ReviewTrends in cognitive sciences2024
Dissociating language and thought in large language models.
Review in Trends in cognitive sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What is being measured by formal thought disorder scales? An item-level content analysis.Psychological medicine · 2026Pooled it
- Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization.JMIR AI · 2026Article
- Estimating the threat of AI-agent responding across online survey platforms.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The Missing Dimension in Cancer Education: Developmental Meaning-Making and the Expert's Illusion of Symmetry.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Review
- Evaluation of Large Language Models as Tools, Models, and Partners in Creative Thinking Research: A Selective Narrative Review with the GCA Framework.Journal of Intelligence · 2026Review
- Persona-prompted LLM agents achieve modest but genuine prediction of human social media reactions.Scientific reports · 2026Article
- The compositionality continuum as a principle for studying the neural basis of intelligence.Nature neuroscience · 2026Review
- Speaking to no one: ontological dissonance and the double bind of conversational AI.Medicine, health care, and philosophy · 2026Article
- Characterizing the spiral: potential mechanisms in AI-associated delusions.NPP - digital psychiatry and neuroscience · 2026Review
- The impact of large language models on collaborative learning processes and outcomes in higher education for English translation.Scientific reports · 2026Article
- Constituent-constrained word prediction during language comprehension.Nature neuroscience · 2026Article
- Munich Sentence (MuSe) Database: Completion norms and audio recordings for 619 German sentences.Behavior research methods · 2026Article
- TechnologicalNature. Mental health · 2026Article
- Evidence Against Syntactic Encapsulation in Large Language Models.Cognitive science · 2026Article
- Review
- Divergent creativity in humans and large language models.Scientific reports · 2026Article
- Knowledge in Animals and Machines.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans.Open mind : discoveries in cognitive science · 2026Article
- A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination.Frontiers in medicine · 2026Article
- Private speech: similarities between a large language model and children.Frontiers in artificial intelligence · 2026Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence (knowledge of linguistic rules and patterns) and functional linguistic competence (understanding and using language in the world). We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. We posit that models that use language in human-like ways would need to master both of these competence types, which, in turn, could require the emergence of separate mechanisms specialized for formal versus functional linguistic competence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.