How to fit transfer models to learning data: a segmentation/clustering approach.

Although transfer models are limited in their ability to evolve over time and account for a wide range of processes, they have repeatedly shown to be useful for testing categorization theories and predicting participants' generalization performance. In this study, we propose a statistical framework that allows transfer models to be applied to category learning data. Our framework uses a segmentation/clustering technique specifically tailored to suit category learning data. We applied this technique to a well-known transfer model, the Generalized Context Model, in three novel experiments that manipulated ordinal effects in category learning. 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, were mostly detected by the segmentation/clustering method. Furthermore, the analysis of the segmentation/clustering outputs using the backward learning curve revealed that participants' performance suddenly improved, suggesting the detection of an "eureka" moment. Our adjusted segmentation/clustering framework allows transfer models to fit learning data while capturing relevant patterns.

Strategy inference during learning via cognitive activity-based credit assignment models.

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.

Investigating interactions between types of order in categorization.

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.