title: Classification image weights and internal noise level estimation.
abstract: For the linear discrimination of two stimuli in white Gaussian noise in the presence of internal noise, a method is described for estimating linear classification weights from the sum of noise images segregated by stimulus and response. The recommended method for combining the two response images for the same stimulus is to difference the average images. Weights are derived for combining images over stimuli and observers. Methods for estimating the level of internal noise are described with emphasis on the case of repeated presentations of the same noise sample. Simple tests for particular hypotheses about the weights are shown based on observer agreement with a noiseless version of the hypothesis.
authors.affiliation: NASA Ames Research Center, Moffett Field, CA, USA. aahumada@mail.arc.nasa.gov
authors.forename: Albert J
authors.initials: AJ
authors.lastname: Ahumada
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016002:Discriminant Analysis; D006801:Humans; D008960:Models, Psychological; D010364:Pattern Recognition, Visual / Q000502:physiology; D017603:Signal Detection, Psychological