Developing Bayesian adaptive methods for estimating sensitivity thresholds (d′) in Yes-No and forced-choice tasks - Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D. (Frontiers in Psychology, 2015)
Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold – the signal intensity corresponding to a pre-defined sensitivity level ( d' = 1)-- in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks --(1) simple YN detection, (2) cued YN detection, which cues the observer’s response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) forced-choice detection -- the quick YN and quick FC methods yield sensitivity thresholds that are independent of the task’s decision structure (YN or FC) and/or the observer’s subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d.=.10-.15 decimal log units), but more trials are needed for forced-choice thresholds. When the same subjects were tested across tasks of simple, cued, rated, and forced-choice detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli, and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods.