The relevance problem is the problem of how cognitive systems can be sensitive to what is relevant despite relevance being holistic, open-ended, and context-sensitive. The problem has typically been ...

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As we count up, number words that occur later in our count denote greater quantities. Here, we tested whether children's understanding of this “later-greater” principle originates from associating nu...

The dominant account of social media's epistemic harms focuses on the spread of misinformation and the production of false beliefs at scale. This paper argues that the misinformation narrative, whate...

This paper demonstrates that in Gitksan, measure phrases (MPs) occurring with relative gradable adjectives (GAs) like ’wii ’nakw ‘tall’ receive differential interpretations even in the absence of an o...

Abstract. Across cultures, humans reason about others’ mental states as being either phenomenal, reflecting shareable experiences, or intentional, reflecting agentive goals. Might our dissociated judg...

An expert at the University of Copenhagen said his team had been sharing its research with the company’s A.I. model, Claude, and that its new finding matched their work.

Most of my students use AI, and I worry about its corrosive effects. We must not entrust tech companies with our long-term health, says Prof Devi Sridhar, chair of global public health at the Universi...

Compositionality is the property of a representational system whereby complex meanings arise from the combination of simpler meaningful elements. Here we take a developmental perspective to…

When asked to count under specific instructions, for example, to count “the forks,” or to identify the referent of “a fork,” children have been shown to treat whole forks and detached parts of fork...

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What must a neural system be capable of to implement language? Current research annotates stimuli with linguistic variables and tests which electrodes, voxels, or language-model layers predict neural ...

The language of thought hypothesis (LoTH) claims that thoughts are underpinned by language-like vehicles. A central motivation is that there is a relevant similarity between language and thought expla...

All content on this site: Copyright © 2026 Elsevier B.V., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.

The nativist–empiricist debate has long organized cognitive science, framing cognitive mechanisms as either innate or learned. Recent advances in controlled rearing and scalable domain-general neural networks now allow this debate to be tested with unprecedented rigor, by raising newborn animals and deep neural networks under tightly matched developmental conditions. Strikingly, when pushed to this empirical limit, the debate does not resolve: the same developmental phenomena support both radical empiricism and radical nativism. We argue that this stalemate reflects a conceptual error. The debate conflates explanatory levels and ontological orientations, with nativists emphasizing stable symbolic structures and empiricists emphasizing the subsymbolic processes that produce those structures. We propose dissolving the dichotomy within a multilevel framework of cognitive development.

Infants use others' emotional expressions to make inferences about the world, for example by inferring candidate causes of their happiness or sadness. However, one emotion—surprise—is less clearly li...

Using a combined approach of monkey electrophysiology, artificial neural networks and human psychophysics, Xue et al. show that neuronal interference from irrelevant information underlies behavioral e...

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OpenAI released 10 AI-generated results over the weekend. Some mathematicians are unhappy with their approach