Expectation and attention in hierarchical auditory prediction

Srivas Chennu, Valdas Noreika, David Gueorguiev, Alejandro Blenkmann, Silvia Kochen, Agustín Ibáñez, Adrian M. Owen, Tristan A. Bekinschtein

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

213 Citas (Scopus)


Hierarchical predictive coding suggests that attention in humans emerges from increased precision in probabilistic inference, whereas expectation biases attention in favor of contextually anticipated stimuli. We test these notions within auditory perception by independently manipulating top-down expectation and attentional precision alongside bottom-up stimulus predictability. Our findings support an integrative interpretation of commonly observed electrophysiological signatures of neurodynamics, namely mismatch negativity (MMN), P300, and contingent negative variation (CNV), as manifestations along successive levels of predictive complexity. Early firstlevel processing indexed by the MMN was sensitive to stimulus predictability: here, attentional precision enhanced early responses, but explicit top-down expectation diminished it. This pattern was in contrast to later, second-level processing indexed by the P300: although sensitive to the degree of predictability, responses at this level were contingent on attentional engagement and in fact sharpened by top-down expectation. At the highest level, the drift of the CNV was a fine-grained marker of top-down expectation itself. Source reconstruction of high-density EEG, supported by intracranial recordings, implicated temporal and frontal regions differentially active at early and late levels. The cortical generators of the CNV suggested that it might be involved in facilitating the consolidation of contextsalient stimuli into conscious perception. These results provide convergent empirical support to promising recent accounts of attention and expectation in predictive coding.

Idioma originalInglés
Páginas (desde-hasta)11194-11205
Número de páginas12
PublicaciónJournal of Neuroscience
EstadoPublicada - 2013


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