The Relationship Between Natural Emotion Vocabularies, Emotion Differentiation and Depressive Symptoms in an Adolescent Sample

There is growing interest in examining whether language influences one’s perception of their internal emotional states. A new linguistic tool, Vocabulate, calculates Emotion Vocabularies (EVs), i.e., the number of unique emotion words in a text, as opposed to other measures which only quantify the frequency of emotion words. Larger Negative EVs have been linked to more depressive symptoms and neuroticism (Vine et al., 2020). The proposed study aims to replicate and extend the original work in this area by examining the relationship between EVs, neuroticism, emotion differentiation (ED), and depressive symptoms in an adolescent sample. This is the first study to examine the relationship between ED, the ability to recognize and label discrete internal emotion states, and EVs. As researchers have speculated that language plays a role in the differentiation of emotional states (Nook, 2021), one might anticipate that larger EVs would be associated with greater ED, as increased range in everyday emotion word choice may reflect a larger range of labels to utilize when differentiating emotions. However, the inverse may also be true: larger EVs may reflect a lack of emotional specificity, and may therefore be associated with lower ED. In line with this latter notion, while high Negative EVs are associated with negative outcomes, including higher depressive symptoms (Vine et al., 2020), high Negative ED is associated with positive outcomes (e.g., lower depressive symptoms; (Tong & Keng, 2017). The current study, the first to examine the association between EVs and ED, will distinguish between these alternative models. The initial studies on EVs laid the foundation for exploring this new construct, but were limited in several ways (Vine et al., 2020). Firstly, the studies utilized written texts exclusively in adult samples, which is common in psycholinguistic research as it is quick to collect and can even be collected passively through online posts such as blogs. Secondly, these studies relied on self-reported or text-derived measures of depression. The present study aims to replicate and extend this work using a rich adolescent dataset. EVs will be harvested from transcripts of UCLA Life Stress Interviews, thereby allowing examination of these constructs in the context of spoken language in reference to emotionally salient topics (DeLap et al., 2022). These transcripts are significantly longer than written responses in prior experimental work. The present study also utilizes multiple measures of depression and ED derived from EMA data. These strengths leave us well positioned to examine EVs in the context of spoken language in a rich developmental sample.