From Eye-tracking Data to Information: Lessons from Dynamic Scenes

From Eye-tracking Data to Information: Lessons from Dynamic Scenes. Carmi, R. & Itti, L. In Proc. Vision Science Society Annual Meeting (VSS06), May, 2006.
abstract bibtex

A common simplifying assumption for dealing with vast amounts of raw eye-tracking data is to focus on spatial rather than temporal analyses. This assumption is supported by studies with still images, which showed that spatial rather than temporal correlations provide the only source of information in eye-tracking data. Here we establish the extent to which this assumption is violated during inspection of dynamic scenes. We collected 50 video clips depicting a heterogeneous collection of natural scenes. These clips were cut into clip segments, which were re-assembled into 50 scene-shuffled clips (MTV-style). Human observers inspected either continuous or scene-shuffled clips, and inter-observer agreement in gaze position was quantified across conditions and over time. On average, the instantaneous eye-positions of 4 human observers were clustered within a rectangle covering 8.51 percent and 6.04 percent of the display area in the continuous and scene-shuffled conditions, respectively. These values increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01) when eye-positions were sampled from the same eye traces in random order. The average cluster area increased further to 35.88 percent (p<0.01) when 4 eye-positions were chosen at random from a uniform distribution of spatial locations. Moreover, preserving time information led to previously unreported patterns of inter-observer agreement. These results demonstrate that increasing stimulus dynamics triggers eye-movement patterns that diverge increasingly from previous accounts based on still images. The limited scalability of conclusions based on still images is likely to be further accentuated by future enhancements in the realism of laboratory stimuli, such as larger field of view and reduced central bias.

@inproceedings{ Carmi_Itti06vss,
  author = {R. Carmi and L. Itti},
  title = {From Eye-tracking Data to Information: Lessons from Dynamic Scenes},
  abstract = {A common simplifying assumption for dealing with vast
amounts of raw eye-tracking data is to focus on spatial rather than
temporal analyses. This assumption is supported by studies with still
images, which showed that spatial rather than temporal correlations
provide the only source of information in eye-tracking data. Here we
establish the extent to which this assumption is violated during
inspection of dynamic scenes.  We collected 50 video clips depicting a
heterogeneous collection of natural scenes. These clips were cut into
clip segments, which were re-assembled into 50 scene-shuffled clips
(MTV-style). Human observers inspected either continuous or
scene-shuffled clips, and inter-observer agreement in gaze position
was quantified across conditions and over time.  On average, the
instantaneous eye-positions of 4 human observers were clustered within
a rectangle covering 8.51 percent and 6.04 percent of the display area
in the continuous and scene-shuffled conditions, respectively. These
values increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01)
when eye-positions were sampled from the same eye traces in random
order. The average cluster area increased further to 35.88 percent
(p<0.01) when 4 eye-positions were chosen at random from a uniform
distribution of spatial locations. Moreover, preserving time
information led to previously unreported patterns of inter-observer
agreement.  These results demonstrate that increasing stimulus
dynamics triggers eye-movement patterns that diverge increasingly from
previous accounts based on still images. The limited scalability of
conclusions based on still images is likely to be further accentuated
by future enhancements in the realism of laboratory stimuli, such as
larger field of view and reduced central bias.},
  booktitle = {Proc. Vision Science Society Annual Meeting (VSS06)},
  year = {2006},
  month = {May},
  type = {mod;bu;td;eye},
  review = {abs/conf}
}

