Journal
CEREBRAL CORTEX
Volume 28, Issue 5, Pages 1771-1782Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhx087
Keywords
EEG; MMN; modeling; novelty; prediction
Categories
Funding
- Australian Research Council (ARC) [DE130101393]
- University of Queensland Fellowship [2016000071]
- ARC Australian Laureate Fellowship [FL110100103]
- ARC Centre of Excellence for Integrative Brain Function (ARC Centre) [CE140100007]
- ARC Special Research Initiative-Science of Learning Research Centre [SR120300015]
- Australian Research Council [DE130101393] Funding Source: Australian Research Council
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Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between 2 alternative computational models. The Opposition model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the Interaction model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modeling showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.
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