Make onset-contingent data.

Divide trials into which AOI participants started on. Calculate switches away from this AOI, using a rolling window to determine what length consitutes a switch. Augment original data with a column indicating whether each row is a switch-away sample.

make_onset_data(
  data,
  onset_time,
  fixation_window_length = NULL,
  target_aoi,
  distractor_aoi = NULL
)

Arguments

data

The original (verified) data

onset_time

When to check for participants' "starting" AOI?

fixation_window_length

Which AOI is currently being fixated is determined by taking a rolling average of this length (ms). This is the width of window for rolling average.

target_aoi

Which AOI is the target that should be switched *to*

distractor_aoi

Which AOI is the distractor that should be switched *from* (default = !target_aoi)

Value

Original dataframe augmented with column indicating switch away from target AOI

Examples

if (FALSE) {
data(word_recognition)
data <- make_eyetrackingr_data(word_recognition, 
                               participant_column = "ParticipantName",
                               trial_column = "Trial",
                               time_column = "TimeFromTrialOnset",
                               trackloss_column = "TrackLoss",
                               aoi_columns = c('Animate','Inanimate'),
                               treat_non_aoi_looks_as_missing = TRUE
)
response_window <- subset_by_window(data, window_start_time = 15500, window_end_time = 21000, 
                                    rezero = FALSE)
inanimate_trials <- subset(response_window, grepl('(Spoon|Bottle)', Trial))
onsets <- make_onset_data(inanimate_trials, onset_time = 15500, target_aoi='Inanimate')
}