Explicit and implicit measures of emotions: Data-science might help to account for data complexity and heterogeneity

TL;DR

This paper discusses integrating explicit and implicit emotion measures in food research, emphasizing the role of data science in analyzing complex, heterogeneous data to better understand emotional experiences in real-life contexts.

Contribution

It proposes a collaborative approach combining food science and data science to develop predictive models linking emotions and physiological responses.

Findings

Data science can manage complex, heterogeneous data sets.

Collaborative training enhances understanding of emotional and physiological data.

Future models can predict emotional responses in real-life eating experiences.

Abstract

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures-explicit to characterize a specific food, and implicit-physiological-to capture the whole experience of a meal in real-life situations. This raises several challenges including development of new and miniaturized sensors and devices but also developing new ways of data analysis. We suggest a path to follow for future studies regarding data analysis: to include Data Science in the game. This field of research may enable developing predictive but also explicative models that link subjective experience of emotions and physiological responses in real-life contexts. We suggest that food scientists should go out of their comfort zone by collaborating with computer scientists and then be trained with the new tools of Data Science, which will undoubtedly enable them 1/ to better manage complex and heterogeneous data sets, 2/ to extract knowledge that will be essential to this field of research.