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Top-down influence on gaze patterns in the presence of social features

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  • Aleya Felicia Flechsenhar
  • Matthias Gamer

Abstract

Visual saliency maps reflecting locations that stand out from the background in terms of their low-level physical features have proven to be very useful for empirical research on attentional exploration and reliably predict gaze behavior. In the present study we tested these predictions for socially relevant stimuli occurring in naturalistic scenes using eye tracking. We hypothesized that social features (i.e. human faces or bodies) would be processed preferentially over non-social features (i.e. objects, animals) regardless of their low-level saliency. To challenge this notion, we included three tasks that deliberately addressed non-social attributes. In agreement with our hypothesis, social information, especially heads, was preferentially attended compared to highly salient image regions across all tasks. Social information was never required to solve a task but was regarded nevertheless. More so, after completing the task requirements, viewing behavior reverted back to that of free-viewing with heavy prioritization of social features. Additionally, initial eye movements reflecting potentially automatic shifts of attention, were predominantly directed towards heads irrespective of top-down task demands. On these grounds, we suggest that social stimuli may provide exclusive access to the priority map, enabling social attention to override reflexive and controlled attentional processes. Furthermore, our results challenge the generalizability of saliency-based attention models.

Suggested Citation

  • Aleya Felicia Flechsenhar & Matthias Gamer, 2017. "Top-down influence on gaze patterns in the presence of social features," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-20, August.
  • Handle: RePEc:plo:pone00:0183799
    DOI: 10.1371/journal.pone.0183799
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    References listed on IDEAS

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    2. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
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