From Exposure to Expectation: Frequency, Surprisal, and Language Across Development in Spanish
- URL: http://arxiv.org/abs/2608.22452v1
- Date: Sun, 23 Aug 2026 15:02:29 GMT
- Title: From Exposure to Expectation: Frequency, Surprisal, and Language Across Development in Spanish
- Abstract summary: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times.<n>We investigate this question across two corpus-based studies of Spanish.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it contribute as much to explaining when children acquire individual words? Frequency reflects a learner's cumulative exposure to a word, whereas surprisal reflects how predictable a single occurrence is given its context. We investigate this question across two corpus-based studies of Spanish. In Study 1, we modeled age of acquisition (AoA) for 225 Spanish nouns using lexical frequency and contextual diversity from child-directed speech, plus surprisal from three language models differing in architecture and training language (BETO, BERTIN, mGPT). Frequency strongly predicted AoA (r=-.597, p<.001); surprisal added little beyond frequency and word length, including in a naturalistic-context analysis. In Study 2, we modeled adult fixation durations in the Chilean Spanish subsample of the Multilingual Eye-movement Corpus (MECO Wave 2), using mGPT surprisal alongside two independent frequency measures. Surprisal robustly predicted longer fixation durations after controlling for frequency and word length, consistent across both frequency sources. A matched word-type-level comparison showed the surprisal-behavior association was stronger in reading than in acquisition (z=3.63, p<.001). The findings suggest cumulative lexical exposure and contextual predictability play different roles across the language trajectory: frequency is particularly informative about when early lexical representations are acquired, whereas surprisal captures moment-to-moment processing difficulty in an already-established linguistic system. We discuss this pattern in relation to usage-based and entrenchment-based accounts of lexical development and to the evaluation of language models as models of human language behavior.
Related papers
- ESCUCHA: A Spanish Speech Benchmark for Heterogeneous Acoustic Conditions [66.93528077633728]
ESCUCHA comprises 1,000 human-curated questions paired with audio, totaling 162.9 hours sourced directly from the wild'' rather than drawn from existing datasets.<n>The benchmark emphasizes reasoning, spanning 9 perceptual and 10 reasoning categories, and it captures linguistic diversity through multiple Spanish accents and non-normative speech.<n> Benchmarking several state-of-the-art multimodal and speech models reveals substantial performance gaps relative to trained humans.
arXiv Detail & Related papers (2026-07-20T10:59:15Z) - The Frequency Confound in Language-Model Surprisal and Metaphor Novelty [12.10361869131849]
We analyse surprisal estimates from eight Pythia model sizes and 154 training checkpoints.<n>Across settings, word frequency is a stronger predictor of metaphor novelty than surprisal.<n>These results suggest that the often-reported optimal LM surprisal settings may incorrectly associate contextual predictability with metaphor novelty and processing difficulty, whereas lexical frequency may be the major underlying factor.
arXiv Detail & Related papers (2026-05-07T16:20:37Z) - Probing for Reading Times [60.30314214800421]
We probe language model representations for human reading times in English, Greek, Hebrew, Russian, and Turkish.<n>We find that the representations from early layers outperform surprisal in predicting early-pass measures such as first fixation and gaze duration.
arXiv Detail & Related papers (2026-04-20T18:12:59Z) - The Limits of Data Scaling: Sub-token Utilization and Acoustic Saturation in Multilingual ASR [6.627057618324123]
We analyze Whisper's decoding behavior during inference across 49 languages.<n>We study the utilization pattern of the model's sub-token space.
arXiv Detail & Related papers (2025-10-26T02:13:26Z) - On the Role of Context in Reading Time Prediction [50.87306355705826]
We present a new perspective on how readers integrate context during real-time language comprehension.<n>Our proposals build on surprisal theory, which posits that the processing effort of a linguistic unit is an affine function of its in-context information content.
arXiv Detail & Related papers (2024-09-12T15:52:22Z) - Testing the Predictions of Surprisal Theory in 11 Languages [77.45204595614]
We investigate the relationship between surprisal and reading times in eleven different languages.<n>By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.
arXiv Detail & Related papers (2023-07-07T15:37:50Z) - Word Acquisition in Neural Language Models [0.38073142980733]
We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words.
We find that the effects of concreteness, word length, and lexical class are pointedly different in children and language models.
arXiv Detail & Related papers (2021-10-05T23:26:16Z) - AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages
with Adversarial Examples [51.048234591165155]
We present AM2iCo, Adversarial and Multilingual Meaning in Context.
It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts.
Results reveal that current SotA pretrained encoders substantially lag behind human performance.
arXiv Detail & Related papers (2021-04-17T20:23:45Z) - Evaluating Models of Robust Word Recognition with Serial Reproduction [8.17947290421835]
We compare several broad-coverage probabilistic generative language models in their ability to capture human linguistic expectations.
We find that those models that make use of abstract representations of preceding linguistic context best predict the changes made by people in the course of serial reproduction.
arXiv Detail & Related papers (2021-01-24T20:16:12Z) - Unsupervised Cross-lingual Representation Learning for Speech
Recognition [63.85924123692923]
XLSR learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.
We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations.
Experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining.
arXiv Detail & Related papers (2020-06-24T18:25:05Z)
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.