title: A contrast- and luminance-driven multiscale network model of brightness perception.
abstract: A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous luminance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring". Computer simulations illustrate the model's competencies.
authors.affiliation: Department of Cognitive and Neural Systems, Boston University, MA 02215, USA.
authors.forename: L
authors.initials: L
authors.lastname: Pessoa
authors.forename: E
authors.initials: E
authors.lastname: Mingolla
authors.forename: H
authors.initials: H
authors.lastname: Neumann
mesh_terms: D015350:Contrast Sensitivity / Q000502:physiology*; D006801:Humans; D008027:Light; D008433:Mathematics; D016571:Neural Networks, Computer; D010783:Photometry; D014796:Visual Perception / Q000502:physiology*