Attracting Visual Attention in a Digital Age: Measuring the Determinants of Interestingness of Videos Using Biosensors

Attracting Visual Attention in a Digital Age: Measuring the Determinants of Interestingness of Videos Using Biosensors

Quincy Conley (A.T. Still University, USA)
Copyright: © 2024 |Pages: 24
DOI: 10.4018/IJCBPL.359336
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Abstract

The purpose of this study was to determine whether previously established visual attention patterns remained intact during video scenes designed to elicit specific emotions using a novel suite of biosensors. To examine the relationship between visual attention and emotion, data from eye tracking, facial expression recognition (FER), and galvanic skin response (GSR) combined with survey data were used to identify the bottom-up and top-down features of saliency in videos that contributed to their “interestingness.” Using a mixed-methods design and convenience sampling, participants (N = 42) watched 60 video clips designed to evoke different emotional responses (positive, neutral, or negative). The results indicated that using a suite of biosensors to examine the impacts of bottom-up and top-down features of visual attention was effective.
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Theoretical Framework

In addition to offering a definition of visual interestingness, Gygli et al. (2013) developed a framework for characterizing it. Interestingness is defined as the qualities of a visual stimuli that “people believe they will remember” that is attributed to why the give visual attention to (Gygli et al. 2013, p. 1633). To make their framework more usable, they categorized interestingness into three factors: (a) aesthetics, (b) unusualness, and (c) preferences (Gygli et al., 2013). Grabner et al. (2013) also attempted to define the elements contributing to interestingness, adding the factors of context and novelty. There have been many investigations of these categories of interestingness of visual information in the hopes of providing a more holistic perspective of what captures a person’s visual attention.

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