ChaRTr

ChaRTr implements quantitative models of choice behavior and response time distributions to analyze mechanisms of decision-making in experimental data.

Key Features:

• Model Diversity: Supports classical and modern decision-making models, including diffusion decision models as well as models with urgency signals and collapsing boundaries.
• Response Time Modeling: Models response time distributions alongside choice data to capture temporal dynamics of decisions.
• Quantitative Evaluation: Provides frameworks for quantitative comparison among competing classes of decision-making models using empirical data.
• Case Study Demonstrations: Includes three distinct case studies that demonstrate the toolbox's ability to discriminate between multiple decision-making models.
• Extensibility: Provides guidance to extend functionality to incorporate future developments in decision-making models.

Scientific Applications:

• Cognitive process inference: Enables analysis of choice and response time data to infer mechanisms underlying decision-making behavior.
• Dynamics of decision formation: Supports investigations of urgency signals, time-varying thresholds (collapsing boundaries), noise, and sensory evidence filtering in decision processes.
• Model comparison and selection: Facilitates distinguishing between competing theoretical approaches to explain empirical decision-making data.

Methodology:

Implemented in R; implements both classical and modern decision-making models (including diffusion decision models, urgency signals, and collapsing boundaries); tests and compares models using empirical choice and response time data; provides quantitative evaluation frameworks for model comparison.