broeder_schuetz_2009_e3: Recognition ROC data from Broeder & Schuetz (2009, Experiment...

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Recognition ROC data from Broeder & Schuetz (2009, Experiment 3)

Description

Binary old/new recognition data from 40 subjects, aggregated to response counts. Each subject was tested under five base-rate conditions: the proportion of old items shifts the decision criterion from conservative (br1) to liberal (br5) while leaving sensitivity unchanged, tracing a five-point binary ROC per subject. That is the cleanest design for estimating the unequal-variance ratio (sdratio) of sdt_yn(), which needs more than one operating point: a single condition with no other varying predictor yields only one hit/false-alarm pair and cannot separate a wider signal distribution from a larger d'. Counts were digitised from the frequencies reported in the original article.

Usage

broeder_schuetz_2009_e3

Format

broeder_schuetz_2009_e3

A data frame with 400 rows (40 subjects x 5 conditions x 2 stimulus types) and 5 columns:

id

Integer uniquely identifying each subject

condition

Factor with five base-rate conditions, ordered from the most conservative (br1) to the most liberal (br5) induced criterion

stimulus

Integer stimulus type: 0 = new/lure, 1 = old/target

n_old

Integer count of "old" responses in that cell: hits for old items (stimulus == 1) and false alarms for new items (stimulus == 0)

n_trials

Integer number of items presented in that cell

Source

Broeder, A., & Schuetz, J. (2009). Recognition ROCs are curvilinear—or are they? On premature arguments against the two-high-threshold model of recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 587–606. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/a0015279")}

Examples

## Not run: 
# Unequal-variance yes/no SDT: the criterion varies across base-rate
# conditions, while sensitivity (d) and the signal/noise SD ratio (sdratio)
# are held constant across conditions.
model <- sdt_yn(
  response = "n_old", stimulus = "stimulus", n_trials = "n_trials"
)
fit <- bmm(
  formula = bmf(
    d ~ 1 + (1 | id),
    criterion ~ 0 + condition + (1 | id),
    sdratio ~ 1
  ),
  data = broeder_schuetz_2009_e3,
  model = model,
  backend = "cmdstanr"
)

## End(Not run)

bmm documentation built on Oct. 16, 2026, 9:08 a.m.