Abstract Number:
2848
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Cammie Newmyer (1)
Institutions:
(1) Math That Makes Sense, N/A
First Author:
Presenting Author:
Abstract Text:
This paper presents the second phase of the Temporal Metrics project, an innovative exploration (using mathematics and AI augmented research) into the human perception of time using deep learning methodologies. Building on the foundational development of the Cr constant - a novel metric quantifying time perception variations - this phase extends the application to a deep learning model. The model predicts individual time perception categories - "Average," "Slower," or "Faster" - based on a comprehensive array of conditions and lifestyle factors, each weighted by the associated Cr value. The data for this study, derived from a theoretical sample representing 0.001% of the U.S. adult population, encompass demographic information, psychological conditions, lifestyle factors, and substance use. This project highlights the potent combination of theoretical constructs with advanced machine learning techniques, offering groundbreaking insights into the subjective experience of time. Our results demonstrate the model's high accuracy in predicting time perception categories, paving the way for future empirical research and potential applications in behavioral monitoring and mental health.
Keywords:
Time Perception|Deep Learning|Weber's Law|Chat GPT 4.0|AI Augmented Research|Human time perception
Sponsors:
Section on Statistics and Data Science Education
Tracks:
Miscellaneous
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