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{"_id":{"_str":"5298a1a09eb585cc26000894"},"__v":0,"authorIDs":[],"author_short":["Carmi, R.","Itti, L."],"bibbaseid":"carmi-itti-fromeyetrackingdatatoinformationlessonsfromdynamicscenes-2006","bibdata":{"html":"<div class=\"bibbase_paper\"> \n\n\n<span class=\"bibbase_paper_titleauthoryear\">\n\t<span class=\"bibbase_paper_title\"><a name=\"Carmi_Itti06vss\"> </a>From Eye-tracking Data to Information: Lessons from Dynamic Scenes.</span>\n\t<span class=\"bibbase_paper_author\">\nCarmi, R.; and Itti, L.</span>\n\t\n</span>\n\n\n\nIn\n<i>Proc. Vision Science Society Annual Meeting (VSS06)</i>, May 2006.\n\n\n\n\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content\">\n \n \n \n <a href=\"javascript:showBib('Carmi_Itti06vss')\"\n class=\"bibbase link\">\n \n\t\n\t\n\t\n BibTeX\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n  \n <a class=\"bibbase_abstract_link bibbase link\"\n href=\"javascript:showAbstract('Carmi_Itti06vss')\">\n Abstract\n <i class=\"fa fa-caret-down\"></i></a>\n \n \n \n\n \n \n \n</span>\n\n<div class=\"well well-small bibbase\" id=\"bib_Carmi_Itti06vss\"\n style=\"display:none\">\n <pre>@inproceedings{ Carmi_Itti06vss,\n author = {R. Carmi and L. Itti},\n title = {From Eye-tracking Data to Information: Lessons from Dynamic Scenes},\n abstract = {A common simplifying assumption for dealing with vast\namounts of raw eye-tracking data is to focus on spatial rather than\ntemporal analyses. This assumption is supported by studies with still\nimages, which showed that spatial rather than temporal correlations\nprovide the only source of information in eye-tracking data. Here we\nestablish the extent to which this assumption is violated during\ninspection of dynamic scenes. We collected 50 video clips depicting a\nheterogeneous collection of natural scenes. These clips were cut into\nclip segments, which were re-assembled into 50 scene-shuffled clips\n(MTV-style). Human observers inspected either continuous or\nscene-shuffled clips, and inter-observer agreement in gaze position\nwas quantified across conditions and over time. On average, the\ninstantaneous eye-positions of 4 human observers were clustered within\na rectangle covering 8.51 percent and 6.04 percent of the display area\nin the continuous and scene-shuffled conditions, respectively. These\nvalues increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01)\nwhen eye-positions were sampled from the same eye traces in random\norder. The average cluster area increased further to 35.88 percent\n(p<0.01) when 4 eye-positions were chosen at random from a uniform\ndistribution of spatial locations. Moreover, preserving time\ninformation led to previously unreported patterns of inter-observer\nagreement. These results demonstrate that increasing stimulus\ndynamics triggers eye-movement patterns that diverge increasingly from\nprevious accounts based on still images. The limited scalability of\nconclusions based on still images is likely to be further accentuated\nby future enhancements in the realism of laboratory stimuli, such as\nlarger field of view and reduced central bias.},\n booktitle = {Proc. Vision Science Society Annual Meeting (VSS06)},\n year = {2006},\n month = {May},\n type = {mod;bu;td;eye},\n review = {abs/conf}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" id=\"abstract_Carmi_Itti06vss\"\n style=\"display:none\">\n A common simplifying assumption for dealing with vast amounts of raw eye-tracking data is to focus on spatial rather than temporal analyses. This assumption is supported by studies with still images, which showed that spatial rather than temporal correlations provide the only source of information in eye-tracking data. Here we establish the extent to which this assumption is violated during inspection of dynamic scenes. We collected 50 video clips depicting a heterogeneous collection of natural scenes. These clips were cut into clip segments, which were re-assembled into 50 scene-shuffled clips (MTV-style). Human observers inspected either continuous or scene-shuffled clips, and inter-observer agreement in gaze position was quantified across conditions and over time. On average, the instantaneous eye-positions of 4 human observers were clustered within a rectangle covering 8.51 percent and 6.04 percent of the display area in the continuous and scene-shuffled conditions, respectively. These values increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01) when eye-positions were sampled from the same eye traces in random order. The average cluster area increased further to 35.88 percent (p<0.01) when 4 eye-positions were chosen at random from a uniform distribution of spatial locations. Moreover, preserving time information led to previously unreported patterns of inter-observer agreement. These results demonstrate that increasing stimulus dynamics triggers eye-movement patterns that diverge increasingly from previous accounts based on still images. The limited scalability of conclusions based on still images is likely to be further accentuated by future enhancements in the realism of laboratory stimuli, such as larger field of view and reduced central bias.