Our Experiment: Each eyetrackingR vignette uses the eyetrackingR package to analyze real data from a simple 2-alternative forced choice (2AFC) word recognition task administered to 19- and 24-month-olds. On each trial, infants were shown a picture of an animate object (e.g., a horse) and an inanimate object (e.g., a spoon). After inspecting the images, they disappeared and they heard a label referring to one of them (e.g., “The horse is nearby!”). Finally, the objects re-appeared on the screen and they were prompted to look at the target (e.g., “Look at the horse!”).
This vignette will cover the basics of preparing your data for use with eyetrackingR.
eyetrackingR is designed to deal with data in a (relatively) raw form, where each row specifies a sample. Each row should represent an equally spaced unit of time (e.g., if your eye-tracker’s sample rate is 100hz, then each row corresponds to the eye-position every 10ms).
This is in contrast to the more parsed data that the software bundled with eye-trackers can sometimes output (e.g., already parsed into saccades or fixations). For eyetrackingR, the simplest data is the best.
This also maximizes compatibility: eyetrackingR will work with any eye-tracker’s data (e.g., Eyelink, Tobii, etc.), since it requires the most basic format.
Note: eyetrackingR does not handle reading your data into R. Most software bundled with your eyetracker should be capable of exporting your data to a delimited format (.csv, tab-delimited .txt), etc. From there, you can use base functions like
read.delim, or (recommended) check out the package readr.
eyetrackingR just needs to the following columns:
subset_by_windowcan help fix this.
add_aoican create them– see below.) These columns specify whether the gaze is in a particular ‘Area of Interest.’ Each AOI should have a corresponding column. The elements of this column specify, for each sample, whether the participant’s gaze was in that AOI.
There are also some optional columns, which you might want to use depending on your analysis:
If your dataset has these columns, you’re ready to begin using eyetrackingR.
Before being used in eyetrackingR, data must be run through the
This lets you provide the information about your dataset that was just described above. The function will perform some checks on your data to make sure it’s in the correct format.
For this dataset, because each participant saw each item only once in this experiment,
trial_column specifies a unique name for each trial (e.g., “FamiliarCow”) and we don’t specify an
set.seed(42) library("Matrix") library("lme4") library("ggplot2") library("eyetrackingR") data("word_recognition") <- make_eyetrackingr_data(word_recognition, data participant_column = "ParticipantName", trial_column = "Trial", time_column = "TimeFromTrialOnset", trackloss_column = "TrackLoss", aoi_columns = c('Animate','Inanimate'), treat_non_aoi_looks_as_missing = TRUE )
You might be wondering about the
treat_non_aoi_looks_as_missing argument above.
Almost all eyetracking analyses require calculating proportion looking–across a trial, within a time bin, etc. One important choice you as the researcher have to make is whether to include non-AOI looking in this calculation. There are two options:
treat_non_aoi_looks_as_missing lets you decide which of these options eyetrackingR will do. If set to TRUE, when it comes time for eyetrackingR to calculate proportion looking to an AOI, this will be calculated as “time looking to that AOI divided by time looking to all other AOIs.” In contrast, if this parameter is set to FALSE, proportion looking to an AOI will be calculated as “time looking to that AOI divided by total time looking (excluding actual trackloss).”
We all wish our data came right out of our eye-tracker ready for analysis, but this isn’t always the case. Two of the more annoying problems you might encounter are:
Your data doesn’t have any columns corresponding to areas-of-interest. Maybe you needed to create or revise these after running the experiment, or your eyetracking software just doesn’t let you specify them.
Your data doesn’t specify when the relevant things in a trial start. Experiments are complicated. There are pre-phases, fixation-contigent attention-getters, etc. etc. All this means that the stuff you actually want to analyze within a trial could be buried among lots of irrelevant data. For example, you might want to only analyze data after stimulus presentation, but have stimuli that starts at a different timepoint on each trial.
Luckily, eyetrackingR has tools to address both of these problems
Your eyetracking data doesn’t have any columns corresponding to areas of interest. However, it does have columns give you the x,y gaze coordinates. You also have a csv file for each AOI, specifying its boundaries on each type of trial.
In that case, it’s easy to add AOIs to your dataframe:
<- read.csv("./interest_areas_for_animate_aoi.csv") animate_aoi # Trial Left Top Right Bottom # 1 FamiliarBird 500 100 900 500 # 2 FamiliarBottle 400 200 800 600 # 3 FamiliarCow 500 300 900 700 # 4 FamiliarDog 300 100 700 500 # 5 FamiliarHorse 500 200 900 600 # 6 FamiliarSpoon 350 300 750 700 <- add_aoi(data = data, aoi_dataframe = animate_aoi, data x_col = "GazeX", y_col = "GazeY", aoi_name = "Animate", x_min_col = "Left", x_max_col = "Right", y_min_col = "Top", y_max_col = "Bottom")
This can be done for each AOI: just load in a csv file and run the
add_aoi function for each.
After using this function, you should probably check that the added AOI column actually indicates that the gaze was ever in the AOI. For example:
## ## FALSE TRUE ## 49681 82460
table(is.na(data$Animate)) # if all TRUE, then something went wrong.
## ## FALSE TRUE ## 132141 63771
(Note that you should typically add your AOIs to your dataframe before running
make_eyetrackingr_data, since that function will check your AOIs.)
subset_by_window has several methods for getting the data you’re interested in. These are powerful because they can be used repeatedly/iteratively to home in on the relevant data. We show this below.
