Affective Dynamics: Principal Motion Analysis of Temporal Dominance of Sensations and Emotions Data

Abstract

Temporal dominance (TD) methods can be used to record temporal changes in multiple sensory and affective responses. TD methods have found wide applications in the analysis of eating experiences of humans. However, extant analyses performed on TD data do not fully utilize the time-series properties of such data. The present study validates the prospect of principal motion analysis (PMA) of TD data. PMA is an extension of principal component analysis, and can be used to resolve multivariate motion data into base principal motions. In this study, panelists were asked to evaluate the tastes of ten types of pickled plums using the temporal dominance of sensations (TDS) and emotions (TDE) methods. Additionally, the panelists were asked to rate the plums using the semantic differential method. Results obtained using both methods were observed to demonstrate good agreement with each other in terms of the structures of reduced variable spaces. As realized in this article, implementation of the combined TD–PMA approach can potentially facilitate statistical discrimination of all food products, whereas conventional methods, such as principal component analysis of data provided via use of the semantic differential method, can at best discriminate only 67 percent of product pairs. PMA can, therefore, be considered as a suitable technique to reveal the characteristics of TD data.