47 citations · 70 across the 17 of their papers we have counts for
20 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…
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
Hua-Dong Xiong, Xinyuan Yan, Ji-An Li +3
Inference-time thinking improves the performance of large language models, but aggregate outcomes do not reveal whether models use available evidence more effectively or seek infor…
Beyond DSA: Conjugacy-based Comparison of Dynamical Systems
Prakhar Godara, Pang Shiang Tay, Marcelo G. Mattar
Comparing whether two dynamical systems implement the same computation despite differences in coordinates or measurements is a central problem in neuroscience and machine learning.…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning
Sixing Chen, Ji-An Li, Saner Cakir +3
Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation over future outcomes. Yet wh…
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…
The Position Curse: LLMs Struggle to Locate the Last Few Items in a List
Zhanqi Zhang, Hua-Dong Xiong, Robert C. Wilson +3
Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant tokens) with near-saturated ac…
Linking In-context Learning in Transformers to Human Episodic Memory
Li Ji-An, Corey Y. Zhou, Marcus K. Benna +1
Understanding connections between artificial and biological intelligent systems can reveal fundamental principles of general intelligence. While many artificial intelligence models…
Brain-Like Replay Naturally Emerges in Reinforcement Learning Agents
Jiyi Wang, Likai Tang, Huimiao Chen +2
Replay is a powerful strategy to promote learning in artificial intelligence and the brain. However, the conditions to generate it and its functional advantages have not been fully…
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
Shi Gu, Marcelo G Mattar, Huajin Tang +1
While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the tempo…
Predecessor Features
Duncan Bailey, Marcelo G. Mattar
Any reinforcement learning system must be able to identify which past events contributed to observed outcomes, a problem known as credit assignment. A common solution to this probl…
Improving Experience Replay with Successor Representation
Yizhi Yuan, Marcelo G Mattar
Prioritized experience replay is a reinforcement learning technique whereby agents speed up learning by replaying useful past experiences. This usefulness is quantified as the expe…
Learning differentially reorganizes brain activity and connectivity
Maxwell A Bertolero, Azeez Adebimpe, Ankit N. Khambhati +4
Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectiv…
Effective learning is accompanied by high dimensional and efficient representations of neural activity
Evelyn Tang, Marcelo G. Mattar, Chad Giusti +2
A fundamental cognitive process is the ability to map value and identity onto objects as we learn about them. Exactly how such mental constructs emerge and what kind of space best…
Brain State Flexibility Accompanies Motor-Skill Acquisition
Pranav G. Reddy, Marcelo G. Mattar, Andrew C. Murphy +4
Learning requires the traversal of inherently distinct cognitive states to produce behavioral adaptation. Yet, tools to explicitly measure these states with non-invasive imaging --…
Brain Network Architecture: Implications for Human Learning
Marcelo G. Mattar, Danielle S. Bassett
Human learning is a complex phenomenon that requires adaptive processes across a range of temporal and spacial scales. While our understanding of those processes at single scales h…
The Energy Landscape Underpinning Module Dynamics in the Human Brain Connectome
Arian Ashourvan, Shi Gu, Marcelo G. Mattar +2
Human brain dynamics can be profitably viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing…
The network architecture of value learning
Marcelo G Mattar, Sharon L Thompson-Schill, Danielle S Bassett
Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain s…
Optimal Trajectories of Brain State Transitions
Shi Gu, Richard F. Betzel, Matthew Cieslak +4
The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how the organization of white matter architecture constrains how the brain transi…
Structural Pathways Supporting Swift Acquisition of New Visuo-Motor Skills
Ari E. Kahn, Marcelo G. Mattar, Jean M. Vettel +3
Human skill learning requires fine-scale coordination of distributed networks of brain regions that are directly linked to one another by white matter tracts to allow for effective…