A connectionist account of the relational shift and context sensitivity in the development of generalisation
Similarity-based generalisation is fundamental to human cognition, and the ability to draw analogies based on relational similarities between superficially different domains is crucial for reasoning and inference. Learning to base generalisation on shared relations rather than (or in the face of) shared perceptual features has been identified as an important developmental milestone. However, recent research has highlighted the context-sensitivity of generalisation: children and adults use perceptual similarity to make inferences in some cases and relational similarity in others, a finding that suggests people track the predictive validity of different types of inferences. Here we demonstrate that this pattern of behaviour naturally emerges over the course of development in a domain-general statistical learning model that employs distributed, sub-symbolic representations. We suggest that this model offers a parsimonious account of the development of context-sensitive, similarity-based generalisation and may provide several advantages over other popular structured or symbolic approaches to modelling relational inference.
Thibodeau, Paul H., Aviva Blonder, and Stephen J. Flusberg. 2020. "A connectionist account of the relational shift and context sensitivity in the development of generalisation." Connection Science 32(4): 384-397.
Taylor & Francis
Analogy, Similarity, Relational shift, Distributed connectionist model, Generalisation, Statistical learning