ArticleAdvances in neural information processing systems2023
Scaling laws for language encoding models in fMRI.
Article in Advances in neural information processing systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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
47 citing papers in PubMed.
- Review
- Article
- Article
- Neural tracking of surprisal and semantic distance in naturalistic movie viewing.bioRxiv : the preprint server for biology · 2026Article
- A cortical gradient in area 55b links language and somato-cognitive action networks.Research square · 2026Article
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- Can neural networks model the human perception of geometric shapes?PLoS computational biology · 2026Article
- Brain states recur across diverse narrative contexts during longitudinal viewing.bioRxiv : the preprint server for biology · 2026Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
- Article
- Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech.bioRxiv : the preprint server for biology · 2026Article
- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Disentangling Superpositions: Interpretable Brain Encoding Model with Sparse Concept Atoms.bioRxiv : the preprint server for biology · 2026Article
- Left-Right Asymmetry in Predicting Brain Activity From LLMs' Representations Emerges With Their Formal Linguistic Competence.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Neural encoding of auditory processing in schizophrenia.Frontiers in neuroscience · 2026Article
- Beyond the dichotomy between controlled and naturalistic paradigms: modeling psychological constructs in cognitive neuroscience.Frontiers in psychology · 2026Article
- Towards decoding individual words from non-invasive brain recordings.Nature communications · 2025Article
- Multilingual Computational Models Capture a Shared Meaning Component in Brain Responses across 21 Languages.bioRxiv : the preprint server for biology · 2025Article
- Increasing alignment of large language models with language processing in the human brain.Nature computational science · 2025Article
- The impact of musical expertise on disentangled and contextual neural encoding of music revealed by generative music models.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing language models to brains have used GPT-2 or similarly sized language models. Here we tested whether larger open-source models such as those from the OPT and LLaMA families are better at predicting brain responses recorded using fMRI. Mirroring scaling results from other contexts, we found that brain prediction performance scales logarithmically with model size from 125M to 30B parameter models, with ~15% increased encoding performance as measured by correlation with a held-out test set across 3 subjects. Similar logarithmic behavior was observed when scaling the size of the fMRI training set. We also characterized scaling for acoustic encoding models that use HuBERT, WavLM, and Whisper, and we found comparable improvements with model size. A noise ceiling analysis of these large, high-performance encoding models showed that performance is nearing the theoretical maximum for brain areas such as the precuneus and higher auditory cortex. These results suggest that increasing scale in both models and data will yield incredibly effective models of language processing in the brain, enabling better scientific understanding as well as applications such as decoding.
Identifiers
39035676PMC11258918What 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.