Each statistic is a posterior-predictive z-score, positive in the suspicious direction:
- `z_gain`
Attempt-2 total score against the distribution predicted from attempt 1 plus the fitted expected growth for this person's covariates. A large gain after long study and remediation is expected; the same gain after two weeks is not.
- `z_exposed`
Attempt-2 score on exposed items against the prediction from attempt 1, expected growth and attempt-2 new items. Gains concentrated on exposed items are the signature of preknowledge.
- `z_rt`
Mean log-speed on exposed minus new items (lognormal RT model with known time intensities); present when response times are.
Examples
sim <- rt_simulate(n_persons = 200, form_exposed = 20, form_new = 10, seed = 1)
fit <- rt_fit(sim)
ev <- rt_evidence(fit)
# preknowledge shows up on exposed items and in speed, not only in the gain
aggregate(ev[c("z_gain", "z_exposed", "z_rt")],
list(preknowledge = sim$truth$preknowledge), mean)
#> preknowledge z_gain z_exposed z_rt
#> 1 FALSE -0.110486 -0.1540647 0.01499349
#> 2 TRUE 1.996404 2.7210563 3.33693387