Infant Relational Learning - Interactions with Visual and Linguistic Factors
Humans are incredible learners, a talent supported by our ability to detect and transfer relational similarities between items and events. Spotting these common relations despite perceptual differences is challenging, yet there’s evidence that this ability begins early, with infants as young as 3 months discriminating same and different (Anderson et al., 2018; Ferry et al., 2015). How? To understand the underlying mechanisms, I examine how learning outcomes in the first year correspond with changes in input and in infant age. I discuss the commonalities in this process with that seen in older children and adults, as well as differences due to interactions with other maturing processes like language and visual attention.
Making neural nets simple enough to succeed at universal relational generalization
Traditional brain-style (connectionist) approaches basically hit a wall when it comes to relational cognition. As an alternative to the well-known approaches of structured connectionism and deep learning, I present an engine for relational pattern recognition based on minimalist reinterpretations of first principles of connectionism. Results of computational experiments will be discussed on problems testing relational learning and universal generalization.
Abstract Semantic Relations in Mind, Brain, and Machines
Abstract semantic relations (e.g., category membership, part-whole, antonymy, cause-effect) are central to human intelligence, underlying the distinctively human ability to reason by analogy. I will describe a computational project (Bayesian Analogy with Relational Transformations) that aims to extract explicit representations of abstract semantic relations from non-relational inputs automatically generated by machine learning. BART’s representations predict patterns of typicality and similarity for semantic relations, as well as similarity of neural signals triggered by semantic relations during analogical reasoning. In this approach, analogy emerges from the ability to learn and compare relations; mapping emerges later from the ability to compare patterns of relations.