MSc Thesis project @ Leiden University, 25/26.
This repository contains all code developed for the master's thesis Tracking Behavioral and Physiological Dynamics in Perceptual Decisions during Sustained Attention. The project investigates how behavioral and physiological signals (primarily pupillometry) evolve over the course of sustained perceptual decision-making, including a confirmatory replication of prior findings and a range of exploratory analyses.
The thesis is openly available through the Leiden University Thesis Repository and can be accessed here: https://hdl.handle.net/1887/4303903
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├── Cleaning/ # Combines behavioral and pupil data; cleans raw data files; adds elapsed time.
├── Descriptives/ # Notebook with descriptive statistics of the sample.
├── EEG_Preprocessing/ # EEG preprocessing pipeline (developed for pilot; not used in final thesis).
├── Exploration/
│ ├── min-inst_replication/ # Exploratory analysis of the minimal instructions condition.
│ ├── window-size/ # Sliding window preprocessing across window sizes + AUC robustness checks.
├── Pupil_preprocessing/ # Preprocesses raw pupillary data into trial-averaged pupil signals
├── Re-analyzing_VDB/ # Reproduces findings from van den Brink et al. (2016)
│ ├── Plotting_VDB/ # Generates plots used in the thesis.
│ ├── Preparing_data/ # Preprocesses VDB files to fit the current analytical pipeline.
│ ├── Stats_VDB/ # Main reproduction.
├── Replication/ # Main confirmatory analyses of the thesis.
└── Visualization/ # All figures and plots generated for the thesis, separated by hypothesis VS non-hypothesis.
Scripts for merging behavioral response data with pupillometric recordings and applying data cleaning procedures (e.g., artifact removal, trial filtering).
A Jupyter notebook providing an overview of the sample's descriptive statistics, including participant demographics and task performance summaries.
A preprocessing pipeline developed during an EEG pilot study. Although EEG data were ultimately not included in the final thesis, this pipeline was developed as part of the original research plan and is retained here for completeness.
- min-inst_replication — Exploratory analyses of the minimal instructions condition, following the same analytic steps as the confirmatory replication to allow for comparison.
- window-size — Implements sliding window preprocessing across a range of window sizes. Includes AUC (Area Under the Curve) calculations to assess whether key findings are robust to the choice of window size.
Preprocesses raw pupillary time-series data into trial-averaged pupil dilation signals ready for downstream analysis.
Code developed to reproduce the key findings reported in:
van den Brink, R. L., et al. (2016). ...
The core of the thesis. Contains all confirmatory (pre-registered in the proposal) analyses examining behavioral and physiological dynamics in sustained perceptual decision-making.
Scripts and notebooks used to generate all figures appearing in the thesis, including pupil time-course plots, performance metrics, and summary statistics visualizations.
This project was conducted as part of a master's thesis in the 2025/2026 academic year. The primary data sources are behavioral responses and pupillometric recordings collected during a sustained perceptual decision-making task. A key component of the thesis is a conceptual replication of previously published findings, complemented by a reproduction of said findings and a series of exploratory analyses to assess the robustness and generalizability of the results.
van den Brink, R. L., Murphy, P. R., & Nieuwenhuis, S. (2016). Pupil Diameter Tracks Lapses of Attention. PLOS ONE, 11(10), e0165274. https://doi.org/10.1371/journal.pone.0165274