Using reaction times and binary responses to estimate psychophysical performance: an information theoretic analysis

As the strength of a stimulus increases, the proportions of correct binary responses increases, which define the psychometric function. Simultaneously, mean reaction times (RT) decrease, which collectively define the chronometric function. However, RTs are traditionally ignored when estimating psychophysical parameters, even though they may provide additional Shannon information. Here, we extend Palmer et al's (2005) proportional-rate diffusion model (PRD) by: (a) fitting individual RTs to an inverse Gaussian distribution, (b) including lapse rate, (c) point-of-subjective-equality (PSE) parameters, and, (d) using a two-alternative forced choice (2AFC) design based on the proportion of times a variable comparison stimulus is chosen. Maximum likelihood estimates of mean RT values (from fitted inverse Gaussians) and binary responses were fitted both separately and in combination to this extended PRD (EPRD) model, to obtain psychophysical parameter values. Values estimated from binary responses alone (i.e., the psychometric function) were found to be similar to those estimated from RTs alone (i.e., the chronometric function), which provides support for the underlying diffusion model. The EPRD model was then used to estimate the mutual information between binary responses and stimulus strength, and between RTs and stimulus strength. These provide conservative bounds for the average amount of Shannon information the observer gains about stimulus strength on each trial. For the human experiment reported here, the observer gains between 2.68 and 3.55 bits/trial. These bounds are monotonically related to a new measure, the Shannon increment, which is the expected value of the smallest change in stimulus strength detectable by an observer.