Decoding Information Processing When Attention Fails: An Electrophysiological Approach

The success of the attentional system in keeping people “on task” in dynamic environments arises from the coordinated operation of multiple neural networks. performance can occur. Here, we investigated the neural bases of attentional failuAlthough this coordinated effort is often successful, errors in res using computational techniques combined with high temporal resolution measures of brain activity using EEG. Attentional failures were induced by presenting two masked targets in rapid succession. In this task, correct identification of the first (T1) leads to impaired identification of the second (T2), a performance failure known as the attentional blink (AB). We applied linear pattern classification algorithms to measures of neural activity acquired during the AB to investigate two key issues about the temporal dynamics of visual attention. First, we tested whether the computational approaches would accurately discriminate the stimulus presented to the observer independent of behavior. Second, we tested whether our computational approaches could predict when the observer would make an error. Our analyses revealed that single-trial EEG activity could be used to not only predict the type of stimulus presented to the observer, but also to predict performance errors. These results are consistent with the notion that the brain represents information about the type of stimuli presented to observers and suggest that computational approaches may be used to provide a moment-by-moment analysis of an observer’s attentional state.

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