z-score pupil timeseries data

The intended use of this method is to scale the arbitrary units of the pupil size timeseries to have a mean of 0 and a standard deviation of 1. This is accomplished by mean centering the data points and then dividing them by their standard deviation (i.e., z-scoring the data, similar to base::scale()). Opting to z-score your pupil data helps with trial-level and between-subjects analyses where arbitrary units of pupil size recorded by the tracker do not scale across participants, and therefore make analyses that depend on data from more than one participant difficult to interpret.

Usage

zscore(eyeris)

Arguments

eyeris

An object of class eyeris dervived from load().

Value

An eyeris object with a new column in timeseries: pupil_raw_{...}_z.

Details

In general, it is common to z-score pupil data within any given participant, and furthermore, z-score that participant's data as a function of block number (for tasks/experiments where participants complete more than one block of trials) to account for potential time-on-task effects across task/experiment blocks.

As such, if you use the eyeris package as intended, you should NOT need to specify any groups for the participant/block-level situations described above. This is because eyeris is designed to preprocess a single block of pupil data for a single participant, one at a time. Therefore, when you later merge all of the preprocessed data from eyeris, each individual, preprocessed block of data for each participant will have already been independently scaled from the others.

Additionally, if you intend to compare mean z-scored pupil size across task conditions, such as that for memory successes vs. memory failures, then do NOT set your behavioral outcome (i.e., success/failure) variable as a grouping variable within your analysis. If you do, you will consequently obtain a mean pupil size of 0 and standard deviation of 1 within each group (since the scaled pupil size would be calculated on the timeseries from each outcome variable group, separately). Instead, you should compute the z-score on the entire pupil timeseries (before epoching the data), and then split and take the mean of the z-scored timeseries as a function of condition variable.

Examples

system.file("extdata", "memory.asc", package = "eyeris") |>
  eyeris::load_asc() |>
  eyeris::deblink(extend = 50) |>
  eyeris::detransient() |>
  eyeris::interpolate() |>
  eyeris::lpfilt(plot_freqz = TRUE) |>
  eyeris::zscore() |>
  plot(seed = 0)