An example computation¶
The following section shows a multimatch-gaze use case to compute the scan path similarities of participants that watched the Hollywood movie Forrest Gump during simultaneous fMRI acquisition.
Data and sample¶
Data for all analyses stems from the 2016 released extension of the studyforrest dataset (Hanke et al., 2016; Sengupta et al., 2016). In this extension, N = 15 right-handed participants (age range 21 - 39 years, mean age 29.4 years, six female, normal or corrected-to-normal vision), who had previously participated in the studyforrest project, watched the audio-visual movie ‘Forrest Gump’ (R. Zemeckis, Paramount Pictures, 1994) during simultaneous fMRI and eye-tracking recording. The video track for the movie stimulus was re-encoded from Blu-ray into H.264 video (1280 x 720px at 25 frames per second (fps)). In accordance to the procedure in an earlier phase of the studyforrest project, the movie was shortened by removing a few scenes less relevant for the major plot to keep the fMRI recording session under two hours. The shortened movie was then split into eight segments of roughly 15 minutes of length (for an overview on segment duration, final stimulus content and detailed procedures see Hanke et al. (2014)). Visual stimuli were projected on to a screen inside the bore of the magnet using an LCD projector, and presented to the subjects through a front-reflective mirror on top of the head coil at a viewing distance of 63cm. The screen dimensions were 26.5cm x 21.2cm (corresponding to 1280 x 1024px) at a resolution of 720p at full width, with a 60Hz video refresh rate (Sengupta et al., 2016). Eye-tracking was performed with an Eyelink 1000 (software version 4.594) using monocular corneal reflection and pupil tracking with a temporal resolution of eye gaze recordings of 1000Hz. The camera was mounted at an approximate distance of 100cm to the left eye of subjects, which was illuminated by an infrared light source (Hanke et al., 2016). Eye-tracking data were normalized such that all gaze coordinates are in native movie frame pixels, with the top-left corner of the movie frame located at (0, 0) and the lower-right corner located at (1280, 546) (ibid.). The amount of unusable data, primarily due to signal loss during eye blinks, ranged from less than 1 to 15% for 13 of the 15 in-scanner subjects (the other two subjects’ data contained 85 and 36% of data loss, respectively). In-scanner acquisition had an approximate spatial uncertainty of 40px according to the calibration procedure (ibid.).
Event detection and scan path derivation¶
Raw gaze data was classified into different categories of eye movements with an adaptive, data-driven algorithm for robust eye movement detection for natural viewing (REMoDNaV ) in Python. The algorithm categorizes the raw data into saccades, fixations, smooth pursuits, and post-saccadic oscillations (glissades), and disregards any unclassifiable data (such as blinks). It was specifically developed to compute robust results even under high noise conditions. For an overview of the algorithmic details and evaluation of REMoDNaV compared to contemporary algorithms and human annotations, please see the respective publication (Dar et al., in preparation) or take a look at the REMoDNaV module. The eye events are reported together with their start and end coordinates, their onsets and durations in seconds, their velocity, and the average pupil size. Fixation vectors as input for multimatch-gaze were derived from the REMoDNaV output. As the stimulus was dynamic with moving targets that evoke smooth pursuit movements, such pursuit events are categorized to be an eye movement category of ‘visual intake’, just as fixation. Therefore, in a first step, the start and end of pursuit movements were included in scan paths to compare as well. In a second step, the continuous eye movement data (~15 min per run) was split into shots corresponding to segments that did not contain scene changes between depicted locales using the published location annotation for the movie (Häusler & Hanke, 2016). This was done to accommodate the fact that subjects gazes have a bias towards the center in Hollywood movies (Tseng et al. 2009). This bias can at least in part be traced back to a strong center bias directly after cuts in dynamic scenes. Lastly, within each segment, scan paths of the median shot length of ~4.92 seconds. To further evade any problems associated with the center bias, scan paths were extracted from the end of the segment: The last oculomotor event within the range of the segment marked the end of a scan path. As such, scan paths began maximally distant to the snippet onset.
Overall scan path similarities were computed in a two-step procedure. First, scan path comparisons of all scan paths from the same shot of two subjects were calculated for all possible pairs of subject. This resulted in 105 combinations for N = 15 subjects. These comparisons were done without any further simplification (i.e. no use of the direction, length, and duration thresholds), as even minor differences in scan paths obtained from a movie can correspond to major differences in attended visual stimuli. In a second step, the resulting similarities for each of the five similarity dimensions were averaged. Thus, for each snippet longer than 4.92s five similarity measures were computed that represented the average similarity of scan paths of all subjects on the given dimension. The results of this computation can be found on Github.
In total, 533 scan paths were extracted from the movie. The median duration of extracted scan path duration was 4.39 seconds (mean = 4.36s). The following figures give an overview of the similarity computations. Figures 1 and 2 display a frame within the segments in the first run of the movie with the lowest and highest group-level similarity (averaged across the five dimensions). The overlayed eye gaze was created with a custom script that is part of the studyforrest phase-2 data release (Hanke et al., 2016) and publicly available in the corresponding Github repository.
The overall similarity of gaze was high, however, there were consistent differences between dimensions. The Shape, Length and Position dimension displayed very high similarities, and the average Duration similarity was the lowest of all dimensions. Medians and means correspond closely, and standard deviations are very small. This is also highlighted by Figure 3.
Variable mean [SD] median Shape 0.97 [0.01] 0.97 Position 0.88 [0.03] 0.89 Length 0.96 [0.01] 0.96 Duration 0.54 [0.05] 0.55 Direction 0.72 [0.05] 0.71
As evident from the previous table and figure, scan paths were almost perfectly similar on the dimensions vector length and vector position. This is likely at least partially due to the scan path alignment based on the scan path shape. Scanpaths were also highly similar on the position dimension, which demonstrates a strong gaze control of the movie stimulus. Subjects scan paths differed more substantially on the dimensions direction and duration, which indicates differences in fixation dwelling times and saccadic angle. Thus, the general points of interest (as evident from high similarities in position, length and shape) were similar across subject, but differences in direction and duration might indicate interindividually different exploration strategies. All dimensions show a remarkable consistency in similarity measures as evident from the small standard deviations. This might indicate a consistently high level of exogenous attentional control by the movie stimulus. This finding is consistent with research on viewing behavior during movies: Unlike during static image viewing, the spatio-temporal gaze behavior of multiple viewers exhibits a substantial degree of coordination in movie watching. Smith and Henderson (2008) cued the term attentional synchrony for this phenomenon. During attentional synchrony, viewers gazes cluster around a small portion of the screen at any one moment. Goldstein et al. (2007), for example, found the distribution of fixations of viewers to occupy less than 12% of the total screen area in more than 50% of the time in six Hollywood movies. In a comparison between different types of static and dynamic visual stimuli, Dorr et al. (2010) found the highest consistency between viewers eyegazes during professionally produced (Hollywood) movies, likely largely due to the use of cinematic composition of scenes, deliberate camera work and editing. Hasson et al. (2008) found high correspondence in gaze behavior across subjects, even for backwards presentations of movies.
The results obtained with the multimatch algorithm from the Hollywood movie Forrest Gump, therefore, are consistent with known properties of gaze behavior during movie watching. This analysis has furthermore demonstrated one way of using multimatchs scan path comparison on a grouplevel similarity computation per segment. If you have any questions about this example, please ask here.
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