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Open Phenomenology: computer vision analyses for exploring the phenomenology of humans in an open field arena.

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Disrupting Reality - A new conceptual architecture for experiments in transcendental phenomenology.

Project under development

Introduction

This project presents a novel approach to overcoming the traditional dichotomy between transcendental phenomenology and experimental framing. The challenge lies in the ontologically dichotomous foundations of the two methodologies. Most experimental investigations are rooted in a Cartesian/Kantian dualist ontology where a detached subject cognizes an external object. On the other hand, transcendental phenomenology strives to resolve this dualism, considering both subject and object as parts of a continuous flow of experience.

Approach

Our approach involves reconsidering the ontological basis of experiments, reimagining their design, methodology, setting, and the analysis and interpretation of findings. We strive to align these elements closer to phenomenology, while still remaining compatible with empirical measurements.

We've found that artistic experiences serve as a viable middle ground. They're infused with key principles of transcendental phenomenology, including meaning formation, intentionality, and responsiveness.

Pilot Experiment: Human Open Field

To assess our approach, we designed a pilot experimental setting, the Human Open Field, which symbolically reveals how subjects form meaning as they traverse various modes of intentional behaviour.

The experiment began with subjects experiencing a novel, unstructured environment while their perceptions were altered using extended reality goggles. The primary goal was to observe the impact of these novel experiences on the subjects' meaning-making process.

Observations were made via objective measurements such as position tracking and EEG, as well as phenomenological interviewing methods. This dual approach allowed us to collect phenomenologically rich data that's also compatible with scientific measurements.

Visualization behavior quantification in human open field
Figure: Visualization of behavior quantification in Human Open Field. (a) Distribution of Beck Anxiety Inventory scores (b) Deep learning-based pose estimation of natural behavior (c-d) Top-down tracking of participants as they move through the Open Field (Left: high anxiety participant; right: mild anxiety participant) (e-f) Thigmotaxis (wall nearness) and center occupancy for the visualized participants.

Conclusion

Our pilot experiment marks the beginning of a principled exploration into how we can design experimental settings that can inform transcendental thinking and gather rich data compatible with scientific measurements. We aim to better understand the conditions under which meaning is formed and how it evolves.

We welcome collaboration and discussion from those interested in furthering this intersection of transcendental phenomenology and experimental methodologies.

Lè code

This repository houses scripts and results for analyzing human behavior in virtual reality through open-field tracking experiments. It provides tools for two camera perspectives: top-down and side-view.

Scripts/top-down-camera

Scripts in this section allow automated tracking and kinematic analysis of individuals in the virtual environment. Key measures include thigmotaxis (movement near the edges of the environment) and velocity tracking.

Scripts/side-view-camera

Scripts in this section offer automated 3D pose estimation and kinematic analysis. Notable measures include 3D pose lifting, eye scanning, and upper limb velocity.

Results

The "results" folder contains data and analysis results generated by the scripts. This output can be used for further exploration and visualization.

Getting Started

To utilize these scripts, clone the repository and install necessary dependencies. Use the scripts in the "src" folder to analyze your open-field tracking data.

Contributing

Contributions to this project are welcome and encouraged. If you notice any bugs or have ideas for additional features, please submit a pull request or open an issue on the GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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