view dependencies in the visual recognition of social interactions

Clicks: 224
ID: 172320
2013
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
Recognizing social interactions, e.g. two people shaking hands, is important for obtaining information about other people and the surrounding social environment. Despite the visual complexity of social interactions, humans have often little difficulties to visually recognize social interactions. What is the visual representation of social interactions and the bodily visual cues that promote this remarkable human ability? Viewpoint dependent representations are considered to be at the heart of the visual recognition of many visual stimuli including objects (Bülthoff & Edelman, 1992), and biological motion patterns (Verfaillie, 1993). Here we addressed the question whether complex social actions acted out between pairs of people, e.g. hugging, are also represented in a similar manner. To this end, we created 3-D models from motion captured actions acted out by two people, e.g. hugging. These 3-D models allowed to present the same action from different viewpoints. Participants task was to discriminate a target action from distractor actions using a one-interval-forced-choice (1IFC) task. We measured participants' recognition performance in terms of reaction times (RT) and d-prime (d'). For each tested action we found one view that lead to superior recognition performance compared to other views. This finding demonstrates view-dependent effects of visual recognition, which are in line with the idea of a view dependent representations of social interactions. Subsequently, we examined the degree to which velocities of joints are able to predict the recognition performance of social interactions in order to determine candidate visual cues underlying the recognition of social interactions. We found that the velocities of the right arm, lower left leg, and both feet correlated with recognition performance.
Reference Key
rosa2013frontiersview Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Stephan ede la Rosa;Sarah eMieskes;Heinrich H Bülthoff;Cristobal eCurio
Journal accounts of chemical research
Year 2013
DOI
10.3389/fpsyg.2013.00752
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.