Binary Signal Detection Theory Sensitivity Bias ROC mROC Overall Likelihood and Posterior Neural Space Psychometric Function Just 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.