Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/887

 Title: Sensitivity and Bias within the Binary Signal Detection Theory, BSDT Authors: Gopych, Petro Keywords: Binary Signal Detection TheorySensitivityBiasROCmROCOverall Likelihood and PosteriorNeural SpacePsychometric FunctionJust Noticeable Difference (jnd)Uniformity or No-Priming Hypotheses Issue Date: 2004 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared. URI: http://hdl.handle.net/10525/887 ISSN: 1313-0463 Appears in Collections: Volume 11 Number 4

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