Supporting data for Reconstructing Event Durations: The Transformation of Retrospective Time Judgments

Human time perception is essential for cognitive function. Specifically, our ability to make retrospective duration judgments helps us assess the significance of experiences and plan appropriate time allocations for future activities. However, the underlying mechanisms driving these temporal evaluations remain contested. Competing theories propose either reconstructive processes based on recalled memory or independent temporal processing systems. This thesis investigates how memory influences retrospective duration judgments, examining which memory components drive temporal perception and the neural mechanisms that enable selective temporal modification while preserving event content.Directory of Files:A. Filename: Chapter 2 Data Short description: Fifty-five individuals (Female = 39, Preferred not to say = 1; Mage = 22.589; SDage = 3.109) were recruited from the University of Hong Kong for this experiment. This file contains data that support the study in Chapter 2, including behavior data, coding report for drawing analysis. All information that can be used to identify human participants have been removed. B. Filename: Chapter 3 Data Short description: Fifty-seven individuals (Female = 45; Mage = 24.1 years; SDage = 2.588 years) were recruited from the University of Hong Kong for this experiment. Some participants were excluded according to ensure data quality and manipulation validity.This file contains data that support the study in Chapter 3, including behavior data, preprocessed event-related spectral power (ERSP) data and ChatGPT codedverbal report results (Gist and Details). All information that can be used to identify human participants have been removed. C. Filename: Chapter 4 Data Short description: EEG analysis for the study in Chapter 3. This file contains data that support the study in Chapter 4, including behavior, Power spectral data and RSA data for the verbal report results. All information that can be used to identify human participants have been removed.