Welcome! forager is a Python-based web interface to analyze verbal fluency task (VFT) data.
You can use forager to obtain cluster-switch designations based on a variety of methods, run computational models of search (based on optimal foraging), and also obtain estimates of semantic similarity, phonological similarity, and frequency for items produced by participants.
To know more about forager, explore the tabs on the sidebar, such as our docs or about page.
Hover over the buttons to learn more about each option.
Timing data options (required for Slope Difference and PEI methods):
Parameter mode:
Upload data as a delimited .txt or CSV file with column headers. Columns are detected by name (case-insensitive). Required: SID (or subject, participant, id) and entry (or item, word, response). Optional: timepoint (to group multiple fluency lists per subject, e.g. different sessions or categories), time (or rt, response_time — cumulative response times for timing-based methods). Please review the docs page for more details.
Please select how you would like forager to handle words that are out of the vocabulary set (OOV) and do not have any reasonable replacements. We recommend excluding them from the analysis.
Evaluation results:
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If you would like to proceed with the corrected data, please click the button below.
Otherwise, please upload a new file and re-run the evaluation by clicking on 'Check Data' again.
This interface currently supports obtaining similarity and cluster-switch values. To run foraging models, please use the Colab notebook to which you have been redirected.
If you use forager, please use the guidelines on the cite page to cite our work!