No abstract

The human brain is inherently limited in the information it can make consciously accessible. When people monitor a rapid stream of visual items for two targets, they can typically report the first, but not the second target, if these appear within 200-500 ms of each other, a phenomenon known as the attentional blink (AB). No work has determined the neural basis for the AB, partly because conventional neuroimaging approaches lack the temporal resolution to adequately characterise the neural activity elicited by each item in a rapid stream. Here we introduce a new approach that can identify the precise effect of the AB on behaviour and neural activity. Specifically, we employed a multivariate encoding approach to extract feature-selective information carried by randomly-oriented gratings within a rapid serial stream. We show that feature selectivity is enhanced for correctly reported targets and suppressed when the same items are missed. By contrast, no such effects were apparent for irrelevant distractor items. Our findings point to a new theoretical account that involves both short- and long-range temporal interactions between visual items competing for consciousness.

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.