7 citations · 27 across the 11 of their papers we have counts for
12 papers
Learning Options for Compositional Motor Control with Adapter Banks
Sreejan Kumar, Marcelo Mattar, Lea Duncker
Learning flexible motor primitives is a hallmark of skilled motor control. Recent neuroscience theory proposes that motor primitives may be implemented as low-rank perturbations of…
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6
Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems lea…
Visual serial processing deficits explain divergences in human and VLM reasoning
Nicholas Budny, Kia Ghods, Declan Campbell +6
Why do Vision Language Models (VLMs), despite success on standard benchmarks, often fail to match human performance on surprisingly simple visual reasoning tasks? While the underly…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity
Raja Marjieh, Sreejan Kumar, Declan Campbell +4
Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has…
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks
Declan Campbell, Sreejan Kumar, Tyler Giallanza +2
Humans possess a remarkable capacity to recognize and manipulate abstract structure, which is especially apparent in the domain of geometry. Recent research in cognitive science su…
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
Sreejan Kumar, Raja Marjieh, Byron Zhang +5
Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game…
Learning to Abstract Visuomotor Mappings using Meta-Reinforcement Learning
Carlos A. Velazquez-Vargas, Isaac Ray Christian, Jordan A. Taylor +1
We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as di…
Relational Constraints On Neural Networks Reproduce Human Biases towards Abstract Geometric Regularity
Declan Campbell, Sreejan Kumar, Tyler Giallanza +2
Uniquely among primates, humans possess a remarkable capacity to recognize and manipulate abstract structure in the service of task goals across a broad range of behaviors. One ill…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
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…
Meta-Learning of Structured Task Distributions in Humans and Machines
Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen +2
In recent years, meta-learning, in which a model is trained on a family of tasks (i.e. a task distribution), has emerged as an approach to training neural networks to perform tasks…