In this example, let’s imagine that our Timestamp doesn’t actually specify the start of the trial– instead, it specifies the time since the eye-tracker was turned on!
Fortunately, our eye-tracker sends a message when each trial starts (this is not always the same as the very first sample for the trial– recording often starts a few hundred milliseconds before the trial does). This message can be used to set the zero-point for each trial.
<- subset_by_window(data, window_start_msg = "TrialStart", msg_col = "Message", rezero= TRUE)data
Unfortunately, the eye-tracker didn’t send a message for when the response-window starts. Instead, it added a column that tells you how long after the start of the trial the response-window started. Now that we have rezero’d our data so that 0 = trial-start, this column specifying the time after trial start can be used easily.
<- subset_by_window(data, window_start_col = "ResponseWindowStart", rezero= FALSE, remove= TRUE)response_window
Finally, our trials always ended after 21 seconds. So we’ll simply remove data from after this.
<- subset_by_window(response_window, window_end_time = 21000, rezero= FALSE, remove= TRUE)response_window
In summary, we have subset the data to focus on our time window of interest.
Trackloss occurs when the eye-tracker loses track of the participant’s eyes (e.g., when they turn away or blink) or when it captures their gaze location but with very low validity.
We need to decide which trials to remove (if any) due to very high trackloss. To do so here, we will:
# analyze amount of trackloss by subjects and trials <- trackloss_analysis(data = response_window))(trackloss
## # A tibble: 155 × 6 ## ParticipantName Trial Samples TracklossSamples TracklossForTrial ## <fct> <fct> <dbl> <dbl> <dbl> ## 1 ANCAT139 FamiliarBottle 330 161 0.488 ## 2 ANCAT18 FamiliarBird 330 74 0.224 ## 3 ANCAT18 FamiliarBottle 330 43 0.130 ## 4 ANCAT18 FamiliarCow 330 159 0.482 ## 5 ANCAT18 FamiliarDog 330 95 0.288 ## 6 ANCAT18 FamiliarHorse 330 165 0.5 ## 7 ANCAT18 FamiliarSpoon 330 95 0.288 ## 8 ANCAT22 FamiliarBird 330 14 0.0424 ## 9 ANCAT22 FamiliarBottle 330 8 0.0242 ## 10 ANCAT22 FamiliarDog 330 55 0.167 ## # … with 145 more rows, and 1 more variable: TracklossForParticipant <dbl>
<- clean_by_trackloss(data = response_window, trial_prop_thresh = .25)response_window_clean
## Performing Trackloss Analysis...
## Will exclude trials whose trackloss proportion is greater than : 0.25
## ...removed 33 trials.
After data cleaning, it’s important to assess how much data you are ultimately left with to (a) report along with your findings and, (b) identify any problematic participants who didn’t contribute enough trials from which to reliably estimate their performance.
<- trackloss_analysis(data = response_window_clean) trackloss_clean <- unique(trackloss_clean[, c('ParticipantName','TracklossForParticipant')]))(trackloss_clean_subjects
## # A tibble: 27 × 2 ## ParticipantName TracklossForParticipant ## <fct> <dbl> ## 1 ANCAT18 0.177 ## 2 ANCAT22 0.0588 ## 3 ANCAT23 0.0626 ## 4 ANCAT26 0.0970 ## 5 ANCAT39 0.0379 ## 6 ANCAT45 0.0131 ## 7 ANCAT50 0.0576 ## 8 ANCAT53 0.0485 ## 9 ANCAT55 0.0430 ## 10 ANCAT58 0.0261 ## # … with 17 more rows
# get mean samples contributed per trials, with SD mean(1 - trackloss_clean_subjects$TracklossForParticipant)
##  0.9313075
##  0.05208985
# look at the NumTrials column <- describe_data(response_window_clean, describe_column = 'Animate', group_columns = 'ParticipantName'))(final_summary
## # A tibble: 27 × 9 ## ParticipantName Mean SD LowerQ UpperQ Min Max N NumTrials ## <fct> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> ## 1 ANCAT18 0.169 0.375 0 1 0 1 660 2 ## 2 ANCAT22 0.581 0.494 0 1 0 1 1650 5 ## 3 ANCAT23 0.780 0.414 0 1 0 1 1980 6 ## 4 ANCAT26 0.598 0.490 0 1 0 1 1320 4 ## 5 ANCAT39 0.650 0.477 0 1 0 1 1980 6 ## 6 ANCAT45 0.679 0.467 0 1 0 1 990 3 ## 7 ANCAT50 0.836 0.370 0 1 0 1 990 3 ## 8 ANCAT53 0.737 0.441 0 1 0 1 990 3 ## 9 ANCAT55 0.745 0.436 0 1 0 1 1650 5 ## 10 ANCAT58 0.731 0.443 0 1 0 1 1650 5 ## # … with 17 more rows
##  4.518519
##  1.369176
Now is the time to make sure that we have all the columns needed for our analyses, because this dataset is going to be shaped and subsetted as we analyze our data and it’s easier to add these columns once then to do it for derivative datasets.
For the present experiment, one thing we want to do is create a “Target” condition column based on the name of each Trial.
In each trial, the participant was told to look at either an Animate or Inanimate objects. Here we create a column specifying which for each column.
$Target <- as.factor( ifelse(test = grepl('(Spoon|Bottle)', response_window_clean$Trial), response_window_cleanyes = 'Inanimate', no = 'Animate') )
Our dataset is now ready for analysis!