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Strategic and Non-Strategic Semantic Expectations Hierarchically Modulate Neural Processing

Vidal-Gran, Consuelo, Sokoliuk, Rodika, Bowman, Howard, Cruse, Damian (2020) Strategic and Non-Strategic Semantic Expectations Hierarchically Modulate Neural Processing. eNeuro, 7 (5). ISSN 2373-2822. (doi:10.1523/ENEURO.0229-20.2020) (KAR id:84690)

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Perception is facilitated by a hierarchy of expectations generated from context and prior knowledge. In auditory processing, violations of local (within-trial) expectations elicit a mismatch negativity (MMN), while violations of global (across-trial) expectations elicit a later positive component (P300). This result is taken as evidence of prediction errors ascending through the expectation hierarchy. However, in language comprehension, there is no evidence that violations of semantic expectations across local-global levels similarly elicit a sequence of hierarchical error signals, thus drawing into question the putative link between event-related potentials (ERPs) and prediction errors. We investigated the neural basis of such hierarchical expectations of semantics in a word-pair priming paradigm. By manipulating the overall proportion of related or unrelated word-pairs across the task, we created two global contexts that differentially encouraged strategic use of primes. Across two experiments, we replicated behavioral evidence of greater priming in the high validity context, reflecting strategic expectations of upcoming targets based on “global” context. In our preregistered EEG analyses, we observed a “local” prediction error ERP effect (i.e., semantic priming) ∼250 ms post-target, which, in exploratory analyses, was followed 100 ms later by a signal that interacted with the global context. However, the later effect behaved in an apredictive manner, i.e., was most extreme for fulfilled expectations, rather than violations. Our results are consistent with interpretations of early ERPs as reflections of prediction error and later ERPs as processes related to conscious access and in support of task demands.

Item Type: Article
DOI/Identification number: 10.1523/ENEURO.0229-20.2020
Uncontrolled keywords: ERP, predictive coding, relatedness proportions, emantic priming
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Howard Bowman
Date Deposited: 02 Dec 2020 16:44 UTC
Last Modified: 16 Feb 2021 14:16 UTC
Resource URI: (The current URI for this page, for reference purposes)
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