5 citations · 8 across the 6 of their papers we have counts for
8 papers
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…
Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths, Brenden M. Lake, R. Thomas McCoy +2
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, produce novelty, and learn quickly. We arg…
Visual symbolic mechanisms: Emergent symbol processing in vision language models
Rim Assouel, Declan Campbell, Yoshua Bengio +1
To accurately process a visual scene, observers must bind features together to represent individual objects. This capacity is necessary, for instance, to distinguish an image conta…
Bound by semanticity: universal laws governing the generalization-identification tradeoff
Marco Nurisso, Jesseba Fernando, Raj Deshpande +9
Intelligent systems must deploy internal representations that are simultaneously structured -- to support broad generalization -- and selective -- to preserve input identity. We ex…
Caption This, Reason That: VLMs Caught in the Middle
Zihan Weng, Lucas Gomez, Taylor Whittington Webb +1
Vision-Language Models (VLMs) have shown remarkable progress in visual understanding in recent years. Yet, they still lag behind human capabilities in specific visual tasks such as…
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
Yukang Yang, Declan Campbell, Kaixuan Huang +3
Many recent studies have found evidence for emergent reasoning capabilities in large language models (LLMs), but debate persists concerning the robustness of these capabilities, an…
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem
Declan Campbell, Sunayana Rane, Tyler Giallanza +8
Recent work has documented striking heterogeneity in the performance of state-of-the-art vision language models (VLMs), including both multimodal language models and text-to-image…
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
Taylor W. Webb, Steven M. Frankland, Awni Altabaa +9
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This has often been framed in terms of a dichotomy between conne…