Feelings are Messy: The Feelings We Study in Affective Science Should Be Too

Abstract

Affective science has taken up the challenge of building a bridge between basic affective science and practical applications. The articles in the Future of Affective Science issue lay out methodological and conceptual frameworks that allow us to expand affective science into real-world settings and to handle naturalistic methods. Along with these advances, accomplishing this goal will require additionally refocusing the types of experiences we study, and the measures of experience we are interested in. This paper explores the necessity for basic affective science to embrace the messy and complex nature of human emotion in order to bridge the gap between theoretical concepts and real-world applicability. Specifically, this involves studying experiences that do not fit as neatly into dominant conceptual frameworks, such as valenced scales and the most common discrete emotion categories, and that may be more difficult to measure or experimentally control. This makes the gap between affective science and real-world feelings larger. To move the field towards incorporating emotional complexity in an empirical manner, I propose measurement standards that err on the side of less fixed-choice options and using stimuli chosen for their potential to elicit highly complex responses over time within the same individual. Designing studies that can measure these experiences will push emotion theories to explain data they were not originally designed for, likely leading to refinement and collaboration. These approaches will help capture the full spectrum of human emotional experience, leading to a more nuanced and applicable understanding of affective science.

Keywords: Emotion measurement; Emotion theory; Mixed emotions; Research methods; Valence.

Fig. 1

Common forced-choice measures of affect may erase essential information. Real-world affect can entail multiple valences, atypical states such as awe and relief, blends between emotion categories, and can be colored by participants feeling certain or uncertain of how they currently feel. These are critical sources of variance in experience that have to be removed to report experience in the most common methods of measurement. Figure created with BioRender.com

Fig. 2

Using qualitative reports to fine-tune deep-imaging analyses. Emphasizing complex stimuli and intrasubject variability allows for a detailed exploration of affective responses, capturing blended, univalent, mixed, and ambiguous emotional states. Deep-imaging analyses using longer complex stimuli can investigate predictive features of affect in the same subject, taking full advantage of lab study control to collect detailed data on varying emotional states. Additionally, this set-up allows us to use qualitative responses to perform exploratory modifications to our original analyses, incorporating parts of the induced experience we did not anticipate. Figure created with BioRender.com