title: Estimating confidence intervals for information transfer analysis of confusion matrices.
abstract: A non-parametric bootstrapping statistical method is introduced and investigated for estimating confidence intervals resulting from information transfer (IT) analysis of confusion matrices. Confidence intervals can be used to statistically compare ITs from two or more confusion matrices obtained in an experiment. Information transfer is a nonlinear analysis and does not satisfy many of the assumptions of a parametric method. The bootstrapping method accurately estimated IT confidence intervals as long as the confusion matrices contained a sufficiently large number of presentations per stimulus category, which is also a condition for reduced bias in IT analysis.
authors.affiliation: Institute for Sensory Research, Syracuse University, 621 Skytop Road, Syracuse, New York 13244 mazadpou@syr.edu.
authors.forename: Mahan
authors.initials: M
authors.lastname: Azadpour
authors.affiliation: Bionics Institute, 384 Albert Street, East Melbourne, Victoria 3002, Australia cmckay@bionicsinstitute.org.
authors.forename: Colette M
authors.initials: CM
authors.lastname: McKay
authors.affiliation: Institute for Sensory Research, Syracuse University, 621 Skytop Road, Syracuse, New York 13244 rlsmith@syr.edu.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Smith
mesh_terms: D000161:Acoustic Stimulation; D001307:Auditory Perception; D015982:Bias; D003198:Computer Simulation; D016001:Confidence Intervals; D003221:Confusion; D006801:Humans; D007257:Information Theory; D015233:Models, Statistical; D009622:Noise / Q000009:adverse effects; D017711:Nonlinear Dynamics; D046709:Pattern Recognition, Physiological; D010470:Perceptual Masking; D059629:Signal-To-Noise Ratio