High-variability training does not enhance generalization in the prototype-distortion paradigm

Author:

Hu Mingjia,Nosofsky Robert M.ORCID

Publisher

Springer Science and Business Media LLC

Reference46 articles.

1. Annis, J., Gauthier, I., & Palmeri, T. J. (2021). Combining convolutional neural networks and cognitive models to predict novel object recognition in humans. Journal of Experimental Psychology: Learning, Memory, and Cognition, 47(5), 785.

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3. Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37(3), 372–400.

4. Battleday, R. M., Peterson, J. C., & Griffiths, T. L. (2020). Capturing human categorization of natural images by combining deep networks and cognitive models. Nature Communications, 11(1), 5418.

5. Bowman, C. R., & Zeithamova, D. (2020). Training set coherence and set size effects on concept generalization and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(8), 1442.

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