Spatial statistics and attentional dynamics in scene viewing
- In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision-making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here, we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on activation dynamics via spatially-limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention toIn humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision-making. Computational neuroscientists have developed biologically-inspired models of visual attention, termed saliency maps, which successfully predict where people fixate on average. Using point process theory for spatial statistics, we show that scanpaths contain, however, important statistical structure, such as spatial clustering on top of distributions of gaze positions. Here, we develop a dynamical model of saccadic selection that accurately predicts the distribution of gaze positions as well as spatial clustering along individual scanpaths. Our model relies on activation dynamics via spatially-limited (foveated) access to saliency information, and, second, a leaky memory process controlling the re-inspection of target regions. This theoretical framework models a form of context-dependent decision-making, linking neural dynamics of attention to behavioral gaze data.…
Author details: | Ralf EngbertORCiDGND, Hans Arne TrukenbrodORCiD, Simon Barthelme, Felix A. WichmannORCiD |
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DOI: | https://doi.org/10.1167/15.1.14 |
ISSN: | 1534-7362 |
Title of parent work (English): | Journal of vision |
Publisher: | Association for Research in Vision and Opthalmology |
Place of publishing: | Rockville |
Publication type: | Article |
Language: | English |
Year of first publication: | 2015 |
Publication year: | 2015 |
Release date: | 2017/03/27 |
Tag: | attention; eye movements; modeling; saccades; scene perception; spatial statistics |
Volume: | 15 |
Issue: | 1 |
Number of pages: | 17 |
Funding institution: | Bundesministerium fur Bildung und Forschung (BMBF) [B3, FKZ: 01GQ1001F, FKZ: 01GQ1001B, FKZ: 01GQ1002]; Deutsche Forschungsgemeinschaft [EN 471/13-1, WI 2103/4-1] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie |
Peer review: | Referiert |
Publishing method: | Open Access |
Institution name at the time of the publication: | Humanwissenschaftliche Fakultät / Institut für Psychologie |