Ranking recognition data from Meyer-Grant & Jakob (2025)

Ranking signal-detection data from 60 subjects. On each trial, participants saw 3, 4, or 5 face images—one a studied target—and ranked them by perceived oldness; the rank assigned to the target is recorded. The set size varies across trials, so the data are aggregated to target rank-frequency counts per subject and set size, in the wide format sdt_ranking() consumes: one count column per rank position, with structural zeros where the rank exceeds the trial's set size. Fitting all set sizes jointly relies on the per-row set-size feature of sdt_ranking(): pass the set-size column to m.

Usage

meyer_grant_jakob_2025

Format

meyer_grant_jakob_2025

A data frame with 180 rows (60 subjects x set sizes 3, 4, 5) and 7 columns:

id

Factor with sequential codes s01–s60 assigned by bmm; the original participant identifiers are not shipped

set_size

Integer number of ranked items on the trial (3, 4, or 5)

rank1, rank2, rank3, rank4, rank5

Integer number of trials in which the target received that rank (rank1 = most likely target). Columns beyond set_size are structural zeros. The counts sum to 56 within each row.

Source

Meyer-Grant, C. G., & Jakob, M. (2025). Ranking tasks in recognition memory: A direct test of the two-high-threshold contrast model. Journal of Experimental Psychology: General, 154(5), 1445–1455. doi:10.1037/xge0001700 . Data on OSF: https://osf.io/gtzu7/.

Examples

if (FALSE) { # \dontrun{
# Ranking SDT with set size varying per row: pass the set-size column to `m`
# so trials with 3, 4, and 5 alternatives are fit jointly.
model <- sdt_ranking(
  response = c("rank1", "rank2", "rank3", "rank4", "rank5"), m = "set_size"
)
fit <- bmm(
  formula = bmf(d ~ 1 + (1 | id)),
  data = meyer_grant_jakob_2025,
  model = model,
  backend = "cmdstanr"
)
} # }