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How to fit transfer models to learning data: a segmentation/clustering approach

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Abstract

Although transfer models are limited in their ability to evolve over time and account for a wide range of processes, they have repeatedly shown to be useful for testing categorization theories and predicting participants’ generalization performance. In this study, we propose a statistical framework that allows transfer models to be applied to category learning data. Our framework uses a segmentation/clustering technique specifically tailored to suit category learning data. We applied this technique to a well-known transfer model, the Generalized Context Model, in three novel experiments that manipulated ordinal effects in category learning. The difference in performance across the three contexts, as well as the benefit of the rule-based order observed in two out of three experiments, were mostly detected by the segmentation/clustering method. Furthermore, the analysis of the segmentation/clustering outputs using the backward learning curve revealed that participants’ performance suddenly improved, suggesting the detection of an “eureka” moment. Our adjusted segmentation/clustering framework allows transfer models to fit learning data while capturing relevant patterns.

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Acknowledgements

The present work was supported by the French government, through the UCAJedi and 3IA Côte d’Azur Investissements d’Avenir managed by the National Research Agency (ANR-15-IDEX-01 and ANR-19-P3IA-0002), directed by the National Research Agency with the ANR project ChaMaNe (ANR-19-CE40-0024-02) and by the interdisciplinary Institute for Modeling in Neuroscience and Cognition (NeuroMod) of the Université Côte d’Azur.

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Authors and Affiliations

  1. Cognition and Decision Lab, Columbia University, New York, US

    Giulia Mezzadri

  2. Laboratoire J.A. Dieudonné UMR CNRS 7351, Université Côte d’Azur, Nice, France

    Thomas Laloë & Patricia Reynaud-Bouret

  3. Laboratoire Bases, Corpus, Langage UMR CNRS 7320, Université Côte d’Azur, Nice, France

    Fabien Mathy

Authors
  1. Giulia Mezzadri
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  2. Thomas Laloë
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  3. Fabien Mathy
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  4. Patricia Reynaud-Bouret
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Contributions

The initial idea of applying the segmentation/clustering technique to transfer models came from P.R.-B. Experiments were designed and supervised by F.M. Data analysis and coding were performed by G.M. The article was drafted by G.M. and critical revisions were provided by P.R.-B., T.L. and F.M. All authors approved the final version of the manuscript for submission.

Corresponding author

Correspondence to Giulia Mezzadri.

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Open practices statement

The data for all experiments and the computer code (including the code for reproducing figures) are publicly available in Open Science Framework at https://osf.io/zv4jf/?view_only=8403629c320d4abfa0906c59443dd4ee. None of the experiments was preregistered.

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The authors declare no conflict of interest with respect to their authorship or the publication of this article.

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Mezzadri, G., Laloë, T., Mathy, F. et al. How to fit transfer models to learning data: a segmentation/clustering approach. Behav Res 56, 2549–2568 (2024). https://doi.org/10.3758/s13428-023-02166-6

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