Fig. 1: RDEX-ABCD model of the Stop-Signal task.
A The “hybrid” racing-diffusion ex-Gaussian (RDEX) model framework combining an evidence-accumulation model for the go process with an ex-Gaussian model for the stop process. In this framework, Go reaction times (RT) results from a competition between accumulators that collect noisy evidence for both the choice matching the stimulus and the choice mismatching it (e.g., a right-facing arrow). These accumulators operate at average rates of vt and vf, respectively, and the process concludes when one of the accumulators reaches a predefined response threshold. The stop process is the Gaussian distribution characterized by a mean (μ) and standard deviation (σ), convolved with an exponential distribution with a mean of τ. B The RDEX-ABCD model accounts for the impact of context independence violations on Stop trials by introducing a perceptual growth function. In this function, evidence signals for both the matching and mismatching accumulators on Stop trials with a given stop signal delay (SSD) are composed of two components: a processing speed component that influences evidence accumulation equally for both choices and a discrimination component that favors the choice aligned with the presented stimulus. The processing speed is determined by parameter v0 and remains constant across all SSDs. At an SSD of 0 s, the discrimination component is absent (equal to 0) since the choice stimulus has not been presented yet. However, this component increases linearly at the same rate g for both the matching and mismatching accumulators as SSD lengthens until it reaches the levels of vt and vf, as observed in Go trials. Consequently, at an SSD of 0 s, the rates for matching and mismatching choices are identical, but as SSD increases, these rates diverge gradually until they match their respective Go trial levels. Panel B illustrates the linear growth form. C Empirical growth patterns of matching (blue lines increasing from SSD = 0) and mismatching (red lines decreasing from SSD = 0) go process accumulator rates by SSD for the sample average parameter estimates (thick lines) and for parameter estimates from several randomly drawn participants (thin lines) to illustrate individual variability. In panel C, the top subpanel shows the linear perceptual growth function, and the bottom subpanel shows the non-linear (power) growth function (determined via model comparison). This figure is adapted and modified from [16] (Figure is licensed under CC BY-NC-ND 4.0).
