Assaf Harel, Ph.D. (Advisor); Kathrin L. Engisch, Ph.D. (Committee Member); Tamera R. Schneider, Ph.D. (Committee Member)
Master of Science (MS)
This thesis explores the interaction between emotions and visual perception using large scale spatial environment as the medium of this interaction. Emotion has been documented to have an early effect on scene perception (Olofsson, Nordin, Sequeira, & Polich, 2008). Yet, most popularly-used scene stimuli, such as the IAPS or GAPED stimulus sets often depict salient objects embedded in naturalistic backgrounds, or “events” which contain rich social information, such as human faces or bodies. And thus, while previous studies are instrumental to our understanding of the role that social-emotion plays in visual perception, they do not isolate the effect of emotion from the social effects in order to address the specific role that emotion plays in scene recognition – defined here as the recognition of large-scale spatial environments. To address this question, we examined how early emotional valence and arousal impact scene processing, by conducting an Event-Related Potential (ERP) study using a well-controlled set of scene stimuli that reduced the social factor, by focusing on natural scenes which did not contain human faces or actors. The study comprised of two stages. First, we collected affective ratings of 440 natural scene images selected specifically so they will not contain human faces or bodies. Based on these ratings, we divided our scene stimuli into three distinct categories: pleasant, unpleasant, and neutral. In the second stage, we recorded ERPs from a separate group of participants as they viewed a subset of 270 scenes ranked highest in each of their respective categories. Scenes were presented for 200ms, back-masked using white noise, while participants performed an orthogonal fixation task. We found that emotional valence had significant impact on scene perception in which unpleasant scenes had higher P1, N1 and P2 peaks. However, we studied the relative contribution of emotional effect and low-level visual features using dominance analysis which can compare the relative importance of predictors in multiple regression. We found that the relative contribution of emotional effect and low-level visual features (operationalized by the GIST model, (Oliva & Torralba, 2006)) had complete dominance over emotional effects (both valence and arousal) on most early peaks and areas under the curve (AUC). We also found out that affective ratings were significantly influenced by the GIST intensities of the scenes in which scenes with high GIST intensities were more likely to be rated as unpleasant. We concluded that emotional impact in our stimulus set of natural scenes was mostly due to bottom-up effect on scene perception and that controlling for the low-level visual features (particularly the GIST intensity) would be an important step to confirm the affective impact on scene perception.
Year Degree Awarded
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