forager: a Python package and web interface for modeling mental search

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Psycholinguistics

Background:

• Verbal fluency tasks are crucial for understanding memory search and retrieval.
• Existing computational models offer insights into memory search mechanisms.
• A gap exists in accessible tools for applying and comparing these models.

Purpose of the Study:

• To introduce "forager," an open-source Python package and web interface.
• To provide automated methods for analyzing verbal fluency data.
• To facilitate the application and comparison of computational models for memory search.

Main Methods:

• Development of the "forager" Python package and web interface.
• Implementation of automated clustering and switching detection algorithms.
• Integration of novel computational models using semantic embeddings to analyze lexical influences (semantic, phonological, frequency) on memory search.

Main Results:

• "Forager" offers automated analysis of verbal fluency lists, identifying clusters and switches.
• The package enables the evaluation of computational models at individual and group levels.
• Demonstrated utility with existing behavioral and clinical semantic fluency datasets.

Conclusions:

• "Forager" removes programming barriers for studying memory search in fluency tasks.
• The tool supports researchers and clinicians with varying programming expertise.
• Provides a unified platform for computational modeling of memory search.