RT Journal Article
SR Electronic
T1 Bayesian mapping reveals that attention boosts neural responses to predicted and unpredicted stimuli
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 084517
DO 10.1101/084517
A1 M. I. Garrido
A1 E. G. Rowe
A1 Veronika Halász
A1 J. B. Mattingley
YR 2016
UL http://biorxiv.org/content/early/2016/10/31/084517.abstract
AB 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 two 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 Modelling (DCM) 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.