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Open Access Publications from the University of California

Modeling joint attention from egocentric vision


Numerous studies in cognitive development have provided converging evidence that Joint Attention (JA) is crucial for children to learn about the world together with their parents. However, a closer look reveals that, in the literature, JA has been operationally defined in different ways. For example, some definitions require explicit signals of “awareness” of being in JA—such as gaze following, while others simply define JA as shared gaze to an object or activity. But what if “awareness” is possible without gaze following? The present study examines egocentric images collected via head-mounted eye-trackers during parent-child toy play. A Convolutional Neural Network model was used to process and learn to classify raw egocentric images as JA vs not JA. We demonstrate individual child and parent egocentric views can be classified as being part of a JA bout at above chance levels. This provides new evidence that an individual can be “aware” they are in JA based solely on the in-the-moment visual information. Moreover, both models trained on child views and those trained on parent views leveraged the visual properties associated with visual object holding to improve classification accuracy—suggesting a critical role for object handling in not only establishing JA, as shown in previous research, but also in inferring the social partner’s attentional state during JA.

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