Music Emotion and Physiological Responses

Potter F. Robert, Yuqian Ni

This project examines whether self-reported ratings of popular music collected after a 40-minute listening session predict physiological responses during prior exposure. We found that songs with higher ratings of liking, arousal, and familiarity elicited greater cardiac deceleration than those with the lower ratings. Also, higher skin conductance levels (SCL) occurred during songs later rated higher in liking, arousal, positivity, and negativity.

Top Liked Songs versus Bottom Liked Songs

Bridging Self-report Arousal Rating and Skin Conductance Level with Random Walk Decision-Making Models

Yuqian Ni, Lucía Cores Sarría

We used a random walk decision-making model to predict self-report arousal rating after video watching based on skin conductance data collected during the video presentation. The predicted and observed results are positively correlated. Estimated parameters from the model appear to explain participants’ bias when reporting their arousal level.

Affective Video and Functional Connectivity

Yuqian Ni, Xia Zheng

The goal of this project is to examine how functional brain networks change when people watch videos evoking four types of emotion states (high-arousal and positive-valence, low-arousal and positive-valence, high-arousal and negative-valence, and low-arousal and negative-valence). We analyzed fMRI data acquired from open fMRI.org. Data were collected by Kim et al., (2018). Results indicate that both local efficiency and global efficiency appeared to be the highest when people watch high-arousal and negative-valence videos.

Flowing Comments and Co-Viewing Experience

Yuqian Ni, Frank A Biocca, Hannah Kum-Biocca

This project explores the effect of flowing comments (Danmaku) on video viewing experience. We found that people gained a sense of co-viewing with the presence of flowing comments. However, the flowing comments appeared to distract people’s attention from the initial video and negatively affected their enjoyment from the video itself.