Exploitation of local and global information in predictive processing

Daniel S Kluger,Ricarda I Schubotz,Nico Broers,Niko A Busch,Marlen A Roehe,Moritz F Wurm,Stefan Kiebel

doi:10.1371/journal.pone.0231021

Daniel S Kluger, Ricarda I Schubotz + Show 5 more

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https://doi.org/10.1371/journal.pone.0231021

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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, 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. Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global uncertainty 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. Over time, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues. Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. As for model-compliant information, multivariate pattern decoding within the P3b time frame demonstrated effective exploitation of local (adjustment cues vs non-informative analogues) and global information (high vs low uncertainty regular endings) sampled from probabilistic events. Finally, superior fit of a global model (disregarding local adjustments) compared to a local model (including local adjustments) in a representational similarity analysis underscored the precedence of global reference frames in hierarchical predictive processing. Overall, results suggest that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Following the hierarchical organisation of predictive processing, model evaluation too can occur at several levels of the processing hierarchy.

Highlights

One of the central faculties of the human brain is to predict upcoming events based on internal models of the world
Aside from on-line adjustments prompted by local cues, global uncertainty was found to modulate the functional signature of checkpoints: Consolidating previous evidence from fMRI [8] and EEG [6], checkpoints were distinctly processed under high uncertainty within the P3b time frame, as Exploitation of local and global information in predictive processing indicated by significant multivariate classification accuracy
Model-compliant events have previously been established as critical reference points informing predictive processing in the absence of prediction errors

Summary

Introduction

One of the central faculties of the human brain is to predict upcoming events based on internal models of the world. On-line exploitation of model-compliant sensory input at these so-called checkpoints (CP) has been shown to be modulated by higher-order context statistics such as irreducible uncertainty with regard to outcome expectancies [8]: In a highly uncertain environment, additional resources are allocated to the processing of informative events to resolve ambiguity regarding the validity of the internal model —even if these events are model-compliant. This effect was not merely driven by event-related surprise but instead reflected exploitation of excess information from probabilistic non-error events (i.e., checkpoints)

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