Computes cumulative trial CIs correlations with final/target CI

Correlates the CI built from the first trials with the final or target CI, adding trials one step at a time.

Usage

computeCumulativeCICorrelation(
  stimuli,
  responses,
  baseimage,
  rdata,
  targetci = list(),
  step = 1
)

Arguments

stimuli

Numeric vector of stimulus numbers, one per response and in the same order. Each must be a positive whole number no larger than the number of trials saved for the selected base image. Numbers may repeat and need not be consecutive. Factors, characters and logicals are rejected: if your data hold stimulus labels, check them against the generated stimulus filenames before converting them to numbers.

responses

Vector of responses in the same order as stimuli: 1 where the original stimulus was chosen, -1 where the inverted one was. Must be numeric and finite; other finite numbers, such as ratings, weight the trials accordingly. Factors, characters and logicals are rejected, as are NA, NaN and infinite values: remove trials without a response from every argument alike.

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.

targetci

Optional target CI to correlate the cumulative CIs with, as returned by generateCI. Without it, the final CI of these trials is used.

step

Number of trials added between successive correlations.

Value

Vector of correlations between each cumulative CI and the final or target CI.

Details

Plot the resulting curve to estimate how many trials your task needs.

Repeated presentations of the same stimulus

This function takes the trials in the order they were presented and does not average repeated presentations of a stimulus. generateCI does average them, per unique stimulus, before building its classification image. Averaging here would discard the presentation order that a cumulative curve is about.

Without a targetci, the final CI is built here from the same trials as the curve. When the evaluated trials reach the last one, as they always do at the default step = 1, the curve's last point compares that CI with itself and is exactly 1. That shows self-consistency, not convergence. A larger step can stop short, because trials are taken at seq(1, length(responses), step): with six responses and step = 2, the last trial evaluated is the fifth, and the curve ends at whatever that partial CI correlates to (0.97 in one such set, not 1).

This assumes the CI compared against varies at all. Responses that cancel exactly, with every presentation of a stimulus answered both ways, average to a CI that is zero everywhere. A correlation with a constant is undefined, so then every point on the curve is NA. Such a curve means the responses carry no net signal, not that the call failed.

A targetci with masked pixels (generateCI stores NA in every pixel a mask excludes) is correlated over the unmasked pixels only. If the mask covers every pixel, no pairs remain and the whole curve is NA, as above.

If every stimulus was shown equally often, the final CI computed here is identical to the one generateCI returns. If not, the two weight the data differently (each trial equally here, each unique stimulus equally there) and they diverge: on an 8-trial set with counts 4/2/1/1 they correlate at 0.77.

So to see how the CI approaches the one you will report, pass that CI as targetci = generateCI(...) instead of relying on the default, which is always built without a mask.

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
)
#> 
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rdata_file <- list.files(stimulus_path, pattern = "\\.Rdata$", full.names = TRUE)[1]

responses <- sample(c(1, -1), 6, replace = TRUE)
correlations <- suppressWarnings(computeCumulativeCICorrelation(
  stimuli = 1:6, responses = responses, baseimage = "face", rdata = rdata_file
))
#> 
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