Abstract
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, so-called checkpoints have been suggested to be sampled for model evaluation, particularly in uncertain contexts.
Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences (i.e., 3-4-5-6-7) that had a regular length of five digits and were occasionally extended to seven digits. Across experimental blocks, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues.
Exploitation of local cue information was reflected in significant decoding of cues vs non-informative analogues using multivariate pattern classification. Modulation of checkpoint processing as a function of global uncertainty was likewise reflected in significant decoding of high vs low uncertainty checkpoints. In line with previous results, both analyses comprised the P3b time frame as an index of excess model-compliant information sampled from probabilistic events.
Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. Finally, we compared the fit of a global model (disregarding local adjustments) and a local model (including local adjustments) using representational similarity analysis (RSA). RSA revealed a better fit for the global model, underscoring the precedence of global reference frames in hierarchical predictive processing.
