How spatial and feature-based attention affect the gain and tuning of population responses

How does attention optimize our visual system for the task at hand? Two mechanisms have been proposed for how attention improves signal processing: gain and tuning. To distinguish between these two mechanisms we use the equivalent-noise paradigm, which measures performance as a function of external noise. In the present study we explored how spatial and feature-based attention affect performance by assessing their threshold-vs-noise (TvN) curves with regard to the signature behavioral effects of gain and tuning. Furthermore, we link our psychophysical results to neurophysiology by implementing a simple, biologically-plausible model to show that attention affects the gain and tuning of population responses differentially, depending on the type of attention being deployed: Whereas spatial attention operates by boosting the gain of the population response, feature-based attention operates by both boosting the gain and sharpening the tuning of the population response.