@article{RuboGamer2021,
  author    = {Rubo, Marius and Gamer, Matthias},
  title     = {Stronger reactivity to social gaze in virtual reality compared to a classical laboratory environment},
  series = {British Journal of Psychology},
  volume    = {112},
  journal   = {British Journal of Psychology},
  number    = {1},
  doi       = {10.1111/bjop.12453},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-215972},
  pages     = {301 -- 314},
  year      = {2021},
  abstract  = {People show a robust tendency to gaze at other human beings when viewing images or videos, but were also found to relatively avoid gaze at others in several real-world situations. This discrepancy, along with theoretical considerations, spawned doubts about the appropriateness of classical laboratory-based experimental paradigms in social attention research. Several researchers instead suggested the use of immersive virtual scenarios in eliciting and measuring naturalistic attentional patterns, but the field, struggling with methodological challenges, still needs to establish the advantages of this approach. Here, we show using eye-tracking in a complex social scenario displayed in virtual reality that participants show enhanced attention towards the face of an avatar at near distance and demonstrate an increased reactivity towards her social gaze as compared to participants who viewed the same scene on a computer monitor. The present study suggests that reactive virtual agents observed in immersive virtual reality can elicit natural modes of information processing and can help to conduct ecologically more valid experiments while maintaining high experimental control.},
  language  = {en}
}
@article{RoeslerRuboGamer2019,
  author    = {R{\"o}sler, Lara and Rubo, Marius and Gamer, Matthias},
  title     = {Artificial faces predict gaze allocation in complex dynamic scenes},
  series = {Frontiers in Psychology},
  volume    = {10},
  journal   = {Frontiers in Psychology},
  number    = {2877},
  issn      = {1664-1078},
  doi       = {10.3389/fpsyg.2019.02877},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193024},
  year      = {2019},
  abstract  = {Both low-level physical saliency and social information, as presented by human heads or bodies, are known to drive gaze behavior in free-viewing tasks. Researchers have previously made use of a great variety of face stimuli, ranging from photographs of real humans to schematic faces, frequently without systematically differentiating between the two. In the current study, we used a Generalized Linear Mixed Model (GLMM) approach to investigate to what extent schematic artificial faces can predict gaze when they are presented alone or in competition with real human faces. Relative differences in predictive power became apparent, while GLMMs suggest substantial effects for real and artificial faces in all conditions. Artificial faces were accordingly less predictive than real human faces but still contributed significantly to gaze allocation. These results help to further our understanding of how social information guides gaze in complex naturalistic scenes.},
  language  = {en}
}