Unveiling Causal Attention in Dogs' Eyes with Smart Eyewear.

Our goals are to better understand dog cognition, and to support others who share this interest. Existing investigation methods predominantly rely on human-manipulated experiments to examine dogs' behavioral responses to visual stimuli such as human gestures. As a result, existing experimental paradigms are usually constrained to in-lab environments and may not reveal the dog's responses to real-world visual scenes. Moreover, visual signals pertaining to dog behavioral responses are empirically derived from observational evidence, which can be prone to subjective bias and may lead to controversies. We aim to overcome or reduce the existing limitations of dog cognition studies by investigating a challenging issue: identifying the visual signal(s) from dog eye motion that can be utilized to infer causal explanations of its behaviors, namely estimating causal attention. To this end, we design a deep learning framework named Causal AtteNtIon NEtwork (CANINE) to unveil the dogs' causal attention mechanism, inspired by the recent advance in causality analysis with deep learning. Equipped with CANINE, we developed the first eyewear device to enable inference on the vision-related behavioral causality of canine wearers. We demonstrate the technical feasibility of the proposed CANINE glasses through their application in multiple representative experimental scenarios of dog cognitive study. Various in-field trials are also performed to demonstrate the generality of the CANINE eyewear in real-world scenarios. With the proposed CANINE glasses, we collect the first large-scale dataset, named DogsView, which consists of automatically generated annotations on the canine wearer's causal attention across a wide range of representative scenarios. The DogsView dataset is available online to facilitate research.