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An order-dependent transfer model in categorization

Abstract : Most categorization models are insensitive to the order in which stimuli are presented. However, a vast array of studies have shown that the sequence received during learning can influence how categories are formed. In this paper, the objective was to better account for effects of serial order. We developed a model called Ordinal General Context Model (OGCM) based on the Generalized Context Model (GCM), which we modified to incorporate ordinal information. OGCM incorporates serial order as a feature along ordinary physical features, allowing it to account for the effect of sequential order as a form of distortion of the feature space. The comparison between the models showed that integrating serial order during learning in the OGCM provided the best account of classification of the stimuli in our data-sets.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03225670
Contributor : Giulia Mezzadri <>
Submitted on : Wednesday, May 12, 2021 - 7:02:15 PM
Last modification on : Wednesday, June 16, 2021 - 12:48:02 PM

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  • HAL Id : hal-03225670, version 1

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Giulia Mezzadri, Patricia Reynaud-Bouret, Thomas Laloë, Fabien Mathy. An order-dependent transfer model in categorization. 2021. ⟨hal-03225670⟩

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