Analyzing data from the verbal fluency task (e.g. “name all the animals you can in a minute”) is of interest to both memory researchers and clinicians due to its implications for cognitive search and retrieval. Recent work has proposed several computational models to examine nuanced differences in fluency task behavior, which can provide insights into the mechanisms underlying memory search.
A prominent account of memory search within the fluency task is the optimal foraging model, proposed by Hills, Jones, and Todd (2012), where mental search is modeled after how animals forage for food by switching from cluster to cluster in resource-rich environments. Despite the broad potential utility of these models to scientists and clinicians, there is currently no open source program to apply and compare existing foraging models or clustering algorithms without extensive, often redundant programming.
To remove this barrier to studying fluency task behavior, we created a Python package and web interface called forager. forager provides multiple automated methods to designate clusters and switches within a fluency list and introduces a novel set of models that can examine the influence of multiple lexical sources (semantic, phonological, and frequency) on memory search. The package provides automated clustering and switching scores at the participant level, implements a range of computational models for memory search, and also enables researchers to evaluate relative model performance at the dataset and group level. Our package and web interface cater to users with various levels of programming experience.
The package is available for use directly via the Python codebase or via this web app. To use the package online, head over to the use tab via the sidebar. We recommend going over the docs before using the package.
You can also download the forager package from GitHub with detailed instructions for new users.
If you use forager, please use the guidelines on the cite page to cite our work!