FIG. 5.

(Color online) Signal detection model of AV and noise effects in speech CP. (A) Schematic identification curves illustrating definitions of lapse and guess rates from the psychometric function. (B) Lapse and (C) guess rates during CP across stimulus conditions. (D) SDT framework for understanding noise and AV effects in CP. (left) Observers' responses are modeled along a perceptual decision axis. In a binary classification task, the probability of responding one or another stimulus class (i.e., /da/ or /ga/) is modeled as two Gaussians. μ_da and μ_ga represent the means of the /da/ and /ga/ distributions; σ is their widths, reflecting response variability. An observer responds “ga” if the signal “energy” falls above the decision criterion (dotted line) and “da” below. (right) Integrating either probability curve results in a cumulative density function, modeling observers' psychometric functions. In an SDT framework, changes in the slope of observers' psychometric functions with multisensory cues and noise [Fig. 2(A)] are well modeled as changes in response variance σ². Reduced height of the psychometric function in the AV+noise condition can be attributed to lapses of attention (see panel B, “AV+noise”), which prevent full unity at the asymptotic end of the curve (Schütt et al., 2016). errorbars = ± 1 s.e.m., **p < 0.01.