We develop a model called Ordinal General Context Model (OGCM) based on the GCM.
•OGCM incorporates serial order as a feature along ordinary physical features.
•OGCM provides the best account of classification of the stimuli in our data-sets.
AbstractMost 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.
KeywordsCategorization
Sequencing
Category transfer models
Generalized Context Model (GCM)
Rule-based order
Similarity-based order
5-fold cross-validation
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