Optimal Deployment of Attentional Gain during Fine Discriminations

Most models assume that top-down attention enhances the gain of sensory neurons tuned to behaviorally relevant stimuli (on-target gain). However, theoretical work suggests that when targets and distracters are highly similar, attention should enhance the gain of neurons that are tuned away from the target, because these neurons better discriminate neighboring features (off-target gain). While it is established that off-target neurons support difficult fine discriminations, it is unclear if top-down attentional gain can be optimally applied to informative off-target sensory neurons or if gain is always applied to on-target neurons, regardless of task demands. To test the optimality of attentional gain in human visual cortex, we used functional magnetic resonance imaging and an encoding model to estimate the response profile across a set of hypothetical orientation-selective channels during a difficult discrimination task. The results suggest that top-down attention can adaptively modulate off-target neural populations, but only when the discriminanda are precisely specified in advance. Furthermore, logistic regression revealed that activation levels in off-target orientation channels predicted behavioral accuracy on a trial-by-trial basis. Overall, these data suggest that attention does not only increase the gain of sensory-evoked responses, but may bias population response profiles in an optimal manner that respects both the tuning properties of sensory neurons and the physical characteristics of the stimulus array.