DOI: 10.5061/dryad.zpc866t5c
title: A relationship between Autism-Spectrum Quotient and face viewing behavior in 98 participants

description: Faces are one of the most important stimuli that we encounter, but humans
 vary dramatically in their behavior when viewing a face: some individuals
 preferentially fixate the eyes, others fixate the mouth, and still others
 show an intermediate pattern. The determinants of these large individual
 differences are unknown. However, individuals with Autism Spectrum
 Disorder (ASD) spend less time fixating the eyes of a viewed face than
 controls, suggesting the hypothesis that autistic traits in healthy adults
 might explain individual differences in face viewing behavior. Autistic
 traits were measured in 98 healthy adults recruited from an academic
 setting using the Autism-Spectrum Quotient, a validated 50-statement
 questionnaire. Fixations were measured using a video-based eye tracker
 while participants viewed two different types of audiovisual movies: short
 videos of talker speaking single syllables and longer videos of talkers
 speaking sentences in a social context. For both types of movies, there
 was a positive correlation between Autism-Spectrum Quotient score and
 percent of time fixating the lower half of the face that explained from 4%
 to 10% of the variance in individual face viewing behavior. This effect
 suggests that in healthy adults, autistic traits are one of many factors
 that contribute to individual differences in face viewing behavior.
description:  <b>Methods</b> <b>Design and
 Participants</b> Participants
 provided written informed consent under an experimental protocol approved
 by the Committee for the Protection of Human Participants of the Baylor
 College of Medicine, Houston, TX. Experimenters who recorded the stimuli
 gave informed consent for publication of identifying images in an online
 open-access publication. All experiments were performed in accordance with
 relevant guidelines and regulations. Participants were primarily recruited
 in a university setting, from Baylor College of Medicine and Rice
 University students and employees. A power analysis was conducted prior to
 collecting the data to determine sample size, based on an expected effect
 size of r=0.45, which provided a suggested sample size of 71
 (G*Power). <b>        
 </b>Participants were presented with stimuli consisting of auditory
 and visual recordings of human talkers and then completd the
 Autism-Spectrum Quotient as a measure of autistic traits. Stimuli were
 presented through Matlab with Psychtoolbox. In experiment 1, participants
 (<i>n </i>= 98; 66 female, mean age 21, age range 18-45) were
 shown short syllable videos recorded in lab and given an explicit task. In
 experiment 2, participants (<i>n = </i>70, 49 female, mean age
 20, range 18-45; all native English speakers) were shown full sentence
 videos taken from longer speeches and interviews and not given a
 task.  <b>Autism-Spectrum Quotient
 </b> <i>           
 </i>Participants completed the Autism-Spectrum Quotient (AQ) online
 before the test session. This self-administered instrument consists of 50
 items designed to assess autistic traits in adults of normal intelligence.
 The items reflect the differences in social skills, communication skills,
 imagination, attention to detail, and attention-switching noted in ASD.
 Each item is a statement about the participant's ability or
 preference, which the participant rates as definitely agree, slightly
 agree, slightly disagree, or definitely disagree. The test was scored
 using the method described in the original presentation of the test: each
 answer was collapsed into one of two categories (yes, for "definitely
 agree" or "slightly agree"; no, for slightly disagree or
 definitely disagree); assigned a value of 1 or 0, depending on the
 question; and the scores across all questions summed. A higher score
 indicates a higher degree of autistic-like traits, with a maximum score of
 50 points.   <b>Experiment 1: Syllable
 Movies</b>             Participants
 (<i>n </i>= 98; 66 female, mean age 21, age range 18-45)
 completed a syllable identification task on short audiovisual speech
 movies. Each trial began with a fixation crosshairs presented outside of
 the location of where the face would appear in order to simulate natural
 viewing conditions in which faces rarely appear at the center of gaze(7).
 The visual crosshairs then disappeared, and participants were free to
 fixate. The visual stimulus appeared for 2 seconds, after which the
 participants had 1 second to report the syllable ("ba,"da,"
 or "ga") they perceived via button press. Four different
 speakers appeared and repeated each syllable 60 times, for a total of 240
 trials. The stimuli were randomized by speaker and syllable, then that
 same order was presented to all participants. The trials were divided into
 4 blocks, separated by rest intervals during which the eye tracker was
 recalibrated. Total eye tracking time was 12 minutes. Only one speaker was
 shown per trial and each directly faced the camera. The face subtended
 approximately 10 cm by 13 cm (6 degrees wide by 8 degrees high).
