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Found 4 papers, 1 papers with code
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
Modularity benefits reinforcement learning agents with competing homeostatic drives
The problem of balancing conflicting needs is fundamental to intelligence.
Generalization to Out-of-Distribution transformations
Humans understand a set of canonical geometric transformations (such as translation, rotation and scaling) that support generalization by being untethered to any specific object.
Modelling the development of counting with memory-augmented neural networks
Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability.
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