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.

We develop a method for selecting meaningful learning strategies based solely on the behavioral data of a single individual in a learning experiment. We use simple Activity-Credit Assignment algorithms to model the different strategies and couple them with a novel hold-out statistical selection method. Application on rat behavioral data in a continuous T-maze task reveals a particular learning strategy that consists in chunking the paths used by the animal. Neuronal data collected in the dorsomedial striatum confirm this strategy.

This study simultaneously manipulates within-category (rule-based vs. similarity-based), between-category (blocked vs. interleaved), and across-blocks (constant vs. variable) orders to investigate how different types of presentation order interact with one another. With regard to within-category orders, stimuli were presented either in a “rule plus exceptions” fashion (in the rule-based order) or by maximizing the similarity between contiguous examples (in the similarity-based order). As for the between-category manipulation, categories were either blocked (in the blocked order) or alternated (in the interleaved order). Finally, the sequence of stimuli was either repeated (in the constant order) or varied (in the variable order) across blocks. This research offers a novel approach through both an individual and concurrent analysis of the studied factors, with the investigation of across-blocks manipulations being unprecedented. We found a significant interaction between within-category and across-blocks orders, as well as between between-category and across-blocks orders. In particular, the combination similarity-based + variable orders was the most detrimental, whereas the combination blocked + constant was the most beneficial. We also found a main effect of across-blocks manipulation, with faster learning in the constant order as compared to the variable one. With regard to the classification of novel stimuli, learners in the rule-based and interleaved orders showed generalization patterns that were more consistent with a specific rule-based strategy, as compared to learners in the similarity-based and blocked orders, respectively. This study shows that different types of order can interact in a subtle fashion and thus should not be considered in isolation.

Models of category transfer do not have the ability to evolve over time. This feature constrains them to only account for participants' generalization patterns. Although they can model fewer processes, transfer models have repeatedly shown to be a useful tool for testing categorization theories and for precisely predicting participants' performance. In this study, we propose a statistical framework that allows transfer models to be applied to learning data. This framework is based on a segmentation/clustering technique, that is here specifically tailored for suiting category learning data. The adjusted technique is then applied to a well-known transfer model (the Generalized Context Model) on three novel experiments. More specifically, these experiments manipulate ordinal effects in category learning by contrasting rule-based vs. similarity-based orders in three contexts. 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 was almost entirely detected by the segmentation/clustering method. We conclude that our adjusted segmentation/clustering framework allows one to fit transfer models to learning, while apprehending essential information in categorization.