Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli

Garrido, Marta I., Rowe, Elise G., Halasz, Veronika and Mattingley, Jason B. (2017) Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli. Cerebral Cortex, . doi:10.1093/cercor/bhx087


Author Garrido, Marta I.
Rowe, Elise G.
Halasz, Veronika
Mattingley, Jason B.
Title Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli
Journal name Cerebral Cortex   Check publisher's open access policy
ISSN 1047-3211
1460-2199
Publication date 2017-04-10
Sub-type Article (original research)
DOI 10.1093/cercor/bhx087
Open Access Status Not yet assessed
Total pages 12
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Abstract 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.
Keyword EEG
MMN
Modeling
Novelty
Prediction
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 10 April 2017. Article in Press

 
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Created: Fri, 11 Aug 2017, 10:44:13 EST by Emma Schleiger on behalf of Queensland Brain Institute