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Taylor Webb

Membre académique associé
Professeur adjoint, Département de psychologie
Sujets de recherche
Apprentissage profond
Conscience
Raisonnement
Science cognitive
Vision par ordinateur

Étudiants actuels

Solim LeGris
Collaborateur·rice de recherche - New York University
Site web
Github
Melody Li
Collaborateur·rice alumni
Site web
Github
Google Scholar
Caleb Musfeldt
Maîtrise recherche - UdeM
Site web
Github

Publications

A brain-inspired agentic architecture to improve planning with LLMs
Taylor Webb
Shanka Subhra Mondal
Ida Momennejad
Large language models (LLMs) demonstrate impressive performance on a wide variety of tasks, but they often struggle with tasks that require … (voir plus)multi-step reasoning or goal-directed planning. To address this, we take inspiration from the human brain, in which planning is accomplished via component processes that are predominantly associated with specific brain regions. These processes include conflict monitoring, state prediction, state evaluation, task decomposition, and task coordination. We find that LLMs are often capable of carrying out these functions in isolation, but struggle to autonomously coordinate them in the service of a goal. Therefore, we propose a modular agentic architecture - the Modular Agentic Planner (MAP) - in which planning is performed via the interaction of specialized brain-inspired LLM modules. We evaluate MAP on three challenging planning tasks – graph traversal, Tower of Hanoi, and the PlanBench benchmark – as well as an NLP task requiring multi-step reasoning (strategyQA). We find that MAP yields significant improvements over both standard LLM methods and competitive agentic baselines, can be effectively combined with smaller and more cost-efficient LLMs, and displays superior transfer across tasks. These results demonstrate the benefit of utilizing knowledge from cognitive neuroscience to improve planning in LLMs. Multi-step planning is a challenge for LLMs. Here, the authors introduce a brain-inspired Modular Agentic Planner that decomposes planning into specialized LLM modules, improving performance across tasks and highlighting the value of cognitive neuroscience for LLM design.
2025-09-29
Nature Communications (publié)
doi.org
Visual serial processing deficits explain divergences in human and VLM reasoning
Nicholas Budny
Kia Ghods
Declan Campbell
Raja Marjieh
Amogh Joshi
Sreejan Kumar
Jonathan D. Cohen 0003
Taylor Webb
Thomas L. Griffiths
Why do Vision Language Models (VLMs), despite success on standard benchmarks, often fail to match human performance on surprisingly simple v… (voir plus)isual reasoning tasks? While the underlying computational principles are still debated, we hypothesize that a crucial factor is a deficit in visually-grounded serial processing. To test this hypothesis, we compared human and VLM performance across tasks designed to vary serial processing demands in three distinct domains: geometric reasoning, perceptual enumeration, and mental rotation. Tasks within each domain varied serial processing load by manipulating factors such as geometric concept complexity, perceptual individuation load, and transformation difficulty. Across all domains, our results revealed a consistent pattern: decreased VLM accuracy was strongly correlated with increased human reaction time (used as a proxy for serial processing load). As tasks require more demanding serial processing -- whether composing concepts, enumerating items, or performing mental transformations -- the VLM-human performance gap widens reliably. These findings support our hypothesis, indicating that limitations in serial, visually grounded reasoning represent a fundamental bottleneck that distinguishes current VLMs from humans.
2025-09-28
ArXiv (prépublication)
doi.org
arxiv.org
Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths
Brenden M. Lake
R. Thomas McCoy
Ellie Pavlick
Taylor Webb
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, pr… (voir plus)oduce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought.
2025-08-06
ArXiv (prépublication)
doi.org
arxiv.org
Visual symbolic mechanisms: Emergent symbol processing in vision language models
Rim Assouel
Declan Campbell
Taylor Webb
To accurately process a visual scene, observers must bind features together to represent individual objects. This capacity is necessary, for… (voir plus) instance, to distinguish an image containing a red square and a blue circle from an image containing a blue square and a red circle. Recent work has found that language models solve this'binding problem'via a set of symbol-like, content-independent indices, but it is unclear whether similar mechanisms are employed by vision language models (VLMs). This question is especially relevant, given the persistent failures of VLMs on tasks that require binding. Here, we identify a set of emergent symbolic mechanisms that support binding in VLMs via a content-independent, spatial indexing scheme. Moreover, we find that binding errors can be traced directly to failures in these mechanisms. Taken together, these results shed light on the mechanisms that support symbol-like processing in VLMs, and suggest possible avenues for addressing the persistent binding failures exhibited by these models.
2025-06-17
ArXiv (prépublication)
doi.org
arxiv.org