Understanding abstraction is a stepping stone towards understanding intelligence. We ask the question: How do abstract representations arise when learning sequences? From a normative perspective, we show that abstraction is necessary for an intelligent agent when the perceptual sequence contains objects of similar interaction properties appearing in identical contexts. A rational agent should identify categories of objects of similar properties as an abstract concept, enabling the discovery of higher-order sequential relations that span a longer part of the sequence. We propose a hierarchical variable learning model (HVM) that learns chunks and abstract concepts from sequential data in a cognitively plausible manner. HVM gradually discovers abstraction via a conjunction of variable discovery and chunking, resembling the process of concept discovery during development. In a sequence recall experiment that demands learning and transferring variables, we observe that the model's sequence complexity can explain human behavior in a sequence memorization experiment.

Cognitive search processes are generally affected by the number of available items. We investigated if this also applies to long-term memory retrieval. Specifically, we explored the effect of set size on retrieval times of cued memories from long-term memory. Participants learned lists of word pairs that varied in the number and the semantic similarity of the pairs. An increase in set size resulted in slower retrieval times, indicating the influence of set size on memory retrieval efficiency. However, participants were faster in retrieving more semantically similar word pairs. These findings are consistent with a search-based model of retrieval, illustrating its sensitivity to the number of memory candidates, while highlighting the role of the quality of the cue in optimizing search performance. Furthermore, we established the validity of using similarity values based on Word2Vec embeddings by showing a high correlation with human similarity ratings and similar model results.