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Abstract Attention to a feature enhances the sensory representation of that feature. However, it is less clear whether attentional modulation is limited when needing to attend to multiple features. Here, we studied both the behavioral and neural correlates of the attentional limit by examining the effectiveness of attentional enhancement of one versus two color features. We recorded electroencephalography (EEG) while observers completed a color-coherence detection task in which they detected a weak coherence signal, an over-representation of a target color. Before stimulus onset, we presented either one or two valid color cues. We found that, on the one-cue trials compared with the two-cue trials, observers were faster and more accurate, indicating that observers could more effectively attend to a single color at a time. Similar behavioral deficits associated with attending to multiple colors were observed in a pre-EEG practice session with one-, two-, three-, and no-cue trials. Further, we were able to decode the target color using the EEG signals measured from the posterior electrodes. Notably, we found that decoding accuracy was greater on the one-cue than on two-cue trials, indicating a stronger color signal on one-cue trials likely due to stronger attentional enhancement. Lastly, we observed a positive correlation between the decoding effect and the behavioral effect comparing one-cue and two-cue trials, suggesting that the decoded neural signals are functionally associated with behavior. Overall, these results provide behavioral and neural evidence pointing to a strong limit in the attentional enhancement of multiple features and suggest that there is a cost in maintaining multiple attentional templates in an active state. 

Abstract Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that—for a given cognitive task and subject—higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity—as estimated from fMRI data—predicted task and age-related differences in reaction times, speaking to the model’s predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making.