title: How spatial and feature-based attention affect the gain and tuning of population responses.
abstract: 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.
authors.affiliation: Department of Psychology, New York University, New York, 10003, USA. s.ling@vanderbilt.edu
authors.forename: Sam
authors.initials: S
authors.lastname: Ling
authors.forename: Taosheng
authors.initials: T
authors.lastname: Liu
authors.forename: Marisa
authors.initials: M
authors.lastname: Carrasco
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology*; D003463:Cues; D006801:Humans; D008959:Models, Neurological; D008960:Models, Psychological; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods; D011601:Psychophysics; D012684:Sensory Thresholds / Q000502:physiology; D013028:Space Perception / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology; D055815:Young Adult