Semantic prediction-errors are context-dependent: An ERP study
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The human brain is an efficient, adaptive, and predictive machine, constructing a generative model of the environment that we then perceive and become conscious of. Here, we show that different types of prediction-errors – the discrepancies between top-down expectations and bottom-up sensory input – are integrated across processing levels and sensory modalities of the cortical hierarchy. We designed a novel, hybrid protocol in which five prediction-establishing sounds were played in rapid succession (e.g., “meow”, “meow”, “meow”, etc.), followed by either a standard (e.g., “meow”) or a deviant (e.g., “woof”) prime sound, then a visual target word that was either congruent or incongruent (e.g., “cat” or “dog”) with the prime sound. We found that the deviants elicited a more negative voltage than the standards at about 150 ms – the mismatch negativity (MMN), an event-related potential (ERP) sensitive to low-level perceptual violations – and that the incongruent words elicited a more negative voltage than the congruent words at about 350 ms – the N400, an ERP sensitive to high-level semantic violations. We also found that the N400 was context-dependent: the N400 was larger when the target words were preceded by a standard than a deviant. Our results suggest that perceptual prediction-errors modulate subsequent semantic prediction-errors. We conclude that our results are consistent with one of the most important assumptions of predictive coding theories: hierarchical prediction-error processing.
This manuscript version is made available under the CC-BY-NC-ND 4.0 license http:// creativecommons.org/licenses/by-nc-nd/4.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited. This author accepted manuscript is made available following 12 month embargo from date of publication (October 2018) in accordance with the publisher’s archiving policy