts. This way, initial model-based expectations are iteratively reaffirmed under uncertainty, even in the absence of prediction errors.