Cognitive Science
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Addressing Longstanding Challenges in Cognitive Science with Language Models
arXiv:2511.00206v3 Announce Type: replace Abstract: Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help to address these longstanding issues. Specifically, they can help map fragmented literatures, formalize verbal theories, identify overlap among...
The Frame Problem
The Frame Problem To most AI researchers, the frame problem is the challenge of representing the effects of action in logic without having to represent explicitly a large number of intuitively obvious non-effects. But to many philosophers, the AI researchers' frame problem is suggestive of wider epistemological issues. Is it possible, in principle, to limit the scope of the reasoning required to derive the consequences of an action?
Can an army of babies and dogs rescue psychology from its reproducibility crisis?
Nature, Published online: 03 June 2026; doi:10.1038/d41586-026-01727-xLabs are teaming up to run a host of huge projects that aim to bring rigour to cognitive science.
Q&A: Experts discuss rise of profanity from politicians
In American politics, cursing and "four-letter words" are no longer confined to hot mics or hidden behind closed doors. Politicians and pundits are increasingly using so-called "bad words" in speeches, social media posts and campaign ads. Benjamin Bergen, professor of cognitive science, and Pamela Ban, associate professor of political science, both from UC San Diego's School of Social Sciences, examine why swearing among politicians is on the rise and what it reveals about persuasion,...
Gleam-glum effect reveals emotional word cues in children as young as five
Gleam-glum effect reveals emotional word cues in children as young as five Lisa Lock Scientific Editor Andrew Zinin Lead Editor The words "tick-tock," "hiss" and "screech" are examples of onomatopoeia because they imitate the sounds they represent: the rhythmic ticking of a clock; an angry cat, or a slowly deflating bike tire; a high-pitched scream. Onomatopoeia is a type of sound symbolism. The sounds of other words, even when they're not strict examples of onomatopoeia, also hint at their...
A MATLAB Toolbox for Standardized Reading Speed Assessment: Implementing and Extending the Perrin Sentence Generator for English Corpora
new Abstract: In the fields of vision science, cognitive psychology, and psycholinguistics, the accurate measurement of reading speed is frequently hampered by the limitations of static reading charts. Repeated testing often leads to memorization effects, while the requirement for oral recitation introduces speech-motor confounds that obscure true information processing speed. To address these methodological hurdles, this paper introduces an open-source MATLAB toolbox that adapts the...
Learning to Theorize the World from Observation
arXiv:2605.03413v2 Announce Type: replace Abstract: What does it mean to understand the world? Contemporary world models often operationalize understanding as accurate future prediction in latent or observation space. Developmental cognitive science, however, suggests a different view: human understanding emerges through the construction of internal theories of how the world works, even before mature language is acquired.
MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
Announce Type: new Abstract: Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived context, the hippocampal system to preserve episodic memory of past experience, and internal...
Deep networks learn to parse uniform-depth context-free languages from local statistics
Announce Type: replace-cross Abstract: Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely...
Resonant Minds: Closed-Loop Social Avatars with Theory of Mind
new Abstract: Creating lifelike digital humans with genuine social intelligence requires unifying cognitive reasoning and multimodal generation within a coherent framework. Current approaches treat these as separate tasks: Large Language Models excel at dialogue but lack embodied expression, while diffusion-based talking head models achieve visual fidelity but ignore social cognition.