When perceiving rich sensory information, some people may integrate its various aspects, whereas other people may selectively focus on its most salient aspects. We propose that neural gain modulates the trade-off between breadth and selectivity, such that high gain focuses perception on those aspects of the information that have the strongest, most immediate influence, whereas low gain allows broader integration of different aspects. We illustrate our hypothesis using a neural-network model of ambiguous-letter perception. We then report an experiment demonstrating that, as predicted by the model, pupil-diameter indices of higher gain are associated with letter perception that is more selectively focused on the letter’s shape or, if primed, its semantic content. Finally, we report a recognition-memory experiment showing that the relationship between gain and selective processing also applies when the influence of different stimulus features is voluntarily modulated by task demands.
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© 2016, © The Author(s) 2016.
- neural gain
- neural network