Jungtak Park. (2025). Neural correlates of time estimation in humans. doi: 10.22677/THESIS.200000841329
Type
Thesis
Description
time estimation, contextual information, fMRI, Bayesian modeling, functional connectivity, dynamic time warping
Abstract
시간 추정은 동적 환경에서 의사결정, 행동 조정 및 적응적 행동을 가능하게 하는 중요한 인지 기능이다. 본 논문은 시간 추정의 인지적 및 신경적 메커니즘을 심층적으로 탐구하며, 특히 맥락적 정보의 역할에 초점을 맞춘다. 행동 실험과 fMRI 연구를 통해 사전 맥락 정보를 제공받은 참가자들이 시간 추정의 정확도와 반응 전략에 미치는 영향을 분석하고, 이러한 효과가 뇌의 신경 활동에 어떻게 반영되는지를 조사하였다. 행동 실험 결과, 사전 맥락 정보를 제공받은 참가자들은 시간 추정의 정확도가 더 높았으며, 맥락에 따라 반응을 효과적으로 조정하는 모습을 보였다. fMRI 분석에서는 맥락 정보를 처리하는 뇌 영역이 예상적 역할을 수행하며, 시간 처리와 관련된 영역보다 먼저 활성화되는 것을 보여주었다. 기능적 연결성 분석에서는 사전 맥락 정보를 제공받은 그룹과 그렇지 않은 그룹 간의 신경 적응 패턴 차이가 드러나, 맥락적 정보가 뇌 네트워크 동역학을 조절함을 시사했다. Dynamic Time Warping 분석 결과, 사전 맥락 정보를 제공받은 그룹은 관련 뇌 영역에서 더 긴 신경 활동 지속 시간을 유지하여 예상적 처리와 맥락 단서 통합의 효율성을 높이고, 시간 추정의 정확도를 향상시키는 것으로 나타났다. 본 연구는 맥락적 정보가 시간 추정 과정에서 행동 및 신경 반응을 조절하는 메커니즘을 밝혀내어, 시간 지각에 관한 인지 모델을 한층 풍부하게 이해하는 데 중요한 통찰을 제공합니다.|Time estimation is a vital cognitive function that underpins decision-making, action coordination, and adaptive behavior in dynamic environments. This thesis investigates the cognitive and neural mechanisms underlying time estimation, with a particular emphasis on the role of contextual information. Through a series of behavioral experiments and fMRI studies, we examine how contextual awareness influences time estimation accuracy and response strategies, and how these effects manifest in neural activity. Behavioral findings indicate that participants with prior contextual knowledge produce more accurate time estimates and adapt their responses effectively according to the context. FMRI analyses reveal that brain regions involved in processing contextual information activate in advance of regions associated with processing duration, highlighting an anticipatory role of contextual information-related regions in time estimation. Functional connectivity analyses further demonstrate distinct neural adaptation patterns between the Informed group, who had access to contextual information, and the Uninformed groups, who did not, illustrating how contextual information shapes brain network dynamics. The dynamic time warping results show that the Informed group sustains prolonged neural activity in regions associated with contextual information, indicating enhanced anticipatory processing and more efficient integration of contextual cues. These findings provide novel insights into how contextual information modulates both behavioral and neural responses in time estimation. This research advances the understanding of how the brain integrates contextual cues with temporal processing, enriching cognitive models of time perception.
Table Of Contents
Ⅰ. Introduction 1 1.1 Preface 1 1.2 Research Background 2 1.2.1 Cognitive Models of Time Perception 2 1.2.1.1 Scalar Expectancy Theory 2 1.2.1.2 Bayesian Models in Time Estimation: The Role of Prior Knowledge 6 1.2.1.3 Iterative Bayesian Model: Dynamic Prior Updating 6 1.2.2 Neural Underpinnings of Duration and Contextual Information Processing 8 1.2.2.1 Duration Processing 8 1.2.2.2 Contextual Information Processing 9 1.2.2.3 Implicit vs. Explicit Contextual Influence 11 1.3 Limitations of Previous Research 12 1.4 Hypothesis 14 1.5 Overview of the Thesis 15
ⅠⅠ. Experiment 1 - Behavioral study 17 2.1 Methods 17 2.1.1 Participants 17 2.1.2 Procedure 17 2.1.2.1 Task Design 17 2.1.2.2 Psychological Assessments 21 2.1.2.3 Post Survey 21 2.1.3 Data Analysis 26 2.1.3.1 Data Preprocessing 26 2.1.3.2 Bayesian Linear Regression Model for Estimation Error 27 2.1.3.3 Kalman Filter-based Iterative Bayesian Model for Time Estimation 29 2.2 Results 34 2.2.1 Bayesian Linear Regression Model Analysis: Contextual Awareness Enhances Accuracy in Time Estimation 34 2.2.2 Kalman Filter-based Iterative Bayesian Model: Contextual Information Drives Response Bias Adjustments in Time Estimation 40 2.3 Discussion 44
ⅠⅠⅠ. Experiment 2 - fMRI Study 47 3.1 Methods 47 3.1.1 Participants 47 3.1.2 Procedure 48 3.1.3 MRI acquisition 49 3.1.4 Data analysis 50 3.1.4.1 Data preprocessing 50 3.1.4.2 Bayesian Linear Regression Model for fMRI Study 52 3.1.4.3 Kalman Filter-Based Iterative Bayesian Model for Time Estimation in fMRI Study 53 3.1.4.4 Seed-based functional connectivity 55 3.1.4.5 Dynamic Time Warping Analysis of Contextual Information's Impact on Time Estimation 57 3.2 Results 60 3.2.1 Contextual Information and Its Role in Modulating Time Estimation Biases 60 3.2.2 Neural Dynamics in Time Estimation: Multi-Factor Influences in fMRI Analysis 64 3.2.3 Impact of Contextual Information on Response Shifts 70 3.2.4 Influence of Contextual Information on Time Estimation Through BOLD Signal in Iterative Bayesian Model 72 3.2.5 Functional Connectivity Analysis: Right Caudate Nucleus’s Role in Contextual Integration in Time Estimation 76 3.2.6 Dynamic Time Warping Analysis Reveals Prolonged Neural Engagement in Contextual Processing for Informed Group 79 3.3 Discussion 84