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Per-participant information-value (infoVal) summary

Source: R/compute_infoval_summary.R
compute_infoval_summary.Rd

Computes per-participant information value (infoVal; Brinkman et al., 2019) via the original rcicr 2IFC pipeline and returns a tidy per-participant table. This is the thin compatibility wrapper around rcicr::computeInfoVal2IFC(). For a richer, paradigm-agnostic diagnostic (group-mean z, random-responder calibration check, face-mask z-lift, interpretation bullets), see diagnose_infoval().

Usage

compute_infoval_summary(
  responses,
  method = c("2ifc", "briefrc"),
  rdata = NULL,
  stimuli = NULL,
  baseimage = "base",
  col_participant = "participant_id",
  col_stimulus = "stimulus",
  col_response = "response",
  iter = 10000L,
  threshold = 1.96,
  ...
)

Arguments

responses

Data frame with one row per trial. Required columns: participant_id, stimulus, response (values in {-1, +1}). Load yours from CSV via read_responses() or utils::read.csv(); column names are configurable via the col_* arguments.

method

"2ifc" (supported) or "briefrc" (returns "skip").

rdata

Path to the rcicr .RData file that produced the stimuli. Required for 2IFC unless stimuli is supplied.

stimuli

In-memory stimuli list (the $stimuli element of an rcisignal_sim object). Use in place of rdata when the file path no longer resolves (e.g. after saveRDS()/readRDS() across R sessions). Internally written to a fresh tempdir- backed .Rdata before the call into rcicr.

baseimage

Name of the base image used at generation time (the key in base_face_files in the rdata). Default "base".

col_participant, col_stimulus, col_response

Column names.

iter

Number of iterations for rcicr's reference-distribution simulation. Default 10000 (the rcicr-recommended value).

threshold

Numeric. Participants with infoVal below this are flagged as likely noise. Default 1.96.

...

Unused.

Value

An rcisignal_diag_result() object. For 2IFC, data$per_participant has one row per participant with participant_id, infoval, and above_threshold (logical: infoval >= threshold). For Brief-RC, a "skip" result pointing the user at diagnose_infoval().

Details

Reported per-producer infoVal is paradigm- and target-dependent. Brinkman et al. (2019, p. 12) report 68% (lab) and 54% (online) of 2IFC gender participants clearing z = 1.96, with mean per-participant infoVal 3.9 (lab) and 2.9 (online) and most participants in the 3-4 range. Schmitz et al. (2024) report Brief-RC infoVals below 1.96 across all conditions in both of their experiments. The status returned here therefore does not "fail" on a per-participant headcount alone: it returns "pass" when the median per-participant z is positive and "warn" otherwise. Use diagnose_infoval() when you need a proper verdict (random-responder calibration + group-mean z + masking).

The 2IFC path delegates to rcicr::batchGenerateCI2IFC() and rcicr::computeInfoVal2IFC() from the original rcicr package (Dotsch, 2016; v1.0.1). The original rcicr uses a pool-size reference distribution and does not expose a Brief-RC path. The Brief-RC route here returns a "skip" result and points users at diagnose_infoval(), which uses an in-package native Brief-RC implementation with a per-trial-count reference distribution.

Side effect (2IFC). rcicr caches a reference distribution inside the supplied rdata file on the first call. Subsequent calls reuse it. Copy your rdata beforehand if you want the original untouched.

References

Brinkman, L., Goffin, S., van de Schoot, R., van Haren, N. E., Dotsch, R., & Aarts, H. (2019). Quantifying the informational value of classification images. Behavior Research Methods, 51(5), 2059–2073.

Dotsch, R. (2016). rcicr: Reverse-correlation image-classification toolbox [R package]. https://github.com/rdotsch/rcicr

Examples

if (FALSE) { # \dontrun{
sim <- simulate_2ifc_data(n_per_condition = 10, n_trials = 60, seed = 1)
compute_infoval_summary(sim$data, method = "2ifc",
                        rdata = sim$rdata_path, iter = 200L)

# After saveRDS(sim, "sim.rds") + restart + readRDS("sim.rds") the
# path on `$rdata_path` will not resolve. Pass `stimuli` instead:
compute_infoval_summary(sim$data, method = "2ifc",
                        stimuli = sim$stimuli, iter = 200L)
} # }