Date of Award
Spring 6-4-2025
Document Type
Thesis (Undergraduate)
Department
Computer Science
First Advisor
Andrew Campbell
Second Advisor
Arvind Pillal
Abstract
This research investigates the capability of Vision Language Models (VLMs), specifically ChatGPT‑4o, to interpret and predict psychological outcomes based on visual representations of time series data. Leveraging conversation duration metrics and psychological flourishing scores from the StudentLife dataset, this study rigorously evaluates the predictive accuracy of VLMs using various methods, including zero‑shot on raw data, zero‑shot on graph data, few‑shot learning, qualitative labeling, and chain‑of‑thought reasoning. Despite multiple methodological enhancements, predictive performance remains modest, revealing significant challenges in quantitative interpretation of visualized temporal data by current multimodal models. We demonstrate that standardizing input tokens by using graph images rather than raw numerical sequences reduces token footprint, enables few‑shot contexts, and improves qualitative reasoning, even if quantitative metrics change only marginally. The outcomes emphasize the need for advancements in both visualization methods and model fine‑tuning to bridge the gap between descriptive and predictive capabilities.
Recommended Citation
Park, Jusung, "Evaluating Vision Language Model Capabilities for Time Series Interpretation: An Empirical Study with Conversation Duration and Psychological Flourishing Data from the StudentLife Dataset" (2025). Computer Science Senior Theses. 84.
https://digitalcommons.dartmouth.edu/cs_senior_theses/84
