Abstract

While prediction errors (PE) have been established to drive learning through adaptation of internal models, the role of model-compliant events in predictive processing is less clear. Checkpoints (CP) were recently introduced as points in time where expected sensory input resolved ambiguity regarding the validity of the internal model. Conceivably, these events serve as on-line reference points for model evaluation, particularly in uncertain contexts. Evidence from fMRI has shown functional similarities of CP and PE to be independent of event-related surprise, raising the important question of how these event classes relate to one another. Consequently, the aim of the present study was to characterise the functional relationship of checkpoints and prediction errors in a serial pattern detection task using electroencephalography (EEG). Specifically, we first hypothesised a joint P3b component of both event classes to index recourse to the internal model (compared to non-informative standards, STD). Second, we assumed the mismatch signal of PE to be reflected in an N400 component when compared to CP. Event-related findings supported these hypotheses. We suggest that while model adaptation is instigated by prediction errors, checkpoints are similarly used for model evaluation. Intriguingly, behavioural subgroup analyses showed that the exploitation of potentially informative reference points may depend on initial cue learning: Strict reliance on cue-based predictions may result in less attentive processing of these reference points, thus impeding upregulation of response gain that would prompt flexible model adaptation. Overall, present results highlight the role of checkpoints as model-compliant, informative reference points and stimulate important research questions about their processing as function of learning und uncertainty.

Highlights

  • Predicting upcoming events constitutes one of the fundamental qualities of brain function

  • The difference between extended and regular sequences was significant as well. This pattern of offset latencies fully replicated the findings from our previous fMRI study

  • Checkpoints are probabilistic, cue-compliant events informing predictive processing. Their functional profile closely resembles that of canonical prediction errors, indicating similar roles of the two event classes in abstract prediction

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Summary

Introduction

Predicting upcoming events constitutes one of the fundamental qualities of brain function. Model adaptation in consequence of such prediction errors (PE) has been proposed to be the foundation of associative learning mechanisms [4,5], as unexpected events are informative with regard to their current context. Probabilistically occurring expected events have been suggested to inform the internal model [6]: While PE instigate model adaptation, expected events verify model-based predictions. These verifications are informative when we face uncertain environments. While the entire stimulus sequence could be predicted reliably in stable environments, unstable environments prompted stepwise predictions This way, CP were used to verify the internal model in order to predict the section . Instead, selected time points carry information about the on-line validity of the internal model, raising the intriguing question of how checkpoints and prediction errors functionally relate to one another

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