Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain
Authors: Jingmin An
Yilong Song
Ruolin Yang
Nai Ding
Lingxi Lu
Yuxuan Wang
Wei Wang
Chu Zhuang
Qian Wang
Fang Fang
Venue: NeurIPS 2025 poster ()
Keywords: Syntactic structure processing
LLM
Syntactic alignment
Primary area: neuroscience_and_cognitive_science
TL;DR: HFTP aligns syntactic processing representations in both large language models (LLMs) and the human brain

Abstract: Large Language Models (LLMs) demonstrate human-level or even superior language abilities, effectively modeling syntactic structures, yet the specific computational units responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human brain. To address these questions, we introduce the Hierarchical Frequency Tagging Probe (HFTP), a tool that utilizes frequency-domain analysis to identify neuron-wise components of LLMs (e.g., individual Multilayer Perceptron (MLP) neurons) and cortical regions (via intracranial recordings) encoding syntactic structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human brain relies on distinct cortical regions for different syntactic levels. Representational similarity analysis reveals a stronger alignment between LLM representations and the left hemisphere of the brain (dominant in language processing). Notably, upgraded models exhibit divergent trends: Gemma 2 shows greater brain similarity than Gemma, while Llama 3.1 shows less alignment with the brain compared to Llama 2. These findings offer new insights into the interpretability of LLM behavioral improvements, raising questions about whether these advancements are driven by human-like or non-human-like mechanisms, and establish HFTP as a valuable tool bridging computational linguistics and cognitive neuroscience. This project is available at https://github.com/LilTiger/HFTP.

Hierarchical Frequency Tagging Probe (HFTP): A Unified Approach to Investigate Syntactic Structure Representations in Large Language Models and the Human Brain
Jingmin An', Yilong Song', Ruolin Yang', Nai Ding". Lingxi Lu² Yuxuan Wang', Wei Wang', Chu Zhuang's Qian Wang' it Fang Fang
*Peking University, *Zhejiang University, "Beijing Language and Culture University, *Beijing Institute for General Artificial Intelligence anjmsstu. pkt.l.kk.ddt. cn, {wangqianpsy, ffang (ang)epku.ediee eackku.edu.com
Abstract
Large Language Models (LLMs) demonstrate human-level or even superior lan- guage abilities, effectively modeling syntactic structures, yet the specific com- putational units responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human brain. To address these questions, we introduce the Hierarchical Frequency Tagging Probe (HFTP), tool that utilizes frequency-domain analysis to identify neuron-wiss wise components of LLMs (c.g., individual Multilayer Perceptron (MLP) neurons) and cortical regions (via intracranial recordings) encoding syntactic structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human brain relies distinct cortical regions for different syntactic levels. Representational similarity analysis reveals stronger alignment between LLM representations and the left hemisphere of the brain (dominant in language processing). Notably, upgraded models exhibit divergent trends: Gemma shows greater brain similarity than Gemma, while Llama 3.1 shows less alignment with the brain compared to Llama 2. These findings offer insights into the interpretability of LLM behavioral improvements, raising questions about whether these advancements are driven by human-like or non-human-like mechanisms, and establish HFTP valuable tool bridging computational linguistics and cognitive neuroscience. This project is thub. com Litiiigr//Te/PP
available httpp:://// tht.bb...ttttt...//gg
Introduction
Language is fundamental to human communication, thought, and cultural transmission. According to the framework proposed by Noam Chomsky, language is divided into three key components: seman- ties (meaning), phonology (sound), and syntax (hierarchical sentence structure) [Chomsky] 1965).
Syntax is particularly crucial as it governs how words combine into meaningful expressions, under- pinning the recursive and generative capacity unique to human language. The theory of Universal Grammar proposes that all human languages share innate structural principles [Chomsky 1980). Building on this foundation, cognitive neuroscience has shown that syntactic processing recruits mechanisms distinet from other linguistic functions, particularly within the left inferior frontal and posterior temporal regions [Matchin and Wood 22000] Friederici] 2011]. Moreover, increases in syntactic complexity yield graded activation in left inferior frontal and posterior temporal cortices, consistent with the computation and maintenance of hierarchical dependencies rather than simple lexical associations [allier al. 2011 Nelson al. 2017. As our understanding of human syntactic
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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computation deepens, artificial intelligence models have increasingly sought to emulate this ability to capture and represent structured language.
In recent years, large language models (LLMs)) have evolved rapidly, achieving human-level or better performance on range of linguistic benchmarks and professional exams (Achiam et al. [023) Their success in understanding, translation, and summarization has led to claims of human- like fluency, particularly in generating text that conforms
surface syntactic regulari- ties [Mahowald et al. 2024] He et al., 2024 Van Veen et al., [022]. Yet it remains unclear whether such models truly represent the hierarchical sentence structures that characterize human syn- tax. Some findings indicate that LLMs can implicitly capture and manipulate structural rela- tions [Manning et al. 2020], while others suggest their success can rely on shallow statistical heuris- ties rather than genuine structural understanding [Linzen et al., [016]] McCoy et al. 2019]. This ongoing debate underscores the need for unified analytic framework capable of directly comparing syntactic representations in human and model systems, which is essential for probing the depth and nature of syntactic alignment between the human brain and artificial models.
Ding et al. [Ding et al. 2016) introduced the hierarchical frequency tagging (HFT) technique to uncover how the human brain processes hierarchical linguistic structures during natural speech comprehension. In this paradigm, monosyllabic words are presented at rate of 4 Hz to form phrases at 2 Hz, which combine into sentences at Hz. Using requency-domalie analysis of clectrophysiological signals, Ding et al. deconstruct the processing of linguistic structures such as phrases and sentences. Subsequent work has extended the HFT framework along complementary axes. Attention is required to group lower-level inputs into higher-order linguistic units, and divert- ing attention attenuates word- and phrase-rate tracking (Ding et al.) 2018). MEG source analyses dissociate cortical signatures for word- versus phrase-level rhythms and link phrasal tracking to comprehension [Keitel et al., 2018). Computational modelling shows that oscillatory architectures can implement hierarchical parsing and reproduce HFT-like spectra [Martin and Doumas 2017]. Natural-speech EEG reveals endogenous word-rate tracking that interacts with exogenous rhythmic cues (Luo and Ding 2020). Naturalistic experiments show that phrase-rate tracking reflects inter- nally generated structure rather than compositional meaning per se, yet remains sensitive to the lexical-syntactic cues that support structure building [Coopmans et al. 2022). Together, these studies demonstrate that HFT effectively isolates neural markers of hierarchical language processing.
Building on hierarchical Dequeecy-tagginn (Ding et al., 2016]. we introduced the Hierarchical Frequency-Tagging Probe (HFTP), a unified framework that probes internal representational structure and systematically assesses the alignment of syntactic representations between LLMs and the human brain. The key contributions of this paper are: (i) We innovatively employed frequency-domain analysis using HFTP to characterize the syntactic structure representations of every computational unit in each layer of LLMs; (ii) HFTP provides a simple, universally applicable approach for detecting and aligning syntactic structure representations in LLMs (via neuron-wiss probing) and the human brain (via population-leve syntactic templates derived from HFTP. we identified brain regions highly correlated with LLMs, predominantly located in key imguage-processiie
analyses), and extends seamlessly to naturalistic text. (iii) Using
areas of the left hemisphere; (iv) By comparing six LLMs, we observed divergent trends in upgraded versions, with some showing increased similarity to brain representations while others exhibited reduced alignment. In sum, HFTP effectively detects syntactic structure representations in both LLMs and the human brain, providing a novel framework for alignment study.
Related work
Syntactic processing in the human brain In humans, syntactic processing recruits a left- dominant fronto-temporal network that supports hierarchical combination from finite ele- ments. A series of classic lesion and neuroimaging work documents a seft-hemisphere advan- tage [Friederici and Brauer [2009] [Hagort| [003]] Blank al.12016), with hemispheric temporal sensitivities aiding speech segmentation [Albouy et al.) 2020]. Converging evidence shows that syntactic operations are distributed across frontal and temporal cortex with substantial overlap with fMRI further indicates that hierarchically structured strings reliably engage left inferior frontal gyrus and posterior
semantic integration [Blank et al. 2016 Fedorenko et al. 2020 Artificial-graman
superior temporal regions [Chen al. 2021). Overall, syntactic processing relies on a left-dominant,

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distributed fronto -empora network that substantially overlaps with semantic integration, rather than single locus.
Syntactic processing in language models Early work shows that LSTM language models able to capture syntax-sensitin dependencies, such the phenomenon of subject-verb agreement [Linzen et al., 2016, Kuncoro et al. 20188. Using the structural probe, a landmark study demonstrates that transformer-based models such BERT encode hierarchical syn- lactic trees, enabling these models to represent complex syntax without explicit supervi- sion [Hewitt and Manning 2019]. Transformer-bass based models excel tracking both local and long- range dependencies through specialized attention mechanisms and distribute syntactic knowledge across layers [Clark] 2019, Tenney et al.) 2019 Manning al. [2020]. However, methodological choices in these language- language-model model studies make it challenging to directly relate the findings to human brain activity.
Alignment between LLMs and the human brain A growing body of work shows that sentence-level contextual embeddings from predictive language models strongly predict cortical responses during comprehension [Sun et al. 2020 Schrimpf et al. 2021]. Disentanglement analyses that factorize activations into lexical, compositional, syntactic, and semantic components indicate distributed contributions-ofii with compositionallexicaa signals explaining much of the alignment, rather than syntax alone [Caucheteux et al. 2021 Caucheteux and King 2022]. Neuroimaging manipulations that dissociate semantics from syntax reveal distinct patterns (including frontal engagement) without establishing syntactic dominance (Wang et al. 2020]. Intervention studies that selectively remove linguistic properties from model representations yield reliable drops in brain alignment, with syntactic properties (e.g., tree depth, top constituents) exerting large cross-layer effects [Oota al. 2023). These findings are consistent with convergence on shared representational axes across brains and models [Hosseini et al. 2024] and with demonstrations that model-derived stimuli can causally drive or suppress the human language network (Tuckute et al. [024], while alignment varies systematically across layers as contextual information accrues [Goldstein et al. 2022]. However, methodological heterogeneityySSpannee
representation choices, tokenization, brain-model alignment metrics, and control mmayses-Prrecudees systematic comparison of how syntactic structure is encoded across models and neural populations. A unified analytical framework is therefore needed to harmonize methodological dimensions and support comparable, interpretable cross- cross-system evaluations.
A
Sentence
Sentence
1H
Sentence
Sentonce
Hr
N phrase
phrase
N phrase
V phrose
H
4
rude dogs apes hunt bugs
(Trensiation: Glant penda falls esleap tourists climbing mountains ...)
B pre-trained LLMs
model activation
Fouseer burafomm
D
Sentence MLL Neuron
Phrase MLP Nouron
AnD"
(rie)
brain activities
tranform
human brain
Sentence sEEG charnel
Phrase SEEG channel
Ano
Sentenco Ptrase
Frrn (19)
Figure I: A framework for Hierarchical Frequency Tagging Probes (HFTP) and an illustration of computational units involved in different levels of hierarchical linguistic processing in both LLMs and the human brain. A, hierarchical linguistic structure in English and Chinese including syllable, phrase, and sentence. B. hierarchical linguistic pattern (1 Hz: sentence feature, 2 Hz: phrase fcature) observed both in LLMs and C, human brain.
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3 Methods
We present the framework of the proposed HFTP methodology (see Figure D). This framework is organized into four parts: Section 3.1 describes the syntactic corpora used and the LLM architectures; Section 3.2 details the syntactic structure probe LLMs to detect syntactic units; Section 3.3 explains the syntactic structure probe applied to human intracranial sterco-electroenephalloggg data; and Section 3.4 syntactic structure representations in LLMs and the human brain by comparing resquency-domaatee representations and detecting similarities in how syntactic structures are encoded across both systems.
(sEEG)
3.1 Data and LLMs
We mainly utilized Chinese and English corpora adapted from (Ding al. 2016], consisting of four-syllable sequences in Chinese or four-word sequences in English, where the first two and last two units form phrases (see Figure [ Further details regarding the corpus can be found in Appendix [H] We also adopted naturalistic texts to test the generalizability of our HFTP method (see E). For both the sEEG and sentence and phrase corpora- frrm (Sheng et al. 2019). While these corpora share similar structure the Chinese syntactic corpus used in the LLM experiments, they differ in content. Guided by evidence that periodic lexical regularities alone can produce peaks at word-, phrase-, and sentence- word-order-randomizze control
alignment experiments, we used the same two Chinese corpora- the
rate frequencies [Frank and Yang 2018), we added a within-sentenee in all experiments: items are permuted across positions so that lexical and part-of-speech categories do not recur at fixed positions and any consistent phrase- or sentence- patterns are prevented, while the lexical set is preserved. This control isolates the syntactic and lexical contribution to 1, 2, and 4 Hz power.
We applied HFTP to six LLMs of varying architectures and sizes-GPP- Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 (see T). Note that the GPT-2 model we used is Chinese-pre- trained and supports both Chinese and English. To avoid tokenization artefacts, we average MLP activations over sub-tokens at the syllable level for Chinese and word level for English. This ensures that the 1 Hz and 2 Hz spectral components reflect linguistic boundaries rather than tokenization boundaries, enabling consistent cross-lingual and cross-model comparisons. Notably, the term "MLP neuron" denotes computational unit in the intermediate hidden layer of the MLP sub-layer within a Transformer model. This sub-layer consists of two linear transformations separated by a nonlinear activation function. We target MLP neurons because they house localized, interpretable units- as "knowledge neurons" [Dai et al. 202T] that causally control factual recall and eexical-syntactte concepts (Geva al. 2022 This mechanistic specificity provides the discrete, concept-aware handles required by HFTP to robustly localize syntactic structures.
3.2 Syntactic structure probe in LLMs
For each LLM, sequences from the Chinese and English corpora were concatenated separately into continuous texts to capture neural-like activations. During this process, each Chinese syllable (or English word) outputs an activation value, allowing the signal corresponding to every individual linguistic unit to be traced. These time-domain activations were then transformed into frequency- domain information via fast-fourier transform (FFT). Because LLM activations are indexed by token order rather than physical time, we imposed explicit 4 Hz clock and sampled at 4 Hz, which by Nyquist confines the analyzable band to 0-2 Hz. This explicit time axis mirrors prior continuous-time encoding approaches that reconstruct model features on a continuous timeline before comparing them with neural data (Jain et al.) 2020], thereby enabling temporally interpretable model-brain correspondence. This adjustment ensured that the syntactic rhythms analogous to those observed in human brain data could be captured within the model activations.
LLMs, with their multiple layers and thousands of MLP neurons per layer, require a systematic approach detect which neurons are responsible for either sentence or phrase processing. HFTP introduces a unified probe to detect significant syntactic processing units, applicable to both LLMs and human brain data. For the LLMs, we conducted a permutation test, randomizing the model activations derived from the structured input corpus 1000 times. The original frequency bins at 1 Hx and 2 Hz, representing sentence and phrase rhythms respectively (their real parts of amplitudes are denoted as real[amp (1 Hz)], real[amp(2 Hz)l), were compared to the 95% confidence interval (CI) generated

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by the distribution of permuted activations. Neurons whose real[amp(1 Hz)] and real/2 Hz)] values exceeded this threshold were classified as significant MLP neurons (see [T.. indicating their involvement in syntactic processing with statistical robustness against random noise.
Definition 1 (Significant MLP Neurons). For a fixed frequency f. a neuron significant MLP neuron, if and only if its FFT result satisfies
real(amp(f)) $ 95% CI of permuted distribution.
The set containing all the significant MLP neurons in terms of frequency f is denoted as Sy-
Since the significant MLP neurons are distributed almost uniformly across all layers, detecting the specific neurons that contribute to sentence and phrase processing requires a more objective and systematic method. We then applied z-scores to the FFT amplitudes at Hz and 2 Hz in both the experimental and control groups for all significant MLP neurons across layers. The z- score deviation (n) between the experimental and control groups was then calculated for each neuron. This deviation helps minimize semantic confounds by isolating frequency-specifi syntactic effects. Sentence and phrase MIP neurons were defined as those whose 7-scores deviated by more than two standard deviations from the mean, at Hz and 2 Hz, respectively (see 2).
Definition 2 (Sentence MLP Neurons and Phrase MLP Neurons). A neuron is defined as sentence/phruse MLP neuron if it satisfies
E ESS, 2)(n) 2 May + 20
(2)
where (n) denotes the z-score deviation of the FFT amplitude between experimental and control for neuron frequency f. U2,, denotes the mean 2-score across all neurons for the frequency f. ozy denotes the standard deviation of 2-scores across all neurons for the frequency f. and the frequency f is specified as 1 Hz and 2 Hz. for sentence and phrase MLP neuron respectively.
Following this, we identified and analysed sentence and phrase MLP neurons across layers and LLMs. with full details provided in Section 4.1 We also conducted bilingual experiments to assess the ability of different L.MSs to perceive syntactic structures across Chinese and English (see Appendix C..
3.3 Syntactic structure probe in the human brain
We recorded sEEG from 26 native Chinese speakers while they listened to two Chinese auditory corpora. In the sentence corpus, nine four-syllable sentences were concatenated per trial: in the phrase corpus, eighteen two-syllable phrases were concatenated. Each corpus comprised 40 trials per subject. Syllables were 250 long, and signals were sampled 512 Hz (2048 Hz for one participant). To minimize onset-related responses, subsequent analyses used only the final 32 syllables of each trial.
To analyze the sEEG data, we employed inter-trial phase coherence (ITPC), a frequency-doman method relatively resistant to noise that quantifies the consistency of phase relationships in oscillatory brain activity across multiple trials [Cohen] 2014). sEEG channel localization was performed similarly to previous studies [Xu et al. 2023 Wang al., 2024]: all channels were mapped to brain regions defined by the Automated Anatomical Labeling (AAL) system. We then grouped certain AAL regions to form 12 brain regions of interest (ROIs) (details in Appendix II. Subsequent experiments were conducted based on brain ROIs
As previously outlined, the HFTP proposes a unified syntactic structure probe for both LLMs and human brain data. For the human brain analysis, we employed the same permutation testing procedure on the time-domain sEEG data that captured cortical activity during listening to Chinese corpora. Specifically, ITPC results were randomized 1000 times for each channel each subject. The original frequency bins, real[smpl Hz)] and real[2 Hz)], were then assessed to determine whether they fell within the 95% confidence interval of the permuted ITPC distribution (see 3
Definition 3 (Sentence channels and Phrase channels). A channel e is defined as a sentence/phrase channel if its ITPC result satisfies
realfamp(f)] & 95% CI of permuted ITPC,
where f = 1 Hz for sentence channel and f Hz for phrase channel.
Using this probe, we identified and analyzed the distribution of sentence and phrase channels across various brain ROIs, with full details provided in Section 4.2
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Layer
pre-trained LLMs
condiaaon
Floganky
Similaiity
sentencee
Sindlarty
phrase
concitioon
Representational Similarity Analysis (RSA)
SRDM Electrodo N
sond
Ozsina Sintbekyy
humsen brain
sentence...
conditio
random word
Frequancy
Figure 2: Alignment pipeline between LLMs and the human brain. SRDMs computed for exclusive sentence/phrase and sentence&phrase MLP neurons and brain channels by comparing cosine similarities across different conditions. Subsequently, RSA (using Spearman correlation) is applied to quantify the similarity between the two SRDMs, thereby assessing the correspondence between model and brain representations.
3.4 Alignment of syntactic structure representations of LLMs with the human brain
To explore syntactic structure representation alignment between LLMs and the human brain, we compared their frequency-domain representations using the same sentence and phrase corpora as in the sEEG experiment. For each computational unit (a MLP neuron in LLMs or an sEEG channel in the human brain), we extracted amplitude in the frequency spectrum as the feature features,
vector. This approach creates multi-dimensiona. space based frequency-domin where each syntactic structure corresponds to specific point in this space (see Figure 2). We then computed the distances between these points for different syntactic structures within the same computational unit using cosine similarity. Through pairwise comparisons, we constructed Structure Representational Dissimilarity Matrices (SRDMs) for each computational unit, which similar to Representational Dissimilarity Matrices (RDMs) but specifically capture the representations of syntactic structures (Cichy al. 2014 Khaligh- Razavi and Kriegeskorte 2014). We then applied Representational Similarity Analysis (RSA) to enable cross-modal comparisons between LLMs and brain data, correlating the representations in both systems [Kriegeskorte et al. 2008). This approach quantified alignment and used statistical tests to detect significant overlaps. We introduced two measures: model-brain similarity S(m, b) and model-region similarity S(m, by). to evaluate alignment globally and in specific brain ROI. We also used the contribution ratio CR, to assess the impact of each region on the alignment (see Appendix D). For more details on the alignment pipeline, see Appendix A The comprehensive discussion of the alignment results can be found in Section 4.3 Additionally, as an orthogonal check, we implemented a predictive encoding analysis (see Appendix B to assess alignment independently of SRDM-RSA.
Experiments
We used HFTP to assess syntactic processing in the human brain and LLMs, and aligned their requenccydomain representations evaluate their similarity.
4.1 MLP neurons represent sentences and phrases in LLMs
Using the HFTP method, we identified neurons in all six models that selectively represent sen- tences (sentence ML.P neurons), phrases (phrase MLP neurons), and neurons that simultaneously represent both (sentence & phrase MLP neurons). In Figure 3 we highlight representative MLP neurons exhibiting four hierarchical frequency patterns: significant peak at the sentence frequency (fsentence 1 Hz), significant peak at the phrase frequency (fphrase 2 Hz), dual peaks both

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Sentence MLP neuron
Phrase MLP nsuron
Sentence Phrase ML nauron
Non sansitive MLP niuron
1.4
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reperimnce
1.0
10
Normalized Amplityde
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Semoecco
Fstrass
Sontenes
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Espetence
Fptnos
Gestenne
Fytnse
0.6
0.b
0.6
0.4
0.4
0.2
2.0
Frequency (Hz)
Frequency (Hi)
Frequeeccy (Hzz)
Frequency
Figure 3: Hicrarchical frequency patterns of MLP neurons selectively represent sentence features, phrase features, shared features of both and non-sensitive feature (from left to the right). Here, "experiment" denotes the original corpus, while "random" indicates the randomized version. Shaded bands show +1 S.c.m. computed across 10 shuffled-impul activation partitions. Significant peaks ($p < 0.05, FDR corrected) indicate amplitudes stronger than neighboring frequencies within +0.5 Hz. "Normalized Amplitude" represents the curves and bands scaled range of 0 to 1.
Paskeaive chone revernollutrra Neadi
certerollatt ftaure
Znai
6.0
2
6.0
(a) GPT-2
(b) Gemma
(c) Gemma
Dotutiiv shroee Heuroea
thee sirore Heurera
6.55
Exckoins sertecee Muriona
6.4
Erck semorcce Muarem
Exclutic aal.uutsssttot. sontence Souerr
2015
600
(d) Llama 2
(c) Llama 3.1
GLM-4
Figure 4: Statistics of exclusive sentence/phrase MLP neurons and sentence & phrase MLP neurons in each layer across six LLMs.
Jsentence and Sphrases and no significant peaks. Frequencies beyond 2 Hz have been artificially set to zero for smoothness in the representation.
Figure shows the distribution of exclusive sentence/phrase neurons and sentence & phrase across different layers, based on experiments using the Chinese syntactic corpus (see Table All the models contain neurons dedicated to capturing sentences and phrases, demonstrating their ability to encode the syntactic hierarchies of human language. However, distinct distribution patterns suggest varied syntactic processing strategies: Llama and GLM primarily process syntactic information in later layers, reflecting more integrated approach. GPT, on the other hand, exhibits higher concentrations of sentence and phrase MLP neurons in middle layers, suggesting balanced intermediate strategy. In contrast, Gemma demonstrates a distinct preference for dense concentrations of neurons in the early layers, indicating an emphasis on initial-stage syntactic processing. These divergences may reflect where hicrarchical composition occurs: models that infer constituent structure from short-range cues may compute sentence/phrase integration carly and propagate it via residual paths, whereas models that rely on broader context may defer integration later layers. Training dynamics may further partition depth into composition versus context-mixing stages, reallocating syntactic signals across layers even under identical inputs.
comparative analysis reveals a significant decrease in the maximum proportions all syntactic ML. neurons in the Ilama and Gemma models, dropping from 11% in Llama 2 to 4.5% in Llama 3.1. 1, and from 27% in Gemma to 22% in Gemma 2. As Ilama 3.1 and Gemma 2 are updated versions of Llama 2 and Gemma, respectively, this trend suggests potential shift in computational resource
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Sentence sEEG dhanne
Phrase sEEG channel
Sentance & Phrase sEEG channel
Non sensitive sEEG channel
14
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Ce
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Frequency (Hz)
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Frequency
Figure 5: Hicrarchical frequency patterns of sEEG channels selectively represent sentence features, phrase features, shared features of both and non-sensitive feature (from left to the right). Here, "setence" denotes the original sentence corpus, while "random" " indicates the randomized version. Shaded bands show =1 s.c.m. computed across channels. Significant peaks (*p 0.05, FDR corrected) indicate amplitudes stronger than neighboring frequencies within =0.5 Hz. "Normalized ITPC" represents the curves and bands scaled to a range of 0 1.
allocation. To enhance performance on complex tasks, Llama 3.1 and Gemma 2 may reduce their specialized processing of syntactic structures (sentence and phrase), reallocating neurons to support these advanced capabilities.
Additionally, correlations between sentence- and phrase-selective MLP neurons across layers were observed in all six models, with high coefficients (GPT-2 (r = 0.754), Gemma (r 0.994), Gemma (r 0.994), Llama 2 (r = 0.912), Llama 3.1 (r 0.886), GLM (r 0.993)). This evidence indicates that the same layers are co-recruited for phrase- and sentence- processing, consistent with a shared compositional mechanism rather than segregated pathways.
4.2 Sentences and phrases representations in the human brain
Using the syntactic structure probe, we identified SEEG channels that selectively represent sentence- and phrase-level structure in the sentence corpus. Each channel reflects collective responses from nearby neuronal populations, providing high temporal resolution and sufficient spatial specificity. As shown in Figure 5 we found channels representing sentence and phrase, as well as channels with shared representations, alongside channels with no selectivity. These patterns parallel those in LLMs, indicating that HFTP effectively interrogates internal syntactic representations in both systems.
Amygdala
Hippocempas
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MFG
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Sensorimator
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pora
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Insu
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05
Rabio (Ler))
Rabio (Right)
(a) SEEG channel locations and Brain ROIS
(b) Significant SEEG channel distribution
Figure 6: (a) Brain ROIs of the left and right hemispheres used in this study. The black markers represent the sEEG channel locations across all participants. (b) Distribution of significant exclusive sentence/phrase and sentence & phrase channels (sentence corpus) in different brain ROIs.
Analogous to our analysis of the distribution of syntactic MLP neurons across LLM layers, we computed the proportions of sentence- and phrase-selective channels within each brain ROL. As shown in Figure 6 phrase channels decrease from lower layers (A1) higher layers (e.g., IFG), while sentence channels show the opposite trend, increasing at higher brain layers. This pattern
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Table Averaged top-100 100 Spearman correlations between sEEG-channel SRDMs and MLP-neuron SRDMs for the sentence corpus, separated by left (L) and right (R) hemispheres. '/' indicates that no channels from that hemisphere appear in the top-100 set. The first data row reports the overall model-brain similarity ((m, b): each subsequent brain ROI row reports the model-region similarity (m, by). Bold in the S(m, b) row flags the model with the highest overall alignment, while bold
entries within each brain ROI row mark the three regions most strongly correlated for that model.
GPT-2
Gemma
Gemma
Llama 2
Llama 3.1
GLM-4
R
L
R
L
R
S(m, b)
0.654
0.442
0.582
0.411
0.644
0.450
0.645
0.439
0.514
0.405
0.630
0.445
A1
0.683
0.423
0.642
0.358
0.702
0.333
0.649
0.547
0.514
0.403
0.664
0.374
STG
0.667
0.422
0.593
0.386
0.654
0.410
0.672
0.453
0.507
0.392
0.647
0.412
MTG
0.674
0.392
0.584
0.383
0.659
0.411
0.674
0,409
0.521
0,408
0.645
0.397
ITG
0,637
0,444
0.578
0.406
0.631
0.448
0.629
0,426
0.509
0,401
0.615
0.439
Insula
0,624
0.460
0.551
0.425
0.600
0.476
0.630
0,446
0.518
0.422
0.604
0.475
TPJ
0,610
0,452
0.566
0.373
0.641
0.410
0.619
0.400
0.518
0.408
0.606
0.438
Temporal Pole
0.648
0.473
0.556
0.470
0.643
0.558
0.610
0.469
0.494
0.448
0.616
0.483
Sensorimotor
0.637
0.462
0.567
0.426
0.622
0.448
0.624
0,446
0.505
0.396
0.617
0.463
IFG
0.694
0.463
0.603
0.466
0.670
0.496
0.665
0,491
0.513
0.410
0.646
0.490
MFG
0.615
0,436
0.557
0.401
0.585
0.489
0.597
0.367
0.510
0.397
0.588
0.473
Hippocampus
0.698
0,405
0.553
0.408
0.626
0.428
0.657
0.413
0.534
0.390
0.613
0,434
Amygdala
/
0.489
0.566
0.454
0.472
0.558
0.496
0.377
/
0.508
aligns with earlier MEG studies Sheng et al. (2019). supporting distinct processing mechanisms for sentences and phrases. Correlations between sentence and phrase channels across brain ROIs in both hemispheres revealed no significant relationship (left: r = -0.169, = 0.870; right: r = -0.1 197. p 0.539), suggesting that sentence and phrase processing operate independently. This contrasts with the behavior of LLMs, implying that while the human brain segregates sentence and phrase processing across different regions, LLMs integrate both syntactic levels within the same model layers. This highlights that the layered representations of LLMs may not align directly with the distinct processing roles observed in brain ROIs
4.3 Alignment of syntactic structure representations between LLMs and the human brain
As the layered representations of LLMs do not correspond directly to the distinct processing functions of different brain ROIs, we sought to investigate whether overall syntactic structure representations in LLMs are comparable to those in the human brain, both globally and across individual brain ROIs. To accomplish this, we used representational alignment to quantify correspondence between model and brain frequency-domain features. Detailed procedures
provided in Appendix
The alignment results across both sentence and phrase corpora revealed consistent patterns of model-brain correspondence. As shown in Tables aand 2 we observed that the similarity between LLMs and the left hemisphere is notably higher than that with the right hemisphere across both structure levels. Brain-wel wise one- one-way ANOVAs confirmed significant model differences for sentence- level (F 59.74, r/ 0.027. p < 0.001) and phrase-level processing (F 12.10, r2² 0.018, p < 0.001). Region- wise ANOVAS (FDR-corrected) further showed a strongly left-lateralized effect across corpora, concentrated in core language cortex (e.g., STG, MTG, IFG).
Examining individual model performance, GPT-2 exhibited the highest average correlation with human brain activity across both sentence (S,m, b) 0.654, denoted S: L) and phrase levels (S 0.654, L). Gemma 2 (S = 0.644, L for sentence; $ = 0.628, L for phrase) consistently outperformed Gemma (S = 0.582, L for sentence; S 0.575, L for phrase) at both corpora, attributed to architec- tural improvements [Tea et al. 2024a) Most notably, Llama 3.1 (S = 0.514, L for sentence: S= 0.522, L for phrase) showed consistently lower alignment than Llama 2 (S = 0.645, L for sentence; S= 0.648, L for phrase) across both processing levels. This counterintuitive pattern is explained at the pretraining level: Llama 3. was trained on a substantially larger corpus emphasizing code, reasoning, and multilingual text, which dilutes language-specific regularities. Additionally, extensive reliance on synthetic data for rapability-targeter curation introduces distributional shifts away from naturalistic language statistics Dubey et al. 2024 Touvron et al. 2023). These findings echo evidence

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Table Averaged top-100 Spearman correlations between SEEG~channel SRDMs and MLP-neuron SRDMs for the phrase corpus, separated by left (L) and right (R) hemispheres. "/' indicates that no channels from that hemisphere appear in the top-100 set. The first data row reports the overall model-brain similarity ((m, b); each subsequent brain ROI row reports the model-region similarity (m, by). Bold in the S(m, b) row flags the model with the highest overall alignment, while bold
entries within each brain ROI row mark the three regions most strongly correlated for that model.
GPT-2
Gemma
Gemma
Llama 2
Llama 3.1
GLM-4
R
L
R
R
Smm, b)
0.654
0.441
0.575
0.416
0.628
0.443
0.648
0.435
0.522
0.404
0.626
0.437
A1
0.669
0.420
0.610
0.315
0.665
0.326
0.628
0.576
0.526
0.372
0.627
0.374
STG
0.665
0.415
0.591
0.388
0.643
0.395
0.674
0.447
0.530
0,402
0.649
0.401
MTG
0.665
0.391
0.564
0.408
0.645
0.415
0.675
0,415
0.521
0.401
0.641
0.408
ITG
0.637
0,447
0.558
0.421
0.611
0.440
0.632
0,421
0.518
0.398
0.621
0.427
Insula
0,627
0.468
0.556
0.433
0.596
0.465
0.632
0,453
0.519
0.409
0.602
0.470
TPJ
0.616
0,442
0.563
0.405
0.608
0.423
0.615
0.384
0.542
0.399
0.588
0,433
Temporal Pole
0.680
0.453
0.555
0.394
0.626
0.501
0.623
0.456
0.505
0.450
0.583
0.442
Sensorimotor
0.645
0.475
0.565
0.443
0.607
0.449
0.629
0,442
0.511
0.391
0.613
0.460
IFG
0.700
0.450
0.635
0.424
0.656
0.491
0.674
0,488
0.514
0.436
0.636
0.480
MFG
0.618
0.409
0.562
0.421
0.583
0.500
0.601
0.365
0.505
0.379
0.577
0.475
Hippocampus
0.656
0.398
0,541
0.418
0.602
0.432
0.640
0.398
0.516
0.406
0.566
0.434
Amygdala
/
0.483
/
0.433
0.475
0.552
0.468
0.386
/
0.492
that scaling alone fails secure robust predicate argument structure especially for long-range, boundary-sensitive roles-revealing persistent saman-modke structural gaps [Cheng et al.) 2024].
Peedicate-argument
Additionally, the key brain ROIs for each model, which are primarily located in the left hemisphere, highlighted regions that are critical for syntactic processing at both levels of the syntactic hierarchy. These regions include the left A1, STG, MTG, and IFG, with many of the LLMs exhibiting particularly strong correlations in these areas across both structure, emphasizing their role in syntactic functions. These converging findings reinforce the robustness of the HFTP approach and suggest its potential as a valuable tool for future studics of model- brain alignment.
5
Conclusion
This study advances syntactic processing by introducing the Hierarchical Frequency Tagging Probe (HFTP), a unified framework for dissecting neuron-wise wiise sentence and phrase representations in LLMs. population-level patterns in the human brain, and generalizing seamlessly to naturalistic text. The results reveal that while I.MMs exhibit hierarchical syntactic processing and alignment with left-hemisphere brain activity, the mechanisms underlying their representations diverge significantly from those in human cortical regions. Notably, newer models like Gemma 2 demonstrate improved alignment, whereas others, such as Llama 3.1, show weaker suman-model correlations despite enhanced task performance. These findings underscore the need to refine LLM architectures for more human-like syntactic processing and establish HFTP bridge between computational linguistics and cognitive neuroscience. Finally, the societal implications of this work are two-sided: positively, HFTP can support safer, more controllable models and inform non-invasive diagnostics via spectral markers; negatively, the same interpretability could be misused to optimize persuasive manipulation and, if linked with personal neural data, undermine privacy.
6 Limitation
Although we applied HFTP to both Chinese and English corpora for LLMs, our SEEG data were collected in China and primarily involve Chinese stimuli. We have preliminary English responses from single Chinese native participant (see Appendix [F), but deeper cross-linguistic analyses are pending; future work will recruit native English speakers to validate alignment on English and enable rigorous cross-linguistic tests. Additionally, we evaluated only small set of model architectures and parameter scales, so the key universal mechanisms driving model-brain syntactic alignment remain unclear and warrant further investigation.
10

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Acknowledgments and Disclosure of Funding
This work supported by the National Natural Science Foundation of China (32441106, 32171039, T2421004) and National Science and Technology Innovation 2030 Major Program (2022ZD0204804, 2022ZID0204022..
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The Abstract and Introduction sections explicitly enumerate the HFTP frame- work, its application to both LLMs and brain data, and summarize the key empirical and methodological contributions, accurately reflecting the paper's scope.
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The answer NA means that the abstract and introduction do not include the claims made in the paper.
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Answer: [Yes]
Justification: In Section 6 We explicitly acknowledge limitations: (i) sEEG data restricted to Chinese stimuli, so future work must include native-English recordings to test cross-language generalization; and (ii) we evaluate only six LLM architectures and sizes, leaving the core drivers of model-brain syntactic alignment for further study.
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: This work is empirical and method-driven. def-ddiiieneefiiiigggggg how to compute sentence and phrase unit responses, layer-wise alignment similarity, and contribution ratios but does not present formal theorems that require assumption lists or mathematical proofs.
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Question:
Answer: [Yes]
We give end mod-to-end details of every experiment how to detect sentence and phrase representations in LLMs (Section 3.2 how to identify corresponding signals in human sEEG data (Section 3.3). and how to compute layer-wise model-brain alignment (Section 3.4 and Appendix A These sections include all parameters, sampling-rate choices, FFT settings, permutation tests, and alignment-pipelin steps needed reproduce the analyses.
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(d)
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Justification: We introduced diverse, curated suite of corpora for the HFTP exper- iments: (i) two four-syllable/wordd syntactic corpora (Chinese and English) adapted from [Ding al. 2016); (ii) four human--enerated naturalistic texts in Chinese and English; and (iii) two Wikipedia-derived corpora in Chinese and English. Detailed descriptions for all non-Wo Non-Wiiipedia corpora are provided in Section H. the Wikipedia-derived corpora are hosted on https: t/uhbb.... thub. com/ hoom/Lili LilTiger/HFT?
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A Alignment pipeline for syntactic processing between LLMs and the human brain
In this appendix, we provide the detailed pipeline used to align the syntactic structure representations in LLMs with those in the human brain, with focus on detecting and comparing sentence- and phrase-level representations across both systems.
Data and experimental setup For system- comparability, presented the exact same input corpora to both systems in the alignment analyses: the sentence corpus (four-syllable Chinese sequences) and the phrase corpus (two-syllable Chinese sequences).. The word-order randomized version each corpus was used as a control condition, as detailed in Section 3.1 Each corpus comprised 40 trials, and each trial contained 36 syllables. For SRDM calculation, the corpora were divided into six experimental conditions, each with 20 trials. To attenuate onset-locked transients, SEEG signals and model activations were extracted for the final 32 syllables of each trial. These time series were then transformed to the frequency domain via FFT, and the resulting amplitude spectra used in the subsequent alignment analyses.
drequen amalysis and SRDM construction For the LLMs, neuron activations were transformed using FFT to capture the frequency components of structure processing across the six conditions. From this transformation, we calculated the cosine similarity between each pair of conditions, constructing
SRDM for each MLP neuron. We then averaged the SRDM for each MLP neuron the same layer to obtain wiserwwise SRDMs. Similarly, for the human brain, we calculated the ITPC to capture frequency-dome domann representations for each brain channel, yielding channel-wise SRDM..
To assess the structural alignment between LLMs and the human brain, we computed the Spearman correlation between the SRDM of each LLM layer and the SRDM of each brain channel. We then formed layer-level model SRDMs by averaging neuron-level cosine-similare each layer. The top 100 most relevant brain channels for each model layer were identified based on Spearman correlation, and the overlap of sentence and phrase channels in these top 100 channels was evaluated using a chi-square test. A significant overlap indicated alignment structural processing between LLMs and brain ROIs.
matrices within
Quantification of cross-system alignment Two key metries were defined to quantify the structural alignment between LLMs and the human brain. The first, model-brain similarity S(m, b). represents the overall similarity of syntactic processing between an LLM m and the human brain b. It is computed the average Spearman correlation between the SRDM of each LLM layer and the top 100 most relevant brain channels:
M
100
1 M j=1 (Etop())
5mm, b)
Σ
p(L,, Ci).
(4)
100
where M is the number of layers in an LLM; Lj and C, denote the model layer and the brain channel indexed by j and i, respectively; top(j) denotes the indices of the top 100 channels for model layer Ly; and p(L,, C,) denotes the Spearman correlation between the model SRDM at layer Ly and the SRDM of brain channel C.
The second metric, model-region similarity S(m, by), measures the alignment between LLMs and specific brain ROIs This is computed by averaging the Spearman correlations over the subset of the top 100 channels that fall within particular brain ROI:
nijrr Σ ieto
b,),
1 M j=1 n(j.r)
Σ
p(Lj, Ct):
(5)
where C, denotes the indices of all channels belonging to region r, and nı(j, r) is the cardinality of top(j) O Cr i.e., the number of channels region that are also among the top 100 channels for model layer Lj.
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Note that we have sentence, phrase, and sentence & phrase neurons for the sentence corpus, thus we average S(m, b) and S(m,b, b,) across the three neuron types; and, for the phrase corpus, there are only phrase neurons, so no averaging is required.
B Predictive encoding control analysis
To provide an orthogonal test of model model-brain alignment that does not depend on representational similarity analysis, we implemented a predictive encoding model that asks whether layer-wise features extracted from an LLM can predict the frequency domain sEEG responses of individual channels. All experimental settings (e.g. sentence/phrase corpora, block structure) were matched to those in the RDM-RSA pipeline (Section A)) We also adopt the same two measures, model-brain similarity (mm,)) b) and model-region similarity S(m, by), so that the predictive encoding results are directly comparable the SRDM-RSA results reported above.
domuan
Model features For each model layer Lj we first selected significant neurons using the same procedure as in the SRDM-RSA pipeline. Within given block, the time courses of the significant neurons were averaged to obtain one 32-syllable sequence per layer and block (the last 32 syllables). We then applied FFT to this layer-average and retained the complex coefficients within 0.5-2 Hz. Each retained coefficient was represented by its real part and imaginary part (the canonical cosine and sine components), which we treat as two features at that frequency. With two blocks per corpus, formed the predictor matrix X, E RN x2 by stacking samples across blocks and frequencies, where K denotes the number of frequency bins in 0.5-2 Hz and N 2K reflects the two blocks times K frequencies. Thus each of the N samples corresponds to one (block, frequency) pair and is described by two-dimensional feature vector containing the real and imaginary parts of the aligned model coefficient.
Brain responses For each SEEG channel C. we computed ITPC exactly as in the SRDM-RSA analysis, yielding a complex spectrum per block. We then aligned the frequency axes of the model and brain spectra by gearest-neighher matching (model sampling 4 Hz; sEEG 512 Hz) within the 0.5 2 Hz band. The target for predictive modeling was the band-limiter spectral profile of stimulus- synchrony for channel C;; specifically, we concatenated the ITPC amplitudes across the two blocks and across all frequencies to obtaín an observation vector yi E TRA. This vector summarizes the oscillatory response of channel C; within the syntactic band while preserving block-specific structure.
Predictive model and cross- volss-valiittion For each pair (Lj, Ci) we fit ridge regression with stan- dardized predictors,
β = arg min ||5- x, al 1112 B
using five random splits (train/test 70/30). The ridge penalty was chosen only on the training split via inner ridge-CV over a logarithmic grid; the fitted model was then evaluated on the held-out test data. Predictive accuracy for split was defined as Spearman correlation p between the predicted and observed test targets. We averaged the split-wise correlations to obtain a predictive score
(7) and repeated this procedure independently for left and right hemispheres and for both corpora. Note that feature selection is performed exclusively the model side; the sEEG data are never used to select features, preventing circularity (double-dig -iipbig-
P(L;, C.) Espits [p(i) ))"")],
Aggregation into alignment metries To summarize predictive alignment, we follow the exact aggregation used for SRDM-RSA pipeline and therefore reuse the definitions of 5m,, b) and (m,, b,) introduced in Section A The only substitution is that SRDM correlations p L.g C.) are replaced by the predictive scores PTLy Ct). Concretely, for each layer L, we rank channels by P(Ly. C.), take the top 100, and compute ((m, b) and S(m, b,) by the same layer-averaging rules before. For the sentence corpus, we compute these summaries separately for sentence, phrase and sentence & phrase neurons and then report their arithmetic mean to yield a single value per model and hemisphere. For the phrase corpus, the selection procedure yields only chrase-selective neurons; consequently no averaging across syntactic MLP neuron types is required. This results in model- score (m, b) and one score per ROI Sm, by)) for each model in each hemisphere, directly comparable to the SRDM-RSA results while relying on predictive encoding rather than representational geometries.
Across both corpora (Tables 3 and a one-way ANOVA on layer-averaged top-100 predictive scores revealed reliable between-model differences in brain-level alignment ((,, (sentence:
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Table 3: Predictive alignment results for the sentence corpus. The first row reports the overall model-brait brain similarity Smm, b); subsequent rows report the model-region similarity S(m, b,). Bold in the S(m, b) row marks the highest value(s) across models/hemispheres: within each model, bold highlights the three highest S (m,, b,) entries across all ROIs and hemispheres (ties bolded).
GPT-2
Gemma
Gemma 2
Llama 2
Llama 3.1
GLM-4
L
R
L
R
L
R
L
R
L
R
L
R
S(m, 6)
0.487
0.371
0.467
0.364
0.469 0.371 0.472 0.364
0.463
0.365
0.471
0.367
A1
0.515 0.375 0.473 0.368
0.474
0.360
0.470
0.388
0.470
0.360 0.485 0.376
STG
0.499 0.384 0.469 0.357 0.472 0.369 0,480 0.369 0.464 0.373 0.476 0.374
MTG
0,487 0.370 0.468 0.366 0.470 0.375 0.475 0.369 0.465 0.366 0.469 0.371
ITG
0,479 0.370 0,463 0.361 0,466 0.367 0,466 0.360 0,457 0.362 0.466 0.364
Insula
0,486 0.373 0.459 0.368 0.468 0.377 0.476 0.376 0.463 0.363 0.473 0.370
TPJ
0,474 0.345 0.468 0.349 0,466 0.361 0,463 0.347 0,462 0.347 0.467 0.350
Temporal Pole
0,484 0,384 0.467 0.362 0.455 0.373 0.475 0.365 0.459 0.362 0.469 0.351
Sensorimotor
0.491 0.373 0.470 0.363 0,471 0.375 0,470 0.362 0,466 0.366 0.472 0.370
IFG
0,480 0.370 0.465 0.365 0.465 0.366 0.466 0.360 0.462 0.359 0.467 0.364
MFG
0.475 0.367 0.457 0.374 0.462 0.364 0,466 0.355 0.453 0.373 0.462 0.376
Hippocampus
0.481 0.358 0.464 0.365 0.458 0.366 0.462 0.353 0.458 0.360 0.469 0.362
Amygdala
0.472 0.334 0.463 0.345 0.465 0.362 0.452 0.353 0.463 0.358 0.471 0.349
Table 4: Predictive alignment results for the phrase corpus. TThe first row reports the overall model- brain similarity (mm, 6): subsequent rows report the model-region similarity S(m, b,). Bold in the S(m, b) row marks the highest value(s) across models/hemispheres within each model, bold highlights the three highest S(m, b,)) entries across all ROIs and hemispheres (ties bolded).
GPT-2
Gemma
Gemma 2
Llama 2
Llama 3.1
GLM-4
L
R
R
L
R
L
R
L
R
L
R
5(m,b) b)
0.454
0.356
0.465
0.362
0.465
0.366
0.446 0.350
0.451
0.354
0.449
0.349
A1
0.448 0.351
0.478
0.343
0.482
0.345
0.451
0.365
0.452
0.333
0.443
0.344
STG
0.463 0.363
0.468 0.364
0.470
0.363
0.448 0,356
0.455
0.352
0.458 0.357
MTG
0.450 0.357 0.464 0.362 0.462 0.366 0.445 0.363
0.448
0.348 0.445 0.345
ITG
0.449 0.348 0.465 0.359 0.460 0.363 0,445 0.346
0,448
0.352 0.448 0.345
Insula
0.453 0.360 0.461 0.360 0.462 0.365 0.446 0.348
0.451
0.354 0.452 0.344
TPJ
0.454 0.350 0.462 0.361 0,460 0.361 0,444 0.343
0,450
0.350 0.448 0.349
Temporal Pole
0.461 0.371 0.464 0.363 0.464
0.365 0.450 0.347
0.448
0.354 0.452 0.344
Sensorimotor
0.461 0.371 0.464 0.363 0.464 0.365 0,450 0.347
0.448
0.354 0.452 0.344
IFG
0.450 0.351 0.454 0.362 0.453 0.358 0.437 0.343
0.454
0.353 0.448 0.347
MFG
0.451 0.328 0.466 0.333 0.460 0.361 0,455 0.330
0.455 0.353 0.457 0.351
Hippocampus
0.454 0.354 0.453 0.363 0.463
0.357 0.436 0.344
0.454
0.354
0.434 0.354
Amygdala
0.403 0.361 0.420 0.391 0.450 0.355 0.385 0.349
0.402 0.386
0.389 0.381
F= 43.37, p- < 0.001, r7² = 0.008; phrase: F = 13.80, p < 0.001, 172 = 0.008), with con- sistently higher alignment in the left hemisphere. At the region level, FDR-controlled one-way ANOVAs showed significant between-model effects within left-dominant language cortices (e.g., STG, MTG, ITG) for both corpora. These findings align with the SRDM-RSA analyses, which likewise demonstrate significant between-mode model variation at both whole-brain and region-specific levels.
Mirroring the SRDM-RSA trend, the predictive results exhibit an almost identical trend for model upgrade comparison. In the sentence corpus, the left-hemisphere model-bran scores reproduce the RSA ordering Gemma 2 exceeds Gemma (S(m, b) 0.469 vs. 0.467, L), while Llama 3.1 falls below Llama 2 (0.463 vs. 0.472, L). In the phrase corpus, the gaps compress: Gemma 2 is essentially tied with Gemma (both 5m, b) 0.465, L), and Llama 3.1 is slightly higher than Llama (0.451 vs. 0.446, L... This compression likely arises because phrase analyses use only phrase neurons and provide fewer, noisier frequency samples, which together reduce model separability. Taken together, the predictive encoding and SRDM-RSA analyses converge on consistent, method-robust
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conclusion: inter-model differences are statistically reliable at both the brain and region levels, and, the brain level, updeldup trends are essentially identical across methods, with only minor fluctuations.
C Bilingual sentence- and phrase-level representations in LLMs
Previous studies have explored how LLMs handle different languages, concluding that while most neurons are shared across languages, smaller subset of neurons is dedicated to processing specific languages [Tang al. 2024]. But does this hold true for syntactic structure perception? This appendix provides insights into this question. It is important to note that the GPT-2 model used in this study was pre- trained on Chinese corpus using the Universal Encoder Representations (UER) framework (Zhao et al. 2019), equipping it with the capability to process both Chinese and English text effectively. This stands in contrast the original GPT-2 model [Radford et al. 2019), which lacks the ability to handle Chinese text.
Exclsied brag sestesce Nouroe
Exckuiive 1g senfence No NeJians
Dicusive ress pestence Narore
Exckalive Chinass sontence Neuror
Chrapedchulinn authatze
CheesSinn cuntanne Naurens
Beap
gare
Oytautve Drg
Dickatva
6.15
Excusive nddo etrose Reartss
Exclelive phicsep Reurers
pivac
Chrisdtiihal an prese Nasbon
g00w
Chrsoe6Dug pross Nourora
500
2om
Layer
Layer
(a) GPT-2
Gemma
Leduudue Erilish sereace Reurnes teduatve
Excluiive Eng sentecce Ne Jians Chinss Neuroro
30.4
0.00
P
9
8
Exckucive Chiose (iicc feurces
Dicanie Coglish phorre Neurtea
0.06
Exclucie Chinct eivace heurers
Cockabie Eng lot atrace leurzee
Crexousthhp bighi uhnas Resters
Chrsosstbi prose feursrs
00
0.00
Layer
(c) Gemma 2
(d) Llama 2
Excisaine End aerience Nouroro Chinetndiingg
Dickaive Dny sentence Nourara Entresddnngiin
630
Sore-
610
17
6125
Escadiv End phoose Neuress
Excuaive English phoaee
0.60
0005
5000
7000
6003
0023
0000
(c) Llama 3.1
(f) GLM-4
Figure 7: Cross-language neural representations extracted from five multilingual models (Gemma,
Gemma 2, Llama 2, Llama 3. and GLM-4) depicting syntactic processing capabilities.
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We used semantically matched Chinese and English syntactic corpora in this bilingual experiment (see Tables Land 77 The results Figure Suggest that language-spectin syntactic MLP neurons (i.e., exclusive sentence/phrase neurons) tend to cluster toward the final layers of Llama 2, Ilama 3.1, and GLM-4, with the proportion of bilingual neurons (Chinese English) increasing progressively in deeper layers. In contrast, Gemma and Gemma 2 display different patterns. In Gemma, both language-specific and bilingual neurons are found not only in the deeper layers but also in the initial layers, whereas this is only observed in the early layers of Gemma 2. GPT-2, on the other hand, exhibits a more balanced distribution, with both language-specific and bilingual neurons present in almost every layer in roughly equal proportions. Furthermore, prominent in the early layers, while Chinese-specific neurons are more concentrated in the later layers. Interestingly, Llama 3.1 shows a notably lower count of Chinese-specific neurons compared to English-specific neurons in the final layer, and fewer Chinese & English neurons than the other five L.Mss. Although Llama 3.1 was pre-trained on 176 languages (Dubey et al.) 2024). it appears to have less specialization in Chinese, which may explain the reduced presence of Chinese-specific neurons and, consequently, fewer bilingual neurons.
neurons are more
D Contribution Ratios of LLMs
To further investigate the role of specific brain ROIs in syntactic processing, we introduced the contri- bution ratio Ctp,, The contribution ratio highlights which brain ROIs contribute most significantly to the syntactic alignment between LLMs and the human brain. Fixing model layer, this metric quantifies the influence of each brain ROIs by calculating the proportion of channels from a given region within the top 100 most relevant channels, normalized by the overall representation of the ROIs. The contribution ratio is defined as:
(Tg)///N Niotal /Niotal
CR,(L,)
where N is the number of channels in region r within the top 100 channels in terms of the LLM layer Lj. Notop is the total number of top channels, which is specified as 100 in this case, Ni is the total number of channels in region r, and Ntotal is the total number of brain channels.
We present the contribution ratio results for six LLMs used in this study: GPT-2, Gemma, Gemma 2, Llama 2, Ilama 3.1, and GL.-4. Specifically, the contribution ratio for each model was caleu- lated based the number of top 100 significant channels within each brain ROIs, described in Appendix A Below, we present the results for both the left (L) and right (R) hemispheres of each model (See Figures 15 16
18 19 and 20). These figures offer further insights into how different LLMs align with human brain ROIS in terms of syntactic processing.
From these figures, we observe that across all LLMs, regions such as A1 and STG in the left hemisphere, and the Insula, Temporal Pole, and Amygdala the right hemisphere contribute more significantly to the alignment with human brain syntactic processing. These regions are known to be involved in language-specific processes in the human brain, particularly in the left hemisphere, where the STG and A1 are crucial for auditory and syntactic processing. Overall, these contribution profiles suggest that these models may be capturing aspects of hierarchical syntactic structures in ways that are functionally similar to human neural mechanisms. The Insula, Temporal Pole, and Amygdala, though not traditionally highlighted as primary language regions, may also play supporting roles in language comprehension, possibly through emotion and memory-related pathways.
E HFTP on naturalistic corpus
Beyond validating HFTP with tightly controlled four-syllable/word sequences, we show that it generalises to naturalistic text spanning everyday dialogue, news reports, literature, and poetry. We tested eight- and nine-syllable Chinese corpora alongside matched English 8- and 9-word corpora (see Tables & 2 IT. Spectral analysis reveals four pronounced peaks each condition. For the eight-syllable set, peaks arise at 0.5, 1.0, 1.5 and 2.0 Hz, corresponding to the full-sentence envelope, the canonical 4-character phrase rhythm, an intermediate 2-a-beat grouping around 1.5 Hz, and the ubiquitous 2-character lexical rhythm (Figure g with the English 8- 8-word corpus exhibiting the
26

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same four-peak pattern at the corresponding sentence and phrase rates (Figure TOp. In nine-syllable sentences the peaks shift 0.44, 0.89, 1.33, and 1.78 Hz: the lowest peak reflects the whole sentence, 1.33 Hz aligns with abundant 3-character phrases, 1.78 Hz captures rapid two-to-three- character alternations, and the 0.89 Hz component indexes a prosodic half-sentence "breath group" of four-to-five characters (Figure and the English 9-word corpus shows the analogous four peaks near these frequencies (Figure
Sentencce MLP nouton
Phrase MLP neuron
Sentence Phrase MIPP nounon
Non sonsitive MLF neuron
1.0
1.0
1.0
1.0
Normaiized Ampllhude
0.8
0.8
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free
0.6-
0.6
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0.4
0.4
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0.2
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2.0
1.0
Frequency (H2)
Frequency (H2)
Frequency (H2)
Frequency (H2)
Figure 8: Hierarchical frequency patterns of MLP neurons, using a naturalistic Chinese 8-syllable corpus, selectively represent sentence features, phrase features, shared features of both, and non- sensitive features (from left to right).
Senteecc MLP neuron
Phrase MLP nsuran
Sentence Phrase ML nauron
Non sens-tiviii MLP neuron
1.0
1.0
3.0
1.0
Normaiized Amplittde
0.8
0.6
0.8
0.8
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0.5
0.b
0.6
6.4
0.4
0.4
0.2
2.0
Frequency (H2)
Frequency (H2)
Frequuency (H2)
Frequency (Hz)
Figure 9: Hicrarchical frequency patterns of MLP neurons, using a naturalistic Chinese 9-syllable corpus, selectively represent sentence features, phrase features, shared features of both, and non- sensitive features (from left to right).
Sentence MLP nouron
Phrase MLP nsuron
Sentence Phrase ML nouron
Non sansitive MLP nsuron
1.0
3.5
3.0
1.0
Normalize ized Amplitude
6.8
0.6
B.B
G
0.6
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C.4
0.4
2.6
0.4
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Frequency (H2)
Frequency (H2)
Frequency (H2)
Fraquency (H2)
Figure 10: Hierarchical frequency patterns of MLP neurons, using a naturalistic English 8-word corpus, selectively represent sentence features, phrase features, shared features of both, and non- sensitive features (from left to right).
We further evaluated HFTP on Chinese and English Wikipedia 8-syllable/word corpora (Fig- ures 12 13 In both languages clear sentence peak appears at 0.5 Hz, and phrase-rate peaks persist at 1.0, 1.5, and 2.0 Hz; however, relative to non-Wikipedia corpora the peaks are attenu- ated, and the separation between experiment and random controls smaller. Although sequence length was strictly controlled to eight syllables/words, Wikipedia text is less regular in content: mixed scripts/orthogrrphiiss (e.g., simplified vs. traditional Chinese), frequent abbreviations and alphanumeric tokens, bibliographic fragments and formulaic titles, heterogeneous named entities,
27

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Sentence MLP neuron
Phrase MLP nsuron
Sentence Phrase ML nauron
Non sansitive MLP niuron
1.4
1.0
1.0
1.0
Normalize Amplityde
0.8
0.5
0.8
0.8
6.8
0.5
0.6
0.6
6.4
0.4
0.4
2.0
Frequency (Hz)
Frequency (H2)
Frequenccy (H2z)
Frequency
Figure 11: Hierarchical frequency patterns MLP neurons, using a naturalistic English 9-word corpus, selectively represent sentence features, phrase features, shared features of both, and non- sensitive features (from left to right).
Senteence MIP
Phrase MLP
Sentennee Phrrse MLL
Non Non-sensitive MLP neuron
1.0
1.0
experiment
1.0
1.0
Normalized Amplitude
Cce
0.5
0.5
0.8
Seree
0.8
0.5
0.5
0.4
Frequency (H2)
Frequency (Hz)
Frequency (H2)
Frequency
Figure 12:
Hierarchical frequency patterns of MLP neurons, using Chinese Wikipedia 8-syllable corpus, selectively represent sentence features, phrase features, shared features of both, and non- sensitive features (from left to right).
inconsistent prosodic phrasing, etc. These factors reduce cross-sentence periodicity and weaken harmonic reinforcement, thereby weakening tracking at sentence- and phrase-rate rhythms.
We speculate that the four dominant frequencies arise from tokenization statistics and prosodic templating shared across languages: frequent two- and three-token words and binomial/trinoiill chunks in English, and abundant 2-/3-character words and 4-character idioms Chinese; both yield stable 2- and 3-beat groupings, while clause-level phrasing produces half-sentence rhythm. Transformer layers further reinforce harmonics of the basic word cycle. For example, in the nine- syllable corpus, a 3-beat unit around 1.33 Hz naturally gives rise a higher harmonic near 1.78 Hz. The half-sentence peak at 0.89 Hz emerges because speakers often place a prosodic break near the nine-word clauses, creating stable sub- sub-sentence rhythm that the model
midpoint of nine-character entrains to. Because the underlying rhythmic structure is shared, we believe HFTP is highly likely to generalize other character-centrie languages (Japanese, Korean) and to space- space-delimine delimid alphabetic languages (French, German).
F Preliminary bilingual HFTP test on native Chinese speakers
As noted above, our HFTP analysis was originally conducted on native Chinese speakers listening to Chinese corpus. To probe cross-linguistic generalization in human sEEG, we constructed bilingual materials by taking the Chinese corpus from [Sheng et al. 2019) and manually translating into four-word English sequences. All experimental settings (c.g., presentation duration, sampling rate) were identical to those the original sEEG experiment. We report preliminary results from one native Chinese speaker who listened to both the Chinese and the English corpora. Hierarchical frequency patterns for the Chinese corpus closely match those in Figure and the corresponding English results shown in Figure 14]
In the English condition, we observe the canonical hierarchical fingerprint: sentence-selectiee channels peak at 1 Hz, phrase-selective channels peak at Hz, channels sensitive to both show peaks both frequencies, and non-sensitive channels show no systematic peaks. This replication in English indicates that the frequency-tagged response generalizes across languages when the hierarchical
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Sentence MLP neuron
Phrase MLP nsuron
Sentence Phrisse ML nauron
Non sansitive MLP neuron
1.0
experimont
2.0
exporimint
1.0
oxporiment
Normalized Amplitude
rardom
0.8
0.0
6rie
6re
0.6
1.0 Frequency
Frequency
Frequency
Frequency
Figure 13: Hicrarchical frequency patterns of MLP neurons, using English Wikipedia 8-word corpus, selectively represent sentence features, phrase features, shared features of both, and non-sensitive features (from left to right).
Sontence SEEG channel
Phrase SEGG channel
Sentiece S: Phrase SEEG channel
Non sensitive SEGG channel
1.8
1.0
1.0
1.0
Normalied mPC
0.8
fyecac
0.6
farrsse
BB
fya
0.
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0.6
0.6
C4
0.4
246
0.4
022
0.2
2.0
0.5
1.5
Frequency (H2)
Frequency (H2)
Prequency (H2)
Frequency (Hz)
Figure 14: Hierarchical frequency patterns sEEG channels from one participant, using four-word
English sequence, selectively represent sentence features, phrase features, shared features of both and non-sensitive feature (from left to the right). Shaded bands show +1 s.e.m. computed within each channel by bootstrapping across sliding- bodding-window window ITPC estimates.
structure of the input is preserved, arguing that HFTP tracks syntactic organization rather than language-specing acoustics or phonotactics. While promising, these observations are based on one participant and should be treated mroof-of-principe rather than definitive. Future work will expand the sample, balance materials across languages, and use more robust statistical analyses to quantify lingu-lingcistic effect sizes.
G Model details
In this appendix, we present the details of the LLMs used in this study. Table 5 summarizes key parameters, including model size, number of layers, attention heads, and MLP neurons. Notably, the GPT-2 variant used here was pre-trained on a Chinese corpus, enabling it to process both Chinese and English; by contrast, the original GPT-2 does not support Chinese.
Table 5: Comparison of model parameters.
Model
Size
Layers
Attention heads
MLP neurons
GPT-2
Radford et al.
2019
774M
36
5120
Gemma
Team al.
2024a
7B
28
16
24576
Gemma
Team et al.
2024b
9B
42
16
14336
Llama
Touvron et
2023
7B
32
32
11008
Llama 3.1
Dubey et al.
2024
8B
32
14336
GLM-4 GLM et al.
2024
9B
40
32
13696
29

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H Syntactic corpora
For LLM experiments, used diverse suite of corpora spanning languages, structure, and length. Specifically, we used Chinese four-syllable and English four-word syntactic corpora adapted from [Ding et al. 2016) (Tables 6 7. alongside auman-generated naturalistic Chinese corpora (eight- and nine-sy nine-syllable; lable; Tables 8 [ and parallel English counterparts (eight- and nine-word; Ta-
bles To I These corpora were used to assess model sensitivity to hierarchical structure. addition, we constructed Wikipedia-derieee
Chinese and English 8 8-syllable/word corpora for out-of -ot-of-domain validation; these corpora are available on https: pttpp://githut git.bb.ttu...... com/LilTiger/HITP
Table Chinese syntactic corpus.
Four-syllable sequences
# t 2 3
m T
::
##
4
i
#
#
+
T
#T
1:
Table 7: English syntactic corpus.
Four-wort sequences
Old ox plows field Bamboo rat gnaws shoots
A friend invites guests
The chef cooks dinner
A farmer plants vegetables
Green grass sprouts up
The monk chants scriptures
A teacher gives lecture
The whale spouts water
The sheep cats grass Pond duck paddles water The doctor treats patients
A hero extinguishes fire
The tourist climbs mountain
A spider spins web
My grandfather plays chess
The nurse administers vaccine
A hen lays eggs
The pedestrian crosses street An eagle catches fish The driver operates vehicle The poet recites verses A fisherman casts net
The judge decides case
A lion eats meat
Honey bee gathers nectar
The child reads books
A painter creates art
The boatman rows oars
The sparrow builds nest
A monkey picks peaches
The camel drinks water A kitten catches fish
The fox catches mice
A seal balances ball A dove carries branch A titmouse catches insects Black bear climbs tree
Old horse pulls cart
The child collects shells
A chick pecks grain
The bluebird pecks wood
A woodcutter chops firewood
A coyote digs burrow
War pigeon delivers messages
The swallow feeds chicks
Wild boar roots earth Silkworm moth spins silk
A fisherman rows boat
The earthworm burrows soil
For the human brain experiment, we utilized two Chinese corpora: the sentence and phrase corpora. To ensure consistent analysis of syntactic processing across both LLMs and the human brain, the same corpora were applied to the alignment experiment. These corpora originated from (Sheng et al., 2019]. Participants received brief oral instruction to listen carefully to each stimulus, completed 40 trials per condition (sentence, phrase, and random), and were compensated approximately Y200 for full participation, with pay prorated if they completed only subset of trials.
I Brain ROIS
As discussed in Section 3.3
we reorganized the original sEEG data by grouping the Auto- mated Anatomical Labeling (AAL) annotations into newly defined brain ROIs for our experi-
30

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Table Naturalistic Chinese corpus.
Eight-syllable sequences
# R Br
# #
?
1
M.. T
#
M 1 ?E?
#
+4
żUЛ 2
-
14T E T?
BIEE HA
Table 9: Naturalistic Chinese corpus.
Nine-syllable sequences
T ?
R # S.
#
fT
ズ 7 /1g?
X 1
10
4
/
Bリ RL K719?
# H K
E. X 0 T I9?
$
#
1T77
R.
-
ments Rolls et al. 2015). In this appendix, we provide the full names of AAL regions, the cor- responding AAL labels used in the sEEG data, and their mapped brain ROIs in Table 12
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Table 10: Naturalistic English corpus.
wirdt-word sequences
With malice toward with charity toward all. A man can destroyed not defeated.
Tonight, shall watch the meteor shower together? Excuse me, where's the nearest restroom around here?
The library felt crowded during rainy examination week.
Market demand seems gradually rebounding during this quarter.
Book sales rankings were refreshed early this morning.
Waiter, cups of hot green tea please.
Quantum research tearn unveiled perplexing entanglement results yesterday. He sorted aging photographs, reminiscing about bygone days.
Students submitted their final graduate theses by noon.
Hello, where you right my friend?
Moonlight spilled gently across ancient city walls. Autonomous huses now operate on every urhan route.
Flights were delayed nationwide due to dense fog. Southern region suffered severe flooding after relentless rain.
Plum blossoms scented the courtyard with delicate sweetness.
Please hold on. I'll check your booking details.
Central bank announced fresh stimulus to boost
Port cranes unloaded containers under brilliant afternoon sky.
cconomy.
He slowly wandered into the rainy narrow alleyway.
Education ministry announced updated national curriculum guidelines today.
Ready to order? have today's special combo.
Rainy night streetlights shimmered like glowing river.
Police dismantled an extensive online fraud network operation.
Spring thunder rolled over rice paddies in Jiangnan.
He put on headphones focused on coding.
Gentle waves lapped softly against the sandy shoreline.
Could you use some sugar in your coffce? The data center completed comprehensive system upgrade.
The rural marketplace opened lively at dawn today. The theater staged a timeless classical ballet tonight.
never imagined you could be this arrogant. Train whistle echoed across fields drenched with mist.
City skyline glittered beneath a crisp winter moon. Research findings published in prestigious journal today.
Tomorrow, what would you like for breakfast, friend?
Ancient stone bridge retained mossy grooves through centuries.
Look, the storm passed; let's explore the streets.
Parcel delivery is currently out for neighborhood distribution.
Financial regulators tightened oversight on speculative securities trading. Travel agency launched discounted spring break tour packages.
Hey classmate, may I horrow your pen briefly?
Teacher, could you explain this problem once more?
Darling, what would you like for dinner tonight?
Osmanthus fragrance drifted along the entire narrow strect.
Reporter delivered live coverage from bustling downtown square. Museum introduced extended evening hours for visitors' convenience.
On rainy days, roads casily accumulate dangerous puddles.
Night market vendors grilled skewers over glowing charcoal.
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Table 11: Naturalistic English corpus.
Nine-word sequences
Clinical trial data were publicly released this morning nationwide.
Shall we share sushi together tonight by the river?
War is peace, freedom is slavery, ignorance is strength.
We offer cola, soda, juice, and soy milk today.
Photography exhibition focuses city's hidden pockets of light.
Driver, how much is the fare the station?
Scientific data platform finally went live to the public.
Jupiter just gained two additional tiny moons last week.
See you at the airport tomorrow seven sharp. alright?
Raindrops drummed against windowpanes in crisp cadence tonight.
Hey, have you reached the company entrance yet today?
Early morning city lights gradually faded as traffic intensified.
Morning rush subway overflowed with restless hurried commuters again.
Doctor, should this new medicine be taken before dinner?
Coffee beans release subtle caramel aroma throughout the cafe.
Book sales leaderboard refreshed, surprising many independent authors today. Sister, docs this blue dress come in medium size?
Undelivered parcels were returned after nobody signed for them. Child, slow down and chew, avoid choking on food.
Smart home automation system is currently installing new firmware.
High school will introduce artificial intelligence elective next year.
He rolled over, consulting a star chart at midnight.
Sudden downpour forced cancellation of tonight's fireworks display completely.
Community basketball tournament tips off under bright evening floodlights. Boss, could this fish be little cheaper please?
Slanting shadows tapped an old window softly at dusk.
Ecommerce campaign now promotes sustainable green packaging materials nationwide. National team claimed first gold medal Paris.
I Luggage carousel kept circling the unattended silver suitcase indefinitely.
New drama premiere received soaring reviews across social media.
Please sit here; the view remains the best available. Classmate, may borrow your notebook for a moment?
Please wait, will retrieve our cinema tickets now. Influencer shared breathtaking drone footage of mountain sunrise yesterday. Payment through WeChat is available, is that alright sir?
Software update completed, critical vulncrabilities have been fixed already.
Campus club seeks enthusiastic freshmen to join this semester.
Hello, has the final exam schedule been changed recently?
Comer flower shop out roses before noon.
He whispered while finishing that fading wartime love letter.
Market supervision bureau conducted an unexpected compliance inspection today.
Dinner menu has been fully updated for the evening.
Satellite captured brilliant images above polar orbit yesterday.
He gently knocked, waiting patiently for someone respond." Music slowed, dancers embraced lighter steps beneath dimmed lights. Hello, kindly complete the visitor registration form reception.
Please bring the bill, waiter, we are finished here.
Lantern glow reflected onto slick cobblestones after evening drizzle. He tapped the keyboard softly, refining his source code.
Volunteers distributed food supplics to displaced families after flood.
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Table 12: Automated Anatomical Labeling (AAL) annotations from the original sEEG data, along with their mapped brain ROIs. Note that the regions are distinguished by the left and right hemi-
spheres.
AAL Full Name
AAL Label
ROI
Heschl Gyrus
Heschl
A1
Superior Temporal Gyrus
Temporal_ Sup
STG
Middle Temporal Gyrus
Temporal_Mid
MTG
Inferior Temporal Gyrus
Temporal_ Inf
ITG
Parahippocampal Gyrus
ParaHiipocampal
ITG
Fusiform Gyrus
Fusiform
ITG
Insular Cortex
Insula
Insula
Angular Gyrus
Angular
TPJ
Supramarginal Gyrus
SupraMarginal
TPJ
Inferior Parietal Lobule
Parietal Inf
TPJ
Superior Temporal Pole
Temporal Pole_Sopp Sup
Temporal_ Pole
Middle Temporal Pole
Cemporal_Pole____
Temporal_Pa Pole
Paracentral Lobule
Loracentral_Lobule
Sensorimotor
Supplementary Motor Area
Hupp_Motor_Ate Area
Sensorimotor
Rolandic Operculum
Rolandic_ Oper
Sensorimotor
Precentral Gyrus
Precentral
Sensorimotor
Postcentral Gyrus
Postcentral
Sensorimotor
Inferior Frontal Gyrus, Opercular part
Frontal Inf Oper
IFG
Inferior Frontal Gyrus, Triangular part
Frontal Inf Tri
IFG
Inferior Frontal Gyrus, Orbital part
Frontal Inf Orb
IFG
Middle Frontal Gyrus
Frontal Mid
MFG
Middle Frontal Gyrus, Orbital part Hippocampus
Frontal Moonaa__Mid___ Mid_Orb
MFG
Hippocampus
Hippocampus
Amygdala
Amygdala
Amygdala
hemisphere
Contcibution Rato
510
#1G
izbi/a
Remoorat Polk
Sansortmator
Hopacamius
Amygdar
R herissperee
A3
570
MYD
inw/a
171
Temgond, Pele
Semormator
go
MPS
Hopocamous
Anyytora
Figure 15: Contribution ratios for GPT-2: Left hemisphere (top) and Right hemisphere (bottom).
34

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hemisphere
Layer
516
M1G
incula
Tumponaa Pole
Sansorimator
H2G
Hoppocampes
R hemisphere
Corpibaatonn
Laver
606
Temppnna Pole
Samsotemator
670
Hpppsrmpps
Anrygtals
Figure 16: Contribution ratios for Gemma: Left hemisphere (top) and Right hemisphere (bottom).
hemisphere
57G
616
undi
Remooral Polk
Sensormmator
SAG
Amyysatee
R hemisphere
STG
N010
X15
Temmond Pale
Sommnrmator
(P0
MPG
Hppocampuus Amyydela
Figure 17: Contribution ratios for Gemma Left hemisphere (top) and Right hemisphere (bottom).
35

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hemisphere
Layer
51G
M16
insul
"Bumporal Poln
Senserimmtto
MFG
Higpocanrpie
Amygabix
R hemisphere
Cortribatonn
87
510
M15
191
Tumponn,, Pole
Senserionctor
go
Wagecemgga
Anaygttii
Figure 18: Contribution ratios for Llama Left hemisphere (top) and Right hemisphere (bottom).
hemisphere
Cortribaion Contribution
516
w1G
insud
Tumppona, Pole
Sensocimotor
MGG
R hemisphere
63
510
M15
(10
Ins/l
171
Tempana tele
Semwnimakrr
(P0
MP5
Higoccrnges
Ampggtoa
Figure 19: Contribution ratios for Llama 3.1: Left hemisphere (top) and Right hemisphere (bottom).
36

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homisphere
Comfibbtion Ratio
M1G
towile
Temporal Pole
Sensormoror
170
Anygettte
R hemisphere
570
MTG
66
Ine/o
05
Tomponal Pole
Sunstrimato
/6
MPG
Hogocampps
Anyglto
Figure 20: Contribution ratios for GL.M4:: Left hemisphere (top) and Right hemisphere (bottom).
37


Reviews:
[{"review_id": "YRqxrOhuOF", "reviewer": "Reviewer_GLpY", "summary": "This paper introduces the Hierarchical Frequency Tagging Probe (HFTP), a novel method designed to investigate how LLMs and the human brain represent syntactic structures. HFTP utilizes frequency-domain analysis to identify specific computational units (MLP neurons in LLMs and cortical regions in the brain) that encode hierarchical linguistic elements like phrases and sentences.", "questions": "Did the authors account for tokenization across LLM when processing English data? How do they think their artificial time-resolution method can scale to more natural data?\nHow do the authors envision the generalizability of their brain-LLM alignment findings to English-speaking brains, especially considering potential cross-linguistic differences in syntactic processing?", "limitations": "The authors have adequately addressed the limitations of their work. Just a small note: authors state \"NA\" for the question about discussing potential positive and negative societal impacts, justifying this by saying their work is \"foundational and methodological\". While it is true that this research is foundational, authors should reflect on potential indirect or long-term negative applications, even for foundational work.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\nThe introduction of the Hierarchical Frequency Tagging Probe (HFTP) is a useful contribution as it extends frequency-domain analysis to LLM internals and brain activity in a unified framework, enabling direct comparison of syntactic processing across artificial and biological systems.\nThe authors do a systematic probing of six diverse LLMs (GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, GLM-4) revealing patterns of syntactic specialization and how these evolve across model versions. They report results showing that models encode sentence and phrase structures at different layers, with differences across architectures (e.g., Gemma vs. Llama families).\nHFTP could be a valuable tool that bridges computational linguistics and cognitive neuroscience, offering new insights into the interpretability of LLMs and their improvements\n\n\n\nWeaknesses\nThe approach of \"artificially defining a time scale\" with a 4 Hz sampling rate for LLM activations is a design decision to enable frequency-domain analyses when drawing a comparison with human brain data (as LLMs do not inherently have a time dimension). However, it means that the \"time-course information\" is imposed on LLMs, as it's not inherent to LLM input structures like it is for the brain. While this is a well-explained design choice, one could argue that it doesn't truly show how LLMs “process time” on their own, but rather how they can adapt to an externally imposed rhythm. Additionally, this might prevent applicability to more realistic language corpora.\nThe paper reveals a divergence: a model (Gemma 2) shows increased brain similarity compared to Gemma, while Llama 3.1 exhibits reduced alignment with the human brain compared to Llama 2, despite both being newer models with enhanced task performance. This is contrast with the authors’ explanation on brain alignment (around line 288) where they report that GPT-2, a model from 2019, has the highest correlation with human activity, and Gemma 2 outperforms Gemma due to modelling and training innovations. Overall the results seem mixed, making it impossible to draw any conclusion of the impact of LLM advances on brain alignment or going beyond empirical evaluation of models.\n\nMinor\n“To enhance performance on complex tasks—such as reasoning and higher cognitive functions—Llama 3.1 and Gemma 2 may reduce their specialized processing of syntactic structures (sentences and phrases), reallocating neurons to support these advanced capabilities.”. LLMs do not have any cognitive functions, I suggest the authors tone down such claims of human-like behavior of LLMs.\n\nProposing a unified framework for comparing syntactic structure representations in LLMs and the human brain is a valuable and timely contribution. The Hierarchical Frequency Tagging Probe (HFTP) offers a novel and methodologically consistent way to bridge AI and neuroscience. However, the core design choice of imposing a 4 Hz temporal structure on LLM activations—while necessary for frequency-domain comparison—raises interpretability concerns. Without ablations or alternative temporal mappings, it remains unclear whether the observed frequency patterns reflect intrinsic model behavior or artifacts of imposed structure. Moreover, the mixed trends in model–brain alignment across newer LLM versions highlight the difficulty of drawing principled conclusions about architectural progress. As it stands, HFTP enables compelling empirical evaluations, but additional controls are needed before it can support stronger theoretical claims.", "was_revised": true, "final_justification": "The paper presents a novel method designed to investigate how LLMs and the human brain represent syntactic structures. The rebuttal proved generalization across different languages (chinese and english) and in a more natural setup. Overall I think it can be accepted at Neurips and I decided to increase my score from 3 to 5", "rebuttal": "Thank you for dedicating your time and expertise to review our work. Your thoughtful comments provide valuable guidance for strengthening the paper. We address each of your concerns in detail below.\n\n---\n> ## Q1: Artificial 4 Hz time scale may impose non-intrinsic rhythm on LLMs.\n\nA1: The 4 Hz sampling is a neutral reference grid that lets us read out latent hierarchical structure with the same FFT used on brain data. When the sEEG experiment presents syllables at 4 Hz, the model must be sampled at 4 Hz so that sentence and phrase rhythms align at 1 Hz and 2 Hz. If the same syllables were played at 8 Hz, we would resample model activations at 8 Hz and the corresponding peaks would appear at 2 Hz and 4 Hz. In other words, **the temporal rate is an external clock that synchronises model and brain for comparability; it does not impose new linguistic content.**   \nWe also applied HFTP to naturalistic corpora in Q4, showing that our resampling procedure generalizes to both Chinese and English natural-text. Consequently, the sampling rate serves only as a temporal granularity parameter—regardless of its value, HFTP consistently recovers the underlying sentence- and phrase-level rhythms.\n\n> ## Q2: Mixed / contradictory model-brain alignment trends hinder conclusions about architectural progress.\n\nA2: Our data show that a model upgrade does not automatically move the network toward human‑like syntax processing: Gemma 2 aligns better with brain rhythms than Gemma, yet Llama 3.1 falls slightly below Llama 2, and GPT‑2 still scores highest overall. Indeed, we observed that upgraded models follow divergent patterns: an architecture’s advance does not guarantee more human‑like syntactic processing. **Rather than assuming that any performance gain yields closer brain alignment, our results invite deeper inquiry into the determinants of syntactic similarity. These varying trajectories are, in fact, more illuminating—they highlight that model improvements can shift network resources away from rhythmic syntax encoding.** These findings set the stage for principled engineering of future LLMs and cognitive robots that can be tuned either toward or away from brain‑like syntactic organisation, depending on application needs.\n> ## Q3: Tokenization across LLMs for English data lacks clear standardization.\n\nA3: We explicitly normalised tokenisation across models. Each LLM employs a different sub‑word scheme; most English‑trained models split a single word into multiple tokens (e.g., Llama‑2‑7B tokenises “A friend invites guests” as ▁A, ▁friend, ▁inv, ites, ▁guests). **Because HFTP treats one word as the minimal semantic unit driving the 4‑word/8‑word rhythmic structure**, we aggregate all sub‑tokens that belong to the same word: their hidden activations are averaged to yield a single word‑level vector before the FFT. This ensures that amplitude at 1 Hz (sentence) and 2 Hz (phrase) reflects linguistic—not tokeniser—boundaries.  \nWe apply the same normalization to Chinese. Although all six models support Chinese, their tokenizers differ: some (e.g., Gemma, Llama) split a single character into multiple tokens, whereas others (e.g., GLM‑4) merge several characters. To enforce a one‑character‑per‑token mapping, we insert explicit delimiters—transforming “朋友请客” into “朋*友*请*客*”—and then average the hidden activations of any sub‑tokens belonging to the same character. **This ensures that each Chinese character (and each English word) produces exactly one activation trace**, making the subsequent frequency‑tagging fully comparable across languages and models.\n\n> ## Q4: Artificial 4 Hz time needs justification for scaling to natural data.\n\nA4: **We have included the Chinese natural‑data scaling experiment in Appendix E of the main text**, with frequency‑domain results shown in Figures 8–9 and corpus details in Tables 6–7. To confirm generalizability, **we also ran an English natural‑text experiment using balanced sets of eight‑ and nine‑word sentences** (50 per set) drawn from everyday dialogue, news reports, literary prose and so on—mirroring the genre diversity of the Chinese stimuli.\n\n|8‑word sentences (excerpt) |genre |\n|---|---|\n| With malice toward none, with charity toward all. | Literary prose |\n| Tonight, shall we watch the meteor shower together? | Dialogue |\n| A man can be destroyed but not defeated. | Literary prose|\n| Excuse me, where’s the nearest restroom around here? | Dialogue |\n| The library felt crowded during rainy examination week. | News report |\n| Market demand seems gradually rebounding during this quarter. | News report |\n\n|9‑word sentences (excerpt)|genre|\n|---|---|\n| Clinical trial data were publicly released this morning nationwide. | News report |\n| Shall we share sushi together tonight by the river? | Dialogue |\n| War is peace, freedom is slavery, ignorance is strength. | Literary prose |\n| We offer cola, soda, juice, and soy milk today. | Dialogue |\n| Photography exhibition focuses on city’s hidden pockets of light. | News report |\n| Driver, how much is the fare to the station? | Dialogue |\n\nThe frequency‑domain patterns are consistent across both Chinese and English naturalistic datasets:\n* Eight‑word set: Robust peaks appear at **0.50 Hz, 1.00 Hz, 1.50 Hz, 2.00 Hz**, corresponding to the sentence envelope, canonical 4‑word phrases, intermediate 2‑to‑3‑word groupings, and the dominant 2‑word lexical rhythm.  \n* Nine‑word set: Peaks shift to **0.44 Hz, 0.89 Hz, 1.33 Hz, 1.78 Hz**: the lowest peak reflects the whole clause, 1.33 Hz aligns with frequent 3‑word phrases, 1.78 Hz captures rapid two‑to‑three‑word alternations, and 0.89 Hz indexes a four‑to‑five‑word prosodic “breath group”.  \n\nThe bilingual results confirm that HFTP scales the artificial 4 Hz tagging to naturalistic corpora in both Chinese and English, proving its cross‑linguistic robustness. Because the underlying rhythmic structure is shared, the method is highly likely to generalize to other character‑centric languages (Japanese, Korean) and to space‑delimited alphabetic languages (French, German).\n\n> ## Q5: Generalizability of the Chinese‑based brain–LLM alignment to English‑speaking brains remains unsubstantiated.\n\nA5: We have already collected **a bilingual sEEG session from one native‑Chinese participant who also understands everyday English**. Using the four‑word English (e.g., Old ox plows field, A friend invites guests) and four‑character Chinese (e.g., 老牛耕地，朋友请客) stimuli, we observed identical spectral signatures—**robust peaks at 1 Hz (sentence) and 2 Hz (phrase)**—for both Chinese and English inputs.  \nFor English speakers who also understand Chinese, we likewise anticipate **dual peaks at 1 Hz and 2 Hz. Hierarchical chunking is driven by universal constraints—working-memory limits and predictive parsing mechanisms—rather than by language-specific lexical cues.** Hence cross-linguistic factors such as function-word density may shift power, but they do not eliminate the peaks themselves. This envision also **aligns with Ding et al. (2016), which showed that the 1–2 Hz hierarchy is a language-general signature of syntactic grouping, independent of word-order typology or writing system.**  \nOur bilingual model results reinforce this view: **Appendix C, Fig. 7 in the main text show both language‑specific and language‑general syntactic neurons within a single LLM.** This finding is especially informative because, although Tang et al. (2024) also identified language-specific neurons, they did not investigate their functional roles—syntactic, semantic, or otherwise. Consequently, these results lead us to hypothesize that the human language network contains both language‑specific channels, tuned to idiosyncratic phonological or orthographic statistics, and language‑general channels that track abstract hierarchical structure independently of the surface code. Such a division is neuro‑computationally efficient: language‑general pathways support rapid transfer of syntactic skills across languages, while language‑specific pathways fine‑tune predictions to each lexicon, minimizing processing cost and error.\n> ## Q6: Long‑term positive and negative societal impacts should be declared.\n\nA6: Thank you for pointing the missing societal impacts out. HFTP offers a transparent, neuro‑inspired lens on LLM internals. Positive impacts include safer, more controllable models—by revealing which neurons drive hierarchical syntax we can design targeted safeguards and debiasing interventions; improved language‑disorder diagnostics—spectral markers could serve as non‑invasive probes of syntactic deficits; and richer human‑AI interfaces that align model parsing with human cognition. Negative impacts also merit consideration: the same interpretability could be weaponised to optimise persuasive text or amplify covert influence; detailed brain‑model correspondences might facilitate adversarial “neuro‑phishing” that mimics neural rhythms; and, if paired with personal neural data, fine‑grained model maps could erode privacy."}, {"review_id": "zt2IIRp2yp", "reviewer": "Reviewer_h9Pq", "summary": "This work uses a method first introduced by Ding et al. (2016) to analyze the syntactic structure of very simple sentences. The authors adapt this method to the study of large language models, by looking at the frequency components of the response of a large language model to carefully designed stimuli (here consisting in sentences of four words, carefully put together into groups that reflect word, phrase or sentence level). This makes it possible to have a common method that allows for a direct comparison between the language processing of the language models and the human brain. Such a comparison is provided, making use of a corpus of sEEG recordings.", "questions": "Why focus only on the hidden layer of the MLPs? The neurons at the output of a transformer layer (output of the MLP, of size d_model) might be interesting to look at. One could imagine that certain syntactic neurons might be visible at the finer resolution of the hidden layer of the MLP but not when putting them all together down to the dimension of the embedding, due to some superposition; but conversely, having a finer resolution might yield responses that are not visible under the criteria that are chosen here but might be visible when combined together at the output of each layer. \n\nConcerning the methods, what do you do when a word is split into several tokens?\n\nWill you make the sEEG corpus available?\n\nWhat is the rationale behind the use of bold font in Table 1?", "limitations": "An important limitation is the generalization to naturalistic text, which is not acknowledged by the authors (see Weaknesses section above).", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Having the same method to analyze both large language models and the brain activity is certainly valuable, and as far as I know, the method of Ding et al. (2016) has never been used to analyze the linguistic processing of large language models.\n\nOne important issue at first sight is the fact that the authors do not specify that the model that they call GPT-2 is actually not the original GPT-2. If this was the case, all the results would be hard to believe, given that the original GPT-2 model was primarily trained on English data, while here it is the model that achieves the best performance at their brain/models comparison in processing Chinese data. Buried in Appendix C, and not announced in the main paper (even the cite to Radford et al., 2019, is misleading, as found in Table 3), there is actually the information that the GPT-2 model that is used is not the original version but another version pretrained on a Chinese corpus. This is far from being a detail and should be stated upfront, right from the presentation of the models, as otherwise the results seem very puzzling.\n\nAn important limitation of the study concerns the generalization to naturalistic text. It is said that the method generalizes \"seamlessly to naturalistic text\" (see Conclusion), but the evidence for such a statement is rather weak. There is only a small appendix (appendix E) addressing this point, and it is actually based on Chinese only, and, as discussed by the authors, due to properties specific to the Chinese language. How all this would work for languages like English is not discussed and is far from obvious -- this goes counter to the statement that the method generalizes, which is misleading. This should be discussed and acknowledged in a more transparent way.\n\nThe origins of the differences between models are not much investigated, notably with respect to their training data. Also, more work on understanding the role of the syntactic neurons would be appreciated, possibly by ablating these syntactic neurons vs. other neurons.\n\nThe Related Work section cites \"methodological inconsistencies\" across previous studies, without providing more details -- it would be nice to state what these inconsistencies are.\n\nMinor points.\n\nThe text contains some typos (eg a few places with no space after a full stop, \"fruquency\").\n\nTable 3, the number of MLP neurons for Gemma 2 seems wrong, as the Gemma 2 paper reports that the feedforward dim for Gemma 2 9B is 28672. Please verify these numbers.", "was_revised": true, "final_justification": "This paper is an interesting extension of the work by Ding et al. (2016), allowing for a common method to study syntactic representation both in language models and in the human brain. Although there is still more investigation needed to understand why the different models behave so differently (as in Fig. 4), and it is not obvious how this work might extend to more complex syntactic phenomena (as noted by another reviewer), the rebuttal and following discussion address my main concerns, particularly the question of the generalization to naturalistic English, which was an important limitation of the original version. The revised version should be clearly improved, and I raise my score to 5.", "rebuttal": "We sincerely appreciate the constructive feedback and the effort you invested in evaluating our manuscript. We respond to every your concern in the following paragraphs.\n\n---\n> ## Q1: Why analyze only hidden MLP neurons? Consider layer outputs as well.\n\nA1: Thank you for pointing this out. **We have tested the HFTP pipeline on layer outputs (i.e. hidden states).** Below are the hidden‑state results for three representative models; values for MLP neurons (reported in the main paper) are systematically higher, confirming their stronger brain alignment.\n| | GPT‑2‑large (L / R) | Llama‑2‑7B (L / R) | Llama‑3.1‑8B (L / R) |\n|-------------------|---------------------|--------------------|----------------------|\n|S(m,b)|0.611 / 0.425|0.626 / 0.430|0.512 / 0.405 |\n|A1|0.640 / 0.339|0.599 / 0.561|0.523 / 0.377|\n|STG|0.630 / 0.382|0.648 / 0.443 |0.505 / 0.381|\n|MTG|0.609 / 0.384|0.649 / 0.419|0.512 / 0.409|\n|ITG|0.606 / 0.427|0.608 / 0.419|0.510 / 0.402|\n|Insula|0.582 / 0.445|0.615 / 0.447|0.509 / 0.421|\n|TPJ|0.566 / 0.430|0.611 / 0.371|0.509 / 0.412|\n|Temporal Pole|0.612 / 0.464|0.597 / 0.459|0.507 / 0.424|\n|Sensorimotor|0.594 / 0.460 |0.616 / 0.428|0.514 / 0.405|\n|IFG| 0.647 / 0.449| 0.644 / 0.483 |0.508 / 0.414|\n|MFG| 0.579 / 0.407|0.584 / 0.355|0.506 / 0.397|\n|Hippocampus| 0.581 / 0.403 |0.585 / 0.396 |0.519 / 0.403|\n|Amygdala|0.496 / 0.439|0.620 / 0.528|0.502 / 0.410|\n\nFeed-forward blocks function as key–value stores (Geva et al. (2021)): the first linear projection expands the signal so individual units can store disentangled lexical, syntactic, or factual features; the second projection then recombines these features back to d_model (i.e. hidden states). Dai et al. (2022) shows that most “knowledge neurons” live in this expanded space. **Probing with HFTP at this inner-MLP stage keeps phrase- and sentence-selective units distinct, producing sharper 1 Hz/2 Hz peaks and higher model–brain alignment. Running the same analysis on the compressed hidden states blurs these rhythms and lowers scores.** We therefore prioritise inner-MLP neurons, listing hidden-state results only for completeness.\n\nGeva, et al. \"Transformer Feed-Forward Layers Are Key-Value Memories.\" EMNLP. 2021.  \nDai, et al. \"Knowledge Neurons in Pretrained Transformers.\" ACL. 2022.\n> ## Q2: Evidence for “seamless” generalization to naturalistic text is weak and limited to a small Chinese appendix. \n\nA2: Thank you for pointing this out. We are sorry for the misleading claims that our method generalizes \"seamlessly to naturalistic text\". **We have now added English natural‑text experiments** as follows, which reveal the same hierarchical peaks as in Chinese. Therefore, **the more precise statement is that *“HFTP generalizes across Chinese and English naturalistic datasets and is highly likely to extend to other character-centric scripts (e.g., Japanese, Korean) as well as further alphabetic languages (e.g., French, German).”***\n\nTo complement the Chinese corpus, we conducted an English natural‑text experiment that uses balanced sets of eight‑ and nine‑word sentences (50 sentences per set).  The materials span everyday dialogue, news reports, literary prose, and so on—mirroring the genre diversity of the Chinese stimuli.\n\n|8‑word sentences (excerpt)|genre|\n|---|---|\n| With malice toward none, with charity toward all. | Literary prose |\n| Tonight, shall we watch the meteor shower together? | Dialogue |\n| A man can be destroyed but not defeated. | Literary prose|\n\n|9‑word sentences (excerpt)|genre|\n|---|---|\n| Clinical trial data were publicly released this morning nationwide. | News report |\n| Shall we share sushi together tonight by the river? | Dialogue |\n| War is peace, freedom is slavery, ignorance is strength. | Literary prose |\n\n### Frequency-domain results  \n* Eight‑word set: Robust peaks appear at **0.50 Hz, 1.00 Hz, 1.50 Hz, 2.00 Hz**, corresponding to the sentence envelope, canonical 4‑word phrases, intermediate 2‑to‑3‑word groupings, and the dominant 2‑word lexical rhythm.  \n* Nine‑word set: Peaks shift to **0.44 Hz, 0.89 Hz, 1.33 Hz, 1.78 Hz**: the lowest peak reflects the whole clause, 1.33 Hz aligns with frequent 3‑word phrases, 1.78 Hz captures rapid two‑to‑three‑word alternations, and 0.89 Hz indexes a four‑to‑five‑word prosodic “breath group”.  \n\nThis pattern **mirrors the hierarchical frequency profile found in the Chinese naturalistic experiment (see Figure 8&9 in the main text),** demonstrating that HFTP reliably tracks sentence-, phrase‑, and clause‑level rhythms in typologically-distinct languages.  The English naturalistic results demonstrate that HFTP scales smoothly from Chinese to an alphabetic writing system. Because the method operates at the rhythm unit that is natural for each script—characters in non-segmented CJK texts, words or sub-words in space-delimited alphabets—we are confident it will likewise generalize to other character-centric (e.g., Japanese, Korean) and alphabetic(e.g., French, German) naturalistic data.\n\n> ## Q3: GPT-2 is actually a Chinese-trained variant; this must be stated prominently in the main text.\n\nA3: We apologise for not stating this at the outset and for any confusion it caused, especially given GPT‑2’s high alignment score. In the revised manuscript **we will explicitly note, on first mention and again in the Results, that our GPT‑2 variant is pre‑trained on a Chinese corpus** because the original English GPT‑2 cannot process Chinese text.\n> ## Q4: Handling of multi-token words\n\nA4: We explicitly normalised tokenisation across models. Each LLM employs a different sub‑word scheme; most English‑trained models split a single word into multiple tokens (e.g., Llama‑2‑7B tokenises “A friend invites guests” as ▁A, ▁friend, ▁inv, ites, ▁guests). **Because HFTP treats one word as the minimal semantic unit driving the 4‑word/8‑word rhythmic structure, we aggregate all sub‑tokens that belong to the same word**: their hidden activations are averaged to yield a single word‑level vector before the FFT. This ensures that amplitude at 1 Hz (sentence) and 2 Hz (phrase) reflects linguistic—not tokeniser—boundaries.  \nWe apply the same normalization to Chinese. Although all six models support Chinese, their tokenizers differ: some (e.g., Gemma, Llama) split a single character into multiple tokens, whereas others (e.g., GLM‑4) merge several characters. To enforce a one‑character‑per‑token mapping, we insert explicit delimiters—transforming “朋友请客”into “朋\\*友\\*请\\*客\\*”—and then average the hidden activations of any sub‑tokens belonging to the same character. This ensures that **each Chinese character (and each English word) produces exactly one activation trace**, making the subsequent frequency‑tagging fully comparable across languages and models.\n> ## Q5: Prior work is said to have “methodological inconsistencies” when exploring syntactic representations between LLMs and the human brain without specifying what they are.\n\nA5: We apologise for the ambiguity. In our text, “methodological inconsistencies” refers to the heterogeneous ways prior studies probe syntax in brains versus LLMs, which makes cross‑study synthesis difficult:\n- Signal source varies. Sun et al. (2020) map whole‑sentence embeddings to fMRI; Caucheteux & König (2021, 2022) first project hidden states into syntactic/semantic subspaces; Wang et al. (2020) use variational disentanglement; Oota et al. (2023) ablates tree‑depth features.\n- Neural coverage is uneven. Data range from single‑region ECoG to ROI‑level MEG to whole‑brain fMRI, meaning the same syntactic signal may be sampled in one study but absent in another.\n\n**Model‑side and brain‑side probes have remained system‑specific—LLM syntax is probed via structural methods (e.g., Hewitt & Manning 2019), while human syntax relies on experimental modalities (fMRI, MEG, ECoG) that sample limited cortical regions—making direct comparisons impossible.** Thus HFTP resolves this by offering a single frequency‑domain probe that treats MLP neurons and cortical channels identically, removing probe asymmetry and unifying the evaluation metric for model‑agnostic, systematic study of hierarchical syntax encoding across both systems.\n> ## Q6: Incorrect Gemma-2 feed-forward dimension (should be 28 672) need correction.\n\nA6: Thank your for pointing this out. The apparent mismatch arises from two counting conventions. In the released HuggingFace checkpoint, Gemma 2 9B has a model width of 3 584 and an intermediate size of 14 336. Because Gemma 2 uses a GeGLU feed‑forward block, the intermediate vector is produced by two parallel projections of size 14 336 each (“gate” and “up”) that are combined element‑wise before being projected back to 3 584. **The Gemma 2 paper reports the effective expanded width—the concatenation of both streams—which is 2 × 14 336 = 28 672. For our neuron counts we follow the transformer‑config convention and list the per‑projection dimension (14 336), as this represents the actual number of activations entering the non‑linearity.** Hence the value in Table 3 is correct, while the paper’s larger figure simply reflects the doubled size created by GeGLU’s two paths.\n> ## Q7: Release plans of sEEG corpus, and bold-font rationale in Table 1.\n\nA7: Yes, we plan to release the sEEG corpus upon obtaining formal permission from the authors of Sheng et al. (2019), as our sEEG data originated from that study.\nWe apologize for not providing a clear explanation in the main text. **For the model–brain similarity row $S(m,b)$, bold numbers indicate the model with the highest syntactic alignment with human brain data (GPT‑2 in Table 1).\nFor the model–region similarity rows (each ROI listed in the first column), bold numbers highlight the top three ROIs where a given model shows the strongest alignment.** This ranking combines both left and right hemispheres to reveal potential lateralization patterns—that is, whether a model’s syntactic processing mirrors the human brain’s known left-dominant language regions."}, {"review_id": "8SaZQVhjPm", "reviewer": "Reviewer_EeaA", "summary": "This paper introduces the Hierarchical Frequency Tagging Probe (HFTP), a unified framework for investigating syntactic structure representations in both LLMs and the human brain using frequency-domain analysis. The authors apply FFT to LLM activations and human sEEG recordings to identify neurons/channels that respond to sentence (1 Hz) and phrase (2 Hz) frequencies. They test six LLMs (GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, GLM-4) and find that while LLMs process syntax in analogous layers, the human brain uses distinct cortical regions. Representational similarity analysis reveals stronger alignment between LLMs and the left hemisphere, with divergent trends in model upgrades.", "questions": "1. How do you justify the 4 Hz sampling constraint for LLMs? This seems to force a temporal structure that doesn't naturally exist in these models. Have you tested other frequencies or more naturalistic approaches?\n2. The correlations between sentence and phrase neurons are extremely high (0.754-0.994). Doesn't this suggest you're detecting general linguistic processing rather than distinct syntactic levels? How do you address this concern?\n3. Given the restriction to Chinese brain data, how confident are you that these alignment patterns would hold for English speakers or other languages? Can you provide any cross-linguistic validation?\n4. You tested relatively simple syntactic structures. How would HFTP perform on more complex syntactic phenomena like long-distance dependencies, garden-path sentences, or recursive structures?\n5. Have you tested whether similar frequency patterns emerge for non-syntactic linguistic features (e.g., semantic or phonological patterns) to ensure syntactic specificity?", "limitations": "The authors adequately acknowledge the language-specific limitations and restricted model coverage. However, they could better address the fundamental methodological concern about artificial temporal constraints and the implications of extremely high sentence-phrase correlations. The computational overhead and scalability of the approach across different model architectures also deserves discussion.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe unified framework for comparing LLMs and brain representations is novel and addresses an important gap in understanding computational vs. biological language processing. The frequency-domain approach is creative and well-motivated, building appropriately on established HFT paradigms. The comprehensive evaluation across multiple LLMs with proper controls (randomized versions) is commendable.\n\nWeaknesses:\n1. The artificial temporal constraints imposed on LLMs are problematic. Constraining activations to 4 Hz and artificially defining time scales seems unmotivated and creates an artificial mapping that may not reflect genuine syntactic processing mechanisms in these models.\n2. The high correlations between sentence and phrase neurons across all LLMs raise questions about whether the method is actually detecting distinct syntactic processes or simply identifying general linguistic sensitivity with slight frequency variations.\n3. Generalizability is limited by restriction to Chinese sEEG data and Chinese/English text corpora. The authors acknowledge this but it significantly constrains the broader applicability of findings. The relatively simple syntactic structures tested may not capture the full complexity of syntactic processing.\n4. The theoretical justification for why frequency-domain analysis should reveal syntactic representations in LLMs lacks depth. While the biological motivation is clear, the computational rationale is underdeveloped.", "was_revised": true, "final_justification": "The questions I raised have been addressed, and I believe the paper makes a valuable contribution deserving of acceptance at NeurIPS. I have updated my score from 4 to 5.", "rebuttal": "We appreciate your detailed and constructive suggestions. Below, we address each of your concerns one by one.\n\n---\n> ## Q1: 4 Hz sampling constraint seems unmotivated.\n\nA1: The 4 Hz grid is not meant to “inject” time into the model; it is simply the clock that matches the sEEG experiment, where syllables are played at 4 Hz and the target phrase‑ and sentence‑beats therefore occur at 2 Hz and 1 Hz. If the stimuli were paced at 6 Hz or 8 Hz we would re‑sample activations at the same rate, and those beats would shift proportionally (e.g., 8 Hz → 2 Hz and 4 Hz peaks). **In other words, the sampling rate is an external synchronisation parameter that lets model and brain be analysed with a shared FFT, without altering the linguistic content inside the network.**\nWe also applied HFTP to naturalistic corpora in Q4, showing that our method generalizes beyond rhythmic input to both Chinese and English naturalistic stimuli. Consequently, the sampling rate serves only as a temporal granularity parameter—regardless of its value, HFTP consistently recovers the underlying sentence- and phrase-level rhythms.\n> ## Q2: Very high sentence–phrase correlations suggest probe may detect general linguistic sensitivity, not distinct syntax levels.\n\nA2: The strong count correlation simply reflects the hierarchical nature of language: **every sentence contains phrases, so layers that allocate more capacity to phrase rhythms inevitably allocate more to sentence rhythms as well.** Importantly, **the neurons themselves remain selective**—phrase‑tagged units peak only at 2 Hz, sentence‑tagged units only at 1 Hz—showing two non‑overlapping spectral signatures rather than a single, undifferentiated “language” response.  \nTo identify the linguistic operation performed by the phrase‑ and sentence‑selective neurons, we implemented a structure‑probe analysis closely following Hewitt & Manning (2019). For every model layer we first isolated the HFTP‑flagged phrase and sentence neurons, then fed the original stimuli forward and recorded their activations for every token.  For each layer ℓ we recorded activations, built a cosine‑distance matrix $D_ ℓ$, and compared it (Spearman ρ) with two reference RDMs: one marking phrase boundaries, the other sentence boundaries. Averaging across trials yields a layer‑wise syntactic profile.\nResults are similar to Figure 4.GPT‑2 peaks for sentence and phrase structure in early-mid layers and declines in last few layers. Llama‑2‑7B shows modest phrase alignment but strong sentence alignment rising in the final third of the network, consistent with late compositional integration. Llama‑3.1‑8B maintains consistently high alignment for both phrase and sentence structures in mid-to-late layers. **Thus the detected syntactic neurons reflect genuine hierarchical syntactic representations, not generic linguistic sensitivity**, and our probe robustly maps each model’s preferred depth for phrase‑ and sentence‑level processing.\n> ## Q3: Generalizability limited by Chinese-only sEEG; need cross-lingual validation.\n\nA3: We have already collected **a bilingual sEEG session from one native‑Chinese participant who also understands everyday English**. Using the four‑word English (e.g., Old ox plows field, A friend invites guests) and four‑character Chinese (e.g., 老牛耕地，朋友请客) stimuli, we observed identical spectral signatures—**robust peaks at 1 Hz (sentence) and 2 Hz (phrase)—for both Chinese and English inputs**.   \n**For English speakers who also understand Chinese, we likewise anticipate dual peaks at 1 Hz and 2 Hz.** Hierarchical chunking is driven by universal constraints—working-memory limits and predictive parsing mechanisms—rather than by language-specific lexical cues. **Hence cross-linguistic factors such as function-word density may shift power, but they do not eliminate the peaks themselves. This envision also aligns with Ding et al. (2016), which showed that the 1–2 Hz hierarchy is a language-general signature of syntactic grouping, independent of word-order typology or writing system.**    \nOur bilingual model results reinforce this view: Appendix C, Fig. 7 show both language‑specific and language‑general syntactic neurons within a single LLM. This finding is especially informative because, although Tang et al. (2024) also identified language-specific neurons, they did not investigate their functional roles—syntactic, semantic, or otherwise. Consequently, these results lead us to hypothesize that the human language network contains both language‑specific channels, tuned to idiosyncratic phonological or orthographic statistics, and language‑general channels that track abstract hierarchical structure independently of the surface code. Such a division is neuro‑computationally efficient: language‑general pathways support rapid transfer of syntactic skills across languages, while language‑specific pathways fine‑tune predictions to each lexicon, minimizing processing cost and error.\n> ## Q4: Request evaluation on complex syntactic phenomena.\n\nA4: **We have included the Chinese natural‑data HFTP experiment in Appendix E of the main text**, with frequency‑domain results shown in Figures 8–9 and corpus details in Tables 6–7. To confirm generalizability, **we also ran an English natural‑text experiment using balanced sets of eight‑ and nine‑word sentences** (50 per set) drawn from everyday dialogue, news reports, literary prose and so on—mirroring the genre diversity of the Chinese stimuli.\n\n|8‑word sentences (excerpt) | genre|\n|---|---|\n| With malice toward none, with charity toward all. | Literary prose |\n| Tonight, shall we watch the meteor shower together? | Dialogue |\n| A man can be destroyed but not defeated. | Literary prose|\n| Excuse me, where’s the nearest restroom around here? | Dialogue |\n| The library felt crowded during rainy examination week. | News report |\n| Market demand seems gradually rebounding during this quarter. | News report |\n\n|9‑word sentences (excerpt)|genre|\n|---|---|\n| Clinical trial data were publicly released this morning nationwide. | News report |\n| Shall we share sushi together tonight by the river? | Dialogue |\n| War is peace, freedom is slavery, ignorance is strength. | Literary prose |\n| We offer cola, soda, juice, and soy milk today. | Dialogue |\n| Photography exhibition focuses on city’s hidden pockets of light. | News report |\n| Driver, how much is the fare to the station? | Dialogue |\n\nThe frequency‑domain patterns are consistent across both Chinese and English naturalistic datasets:\n* Eight‑word set: Robust peaks appear at **0.50 Hz, 1.00 Hz, 1.50 Hz, 2.00 Hz**, corresponding to the sentence envelope, canonical 4‑word phrases, intermediate 2‑to‑3‑word groupings, and the dominant 2‑word lexical rhythm.  \n* Nine‑word set: Peaks shift to **0.44 Hz, 0.89 Hz, 1.33 Hz, 1.78 Hz**: the lowest peak reflects the whole clause, 1.33 Hz aligns with frequent 3‑word phrases, 1.78 Hz captures rapid two‑to‑three‑word alternations, and 0.89 Hz indexes a four‑to‑five‑word prosodic “breath group”.  \n\nThe bilingual natural‑text study shows that HFTP scales from controlled four‑syllable strings to unrestricted prose: **for both Chinese and English, we recover a clean hierarchy of clause, phrase, and lexical rhythms even when sentence length and genre vary.** This demonstrates that the probe is not limited to simple structures but generalises to the richer combinatorics of everyday language.  \nFor long‑distance dependencies (e.g., wh‑movement over multiple clauses) we predict an additional, lower‑frequency peak (< 0.5 Hz) reflecting the longer integration window; all higher‑level peaks should remain, merely compressed along the frequency axis.\nFor garden‑path sentences the initial mis‑parse and subsequent reanalysis should appear as two successive bursts in the same frequency band (first at the garden‑path interpretation, then at the corrected structure), a pattern we can time‑lock in sliding‑window FFTs.\nFor recursive embeddings we expect a nested harmonic series: each new level of recursion introduces a further sub‑harmonic (½, ⅓, ¼ × the phrase rate) while preserving the base phrase and lexical peaks.\nBecause HFTP reads frequency rather than word position, these phenomena require only appropriate presentation rates (longer stimuli → lower sampling) to fall within the analyzable band. \n\n> ## Q5: Test on non-syntactic controls for specificity.\n\nA5: Yes. **In the main text, we ran random‑label control in which all words were permuted so that sentence and phrase structure**—and most semantics—were destroyed while lexical statistics were preserved. **Across models this control produced flat spectra with no 1 Hz/2 Hz peaks (see Figure 3, 5, 8, 9, “random” labels).** We also include a **phrase‑only data (phrase corpus in the main text)** that preserves two‑word phrase boundaries but removes sentence structure. FFT results show **a strong 2 Hz peak while the 1 Hz sentence peak disappears.** Conversely, the random‑label control flattens both peaks. Together these manipulations demonstrate that HFTP responds precisely to the syntactic boundary present—phrase or sentence—rather than to general lexical, phonological, or semantic patterns.\n\n> ## Q6: Explain why should FFT expose syntax in LLMs, and how heavy is the method.\n\nA6: Hierarchical syntax produces nested periodicities whenever tokens are serialized—clauses recur slower than phrases, phrases slower than lexical pairs. **The transformer’s feed‑forward clock preserves this rhythm in its hidden activations; applying an FFT simply makes the nested rates explicit.** For all models, we performed activation extraction in float16 precision on two NVIDIA A100 GPUs (40 GB each), with end‑to‑end runtimes spanning from tens of seconds to a few minutes depending on model size."}, {"review_id": "oE95PZGmh0", "reviewer": "Reviewer_2nNc", "summary": "The authors present an extension of the experimental method of Ding et al. (2016) to both a  dataset of human subjects with intracranial sEEG recording and a fleet of text-based Transformer language models. This method revealed, in both Transformer models and brains, individual units (or electrodes) which exhibited structure tracking (=frequency tracking) at the phrase- and sentence-level. They found that different language models exhibited vastly different distributions of phrase- and sentence-selectivity, but that within any model, the distribution of phrase- and sentence-level neurons is strongly correlated across layers. This latter fact does not hold in a similar comparison applied across brain regions, suggesting a distinct gross architecture for syntactic processing between models and the human brain.", "questions": "Please minimally address the points given with asterisks above.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "This is an ambitious paper which transfers a popular method from cognitive neuroscience to AI, and draws broad conclusions about an architectural dissociation between models and humans as a result. The experiments are extensive on the modeling side, covering a large class of LLMs.\n\nHowever, there are some major analytic and experimental issues that make this paper difficult to accept at this point. These are given below with asterisks. Other conceptual / presentational issues are also highlighted.\n\n- 4.1 MLP neurons represent sentences and phrases\n\t- You find a strong correlation between the number of sentence and phrase neurons within-model across layers (L254–257). This is interesting. How do you reconcile this with existing claims that LLMs distribute different syntactic functions across distinct layers (e.g. Tenney et al. (2019))?\n\t- \\* L237–246: The interpretation of differences between phrase- and sentence-selectivity layer distributions across models is pretty unsatisfying (\"reflecting a more integrated approach ... suggesting a balanced intermediate strategy ... indicating an emphasis on initial-stage syntactic processing\"). These labels aren't very meaningful without further analysis. Given that this is a core finding of your paper (and part of the critical mismatch with the brain findings) I think you need to do more work to detail what kind of process it is you are locating which is in wildly distant layers across different LLMs. You could use probing methods, for example, to ask whether the populations you're picking out correspond to theoretical syntactic concepts.\n- 4.3 SRDM analysis\n\t- Your SRDM method evaluates the cosine distance between two frequency spectra, and is thus sensitive to differences across the entire spectrum. But we know by the controlled design of your setup that there are only a few bands that actually matter. For simplicity and interpretability, why not just take the amplitude of the two relevant peaks at 1 and 2 Hz? Is there something we gain with the cosine distance metric here?\n\t- How exactly are SRDMs computed at each model layer? Are you averaging SRDMs across all neurons? Or just the ones found to be sentence/phrase-selective?\n\t- I am having a lot of trouble interpreting Table 1.\n\t\t- These are text-based models and yet, for each model, the best- or near-best-predicted region is A1. How can this be? This does not match, for example, the performance distribution of the ECoG study of Goldstein et al. (2022), who seem to find highest predictability in frontal regions.\n\t\t- \\* We need error bars on these values. Consider, for example, bootstrapping the set of stimuli that enter into each SRDM in order to estimate nonparametric confidence intervals, and/or bootstrap-sampling the subjects that enter into the SRDM evaluation.\n\t- \\* Comparisons between ROIs and hemispheres here are likely confounded with major between-subject differences in signal quality and SRDM performance, since the placement of electrodes is highly variable across subjects. As far as I can tell this was not taken into account in any evaluations. I would suggest running per-ROI model–brain alignment within-subject, and then using a mixed-effects regression model to explain per-ROI and per-hemisphere performance controlling for random subject-level intercepts.\n- General questions\n\t- Why do you not analyze at the syllable level (4 Hz) as done in Ding et al. (2016)?\n- Minor questions/comments\n\t- Table 1: What do bold numbers indicate? This is not labeled anywhere as far as I can see.\n\t- Why is there no second constraint (as in Definition 2) on electrode responses as well? Without this, the comparison is not exactly matched: you're finding MLP neurons that are *exclusively* sentence- and/or phrase-tuned, but electrodes which are tuned (not necessarily exclusively). Do you not have the relevant control materials for the sEEG data?\n\nDing, N., Melloni, L., Zhang, H. _et al._ Cortical tracking of hierarchical linguistic structures in connected speech. _Nat Neurosci_ **19**, 158–164 (2016). https://doi.org/10.1038/nn.4186\n\nGoldstein, A., Zada, Z., Buchnik, E. _et al._ Shared computational principles for language processing in humans and deep language models. _Nat Neurosci_ **25**, 369–380 (2022). https://doi.org/10.1038/s41593-022-01026-4\n\nIan Tenney, Dipanjan Das, and Ellie Pavlick. 2019. [BERT Rediscovers the Classical NLP Pipeline](https://aclanthology.org/P19-1452/). In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pages 4593–4601, Florence, Italy. Association for Computational Linguistics.", "was_revised": true, "final_justification": "The authors have shared an impressive amount of extra work in their rebuttal. The structural probe-style analysis along with statistical controls settle some of my questions, and I also appreciate the naturalistic English experiment presented in response to other reviewers. Answers to methodological questions are also satisfactory. I will raise my score from 2 to 4.", "rebuttal": "Thank you for your detailed evaluation and thoughtful recommendations. Your feedback has significantly contributed to the refinement of our study. We respond to all of your points in the following part.\n\n---\n> ## Q1: Reconcile strong sentence–phrase layer correlations with prior layer-specialisation results of Tenney et al. (2019).\n\nA1: Our correlation shows that when a layer devotes many units to phrase rhythms it also hosts sentence rhythms, revealing a shared hierarchical scaffold. Tenney et al. locate the depths where supervised edge‑probes become linearly decodable, yielding an early‑vs‑late split. **Co‑location of phrase‑ and sentence‑responsive neurons does not contradict that split; it means both levels ride the same representational backbone before different linguistic functions are teased apart by linear probes.**\n\n> ## Q2: Current interpretation of model-specific layer distributions is “unsatisfying” and needs deeper analysis (e.g., probing).\n\nA2: To identify the linguistic operation performed by the phrase‑ and sentence‑selective neurons that appear in widely separated layers, we implemented a structure‑probe analysis closely following Hewitt & Manning (2019). For every model layer we first isolated the HFTP‑flagged phrase and sentence neurons, then fed the original stimuli forward and recorded their activations for every token.  For each layer ℓ we recorded activations, built a cosine‑distance matrix $D_ ℓ$, and compared it (Spearman ρ) with two reference RDMs: one marking phrase boundaries, the other sentence boundaries. Averaging across trials yields a layer‑wise syntactic profile.\n\nResults are similar to Figure 4.GPT‑2 peaks for sentence and phrase structure in early-mid layers and declines in last few layers. Llama‑2‑7B shows modest phrase alignment but strong sentence alignment rising in the final third of the network, consistent with late compositional integration. Llama‑3.1‑8B maintains consistently high alignment for both phrase and sentence structures in mid-to-late layers. **Thus the “distant” selective layers reflect genuine hierarchical syntactic representations, not generic linguistic sensitivity**, and our probe robustly maps each model’s preferred depth for phrase‑ and sentence‑level processing.\n> ## Q3: Why use cosine-distance SRDMs instead of simply comparing 1 & 2 Hz peak amplitudes?\n\nA3: Cosine-distance SRDMs encode the full spectral geometry of each condition, **making the metric immune to baseline shifts or local noise spikes that often distort single-peak measures**. In pilot tests, peak-only scores sometimes failed to detect true model–brain matches when slight frequency drifts occurred, whereas SRDM-based RSA consistently retrieved the same top layers and channels. SRDMs therefore deliver a more robust and sensitive similarity estimate without adding methodological complexity.\n> ## Q4: Explain exactly how SRDMs are computed (which neurons, averaging procedure, etc.).\n\nA4: For each layer ℓ we isolate phrase-only, sentence-only, and dual-responsive neurons, obtain their activations for every stimulus, and **build three SRDMs based on phrase, sentence, dual neurons** by taking cosine distances between FFT spectra of all stimulus pairs. Each sEEG channel receives an analogous SRDM. We correlate each model SRDM with every channel SRDM (Spearman ρ), average the top 100 ρ values to yield the layer-brain similarity for that neuron group, and then average across layers to obtain three model–brain scores. The mean of these three scores is reported as $S(m,b)$.\n> ## Q5: A1 emerges as best-predicted region in Table 1—contradicts prior ECoG studies of Goldstein et al. (2022)\n\nA5: In fact, our study and Goldstein et al. (2022) address fundamentally different questions with distinct methods, so it is unsurprising that their peak regions diverge from ours. **In reality, Goldstein et al. (2022) did not investigate syntactic processing; they focused on semantic next‑word prediction**, showing that both the brain and GPT‑2 “match their pre‑onset predictions to the incoming word”. They model high‑γ (70–200 Hz) ECoG power recorded from surface grids and strips that do not sample the core of Heschl’s gyrus (A1) because subdural electrodes cannot reach that medial sulcal cortex. When contextual embeddings are added, they highlight additional fits in frontal/temporal‑pole areas, but early auditory contacts remain in the significant set.\n\nOur work, by contrast, targets hierarchical syntactic rhythms (0.5–2 Hz) and uses depth‑SEEG shafts that traverse A1, giving much denser coverage and allowing us to measure phase‑locked low‑frequency responses that originate in early auditory cortex. Because syntax‑driven rhythmic entrainment is strongest at the auditory core, A1 naturally emerges as the best‑predicted ROI. Hence **our findings complement Goldstein’s semantic‑frontal effects rather than contradict them.**\n> ## Q6: Provide error bars and control for electrode-placement confounds (suggest mixed-effects analysis).\n\nA6:We implemented two complementary analyses exactly as suggested:  \n1. Bootstrapped 95% confidence intervals \n   * For every model‑layer we averaged the top‑100 channel correlations, then bootstrapped (10 000 resamples of channels × layers) to obtain mean ± 95 % CI.\n   * For each brain region the same procedure was applied across layers, and the three syntactic neuron types were then averaged to give a single region‑level value.\n2. Mixed‑effects regression to account for the confound that different subjects contribute different sets of electrodes.  \n   * Model: Alignment ~ ROI + (1 | Subject)  (random intercept per subject).  \n   * Significance therefore reflects within‑subject ROI effects, while the group‑level intercept absorbs between‑subject signal‑quality differences.\n   * We additionally report per‑subject OLS tables (four examples shown below) to demonstrate that effects are not driven by a single individual.\n\n### Table S1: Llama 2 | Sentence condition — Bootstrap 95 % CI\n| ROI |L(μ ± CI)|R(μ ± CI)|\n|-----|--------------|--------------|\n|S (m, b)| 0.651 (0.646 – 0.655) | 0.446 (0.443 – 0.450) |\n| A1 | 0.653 (0.644 – 0.662) | 0.401 (0.379 – 0.424) |\n| STG | 0.666 (0.660 – 0.673) | 0.417 (0.408 – 0.425) |\n| MTG | 0.666 (0.659 – 0.672) | 0.425 (0.418 – 0.431) |\n| ITG | 0.637 (0.631 – 0.643) | 0.439 (0.435 – 0.443) |\n| Insula | 0.634 (0.626 – 0.643) | 0.487 (0.477 – 0.497) |\n| TPJ | 0.628 (0.617 – 0.638) | 0.448 (0.435 – 0.460) |\n| Temporal Pole | 0.658 (0.648 – 0.669) | 0.481 (0.470 – 0.491)|\n| Sensorimotor | 0.640 (0.634 – 0.645) | 0.461 (0.456 – 0.466) |\n| IFG | 0.688 (0.679 – 0.696)|0.476 (0.466 – 0.485)|\n| MFG | 0.594 (0.587 – 0.602)|0.409 (0.397 – 0.422)|\n|Hippocampus|0.621(0.607 – 0.636)| 0.421 (0.414 – 0.428)|\n|Amygdala| — |0.478(0.462 – 0.495)|\n\n### Tabls S2: Mixed‑effects highlights († p < 0.01 vs baseline A1)\n| Hemi | $σ_{\\text{subj}}$ | ↑ ROI | ↓ ROI |\n|------|-----------|-----------------------|--------------------|\n|L|0.032| IFG (+0.057), STG (+0.025) | Hippocampus (–0.040), Insula (–0.021), MFG (–0.063), Sensorimotor (–0.023), TPJ (–0.020) |\n|R|0.037| Amygdala (+0.072), Insula (+0.052), Temporal Pole (+0.053), IFG (+0.038) | — |\n\n*σ_subj is the random‑intercept SD; its small value confirms residual\ninter‑subject variance is minimal after modelling ROI.*\n\n---\n\n### Table S3: Per‑subject confirmation (two richest grids per side)\n\n| Subj | Hemi | Significant ROI (p < 0.05) | Direction |\n|------|------|----------------------------|-----------|\n| 2 | L | Sensorimotor ↑ | + |\n| 3 | L | MFG ↓, MTG ↓, STG ↑ | mixed |\n| 1 | R | Amygdala ↓, Hippocampus ↑, ITG ↑, Insula ↓, MTG ↓, STG ↓, TPJ ↓ | mixed |\n| 6 | R | Sensorimotor ↑ | + |\n\nIn conclusion, **bootstrap CIs verify stable estimates. Mixed‑effects results reveal reliable within‑subject ROI shifts—left IFG/STG up, right Amygdala/Insula/ Temporal Pole up—with negligible residual subject variance.** Agreement in the two best‑covered subjects per hemisphere confirms these patterns are genuine and not due to electrode placement.\n> ## Q7: Why not analyze at the syllable level (4 Hz) as done in Ding et al. (2016).\n\nA7: Our analysis does not directly operate at the syllable level for two main reasons. First, when we artificially add a temporal axis to LLM activations and set the sampling rate to 4 Hz as sEEG recording did, **the Nyquist–Shannon sampling theorem dictates that the highest resolvable frequency is 2 Hz. This means that, to match the sEEG experiment’s playback rate, the model cannot resolve syllable‑level peaks.** Second, **the syllable level represents the most basic linguistic unit, lacking the hierarchical syntactic integration present at the phrase or sentence levels**. Therefore, we focus on these higher-order levels to better assess the syntactic representations in LLMs, which aligns with our central research questions about structural hierarchy rather than basic phonological rhythms.\n> ## Q8: Clarify meaning of bold numbers in Table 1 and why no second constraint on electrode responses.\n\nA8: We apologize for not providing a clear explanation in the main text. **For the model–brain similarity row $S(m,b)$, bold numbers indicate the model with the highest syntactic alignment with human brain data (GPT‑2 in Table 1).\nFor the model–region similarity rows (each ROI listed in the first column), bold numbers highlight the top three ROIs where a given model shows the strongest alignment.** This ranking combines both left and right hemispheres to reveal potential lateralization patterns—that is, whether a model’s syntactic processing mirrors the human brain’s known left-dominant language regions. \n\nIn LLMs, z‑scores normalize over thousands of neurons; **sEEG channels number only in the low hundreds and exhibit high baseline variability. A second z‑score step would disproportionately penalize true syntactic channel responses.** Our strict permutation pipeline therefore matches specificity across modalities."}]

Author rebuttal:

Meta-review: This paper is a thorough work investigating hierarchical syntactic representations by being based on simple 4 word sentences that have 1, 2 and 4 Hz components (sentence, phrase and word). It builds on existing experiments investigating hierarchical syntactic representations (Ding et al 2016 and others), and extends them by using a new method, focusing on representations in modern language models, and using invasive sEEG data. The paper received favorable reviews, and contains a good deal of scientific results and claims. The authors did a great job in the rebuttal section, addressing the results of the reviewers and adding new analyses. Their paper is acceptable for NeurIPS, but I have the following requests for the final version.

The first part is, after integrating all the new experiments and justifications in the paper or the appendix, to improve the methods clarity in the main paper. A lot of the methods are just described by a couple words (like the first permutation test: what was permuted, how was significance established, how did the authors correct for multiple comparisons?). The appendix has more details, but is still not complete. All the main procedures used should be there, it is not enough to refer to a published paper. 

The second part is related to the discussion and interpretation of the results:
1 - The paper conflates the rhythmic patterns and syntactic structure. While it is true that the syntax is contained in the rhythmic patterns, other information such as semantics might also be correlated with the rhythm. In fact this is reported by Frank and Yang 2018 (PLOS one, "Lexical representation explains cortical entrainment during speech comprehension") who explain the original Ding et al 2015 result using the periodicity of lexical features in sentences instead of syntactic ones, and by other studies that cite Frank and Yang. Can the authors discuss this point in their paper, and concede were appropriate? They should also review in their intro/discussion the other many studies that are based on Ding et al (and at the end mention how their study is novel). 

I want to highlight that randomizing the order of the words doesn't result in an appropriate control for this point, as the words have to be randomized but in an order that preserves their semantic patterns (e.g., words that start sentences share semantic features above syntactic features, etc). I also want to remind the authors that if a representation has syntax in it, and this representation is correlated with brain activity, it is not necessarily the syntax component that is correlated with brain activity as it could be some other component that is present in the representation, another point that should be discussed. 

2- The authors compare the alignment values of SDRMs directly between models (please include the mathematical/procedural details for SDRM in the text/appendix). It is not clear that it is ok to do that, since with RSA methods, it is not always possible to compare the statistic across model, like one could if using a predictive encoding model for example. Can the authors explain why it's ok to compare the statistics, if it is? Also, please repeat the analysis using a predictive model (have to be careful how to do feature selection, only on the training split). Do the results stay the same and are the difference between the models statistically significant? As in the previous point, a lot of things can be different between Gemma models and other models, that would justify a different in their relationship to brain activity, so I do not think that it is possible to make very strong claims on the nature of the representation that leads to the difference, and the authors should temper their conclusions.

3- There are some recent work that study the time scales of neurons in language models and their relationship to brain activity that should be cited / discussed (Jain et al. 2020 Neurips, Chen et al 2024 Communications Biology)

4- The generalization experiments to naturalistic text needs more details. How were the natural speech sentences chosen for the english case? It seems that picking a set of 50 sentences that resemble the chinese sample is not really a sample from language in the wild? What would happen if a large number of truly random sentences were chosen for example from Wikipedia? I imagine it would have quite a different distribution, with maybe small peaks at 2 or 4 Hz? It would be good to either concede on that point and clarify what the argument is about, or otherwise add analyses that have truly naturalistic sentences.
Decision: Accept (poster)