Name: Yiren Ren

Ph.D. Dissertation Proposal Meeting

Date: Monday, October 23, 2023

Time: 1:30 pm

Location: Zoom click here

Advisor: Thackery Brown, Ph.D. (Georgia Tech) 

Dissertation Committee Members:

Paul Verhaeghen, Ph.D. (Georgia Tech)

Sashank Varma, Ph.D. (Georgia Tech)

Lila Davachi, Ph.D. (Columbia University)

Elizabeth Race, Ph.D. (Tufts University)

Title: Using Music Schema as Context to Improve Visual Sequence Regularity Detection and Encoding 

Abstract: In our daily lives, we encounter and engage with events presented in the form of sequences. Many of these event sequences possess regular sequential patterns, such as our daily commute from home to work. The ability to detect and learn these sequential regularities enhances our understanding, prediction, and response to such events. While decades of research have focused on unraveling how temporally structured event representations are encoded, stored, and retrieved in the brain, a prevalent question emerges: How can human sequential memory be modulated or enhanced? The existing literature underscores the significance of context in learning the sequential relationships between pieces of information. In this study, I propose to investigate whether music, characterized by its high predictability and rule-based sequential nature, can facilitate the learning and encoding of visual sequences. Furthermore, this study aims to explore the dynamic neural interactions among brain regions. Employing a continuous sequential encoding paradigm wherein participants are exposed to a continuous presentation of images and are tasked with detecting the regularity in the sequential relationships between these images, I hypothesize that music can provide temporal structural schemas that expedite the detection and encoding of visual image sequences. Using fMRI, I seek to unveil the underlying mechanisms driving the impact of music. My prediction is music's influences on sequence memory arise via increased connectivity within the frontal-striatal-hippocampus network. Through dynamic causal modeling, I aim to elucidate the effective modulatory interactions between these regions, shedding light on how music imparts its benefits, whether by activating existing memory schemas or by enhancing the reward and prediction error feedback mechanisms. This study may deliver insights into cross-modal sequential memory and the frequently asked question: Should individuals listen to music, and if so, what type of music is conducive to productivity during work and study?