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Abhilasha A Kumar1, Molly Apsel2, Larry Zhang2

  • 1Bowdoin College, Brunswick, ME, USA. a.kumar@bowdoin.edu.

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Summary
This summary is machine-generated.

Researchers can now analyze memory search patterns using the "forager" Python package. This tool simplifies computational modeling of verbal fluency tasks for memory researchers and clinicians.

Keywords:
ClusteringComputational modelingForagingMemory retrievalMemory searchOpen scienceSwitchingVerbal fluency

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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.