Compared to those available for forced-choice tasks, there are few adaptive methods for estimating thresholds in Yes/No (YN) tasks. Even existing methods^1,2, aimed at estimating thresholds for specific Yes rates (e.g., 50%), are unreliable, due to contamination of Yes rate by response bias. We present a novel adaptive procedure (the “quick YN” or “qYN” method), to estimate thresholds at levels of criterion sensitivity (d'), rather than Yes rate. The method uses Bayesian estimation and a minimum entropy criterion^3,4,5 to place stimuli at signal intensities providing the most information about three parameters: (1) the observer's response bias,ß; (2) the signal intensity, &alpha;, corresponding to d'=1; and (3) the steepness of the d' psychometric function, &gamma;. Using simulations and a psychophysical experiment, we compared threshold estimates obtained in a YN task, using the qYN and the method of constant stimuli (MCS). Simulations showed that, for false-alarm (FA) rates from 10-50%, the qYN needed only 25 trials to provide accurate (bias <.5dB) and precise (sd < 2.5dB) estimates of d'=1. For FA=1%, d' estimates were biased by 2dB. For all FA rates, the number of MCS trials needed to match the qYN's precision was roughly five-fold. Psychophysical results showed that qYN threshold estimates (sd < 2.5dB), deviated little from MCS estimates (<1dB). The qYN exhibits strong advantages over previous methods. Most notably, by measuring (and accounting for) response bias, the qYN estimates thresholds at criterion sensitivities with reduced data collection.

_{1. Green (1993)}

2. Kaernbach (1990)

3. Cobo-Lewis (1996)

4. Kontsevich & Tyler (1999)

5. Lesmes et al (2005)