DOI: 10.5061/dryad.mp332
title: Data from: Pupil diameter tracks lapses of attention

description: Our ability to sustain attention for prolonged periods of time is limited.
 Studies on the relationship between lapses of attention and
 psychophysiological markers of attentional state, such as pupil diameter,
 have yielded contradicting results. Here, we investigated the relationship
 between tonic fluctuations in pupil diameter and performance on a
 demanding sustained attention task. We found robust linear relationships
 between baseline pupil diameter and several measures of task performance,
 suggesting that attentional lapses tended to occur when pupil diameter was
 small. However, these observations were primarily driven by the joint
 effects of time-on-task on baseline pupil diameter and task performance.
 The linear relationships disappeared when we statistically controlled for
 time-on-task effects and were replaced by consistent inverted U-shaped
 relationships between baseline pupil diameter and each of the task
 performance measures, such that most false alarms and the longest and most
 variable response times occurred when pupil diameter was both relatively
 small and large. Finally, we observed strong linear relationships between
 the temporal derivative of pupil diameter and task performance measures,
 which were largely independent of time-on-task. Our results help to
 reconcile contradicting findings in the literature on pupil-linked changes
 in attentional state, and are consistent with the adaptive gain theory of
 locus coeruleus-norepinephrine function. Moreover, they suggest that the
 derivative of baseline pupil diameter is a potentially useful
 psychophysiological marker that could be used in the on-line prediction
 and prevention of attentional lapses.
description: All
 dataData for van den Brink,
 Murphy & Niewenhuis: Pupil diameter tracks lapses of attention. Three
 types of data are provided: 1) Raw data; 2) the processed data that were
 used to compute metrics for inferrential stistics; 3) and the metrics
 themselves. (1) Raw data are contained in the folder 'raw_data'.
 The folder 'pupil_data contains four sub folders: * edfs: Raw EDF
 files as produced by the EyeLink. * samples: ASCII file containing data
 points from the EDF files (so the pupil data). * events: ASCII file
 contaning event type and timing information. Type: 0 = Scrambled image; 1
 = Mountain; 2 = City; 32 = Response (space bar press). * converted: MATLAB
 files containing the data imported into EEGLAB format. Each block is
 contained in a separate EEG entry within ALLEEG. The first channel is
 pupil diamter in pixels. The second and third channel are gaze x and gaze
 y respectively. Event type and timing are contained in EEG.event. The
 folder 'behavior' contains a MATLAB file per participant and
 block that contains the behavioral data. * The relevant matrix here is
 'response', which is organized as trials (rows) by variables
 (columns). Relevant columns are: Column 1 contains trial types (0 =
 Scrambled image; 1 = Mountain; 2 = City), Column 2 contains key code (32 =
 space bar; 0 = no response), Column 5 contains RTs (RT = 0 if no
 response), Comlumn 7 contains response type (-1 = false alarm; 0 = miss; 1
 = hit). (2) Processed data are contained in the folder
 'processed_data.’ Within are text files that resulted from the
 sliding window analysis. In all files the first column is participant
 number, and the second column is block number. All following columns are
 data points (a value per window). These data served as regressors in all
 the major analyses. Folder and file names will tell you what's what.
 (3) Regression coefficients and slopes are contained in
 'statistics'. All MATLAB files containing matrices on which the
 stats were run. * Slopes, indicative of linear changes over time, are
 contained in 'Slopes_xxx.mat;’ Size: participant (rows) by block
 (columns). * Linear regression coefficients are contained in
 'Linear_betas_diameter/derivative.mat.’ * Quadratic regression
 coefficients are contained in 'Quadratic_betas_diameter.mat.’ * File
 suffix _noTOT indicates that these are regression coefficients after
 taking time on task into account. The matrices that contain regression
 coefficients are of size Participant by block by measure. Measure: 1 =
 False alarm; 2 = Slow quintile 3 = RT; 4 = RTCV.In all of the above, the
 participants are in the same order as in the text files in the folder
 'processed_data'. Note that all statistics were run on the
 block-average of these matrices.vandenBrinketal2016PONE.zip