123 citations · 238 across the 22 of their papers we have counts for
24 papers
Modularity benefits reinforcement learning agents with competing homeostatic drives
Zack Dulberg, Rachit Dubey, Isabel M. Berwian +1
The problem of balancing conflicting needs is fundamental to intelligence. Standard reinforcement learning algorithms maximize a scalar reward, which requires combining different o…
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw +2
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Ag…
Distill: Domain-Specific Compilation for Cognitive Models
Jan Vesely, Raghavendra Pradyumna Pothukuchi, Ketaki Joshi +3
This paper discusses our proposal and implementation of Distill, a domain-specific compilation tool based on LLVM to accelerate cognitive models. Cognitive models explain the proce…
A Self-Supervised Framework for Function Learning and Extrapolation
Simon N. Segert, Jonathan D. Cohen
Understanding how agents learn to generalize -- and, in particular, to extrapolate -- in high-dimensional, naturalistic environments remains a challenge for both machine learning a…
Modelling the development of counting with memory-augmented neural networks
Zack Dulberg, Taylor Webb, Jonathan Cohen
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…
People construct simplified mental representations to plan
Mark K. Ho, David Abel, Carlos G. Correa +3
One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency and flexibility. Efficiency is especially impr…