THREAT - A database of line-drawn scenes to study threat perception

Abstract

Efficient extraction of threat information from scene images is a remarkable feat of our visual system, but little is known about how it is accomplished. To facilitate studies of threat perception with well-controlled scene images, we created a set comprising 500 hand-traced line drawings of photographic visual scenes depicting various dimensions of threat. We used color-photo scene images previously reported in Kveraga et al. (2015) depicting direct threat, indirect threat, threat aftermath, and low threat scenes. Sixty participants were randomly assigned to rate all 500 scenes answering one of three questions: 1) How much harm might you be about to suffer in this scene if this was your view of the scene?; 2) How much harm might someone (not you) be about to suffer in this scene?; 3) How much harm might someone (not you) have already suffered in this scene?. Another 134 participants were randomly assigned to rate the images on various other threat dimensions. The mean ratings on these threat dimensions were submitted to a factor analysis, which resulted in three distinct factors including Affect (comprised of perceived emotional intensity, physical and psychological harm, and affect), Proximity (comprised of perceived threat clarity, its proximity in space and time, and degree of motion), and Agency (comprised of perceived human and animal agency, and whether inanimate objects present in the scene could be used as a potential weapon). Mean ratings on three harm questions and three factors were then submitted to cluster analyses, which grouped images into six distinct categories. This unique set of images, accompanied by ratings assessing multiple dimensions of threat and their clusters, is well suited for investigating research questions on emotion regulation and threat perception in neurotypical and clinical populations. Information on using it can be found at http://www.kveragalab.org/stimuli.html.

Meeting abstract presented at VSS 2017