Language is primarily a tool for communication rather than thought

Nature, Journal Year: 2024, Volume and Issue: 630(8017), P. 575 - 586

Published: June 19, 2024

Language: Английский

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Driving and suppressing the human language network using large language models

Nature Human Behaviour, Journal Year: 2024, Volume and Issue: 8(3), P. 544 - 561

Published: Jan. 3, 2024

Language: Английский

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Large-scale evidence for logarithmic effects of word predictability on reading time

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(10)

Published: Feb. 29, 2024

During real-time language comprehension, our minds rapidly decode complex meanings from sequences of words. The difficulty doing so is known to be related words’ contextual predictability, but what cognitive processes do these predictability effects reflect? In one view, reflect facilitation due anticipatory processing words that are predictable context. This view predicts a linear effect on demand. another the costs probabilistic inference over sentence interpretations. either logarithmic or superlogarithmic demand, depending whether it assumes pressures toward uniform distribution information time. empirical record currently mixed. Here, we revisit this question at scale: We analyze six reading datasets, estimate next-word probabilities with diverse statistical models, and model times using recent advances in nonlinear regression. Results support word difficulty, which favors as key component human processing.

Language: Английский

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Evaluating large language models in theory of mind tasks

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(45)

Published: Oct. 29, 2024

Eleven large language models (LLMs) were assessed using 40 bespoke false-belief tasks, considered a gold standard in testing theory of mind (ToM) humans. Each task included scenario, three closely matched true-belief control scenarios, and the reversed versions all four. An LLM had to solve eight scenarios single task. Older solved no tasks; Generative Pre-trained Transformer (GPT)-3-davinci-003 (from November 2022) ChatGPT-3.5-turbo March 2023) 20% ChatGPT-4 June 75% matching performance 6-y-old children observed past studies. We explore potential interpretation these results, including intriguing possibility that ToM-like ability, previously unique humans, may have emerged as an unintended by-product LLMs' improving skills. Regardless how we interpret outcomes, they signify advent more powerful socially skilled AI-with profound positive negative implications.

Language: Английский

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Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 30, 2024

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally the symbolic representations posited by traditional psycholinguistics. We hypothesize that areas in human brain, similar to DLMs, rely on represent To test this hypothesis, we densely record neural activity patterns inferior frontal gyrus (IFG) three participants using dense intracranial arrays while they listened 30-minute podcast. From these fine-grained spatiotemporal recordings, derive for each word (i.e., brain embedding) patient. Using stringent zero-shot mapping demonstrate embeddings IFG and DLM contextual have common geometric patterns. The allow us predict given left-out based solely its geometrical relationship other non-overlapping words Furthermore, show capture geometry better than static embeddings. exposes vector-based code natural processing brain.

Language: Английский

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Large language models predict human sensory judgments across six modalities

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 13, 2024

Language: Английский

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Data science opportunities of large language models for neuroscience and biomedicine

Neuron, Journal Year: 2024, Volume and Issue: 112(5), P. 698 - 717

Published: Feb. 9, 2024

Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer primer on defining properties of these modeling techniques. We then reflect modes investigation which LLMs can be used to reframe classic neuroscience questions deliver fresh answers. reason that have potential (1) enrich datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources overcome divides between siloed communities, (3) enable previously unthinkable fusion disparate relevant brain, (4) help deconvolve cognitive concepts most usefully grasp phenomena and much more.

Language: Английский

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Large language models can segment narrative events similarly to humans

Behavior Research Methods, Journal Year: 2025, Volume and Issue: 57(1)

Published: Jan. 3, 2025

Language: Английский

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Incremental accumulation of linguistic context in artificial and biological neural networks

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 18, 2025

Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how brain, unlike LLMs process text windows parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data 219 participants listening spoken narratives, first demonstrate predict brain activity effectively only when using short up a few dozen words. Next, introduce alternative LLM-based incremental-context model combines incoming aggregated, dynamically updated summary prior context. This significantly enhances prediction higher-order regions involved long-timescale processing. Our findings reveal brain's hierarchical temporal processing mechanisms enable flexible integration time, providing valuable insights for both cognitive neuroscience AI development.

Language: Английский

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Robust Effects of Working Memory Demand during Naturalistic Language Comprehension in Language-Selective Cortex

Journal of Neuroscience, Journal Year: 2022, Volume and Issue: 42(39), P. 7412 - 7430

Published: Aug. 24, 2022

To understand language, we must infer structured meanings from real-time auditory or visual signals. Researchers have long focused on word-by-word structure building in working memory as a mechanism that might enable this feat. However, some argued language processing does not typically involve rich building, and/or apparent effects are underlyingly driven by surprisal (how predictable word is context). Consistent with alternative, recent behavioral studies of naturalistic control for surprisal shown clear effects. In fMRI study, investigate range theory-driven predictors demand during comprehension humans both sexes under rigorous controls. addition, address related debate about whether the mechanisms involved specialized domain general. do so, each participant, functionally localize (1) language-selective network and (2) “multiple-demand” network, which supports across domains. Results show robust surprisal-independent no effect multiple-demand network. Our findings thus support view involves computationally demanding operations memory, addition to any prediction-related mechanisms. Further, these appear be primarily conducted same neural resources store linguistic knowledge, evidence involvement brain regions known SIGNIFICANCE STATEMENT This study uses signatures (WM) story listening, using broad theoretically motivated estimates WM demand. strong distinct predictability. demands register regions, rather than previously been associated nonlinguistic core role incremental processing, language.

Language: Английский

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