batchGenerateCI2IFC: Generates multiple 2IFC classification images by participant...

In rcicr: Reverse-Correlation Image-Classification Toolbox

Generates multiple 2IFC classification images by participant or condition

Description

Generate one classification image per participant or condition, for a two-image forced-choice reverse correlation task.

Usage

batchGenerateCI2IFC(
  data,
  by,
  stimuli,
  responses,
  baseimage,
  rdata,
  save_as_png = TRUE,
  targetpath,
  antiCI = FALSE,
  scaling = "autoscale",
  constant = 0.1,
  label = ""
)

Arguments

data	Data frame with one row per trial.
by	Name of the column that splits the data into units, such as participants or conditions. One CI is computed per unit.
stimuli	Name of the column holding the stimulus numbers of the presented stimuli.
responses	Name of the column holding the responses: 1 where the original stimulus was chosen, -1 where the inverted one was.
baseimage	String naming the base image: not its file name, but its key in the base_face_files list passed to generateStimuli2IFC.
rdata	Path to the .Rdata file written when the stimuli were generated. It holds the contrast parameters of every stimulus.
save_as_png	Boolean: also save the CI as a PNG image.
targetpath	Directory to save PNGs to. Required when save_as_png = TRUE; there is no default. The directory is created if it does not exist; to just try the function out, use tempdir().
antiCI	Boolean: compute the anti-CI, the classification image with its sign flipped, instead of the CI.
scaling	Scaling method: none, constant, matched, independent or autoscale (default). autoscale computes the CIs unscaled and then puts them on one scale with autoscale.
constant	Scaling constant for the noise. Used only when scaling = 'constant'.
label	Optional string added to the PNG file names to make them easier to identify.

Details

Splits data by the by column, calls generateCI2IFC for each part, and returns the CIs. By default each CI is also saved as a PNG.

Value

Named list with one classification image per unit. Each is itself a list of pixel matrices: the raw noise (ci), the scaled noise (scaled), the base image (base) and the two combined (combined).

Examples

# a synthetic square grayscale image stands in for a real base face photo
base_face <- tempfile(fileext = ".png")
png::writePNG(matrix(runif(32 * 32), 32, 32), base_face)

stimulus_path <- tempfile("stimuli")
generateStimuli2IFC(
  base_face_files = list(face = base_face),
  n_trials = 6,
  img_size = 32,
  stimulus_path = stimulus_path,
  seed = 1,
  ncores = 1,
  nscales = 1,
  save_as_png = FALSE
)
rdata_file <- list.files(stimulus_path, pattern = "\\.Rdata$", full.names = TRUE)[1]

# two "participants", three trials each
data <- data.frame(
  participant = rep(c("p1", "p2"), each = 3),
  stimulus = 1:6,
  response = sample(c(1, -1), 6, replace = TRUE)
)

cis <- suppressWarnings(batchGenerateCI2IFC(
  data = data, by = "participant", stimuli = "stimulus", responses = "response",
  baseimage = "face", rdata = rdata_file, save_as_png = FALSE
))

rcicr documentation built on Sept. 27, 2026, 5:07 p.m.