uSDT estimates hierarchical signal detection theory (SDT) models for research on unconscious processing. It jointly models sensitivity in paired direct and indirect measures, allowing researchers to compare both sensitivities, estimate their latent association, and test indirect sensitivity when direct sensitivity is zero.
Documentation
The package website is at https://ricardoreysaez.github.io/uSDT/. It contains:
- Getting started, a tutorial that walks through the whole workflow with real data, from two trial-level data frames to the three hypotheses.
- Reference, the help page of every function.
- Changelog, the changes in each release.
Installation
Install the development version from GitHub:
install.packages("remotes")
remotes::install_github("RicardoReySaez/uSDT")Example: Vadillo et al. data
The package includes the two trial-level data frames from Experiment 2 of Vadillo et al. (2025): vadillo_awareness for the direct task and vadillo_cuing for the indirect task.
library(uSDT)
data(vadillo_awareness)
data(vadillo_cuing)
d <- usdt_data_tasks(
direct = vadillo_awareness,
indirect = vadillo_cuing,
subject_col = "subj",
condition_col = "condition",
condition_levels = c(signal = "old", noise = "new"),
response_col = list(direct = "judged.old", indirect = "rt"),
response_levels = list(
direct = c(signal = 1, noise = 0),
indirect = c(signal = "faster", noise = "slower")
),
dichotomize = list(direct = FALSE, indirect = TRUE)
)
fit <- hsdt(d)
summary(fit)Every column, level and format argument takes either one value for both tasks or one per task as list(direct = ..., indirect = ...), so the two tasks may differ in the columns they use, in the values those columns take, and in the format they arrive in.
The source data are from Vadillo, Malejka, and Shanks (2025), Mapping the reliability multiverse of contextual cuing, doi:10.1037/xlm0001410.