The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance
- URL: http://arxiv.org/abs/2605.01537v1
- Date: Sat, 02 May 2026 17:03:27 GMT
- Title: The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance
- Abstract summary: Isolated word meanings are inherently uncertain.<n>This uncertainty reduces when they are combined and anchored in context.<n>We propose that grammar compresses meaning uncertainty cross-linguistically, which is reflected in brain and selectively disrupted in disorders.
- Score: 11.630388936897218
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cross-linguistically, which is reflected in brain and selectively disrupted in disorders. Compression was operationalized as the relative difference between non-contextual surprisal estimated from lexical frequency, and contextual surprisal from grammar-sensitive models. In narratives from 20 languages, contextual surprisal reduced frequency-based surprisal. This reduction closely tracked the surprisal cost of reversing word order, and scaled with richer, non-redundant lexis as organized by more complex but optimal dependency structure. During fMRI, surprisal and its reduction explained BOLD activity for comprehension and production in overlapping but distinct regions. Uncertainty reduction was significantly attenuated in aphasia, dementia, and schizophrenia, but remained intact where primary deficit is not language. These findings position uncertainty reduction via grammar as a foundational concept that illuminates principles, brain basis, and disruptions of language.
Related papers
- Psychosis involves a deficit of information compression in connected speech [4.907371324386136]
Large language models (LLMs) with human-like performance on linguistic tasks have transformed the study of language in neurodiverse conditions.<n>Previous crosslinguistic evidence suggests a complexity reduction in the form of both lower intrinsic dimensionality (ID) of LLM representations and higher mean surprisal (prediction error) in psychosis.<n>We operationalized surprisal difference as the difference between surprisal as estimated from word frequency and surprisal as based on a contextual LM, which is sensitive to grammatical organization over and above lexical concepts.
arXiv Detail & Related papers (2026-09-14T13:13:30Z) - A Formal Limitation on Learning Human Language From Textual Corpora [54.22186267212778]
We derive upper bounds on the probability that a decoder recovers a speaker's intended meaning from a representation of the utterance.<n>Experiments on artificial languages, Mandarin zero-pronoun resolution, and color reference provide empirical evidence in support of the theory.
arXiv Detail & Related papers (2026-08-28T17:38:24Z) - Neural Uncertainty Principle: A Unified View of Adversarial Fragility and LLM Hallucination [60.197429875410286]
Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems.<n>Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion.<n>In vision, masking highly coupled components improves robustness without costly adversarial training.<n>In language, the same prefill-stage probe detects hallucination risk before generating any answer tokens.
arXiv Detail & Related papers (2026-03-20T02:07:10Z) - Revisiting Modality Invariance in a Multilingual Speech-Text Model via Neuron-Level Analysis [15.638379666159127]
We investigate where language and modality information is encoded, how selective neurons causally influence decoding, and how concentrated this influence is across the network.<n>We identify language- and modality-selective neurons using average-precision ranking, investigate their functional role via median-replacement interventions at inference time, and analyze activation-magnitude inequality across languages and modalities.
arXiv Detail & Related papers (2026-01-24T09:22:18Z) - Coherence in the brain unfolds across separable temporal regimes [1.3874648807526748]
Coherence in language requires the brain to satisfy two competing temporal demands.<n>We show that coherence is implemented through dissociable neural regimes of slow contextual integration and rapid event-driven reconfiguration.
arXiv Detail & Related papers (2025-12-23T16:16:42Z) - Far from the Shallow: Brain-Predictive Reasoning Embedding through Residual Disentanglement [43.96899536703126]
Modern large language models (LLMs) are increasingly used to model neural responses to language.<n>Their internal representations are highly "entangled," mixing information about lexicon, syntax, meaning, and reasoning.<n>This entanglement biases conventional brain encoding analyses toward linguistically shallow features.
arXiv Detail & Related papers (2025-10-26T22:46:26Z) - Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations [51.92795774118647]
We find that verbal uncertainty'' is governed by a single linear feature in the representation space of LLMs.<n>We show that this has only moderate correlation with the actual semantic uncertainty'' of the model.
arXiv Detail & Related papers (2025-03-18T17:51:04Z) - Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection [46.930149191121416]
Large Language Models (LLMs) are prone to hallucination with non-factual or unfaithful statements.<n>We propose a method to enhance uncertainty modeling with semantic graph for hallucination detection.
arXiv Detail & Related papers (2025-01-02T16:45:05Z) - Information-Restricted Neural Language Models Reveal Different Brain
Regions' Sensitivity to Semantics, Syntax and Context [87.31930367845125]
We trained a lexical language model, Glove, and a supra-lexical language model, GPT-2, on a text corpus.
We then assessed to what extent these information-restricted models were able to predict the time-courses of fMRI signal of humans listening to naturalistic text.
Our analyses show that, while most brain regions involved in language are sensitive to both syntactic and semantic variables, the relative magnitudes of these effects vary a lot across these regions.
arXiv Detail & Related papers (2023-02-28T08:16:18Z) - On Hallucination and Predictive Uncertainty in Conditional Language
Generation [76.18783678114325]
Higher predictive uncertainty corresponds to a higher chance of hallucination.
Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties.
It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
arXiv Detail & Related papers (2021-03-28T00:32:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.