Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata
Contributor : Giulia Mezzadri Connect in order to contact the contributor
Submitted on : Wednesday, May 12, 2021 - 7:02:15 PM
Last modification on : Saturday, June 25, 2022 - 11:50:42 PM
Long-term archiving on: : Friday, August 13, 2021 - 6:53:31 PM


An order-dependent transfer mo...
Files produced by the author(s)



Giulia Mezzadri, Patricia Reynaud-Bouret, Thomas Laloë, Fabien Mathy. An order-dependent transfer model in categorization. Journal of Mathematical Psychology, Elsevier, 2022, 107, ⟨10.1016/⟩. ⟨hal-03225670⟩



Record views


Files downloads