title: Explaining attention-related changes in behavior and electroencephalography data through computational modeling.
abstract: In a recent article, Itthipuripat and colleagues combined psychophysics, neurophysiology, and mathematical modeling to investigate the neural mechanism underlying behavioral benefits of spatial attention (Itthipuripat S, Ester EF, Deering S, Serences JT. J Neurosci 34: 13384-13398, 2014). They found that attention-related effects on behavior as well as neural signals could be better explained by a response gain model than by a noise reduction model or an efficient read-out model. In this Neuro Forum we discuss these results and raise several interesting questions and potential interpretations.
authors.affiliation: Key Laboratory of Brain Functional Genomics, Ministry of Education, Shanghai Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China; and Departments of Neurology, Physiology and Psychiatry, University of California, San Francisco, California yixuanku@gmail.com.
authors.forename: Yixuan
authors.initials: Y
authors.lastname: Ku
authors.affiliation: Departments of Neurology, Physiology and Psychiatry, University of California, San Francisco, California.
authors.forename: Martine R
authors.initials: MR
authors.lastname: van Schouwenburg
mesh_terms: D001288:Attention / Q000502:physiology*; D004192:Discrimination, Psychological / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D014796:Visual Perception / Q000502:physiology*
keywords: computational models; sensory gain; spatial attention; vision