Cross-validate infoVal against RT quality

Correlates per-participant infoVal with per-participant median response time. The check looks for two patterns that are individually plausible but jointly suspicious:

Usage

cross_validate_rt_infoval(
  responses,
  method = c("2ifc", "briefrc"),
  rdata,
  baseimage = "base",
  col_participant = "participant_id",
  col_stimulus = "stimulus",
  col_response = "response",
  col_rt = "rt",
  iter = 10000L,
  ...
)

Arguments

responses

A data frame of trial-level responses.

method

"2ifc" or "briefrc".

rdata

Path to the rcicr .RData file.

baseimage

Name of the base image in rdata$base_face_files.

col_participant, col_stimulus, col_response, col_rt

Column names. col_rt is required.

iter

Reference-distribution iterations. Default 10000.

...

Unused.

Value

An rcdiag_result() object. data$per_participant has participant_id, infoval, median_rt. data$correlation is the Pearson correlation between the two.

Details

1. High infoVal with fast median RT. A participant producing a seemingly informative CI while responding faster than others is more likely to have produced a spurious signal than genuine meaningful information — the signal may be an artefact of a button-mashing strategy that happens to correlate with the noise.

2. A negative correlation between infoVal and median RT across participants. If fast responders systematically score higher on infoVal, something about the measurement is off.

The function computes cor(infoval, median_rt) across participants and returns the value plus a per-participant table for further inspection. It does not auto-exclude anyone; interpretation requires judgement about the specific experiment.

Requires rcicr via compute_infoval_summary().

Examples

if (FALSE) { # \dontrun{
cross_validate_rt_infoval(
  responses, method = "2ifc",
  rdata = "stimuli.RData",
  col_rt = "rt"
)
} # }