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Humans recognize social interactions using relational visual information, not complex mental inference. A new graph neural network model, SocialGNN, accurately predicts human judgments, highlighting the importance of structured visual representations for social cognition.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Neuroscience

Background:

  • Human social interaction recognition is effortless but difficult to model.
  • Prior models used generative inverse planning, inferring goals and mental states.
  • Existing visual neural networks fail to replicate human-like interaction recognition.

Purpose of the Study:

  • Investigate the role of relational visual information in social interaction recognition.
  • Develop a novel computational model for predicting human judgments of social interactions.
  • Challenge the necessity of mental state inference for social cognition.

Main Methods:

  • Developed SocialGNN, a relational graph neural network model.
  • Trained and tested SocialGNN on animated and natural videos.
  • Compared model predictions against human interaction judgments.

Main Results:

  • SocialGNN accurately predicts human judgments of social interactions.
  • The model's success is attributed to its focus on relational visual information.
  • Demonstrated that complex social judgments can be made without explicit world models.

Conclusions:

  • Relational visual representations are crucial for human social interaction recognition.
  • Social cognition may not require complex mental state inference.
  • SocialGNN provides a new framework for understanding visual social perception.