\n</div>\n\n\n</div>\n","downloads":0,"bibbaseid":"carmi-itti-fromeyetrackingdatatoinformationlessonsfromdynamicscenes-2006","role":"author","year":"2006","type":"mod;bu;td;eye","title":"From Eye-tracking Data to Information: Lessons from Dynamic Scenes","review":"abs/conf","month":"May","key":"Carmi_Itti06vss","id":"Carmi_Itti06vss","booktitle":"Proc. Vision Science Society Annual Meeting (VSS06)","bibtype":"inproceedings","bibtex":"@inproceedings{ Carmi_Itti06vss,\n author = {R. Carmi and L. Itti},\n title = {From Eye-tracking Data to Information: Lessons from Dynamic Scenes},\n abstract = {A common simplifying assumption for dealing with vast\namounts of raw eye-tracking data is to focus on spatial rather than\ntemporal analyses. This assumption is supported by studies with still\nimages, which showed that spatial rather than temporal correlations\nprovide the only source of information in eye-tracking data. Here we\nestablish the extent to which this assumption is violated during\ninspection of dynamic scenes. We collected 50 video clips depicting a\nheterogeneous collection of natural scenes. These clips were cut into\nclip segments, which were re-assembled into 50 scene-shuffled clips\n(MTV-style). Human observers inspected either continuous or\nscene-shuffled clips, and inter-observer agreement in gaze position\nwas quantified across conditions and over time. On average, the\ninstantaneous eye-positions of 4 human observers were clustered within\na rectangle covering 8.51 percent and 6.04 percent of the display area\nin the continuous and scene-shuffled conditions, respectively. These\nvalues increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01)\nwhen eye-positions were sampled from the same eye traces in random\norder. The average cluster area increased further to 35.88 percent\n(p<0.01) when 4 eye-positions were chosen at random from a uniform\ndistribution of spatial locations. Moreover, preserving time\ninformation led to previously unreported patterns of inter-observer\nagreement. These results demonstrate that increasing stimulus\ndynamics triggers eye-movement patterns that diverge increasingly from\nprevious accounts based on still images. The limited scalability of\nconclusions based on still images is likely to be further accentuated\nby future enhancements in the realism of laboratory stimuli, such as\nlarger field of view and reduced central bias.},\n booktitle = {Proc. Vision Science Society Annual Meeting (VSS06)},\n year = {2006},\n month = {May},\n type = {mod;bu;td;eye},\n review = {abs/conf}\n}","author_short":["Carmi, R.","Itti, L."],"author":["Carmi, R.","Itti, L."],"abstract":"A common simplifying assumption for dealing with vast amounts of raw eye-tracking data is to focus on spatial rather than temporal analyses. This assumption is supported by studies with still images, which showed that spatial rather than temporal correlations provide the only source of information in eye-tracking data. Here we establish the extent to which this assumption is violated during inspection of dynamic scenes. We collected 50 video clips depicting a heterogeneous collection of natural scenes. These clips were cut into clip segments, which were re-assembled into 50 scene-shuffled clips (MTV-style). Human observers inspected either continuous or scene-shuffled clips, and inter-observer agreement in gaze position was quantified across conditions and over time. On average, the instantaneous eye-positions of 4 human observers were clustered within a rectangle covering 8.51 percent and 6.04 percent of the display area in the continuous and scene-shuffled conditions, respectively. These values increased to 11.48 percent (p<0.01) and 9.36 percent (p<0.01) when eye-positions were sampled from the same eye traces in random order. The average cluster area increased further to 35.88 percent (p<0.01) when 4 eye-positions were chosen at random from a uniform distribution of spatial locations. Moreover, preserving time information led to previously unreported patterns of inter-observer agreement. These results demonstrate that increasing stimulus dynamics triggers eye-movement patterns that diverge increasingly from previous accounts based on still images. The limited scalability of conclusions based on still images is likely to be further accentuated by future enhancements in the realism of laboratory stimuli, such as larger field of view and reduced central bias."},"bibtype":"inproceedings","biburl":"http://ilab.usc.edu/publications/src/ilab.bib","downloads":0,"search_terms":["eye","tracking","data","information","lessons","dynamic","scenes","carmi","itti"],"title":"From Eye-tracking Data to Information: Lessons from Dynamic Scenes","year":2006,"dataSources":["wedBDxEpNXNCLZ2sZ"]}