Affective Priming Score: A Data-Driven Method to Detect Priming in Sequential Datasets

TL;DR

This paper introduces the Affective Priming Score (APS), a novel data-driven method to detect and mitigate priming effects in physiological datasets, improving classification accuracy in affective computing.

Contribution

The study presents the APS, a new scoring method to identify primed data points, and demonstrates its effectiveness in reducing misclassification in emotion recognition datasets.

Findings

APS effectively detects primed data points.

Using priming-free data reduces misclassification rates.

APS enhances robustness of affective computing models.

Abstract

Affective priming exemplifies the challenge of ambiguity in affective computing. While the community has largely addressed this issue from a label-based perspective, identifying data points in the sequence affected by the priming effect, the impact of priming on data itself, particularly in physiological signals, remains underexplored. Data affected by priming can lead to misclassifications when used in learning models. This study proposes the Affective Priming Score (APS), a data-driven method to detect data points influenced by the priming effect. The APS assigns a score to each data point, quantifying the extent to which it is affected by priming. To validate this method, we apply it to the SEED and SEED-VII datasets, which contain sufficient transitions between emotional events to exhibit priming effects. We train models with the same configuration using both the original data and priming-free sequences. The misclassification rate is significantly reduced when using priming-free sequences compared to the original data. This work contributes to the broader challenge of ambiguity by identifying and mitigating priming effects at the data level, enhancing model robustness, and offering valuable insights for the design and collection of affective computing datasets.