Estimates stop-signal reaction time (SSRT) using the integration and mean methods of Verbruggen et al. (2019), and extends these point estimates with three families of tools:
data(adaptive)
d <- adaptive[adaptive$SubjID == 1, ]integration_adaptiveSSD(d) # point estimate ssrt_boot(d) # + bootstrap CI ssrt_stan(d) # + full posterior (requires cmdstanr/rstan)
Maintainer: Anton Leontyev anton.leontyev@example.com (ORCID)
Monte Carlo (ssrt_boot, ssrt_simulate,
ssrt_power, ssrt_robustness): nonparametric
bootstrap confidence intervals, parametric ex-Gaussian simulation,
minimum-trial-count / power analysis, and sensitivity of SSRT to
violations of the horse-race assumptions.
Bayesian / Stan (ssrt_stan and friends):
single-subject and hierarchical ex-Gaussian horse-race models fit via
Hamiltonian Monte Carlo, with an optional trigger-failure parameter
following Matzke et al. (2013), posterior inhibition functions, and
posterior predictive checks. Works with either the cmdstanr or
rstan backend.
run_all_mc and ssrt_stan_compare:
convenience wrappers that run a full battery of analyses in one call.
Verbruggen, F., Aron, A. R., Band, G. P. H., Beste, C., Bissett, P. G., Brockett, A. T., ... Boehler, C. N. (2019). A consensus guide to capturing the ability to inhibit actions and impulses: the stop-signal task. eLife, 8, e46323. tools:::Rd_expr_doi("10.7554/eLife.46323")
Matzke, D., Dolan, C. V., Logan, G. D., Brown, S. D., & Wagenmakers, E.-J. (2013). Bayesian parametric estimation of stop-signal reaction time distributions. Journal of Experimental Psychology: General, 142(4), 1047--1073. tools:::Rd_expr_doi("10.1037/a0030543")
Useful links: