Title: Scaling and context steer LLMs along the same computational path as the human brain
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Language Models

LLMs

Emergence

Neuroscience

Brain

Alignment

Language

Magnetoencephalography
Abstract: Recent studies suggest that the representations learned by large language models (LLMs) are partially aligned to those of the human brain. 
However, whether this representational alignment arises from a similar sequence of computations remains elusive. 

In this study, we explore this question by examining temporally-resolved brain signals of participants listening to 10 hours of an audiobook. 
We study these neural dynamics jointly with a benchmark encompassing 17 LLMs varying in size and architecture type. 

Our analyses reveal that LLMs and the brain generate representations in a similar order: specifically, activations in the initial layers of LLMs tend to best align with early brain responses, while the deeper layers of LLMs tend to best align with later brain responses. 

This brain-LLM alignment is consistent across transformers and recurrent architectures. 
However, its emergence depends on both model size and context length. 

Overall, the alignment between LLMs and the brain provides novel elements supporting a partial convergence between biological and artificial neural networks.

Scaling and context steer LLMs along the same computational path as the human brain

Abstract

Recent studies suggest that the representations learned by large language models (LLMs) are partially aligned to those of the human brain. However, whether and why this alignment score arises from a similar sequence of computations remains elusive. In this study, we explore this question by examining temporally-resolved brain signals of participants listening to 10 hours of an audiobook. We study these neural dynamics jointly with a benchmark encompassing 17 LLMs varying in size and architecture type. Our analyses confirm that LLMs and the brain generate representations in a similar order: specifically, activations in the initial layers of LLMs tend to best align with early brain responses, while the deeper layers of LLMs tend to best align with later brain responses. This brain-LLM alignment is consistent across transformers and recurrent architectures. However, its emergence depends on both model size and context length. Overall, this study sheds light on the sequential nature of computations and the factors underlying the partial convergence between biological and artificial neural networks.

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MEG

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Llama 3

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Evaluation:Rtayer = Corr(XWiayer, Yayer)

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Temporal score: r = Corr(Tmax,Layers)

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Lastlayer

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Y∈Rwxd

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p = 3e-04

"Onceupon a time..."

'Once upon a time..

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Stéphane d'Ascoli

Meta AI

Meta AI

Meta AI

*shared senior authorship

1 Introduction

Motivation. While large language models (LLMs) are not designed to resemble the human brain, recent studies show that their activations share similarities with those of the brain in response to speech [1, 2, 3, 4, 5, 6]. In the same way bats and birds independently evolved wings [7], LLMs and the human brain thus seem to follow a partial convergence [4].

State of the art. Recent studies showed that an anatomical alignment exists between LLM layers and functional regions of the human brain, in the sense that their first layers tend to align with low-level areas of the brain such as primary sensory cortices whereas their deeper layers tend to align with higher-level areas such as secondary sensory or associative cortices [5, 8, 3, 9, 1].

Remaining challenges. While this anatomical alignment between LLMs and the brain is increasingly established, the order in which these representations emerge remains poorly understood. While it has been shown by [6] that GPT2-XL exhibits a form of temporal alignment, (i) whether this temporal alignment with the brain is systematically found across LLMs, (ii) whether this alignment depends on the type of architecture, (iii) on its size, or (iv) on the length of its context remains currently unknown. In sum, the factors that lead an LLM to adopt a computational path analogous to the human brain's remain unknown.

Approach. To address these issues, we analyze the temporally resolved brain signals of healthy individuals recorded with magnetoencephalography (MEG) [10], while they listened to 10 hours of audiobooks. We then systematically analyze these neural dynamics in conjunction with a benchmark of 17 LLMs varying in architecture, size and training choices.

2 Methods

Problem formalization. We compare (i) the representations of the human brain in response to natural speech, to (ii) the representations of LLMs in response to the corresponding textual input. Brain activity, here measured with magnetoencephalography (MEG), leads to a high-dimensional time series that depends, in part, on speech input. To test whether the order of computations is similar between the brain and LLMs, we test whether the orders in which representations are generated are correlated between the two systems.

Linear mapping. Following others [11, 12, 13], we operationally define a neural 'representation' as 'linearly readable information'. As there is no one-to-one alignment between each MEG sensor and each activation of an LLM, we compare the representations across these two systems through a linear mapping. Specifically, we fit a ridge regression to predict LLM activations ( Y ∈ R w × d ) from brain activity ( X t ∈ R w × s ):

with w the number of words, d the number of LLM activations, s the number of MEG sensors and t the time point relative to word onset. For this, we use scikit-learn's RidgeCV , with tuning of logarithmically spaced regularization strength through a grid search approach ( α = 10 -4 to 10 8 , tuned for each dimension independently).

Alignment score. To evaluate this alignment score between an LLM and a human brain, we compute, for each time sample relative to word onset, a Pearson correlation between Y and WX t on a held-out test set. We repeat this procedure across all five train-test folds of the cross-validation.

Temporal alignment. After computing this LLM-brain alignment score of l = 9 equally spaced layers of the LLM, we evaluate whether the time at which the score peaks correlates with the depth of the layer in the LLM considered. We refer to this as temporal alignment . Specifically, we compute, for each layer T max, the mean of the temporal window during which ˜ R ≥ 95% , where ˜ R is the normalized alignment score of the layer, obtained by dividing the alignment score by its maximum value across time. Finally, we compute the Pearson correlation between the T max and the relative depth of the 9 layers. The result of this correlation is hereafter referred to as temporal score .

Brain data and preprocessing. We here focus on a large within-subject MEG dataset publicly available [10]. This dataset consists of three healthy participants who listened to 10 h of audio books

in a CTF MEG scanner. To limit the impact of noise we apply a band-pass filter between 0.1 and 20 Hz, down-sample the signal at 30 Hz, time-lock the brain responses to individual words, and epoch the corresponding neural data between -2.5 s and +3 s relative to word onset using MNE-Python [14]. Finally, we z-score MEG signals across words, for each MEG channel and each time point independently.

LLM activations and preprocessing. We use a selection of SOTA LLMs to ensure a comprehensive evaluation ranging through architectures, scales, design and training choices. Specifically, we benchmark models such as Llama-3-8B [15, 16], Llama-3.2 (1B, 3B) [15, 17, 18], Mistral-7B-v0.1 [19], Gemma-7B [20], Qwen1.5-7B [21], and GPT-2-XL [22, 23], the latter serving as a historical reference. We also investigate dynamics at play in SOTA state space models: Mamba-1.4B-hf [24, 25] and RecurrentGemma-9B [26]. Additionally, we leverage the Pythia family [27] consisting of 8 models of increasing size and same training setup. Except when explicitly stated, context length is 50 words. For each LLM, we investigate 9 layers linearly distributed between 10% and 90% of the model hierarchy. To mitigate the issue of heterogeneous sizes, we transform these activation patterns with a Principal Component Analysis (n=50) with scikit-learn [28].

Text preprocessing. To ensure the processing of the most semantically meaningful words, we study only content words (as opposed to function words), specifically those which belong to the following part-of-speech categories as defined by Spacy [29]: NOUN, VERB, ADJ, ADV. We ensure the replicability of findings for all words (function and content) via control figures in App. F.

Compute resources. Evaluating the largest LLM on our dataset to extract both activation patterns for all 9 layers and next-word probability requires 3 V100 GPU-hours and 12 CPU-hours. Pre-processing both LLMs' and brain's activations, fitting and testing a decoding model on our dataset, for each layer and each time step along our temporal window of 5.5s requires in total 4 hours x 9 layers x 15CPUs (Allocated memory: 100 GB) x 3 subjects = 1,620 CPU-hours - for the largest LLM. In total, experiments on 17 LLMs and 9 varying contexts required ~75 GPU-hours and ~42,500 CPU-hours. An internal cluster was used for all experiments. Licenses for the models and dataset used are presented in App. J.

3 Results

Alignment score. We compare the representations of LLMs to those of the human brain in response to natural speech. For this, we fit a linear model to predict the LLMs' contextual representations from the MEG activations, time locked to word onset, and we evaluate this linear mapping with a correlation between true and predicted activations on a held-out test set. The results show that the alignment scores between each layer of the LLMs and the brain increase around 0.4s post-word onset (Fig. 1A-C).

Temporal alignment. We next examine the existence of brain-like dynamics of computations in the nine LLMs specified above. On average, these models show a 'temporal score' of r = 0.99 ( p < 1e-06), between the depth of their layers and the MEG responses to words (Fig. 1A-B). This temporal alignment is observed in all studied models, even non-transformer models like ReccurrentGemma-9B and Mamba-1.4B. (Fig. 2C-D). Oldest model GPT2-XL exhibits the lowest Temporal score, though significant, r = 0.85. While being grounded in the same seminal transformer design, GPT2-XL is smaller and lacks modern advancements present in the other studied transformers [30, 31]. When not pretrained, the LLMs do not show this alignment, and encode very poorly the brain activity. Together, these results suggest that the order of computations activated through recent LLMs' layers is similar to the order of computations of the human brain listening to natural speech.

Impact of causality. To assess the impact of contextual directionality, we compare two bidirectional LLMs- BERT [32] and RoBERTa [33] - and one bidirectional speech model - Wav2vec2.0 [34] - to the previous causal models, more faithful to the brain's causal mechanisms of language processing. While alignment scores are comparable, the temporal scores of these bidirectional models are substantially lower than those of causal LLMs (see App. B).

Impact of model size. Various architectures, scales and training choices all yield above-chance representational and temporal alignments. To identify the factors that impact the emergence of this phenomenon, we repeat these analyses on a family of LLMs that solely vary in model size. For this,

A

Decoding brain-scores of layers along time, all models

0.1

B

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Correlation between LLM depth and Tmax, all models

Qwen1.5-7B

Temporal alignment with the brain across LLMs

·0.98 *

0.05

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Mistral-7B-v0.1

·0.96 *

scores

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0.3

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Gemma-7b

·0.95 *

Alignment

0.03

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0.9

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Llama-3.2-3B

·0.93 *

· 0.94 *

Unpretrained

0.02

layers

Llama-3.2-1B

· 0.92 *

0.01

0.2

r=0.99

Recurrentgemma-9b

· 0.92 *

p=3e-07

Mamba-1.4b-hf

·0.93 *

0.00

0.0

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1

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0

1

2

3

0.44

0.5

Tmax (S)

0.55

0.6

0.65

Gpt2-xl

0.8

TemporalPearson score

0.85 *

0.9

1.0

1.1

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0.3

0.1

.057

Qwen1.5-7B

059

Mistral-7B-v0.1

06

Gemma-7b

.057

Meta-Llama-3-8B

055

Llama-3.2-3B

056

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Recurrentgemma-9b

.053

.061

Mamba-1.4b-hf

.054

Gpt2-x1

0.4

0.5

0.6

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0.8

0.9

Unpretrained

Time (s)

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we leverage the Pythia family [27]: eight models of increasing scales. These models are trained in the same way, on the same data, in a highly controlled setup where only model size varies. We find that both the representational and the temporal alignment increase with model size (Fig. 3), from a non-significant temporal score (r = 0.44, p > 0.05) for the smallest model of 14M parameters, to a highly significant temporal score (r = 0.96, p < 1e-4) for the biggest model of 12 billion parameters. The correlation between the temporal score and the log model size reaches r = 0.87 ( p = 0.01). This emergence follows a logarithmic trend, where the temporal and alignment scores of the biggest models tend to plateau. A similar trend is observed for alignment score.

Impact of context size. When listening and processing language, humans accumulate narrative context in the form of evidence to extract the richest meaning out of the currently heard word, and anticipate the next one [35]. Motivated by this incremental nature of human language processing, we postulate that brain-like inference dynamics in LLMs emerge with context size. To test how context size impacts representational and temporal alignments, we repeat our analyses on a single model (Llama-3.2 3B) while varying the amount of words in the input context. The results show that representational and temporal alignments increase with context size (r = 0.81, p < 5e-2, Fig. 4), from a non-significant temporal score without context (r = 0.19, p > 0.5) to a highly significant temporal score for context of 1000 words (r = 0.93, p = 3e-4). This temporal score increases logarithmically and considerably slows down from context lengths of 50 words. A similar - though lower - correlation is observed for alignment score. We find similar logarithmic increases of both temporal and alignment scores for state-space model Mamba (see in App. E).

Impact of word predictability. Autoregressive LLMs are trained to predict incoming words/tokens. In a similar way, predictive coding theory suggests that the brain anticipates upcoming words [36]. Could this similarity be the driving factor of temporal alignment? To test this possibility, we evaluate how temporal alignment varies with word's predictability: if this hypothesis was true,

C

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pythia-6.9b

pythia-12b

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Temporal score: r = 0.87, p = 0.01

Alignment score: r = 0.86, p = 0.01

0.71,

+0.92

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Tmax (s)

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r = 0.44

p = 0.24

0.54

0.58

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0.48

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Tmax (S)

r = 0.71

p = 0.03

ES'0

0.55

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0.49

Tmax (s)

0.53

p = 0.02

= 0.74

0.57

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p = 4e-05

0.59

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160m

Model size (Log)

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410m

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Pythia-

3-6.96-12b

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10-wordcontext

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1000-wordcontext

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0.75

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0.37

0.41

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0.45

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0.39

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0.57

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p = 3e-04

0.57

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0.045

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Tmax (s)

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0.54

highly unpredictable words should exhibit low temporal alignment. To control for the fact that predictability can be impacted by the word being a content or function word, for this analysis we include all words of our dataset. For this, we first retrieve the predictability of each word in its context from the softmax-transformed logits of Llama-3-8B. We then separate these words into four

predictability quartiles. Finally, we evaluate temporal alignment for each quartile independently. The results show that most and least predictable words both lead to above chance representational and temporal alignments: r = 0.92, p < 1e-3 for the quartile "most expected" and r = 0.83, p < 1e-2 for the quartile "most surprising", respectively (Fig. 5). The temporal score for the most surprising quartile does exhibit a lower r value and p . However, when computing the Pearson correlation of the layer-wise differences of T max between "most expected" and "most surprising" quartiles, we do not find a significant impact of contextual predictability on Temporal score ( p = 0.61, Fig. 5E). This result holds with more layers too (see App. H). Overall, this control analysis indicates that word-predictability alone does not explain the emergence of temporal alignments between LLMs and the brain.

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r = -0.20

p = 0.61

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Layer 0.1

Layer 0.2

Layer 0.3

Layer 0.4

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Quartilemostexpected

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Quartile most surprising

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Correlation between temporal and alignment scores. We investigate whether, and why, temporal and alignment scores are correlated, while arising from different measurements. We study jointly the temporal scores across all previously studied models - clustered by families - and context lengths, with their respective maximal alignment score - i.e. through their best predictive layer. When plotting both alignment and temporal scores across families of models (Fig. 6A), we find that both alignment and temporal scores increase with model size and context length. When correlating both alignment and temporal scores, we find that temporal score indeed correlates significantly with alignment score (Pearson score = 0.54, p = 9e-04) (Fig. 6B). This result indicates that the capacity of an

LLM to predict neural signals of the human brain is correlated to how tightly aligned its pathway of computations is with the one at play in the brain, when processing language.

A

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Temporal score

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Alignmentscore

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Recurrent models

Meta-Llama-3-8B models

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Models

Alignment score

4 Discussion

Temporal alignment. This work investigates whether and when LLMs generate representations in an order similar to the human brain's during natural speech listening. This study provides three main contributions. First, the order of representations generated by LLMs' layers strongly correlates with the sequence of neural activations observed in the human brain recorded with MEG. This temporal alignment complements previous work on the anatomical alignment observed between LLMs' layers and functional regions of the human brain [5, 8, 3, 9, 1], and systematize early report of temporal alignment between language models and the brain [4, 6].

A shared computational path. We show that temporal scores is independent of the word predictability (Fig. 5). Beyond indicating the autoregressive generation capacities of the LLM, the temporal score thus seems to indicate its brain-like inference dynamics. Additionally, as previously reported [2, 4, 3, 37], the best alignment scores are achieved in the intermediary layers. This result suggests that the intermediary representations (layer=0.6 of architecture) - as opposed to the input and the prediction - are best aligned between brains and LLMs. Finally, while part of the alignment score can be explained by the latent representations present from the very first layer - accounting for the relatively high alignment score observed from the first layers of the LLM - the temporal score accounts for evolving representations, a sequentially ordered mechanism of processing. Together, these results indicate that the brain and LLMs don't just share similar representations, but a similar computational path too. The increase of alignment score in more brain-like models may stem from a shared sequential structure of computations between the LLMs and the brain, as captured by the temporal score.

Impact of model architecture. Second, this LLM-brain alignment is observed in non-transformer architectures, such as state-space models. While other architectures (e.g. fully connected networks [38], Kolmogorov-Arnold Networks [39]) remain to be investigated, this finding suggests that the convergence between LLMs and the brain is architecture-independent. If confirmed, this result provides additional evidence that alignment score does not result from a trivial inductive bias. It remains to be investigated, however, whether it relates to model objective (next token prediction) or to the structure of language [40].

Impact of context and model size. Third, our experiments show that this alignment directly depends on (i) context size and (ii) model size - although with a saturation beyond 70B parameters models (App. D). These MEG results extend previous works on scaling laws in neuro-ai [37, 41, 42].

In particular, [37, 43] showed that LLMs that best predict functional magnetic resonance imaging (fMRI) responses to natural speech are those with the largest amount of parameters. In parallel, [44, 45] showed that context size improved the alignment between brain and fMRI. Here, we further show the effect of context size and model size to increase logarithmically, hence pointing to diminishing returns, if not a plateau. Extending our analyses to larger LLMs, e.g. Llama 3.3 70B, does not yield major improvement as compared to smaller LLMs e.g. Llama 3.2 3B, hence pointing to a plateau effect of scaling laws identifiable with MEG. Together, these findings clarify the specific conditions required for brain-like representations and computations to emerge. It remains unclear, however, whether context and size act directly on the alignment, or are confounded by other uncontrolled variables, such as linguistic performance. For example, in App. I, we find that performance is correlated with temporal alignment for specific conditions - LLM with increasing context lengths - but not others - LLMs with increasing sizes. Disentangling the causal chain that links these factors remains a major research avenue.

Working memory. Interestingly, the comparison between State Space Models (SSMs) and LLMs offers a new perspective to investigate context-size. Indeed, transformers compute contextual representations, thanks to a non linear combination of the past context, and hence require very large memory buffers that are implausible in the brain. By contrast, SSMs can be thought of as recurrent neural networks (RNNs) with hidden states that linearly evolve over time. At each time point, they thus represent the full context with their hidden state - an approach presumably similar to the brain. Our results show that SSMs do exhibit modest yet consistent improvements in temporal scores even at very large context sizes (App. E), and thus show that it is, in fact possible, to build and maintain a long context in a single hidden state. However, further research remains necessary to evaluate the degradation of such memory in the absence of meaningful context, such as during the memorization of a random digit sequence like a phone number - a task recognized as highly constrained in human subjects [46].

Limitations. Three main limitations should be highlighted. First, MEG has a limited spatial resolution of brain activity. This recording device does not allow a single-neuron recording, and is largely unable to pick up deep sources. Consequently, the precise neuronal bases of the present findings remain to be further explored with intracranial recordings. Second, the present dataset only consists of 3 subjects. This design choice was motivated by the fact that subjects each listen to 10 h of audiobooks, making this dataset the largest per-subject MEG listening dataset authors found, among multi-subject datasets. As alignment models are trained subject-wise, the biggest possible per-subject amount of data allows the most precise brain-alignment with LLMs dynamics. While App. G shows similar findings can be identified within each of these three subjects, how these alignments vary across individuals thus remains unknown. Finally, the present work only focuses on pretrained text-models. Yet, humans necessarily process language through sensory modality. Consequently, future work remains necessary to investigate the similarities between brains and speech models [47, 48, 5, 9].

Impact. The present findings reveal striking similarities between human brain responses and sequential representations in multiple kinds of large language models (LLMs). Yet, their architecture, sensory modality, learning objective, and training regime are remarkably disjoint [49, 50, 51, 52]. Furthermore, specific features like syntactic structures and semantic roles may be represented fundamentally differently [53, 54]. That partially converging computational paths arise despite these differences is thus all the more surprising, and highlights the necessity to clarify the computational principles that lead language processing to be partially shared between biological and artificial systems. We hope that systematically investigating the factors that steer LLMs to function similarly to the brain will help reduce this major gap, and ultimately chart a path toward building artificial systems that learn as efficiently as the human brain.

5 Acknowledgments

We warmly thank the authors of [10] for building the MEG dataset on which we relied for this study.

We wish to thank every member of Meta's legal team for helping us to process quickly through the legal process in order to submit in time to the Neurips 2025 conference.

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Supplementary Material and Technical Appendices

A Encoding scores for two LLMs.

Per-channel encoding scoresfor layer 0.9

Qwen1.5-7B

-1.0 s

0.2 s

1.4 S

Meta-Llama-3-8B

-1.0 s

0.2 s

1.4 s

B Temporal and alignment scores of bidirectional models.

Below are presented results for bidirectional LLMs BERT and RoBERTa, alongside bidirectional speech model Wav2vec2. All three models show alignment scores comparable to larger, more recent and causal LLMs, but much lower, non-significant temporal scores.

Wav2Vec

google-bert/bert-base-uncased

FacebookAl/roberta-large

0.055

0.055

0.058

0.1

0.1

0.1

0.2

0.2

0.2

0.3

0.3

0.3

0.4

0.4

0.4

R

0.5

R

0.5

R

0.5

0.6

0.6

0.6

0.7

0.7

0.7

0.8

0.8

0.8

0.9

0.9

0.9

0.000

0.000

0.000

2

-1

Time (s)

0

1

2

3

2

1

Time (s)

0

1

2

3

2

-1

Time (s)

0

1

2

1.2

1.2

0.75

1.0

1.0

0.50

Layers

0.25

0.8

0.00

0.25

0.4

0.4

0.50

p=1.00

0.2

r=0.38

p = 0.31

0.2

r=-0.18

p=0.64

0.75

0.03

0.05

0.07

0.09

0.11

0.0

0.4

0.42

0.45

0.47

0.49

0.46

0.47

0.49

0.51

0.52

Tmax (s)

Tmax (s)

Tmax (s)

C Alignment scores across subjects, per LLM

Qwen1.5-7B

Mistral-7B-v0.1

Gemma-7b

一

Meta-Llama-3-8B

Llama-3.2-3B

Llama-3.2-1B

Recurrentgemma-9b

Mamba-1.4b-hf

Gpt2-xl

0.00

0.02

0.04

0.06

0.08

R

D Temporal and alignment scores of the largest models

meta-llama/Llama-3.3-70B-lnstruct

mistralai/Mixtral-8x7B-v0.1

0.058

0.056

0.1

0.1

0.2

0.2

0.3

0.3

0.4

0.4

R

R

0.5

0.5

0.6

0.6

0.7

0.7

0.8

0.8

0.9

0.9

0.000

0.000

-2

-1

0

1

2

3

-2

-1

0

1

2

3

Time (s)

Time (s)

1.0

0.8

0.8

0.6-

Layers

0.4

/ers

0.6

0.4

0.2

0.2

0.0

r = 0.93

r =0.88

-0.2

0.0

p = 2e-04

p =2e-03

-0.4

-0.2

0.46

0.5

0.54

0.58

0.63

0.48

0.51

0.55

0.58

0.61

Tmax (s)

Tmax (s)

E Temporal alignment increases with the length of the context provided to State-Space Model Mamba-1.4B

C

A 1-word context

10-word context

500-wordcontext

1000-wordcontext

0.035

0.1

0.052

0.055

0.055

1.2

0.070

0.000

2

0.9

0.2

0.3

0.4

0.6

0.7

0.8

0.5

-1

Time (s)

0.000

-2

0.7

0.1

0.2

0.3

0.8

0.4

0.5

0.6

0.9

-1

Time (s)

0

0.000

2

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-1

Time (s)

0.000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

-1

Time (s)

0

1

Temporal score

1.1

1.0

0.9

Temporal score: r = 0.87, p = 0.03

Alignment score: r = 0.78, p = 0.07

0.82

0.91

0.91

E6°0

0.065

0.060

lignment score

B

1.0

0.5

1.4

1.2

1.0

0.8

1.2

1.0

1.0

1.2

0.8

0.70.65

0.61

0.050

0.055

Layers

0.5

1.0

0.0

= 0.65

p = 0.06

0.2

0.4

0.0

0.48

0.52

Tmax (S)

0.56

= 0.82

p = 7e-03

9'0

0.63

0.4

0.2

0.51

0.55

Tmax (S)

0.6

= 0.91

p = 6e-04

0.64

0.69

0.2

0.4

0.51

0.55

Tmax (S)

0.6

= 0.93

p = 3e-04

0.65

0.69

0.6

Context length in words (Log)

0.045

0.47

0.49

0.51

0.55

Tmax (s)

F Human brain and LLMs exhibit temporal alignment. Analysis performed for all types of words: function and content words

To ensure the processing of the most semantically meaningful words, we often study only content words in the main text of this paper (as opposed to function words), specifically those which belong to the following part-of-speech categories as defined by Spacy: NOUN, VERB, ADJ, ADV. Here, we test for replicability of findings for all words (function and content).

Analysisonbothcontentandfunctionwords

A

Decoding brain-scores of layers along time,all models

0.1

B

1.2

Correlation between LLM depth and Tmax all modelsC

Qwen1.5-7B

Temporal alignment with the brain across LLMs

· 0.91 *

Brain-scores

0.05

0.04

0.2

0.3

0.4

0.5

0.6

0.7

1.0

0.8

Meta-Llama-3-8B

Mistral-7B-v0.1

Gemma-7b

· 0.75 *

·0.8 *

· 0.87 *

0.03

0.8

0.9

Llama-3.2-3B

· 0.66

0.02

Unpretrained

layers

Llama-3.2-1B

·0.65

0.01

0.4

r=0.96

Recurrentgemma-9b

·0.93 *

0.2

p=5e-05

Mamba-1.4b-hf

· 0.79 *

0.00

2

1

Time (s)

0

1

2

0.31

0.36

Tmax (s)

0.41

Gpt2-xl

0.6

0.7

TemporalPearsonscore

0.8

·0.8*

0.9

1.0

D

0.1

Qwen1.5-7B

Mistral-7B-v0.1

Gemma-7b

Meta-Llama-3-8B

Llama-3.2-3B

Llama-3.2-1B

Recurrentgemma-9b

Mamba-1.4b-hf

Gpt2-xl

0.2

.057

.057

.057

.057

.056

.056

.057

.053

.059

0.3

0.4

Brain-scores

0.5

0.6

0.7

0.8

0.9

Unpretrained

2

Time (s)

0

2

-2

Time (s)

0

2

-2

Time (s)

0

2

=2

Time (s)

0

2

=2

Time (s)

0

2

2

Time (s)

0

2

=2

Time (s)

0

2

2

Time (s)

0

2

Time (s)

0

layers

G Human brain and LLMs exhibit temporal alignment - Subject 1, 2 and 3 plotted individually

We ensure reproducibility of results presented in the main text of this paper - averaged across subjects - this time for individual subjects.

Subject 1

0.05

0.04

Decoding brain-scores of layers along time, all models

0.2

B

0.8

1.0

Correlation between LLM depth and Tmax, all models

Mistral-7B-v0.1

Qwen1.5-7B

Gemma-7b

Temporal alignment with the brain across LLMs

· 0.95 *

· 0.95 *

· 0.95 *

0.6

Meta-Llama-3-8B

· 0.96 *

0.8

0.9

0.4

Llama-3.2-3B

· 0.91 *

Llama-3.2-1B

· 0.85 *

0.01

0.2

r = 0.99

Recurrentgemma-9b

· 0.87 *

0.00

0.0

p = 7e-07

Mamba-1.4b-hf

· 0.96 *

2

1

Time (s)

0

0.46

0.51

0.56

Tmax (S)

0.61

0.66

0.71

Gpt2-xl

0.8

Temporal Pearson score

0.83 *

0.9

1.0

1.1

D

0.1

.059

Mistral-7B-v0.1

.059

Gemma-7b

Meta-Llama-3-8B

50

ES0

Llama-3.2-3B

.055

Llama-3.2-1B

Recurrentgemma-9b

054

Mamba-1.4b-hf

ES0

Gpt2-xl

0.9

ained

Time (s)

Z-

Time (s)

-2

Time (s)

2

Time (s)

2

Time (s)

Time (s)

Timne (s)

-Z

Tine(s)

Subject 2

0.05

Decoding brain-scores of layers along time, all models

B

1.2

Correlation between LLM depth and Tmax, all models

Mistral-7B-v0.1

Qwen1.5-7B

Temporalalignment with the brain across LLMs

· 0.84 *

· 0.86 *

0.04

1.0

Gerfr63-7b

Meta-Llama-3-8B · 0.76 *

uneretr

0.6

Llama-3.2-1B 0.75*

Llama-3.2-3B

· 0.96 *

0.01

0.4

r = 0.97

Recurrentgemma-9b

· 0.84 *

0.00

0.2

p = 2e-05

-1

Time (s)

0

0.45

0.5

Tmax (s)

0.55

Gpt2-xl

0.8

0.8 *

Temporal Pearson score

0.9

1.0

1.1

D

.058

Mistral-7B-v0.1

.057

Gemma-7b

Meta-Llama-3-8B

.053

Llama-3.2-3B

.053

Llama-3.2-1B

Recurrentgemma-9b

052

Mamba-1.4b-hf

.054

Gpt2-xl

Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Time (s)

Subject 3

A

0.06

0.05

Decoding brain-scores of layers along time, all models

.2

B

1.0

Correlation between LLM depth and Tmax ll models

C

Mistral-7B-v0.1

Qwen1.5-7B

Temporal alignment with the brain across LLMs

· 0.91 *

• 0.92 *

0.8

Gemma-7b

· 0.96 *

0.7

Meta-Llama-3-8B

Llama-3.2-3B

· 0.94 *

· 0.94 *

0.02

0.4

Llama-3.2-1B

· 0.95 *

0.01

0.2

r = 0.99

p = 3e-07

Recurrentgemma-9b

Mamba-1.4b-hf

• 0.91 *

· 0.94 *

0.00

-1

Time (s)

0

0.0

0.46

0.51

0.56

Tmax (S)

0.61

0.66

Gpt2-xl

0.8

0.81 *

Temporal Pearsonscore

0.9

1.0

1.1

D

0.2

0.1

Qwen1.5-7B

062

Mistral-7B-v0.1

.064

Gemma-7b

064

Meta-Llama-3-8B

.061

Llama-3.2-3B

.062

Lama-3.2-1B

Recurrentgemma-9b

.057

Mamba-1.4b-hf

.065

.057

Gpt2-xl

0.3

0.5

0.6

0.8

0.7

0.9

=Z

nime (s)

Time (s)

ime (s)

Time (s)

-

(s) awy

Correlation between time of peaking alignment scores ( T max, x-axis) and layer depth shows a highly significant temporal alignment. A. Alignment scores of 9 representative layers across each of the 9 studied LLMs, as a function of word-onset (t=0). Alignment scores have been averaged across models. B. The time steps of peaking alignment scores ( T max, x-axis) are plotted for each representative layer (y-axis), averaged across models. The Temporal score r and associated P value are printed on the figure. The grey area indicates the confidence intervals of the regression estimate. Here, colored error bars indicate standard deviations of the layer-wise distributions of T max across the 9 presented models. C. Temporal scores are computed and presented for each model studied independently. An asterix next to the Temporal score indicates the score is significant with P value < 5e-3. D. Alignment scores of 9 representative layers across each of the 9 presented LLMs, as a function of word-onset (t=0). Each figure presents one model studied independently.

H Temporal alignment holds independently of word predictability

When computing the Pearson correlation of the layer-wise differences of T max between "most expected" and "most surprising" quartiles, we do not find a significant impact of contextual predictability on Temporal score. To ensure this result, we perform here the same analysis on twice as many layers across the depth of Llama-3-8B architecture.

A

B

Quartilemostexpected

Quartilemostsurprising

.056

.061

Brain-scores

E

onTemporalalignment

Layer 0.05

1.0

Layer 0.1

Layer 0.15

-2

-1

0

1

2

3

-2

-1

0

1

2

Layer 0.2

Layer 0.25

C

Time (s)

D

Time (s)

yers

0.6

Layer 0.3

Layer 0.35

Layer 0.4

1.0

1.0

.4

Layer 0.45

Layer 0.5

.2

-0.07

Layer 0.55

Layer 0.6

p =0.76

Layer 0.65

5

0.5

.0

-0.08

0.06

0.04

Difference in Tmax

0.02

0.00

0.02

0.04

Layer 0.75

Layer 0.8

Layer 0.7

0.0

r = 0.91

p = 4e-08

0.0

r = 0.89

p =3e-07

Layer 0.85

Layer 0.9

Layer 0.95

0.28

0.33

0.38

0.43

0.28

0.33

0.38

0.43

0.48

permuted_r

Tmax (s)

Tmax (s)

I Temporal scores along top-3 accuracies evaluated on our dataset for next-token prediction across LLMs

We find that performance is correlated with temporal alignment for specific conditions - LLMs with increasing context lengths - but not others - LLMs with increasing sizes. Regression estimates illustrate how scaling model size or context affect temporal alignment with brain activity. Increasing context lengths significantly correlates with temporal alignment (p = 0.01). Increasing model sizes does not significantly correlate with temporal alignment.

0.002

0.004

0.006

Top-3 accuracy

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Temporal score

r = 0.2

p = 0.64

A

pythia-14m_50

pythia-70m_50

pythia-160m_50

pythia-410m_50

pythia-1b_50

pythia-1.4b_50

pythia-6.9b_50

pythia-12b_50

Regression on Increasing size

0.00

0.02

0.04

0.06

0.08

Top-3 accuracy

0.4

0.2

0.0

0.2

0.4

0.6

0.8

1.0

Temporal score

r = 0.77

p = 0.01

B

Meta-Llama-3-8B_1

Meta-Llama-3-8B_2

Meta-Llama-3-8B_3

Meta-Llama-3-8B_4

Meta-Llama-3-8B_5

Meta-Llama-3-8B_7

Meta-Llama-3-8B_10

Meta-Llama-3-8B_20

Meta-Llama-3-8B_50

Meta-Llama-3-8B_100

Regression on Increasing context

J Licenses

The dataset used in this paper is licensed under a Creative Commons Attribution 4.0 International License.

Regarding the models used, here follows a list of their licenses:

Qwen/Qwen1.5-7B: Tongyi Qianwen License

mistralai/Mistral-7B-v0.1: Apache 2.0 License

google/gemma-7b: Gemma License

meta-llama/Meta-Llama-3-8B: Llama 3 Community License Agreement

meta-llama/Llama-3.2-3B: Llama 3.2 Community License Agreement

meta-llama/Llama-3.2-1B: Llama 3.2 Community License Agreement

google/recurrentgemma-9b: Gemma License

state-spaces/mamba-1.4b-hf: Apache 2.0 License

openai-community/gpt2-xl: MIT License

Pythia family of models: Apache 2.0 License

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper focuses on the temporal alignment of LLMs to human brain MEG responses, i.e., later model layers show greatest representational similarity to neural responses at greater times past word onset compared to earlier model layers (temporal alignment). They conduct many analyses to study how temporal alignment of LLMs is related to overall brain alignment, context size, and model size.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Interesting finding that LLMs exhibit temporal alignment to human brains. \n- Interesting findings that temporal alignment is correlated to overall brain alignment, context size, and model size\n\nWeaknesses:\n- Pearson correlation of roughly 0.06 as the maximum across different models is quite low, it would be helpful if the paper clarified what the noise ceiling is for the data, to make it easier to understand how well these models are predicting the neural responses.\n- \"To ensure that the strong Temporal scores we find are not led by this parallel between predicting of next word by the model and hearing of next-word by the human, we study the impact of next words’ predictability on temporal alignment.\" However, I think one alternative hypothesis is that the model's next-word prediction aligns with the humans' next-word prediction, not next-word hearing. Since deeper model layers build up representations for next-word prediction, it might align more to next-word prediction that humans are doing, i.e., it might simply suggest a similar objective of next-word prediction, not a similar order of computations between LLMs and humans. Having a similar objective would also explain that there is no significant layer-wise difference of T-max between \"most expected\" and \"least expected\" quartiles, because humans might also find the model's \"most expected\" words to be more expected and the model's \"least expected\" words to be less expected.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper generates a number of language encoding models for MEG and then measures what they refer to as \"temporal alignment\" between the models and the brain. In particular they show that earlier layers of language models have earlier alignment peaks (the time at which the encoding performance is highest) than later layers.  They further show that this correspondence is strongest in largest models, scales with context size, and that this temporal alignment score is itself correlated which general encoding performance.\n\nSTRENGTHS AND WEAKNESSES:\nMy primary concern here is about the providence of the temporal alignment score that the authors heavily base their paper around. The observation that later layers of LMs model later times in the brain is not really that surprising - the layers are more lexical (and therefore contain information that is earlier in the language processing pipeline) in the early stages of a model and more contextual in the later layers of a model, which is a natural consequence of the hierarchical manner in which LLM architectures build up their representations. In a similar way, auditory cortex (earlier) is more lexical than prefrontal cortex (later).  In addition to being unsurprising, the observation is also not particular novel; a number of the papers that the authors cite contain the same basic trend. The observation that larger models are more \"temporally aligned\" than smaller models is not something I have explicitly seen mentioned before, but it is also not something I find especially notable, mostly because I would expect larger models to have a more finely-tuned and elongated processing pipeline simply by virtue of being less constrained in parameter count. There is also limited-to-no attention given in the paper to the now fairly extensive literature that already exists regarding scaling laws for encoding models, although I consider this a minor point.\n\nAs another minor point, I was somewhat confused about what the brain-score number that is being plotted is. I understand it to be some sort of Pearson correlation metric, however it is unclear how this metric is averaged across sensors. My assumption is that the Pearson correlation is independently computed for each sensor and then averaged across the brain, since this is the general convention in this field, although please correct me if I am wrong. \n\nI do think the paper is very well written and exceptionally clear. I have little to say about obvious scientific mistakes that are made by the authors. Overall, my general opinion is that this paper has no major scientific issues, but that the results are somewhat low impact given that they appear to be something of a replication. I have no problem with replications, but I am also somewhat unsure whether they belong at NeurIPS. I have given the paper a 3.\n\nQUESTIONS:\nPlease clarify the method of computing brain-score. The existing paragraph is somewhat inadequate. \n\nSince everything appears to be averaged across sensors, it is unclear to me what the spatial patterns driving this temporal alignment are. It would be nicer to see how this temporal alignment shifts on a per-sensor basis. I think this would be more along the lines of something that I might be interested to see. The general lack of spatial specificity in the results here is something that could be improved. \n\nAre the temporal alignment trends observed in the paper a property of LLMs specifically? Do the same results apply to speech models like Whisper or Wav2Vec?\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper investigates the alignment between the computations performed by large language models (LLMs) and the processing observed in the human brain during language comprehension. The authors explore whether the sequence of activations in LLMs, as they process natural language, corresponds to the temporal order of brain activity measured using MEG as individuals listen to an audiobook. The study compares neural dynamics and LLM activations across different model architectures and sizes. The study reports strong temporal alignment. Early layers of the LLMs align with early brain responses, while deeper layers align with later brain activity, a pattern consistent across both transformer and recurrent architectures.

STRENGTHS AND WEAKNESSES:
This is a very timely and interesting study that sheds light on high-level simialrities between human and machine information processing.

The paper is well written and clearly structured.

I appreciate the broad model comparison but would have loved to see a larger model included to see whether the trend outlined in figure 2c continues for models in the 70B parameter neighborhood.

It would have been interesting to contrast the processing order of humans and large models with older ones such as RNNs to test whether processing order is simply always like this or whether generative models are truly different and more similar to humans.

Sec 3.2 and Fig 4: It would have been interesting to study very long contexts for SSMs such as Mamba that are not, in principle, limited at all in permissible context length.

QUESTIONS:
No additional questions.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to assess whether brain---neural network representation alignment is a consequence of the two systems performing similar computations. To this end, authors make a clever experimental design wherein temporally-resolved brain signals are matched with model representations. If representations generate in a similar order, the alignment across layers will reflect that---a finding authors make.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths.** I really enjoyed the paper! While arguably the experiments are behavioral in nature, i.e., merely representational alignment measures are used, I found the experimental design very well motivated. The findings themselves are reasonable, though I am somewhat confused why better performing models (often the larger one) show a better temporal alignment score (see questions).\n\n**Weaknesses.** I don't have much to note here. Adding some very minor points below.\n\n- I think the writing can improve on points of discussion w.r.t. past work. For example, on page 4, in section 3.2, I expected authors would need to discuss working memory constraints, since a Transformer's ability to (in theory) keep track of infinitely long contexts is often used to argue against their use for neuroscientific comparisons between LLMs and human brain. Similarly, the impact of model scaling would be worth discussing at several points: authors often saw larger models are performing equally well and scoring high on temporal alignment, which stands in contrast to expectations one would have based on past work that shows scaling model does not improve brain alignment.\n\n- Figure 6's design is somewhat awkward. Individual markers are different models and colors are model families. Connecting these markers with lines does not make sense if you're not trying to put together a trend. Arguably, that single plot in Figure 6a should be three separate plots.\n\nQUESTIONS:\nBuilding on one of the minor weaknesses, I would appreciate authors' thoughts on why they found model scaling to not be harmful for achieving temporal alignment. This is in contrast to prior finding on brain representational alignment. My working hypothesis would be that a good modeling of context is necessary to get good temporal alignment, which is not the target of analysis of past work, and hence scaling models eventually saturates alignment score. Alternatively, it could be the case that the temporal alignment score is too generously defined, i.e., it is trivial to achieve high scores on it. Having baselines, e.g., random or relatively less competent models (e.g., GPT-2 small), could help assess this.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}