University of New South Wales, Sydney, Australia.
University of Queensland, Brisbane, Australia.
Sci Rep. 2023 Jan 16;13(1):783. doi: 10.1038/s41598-022-25268-1.
Faces are key to everyday social interactions, but our understanding of social attention is based on experiments that present images of faces on computer screens. Advances in wearable eye-tracking devices now enable studies in unconstrained natural settings but this approach has been limited by manual coding of fixations. Here we introduce an automatic 'dynamic region of interest' approach that registers eye-fixations to bodies and faces seen while a participant moves through the environment. We show that just 14% of fixations are to faces of passersby, contrasting with prior screen-based studies that suggest faces automatically capture visual attention. We also demonstrate the potential for this new tool to help understand differences in individuals' social attention, and the content of their perceptual exposure to other people. Together, this can form the basis of a new paradigm for studying social attention 'in the wild' that opens new avenues for theoretical, applied and clinical research.
面部是日常社交互动的关键，但我们对社交注意力的理解是基于在计算机屏幕上呈现面部图像的实验。可穿戴式眼动追踪设备的进步现在使我们能够在不受限制的自然环境中进行研究，但这种方法受到手动注视编码的限制。在这里，我们引入了一种自动的“动态感兴趣区域”方法，该方法可以在参与者在环境中移动时记录注视到的身体和面部。我们发现，只有 14%的注视是看向路人的脸，这与之前基于屏幕的研究形成对比，后者表明脸会自动吸引视觉注意力。我们还展示了这个新工具帮助理解个体社交注意力差异以及他们对其他人的感知接触内容的潜力。总的来说，这可以为研究“自然状态下”的社交注意力提供一个新的范式，为理论、应用和临床研究开辟新的途径。