   <b>Experiment 2: Sentence
 Movies</b> <b>            </b>A
 subset of the Experiment 1 participants (<i>n = </i>70, 49
 female, mean age 20, range 18-45; all native English speakers) completed a
 short movie watching task. Participants watched 40 short clips taken from
 interviews or speeches that had been uploaded to YouTube under the
 Creative Commons license. As in the syllable videos, each trial began with
 a fixation cross outside of where the face would later appear. The clips
 lasted an average of 23 seconds, ranging from 13 and 42 seconds. The clips
 were shown in approximately 4-minute blocks of 10 videos each, separated
 by rest intervals where the eye tracker was recalibrated. The total task
 time was 15.5 minutes. Movies were balanced between blocks by length and
 speaker gender, then the stimuli were presented in the same order to all
 participants.  Participants were not given a specific task or required to
 make a behavioral response following the videos in order to encourage as
 close to natural free viewing as possible. Each movie showed only one
 speaker and was cropped so that the speaker's face filled most of the
 frame in order to minimize background distractions. Some clips featured
 direct gaze from the speakers, while speakers looked to the side in
 others.  A different speaker appeared each trial, with 18 clips featuring
 female speakers and 22 featuring male speakers. The stimuli filled the
 entire screen (70 cm x 40 cm) and the face subtended approximately 14 cm
 by 18 cm (9 degrees wide by 11 degrees high). 
   <b>Eye Tracking Methods and
 Analysis</b>             Participants' eye
 movements were recorded using an infrared eye tracker (Eye Link 1000 Plus,
 SR Research Ltd., Ottawa, Ontario, Canada) as they viewed visual stimuli
 presented on a display (Display++ LCD Monitor, 32" 1920 × 1080,
 120 Hz, Cambridge Research Systems, Rochester, UK) and listened to speech
 through speakers located on either side of the screen. Eye tracking
 stability was increased with a chin rest placed 90 cm from the display.
 Eye tracking was performed with a sampling rate of 500 Hz. The eye tracker
 was calibrated using a 9-target array before each block, for a total of 4
 times in each task. Fixations, saccades, and blinks were identified by SR
 Research's Eyelink software.  Saccades, blinks and fixations that
 started before stimulus onset were excluded from the analysis.
             In order to summarize the fixation data, we used a
 region of interest (ROI) approach. For syllable videos, each fixation in
 each trial was marked as within or outside the lower face region of
 interest on each trial. ROIs were hand drawn for each speaker. A dividing
 line was drawn at the tip of the speaker's nose. Since the syllable
 videos were only 2 seconds and the speaker's head did not move, the
 same ROI coordinates were used for each frame. Fixations falling in the
 lower face region were then summed and divided by the total trial fixation
 time to calculate a percent time spent fixating lower face on each trial.
 These trial percentages were then averaged, weighted by the trial
 duration, to calculate the percent of time spent fixating the lower face
 for each participant.             The sentence videos
 were taken from television programs rather than created in the lab,
 resulting in variation in the face position from frame to frame. Together
 with the length of the videos, this precluded the use of hand-drawn ROIs.
 Instead, the Cascade Object Detector in Matlab's Computer Vision
 System Toolbox was used to automatically generate a box surrounding the
 face in each video frame. A location at 40% of the face box height was
 selected as the dividing line before the upper and lower face in order to
 align most closely to the tip of the nose dividing line used in the
 syllable task. To check the accuracy of the face detection tool, all ROIs
 were visualized on top of the original videos. If a face was not
 identified in a frame or the location was not identified incorrectly, the
 coordinates from the preceding and succeeding frames were averaged to
 estimate a location.             The fixation location
 at the time of presentation of each video frame was measured. To calculate
 percentage of fixation time, the number of frames with fixation in the
 lower face were divided by the total number of frames. This value was then
 averaged across all videos, weighted by the number of frames (since all
 videos had the same frame rate) in order to calculate a grand mean
 lower-face fixation percentage for each participant.