Observation-Based psychometrics for human behavior research taking an ecological perspective on personality
Read more from the book chapter written on it linked here: https://docs.google.com/document/d/1tTwY2wWmlTAHcGy07YjWPE2EKPEZYsH-JGJMcBgE0QA/edit?usp=sharing
Computational Psychodynamics represents a novel approach in psychometrics, focusing on the ecological perspective of personality through the lens of a Hierarchical Behavioral Schema. This framework draws an analogy to the hierarchical syntactic schema in language, providing a robust structure for understanding and analyzing personality and behavior through observable data.
Hierarchical Behavioral Schema: A structured approach to categorize and understand behaviors, akin to 'parts of speech' in language. Transition Matrix Formulation: Deriving matrices from observed behaviors to map the transitions between different behavioral states. Inferential Modeling with HMMs: Utilizing Hidden Markov Models to link observed behaviors with unobserved affective states, offering a nuanced view of personality dynamics. Applications
This framework has a wide range of applications in psychological research and practice, including:
** Stationary Distribution: Analyzing long-term behavioral tendencies.
** Clustering or Grouping: Segmenting populations based on shared behavioral dynamics.
** Behavioral Sequence Analysis: Tracking the progression of behaviors over time.
** Simulation: Projecting potential future behavioral patterns.
** Comparative Analysis: Evaluating the impact of different interventions or demographic factors.
** Absorption Probabilities: Identifying transition likelihoods to critical behavioral states.
** Transient and Recurrent States: Distinguishing between stable and momentary behaviors.
** Entropy Rate: Measuring the unpredictability of behavioral transitions.
** Mean First Passage Time: Estimating the time or steps required for specific behavioral transitions.
/src: Source code for data collection, annotation, and analysis. /data: Contains data used for experimentation and testing /docs: Detailed documentation and methodology, instructions of use. /examples: Example datasets and case studies. CONTRIBUTING.md: Guidelines for contributing to this project. Getting Started
To get started with Computational Psychodynamics, clone this repository and refer to the /docs directory for detailed instructions on setup and usage.
We welcome contributions from the community. Please read our CONTRIBUTING.md for guidelines on how to contribute.
This project is licensed under PolyForm Noncommercial License 1.0.0 - see the LICENSE file for details.
If you use this framework in your research, please cite it as follows: APA (American Psychological Association) Senthil, A. (2024). CompPsychoToolBox. [Software]. GitHub. URL of the repository
MLA (Modern Language Association) Senthil, Ajith. CompPsychoToolBox. 2024, URL of the repository.
Chicago Senthil, Ajith. 2024. CompPsychoToolBox. GitHub repository. URL of the repository.
IEEE A. Senthil, "CompPsychoToolBox," GitHub, 2024. [Online]. Available: URL of the repository.