Observation-Based psychometrics for human behavior research taking an ecological perspective on personality
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. /docs: Detailed documentation and methodology. /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 [LICENSE NAME] - see the LICENSE file for details.
If you use this framework in your research, please cite it as follows: [Your preferred citation format]