Semantic similarity across languages reflects neurocognitive dimensions shaped by climate

Domain: biology
Published: 2026-03-16
Editorial outcome: major_revision

Abstract
Human languages differ widely, yet they share systematic regularities in the underlying semantic representations being expressed. How such similarities and differences arise remains unclear, in part because semantic theories often lack a principled link to neurocognitive constraints. Drawing on neurocognitive accounts in which semantic knowledge is grounded in biologically salient information dimensions, we examine how environmental factors shape conceptual representations in language. Here we show that word meanings across languages are organized along shared neurocognitive dimensions, while systematic variation along these dimensions is associated with climate. Using word embeddings from 53 languages and behavioral ratings from speakers of 8 languages, we find converging evidence that climatic variables explain semantic variation beyond commonly considered sociocultural factors. Complementary exploratory brain data further suggest climate-related modulation of activity patterns in the right anterior temporal lobe. Together, these findings indicate that semantic representations in language reflect biologically grounded dimensions that are flexibly shaped by long-term environmental conditions.

Introduction
There are currently over 7,000 spoken and signed languages worldwide, each with distinct sound and visual forms and syntactic rules. Do speakers of different languages simply use different word forms to map onto a common conceptual structure, i.e., do people speaking rose and 玫瑰 have the same semantic representation? If not, what is lost in translation? This question is part of the classic debate on language universality versus relativity of whether speaking different languages is associated with different cognitions more broadly1-3. It seems trivial that both commonalities and variations exist — most languages have words referring to phenomena such as the sun or the color red, while the words for the object rose entail notions of romance in some languages but not in others. The key question is what principles underlie these commonalities and variations across languages. Answers to this question not only address semantic representation principles but also provide a foundation for better communication across languages and cultures.

Rich commonalities and variations have been documented across multiple disciplines, including anthropology and linguistics. Ethnographic descriptions have identified common concepts across semantic domains such as color4 and emotion5,6, while also revealing different words for the same perceptual referents, culturally bound words resistant to translation, and different categorization systems3,7-9. Recent advances in computational linguistics allow for cross-linguistic comparisons through analysis of word representations derived from extensive language corpora (see review10). These approaches assess cross-language semantic alignment (e.g., beautiful in English and bella in Italian) by comparing their distances to a set of anchor words in their respective high-dimensional representational spaces, allowing for meaningful comparisons across languages. The selection of different anchor word sets (whether domain-specific, proximally neighboring, or the entire word space) and the utilization of diverse data types (including word embeddings and colexification networks) have yielded different results and conclusions accordingly, supporting either word semantics being innate11 or culturally driven12,13. It is difficult to evaluate whether these differences reflect those certain aspects of word meaning (approximated by anchor words) are more universal than others across experiments, given the differences in stimuli and computational methods. To this end, what has been missing is an overarching theoretical framework: What aspects (underlying dimensions) of human semantic mechanisms are hypothesized to be universal? How do variations arise from this universal mechanism?

We propose that the intrinsic way in which the human brain represents semantic knowledge offers a strong candidate framework for understanding both cross-linguistic semantic universality and variation. The biological constraints of the human brain are the result of the biological evolution of Homo sapiens and lay the foundation for universality (see similar arguments for color space4 and emotion space14), and such a biological structure would respond to different environmental inputs (e.g., naturally varied across different types of climates), resulting in phenomenal variations. Neurocognitive research reveals a consensus framework that semantic representation in the brain is derived from sensory (and language) experiences in ways that respect the specific information processing architectures of the brain: Brain responses to word meanings are distributed along sensorimotor and related associative cortical networks, respecting domains of evolutionary saliency, with activity strength modulated by the meaning’s loading on corresponding attributes/domains15-18. Lesions in the brain lead to deficits along the lines of these sensory-motor modalities and domains19-21. Some aspects of the neural organization are present early in human infancy22,23, have been found across diverse cultures and languages (e.g., US, UK, Italy, and China), even in individuals with drastic experiential differences such as complete visual deprivation24-27. Several mainstream neuroanatomical semantic models — including GRAPES28, Embodied Abstraction29, Hub-and-Spoke Model30,31, and Neural Dual Coding Theory26 — converge in their recognition of this foundational biological structure underlying semantic representation, but the commonalities and variations along this framework have not been systematically tested across a large number of languages.

We extend this neurocognitive framework of semantic representations, i.e., representing word semantics along the core neurocognitive dimensions, to investigate its effectiveness in explaining semantic commonalities and variations across languages. To operationalize, we gleaned 13 (primitive) dimensions that have extensive evidence for their neural correlates, and constructed word semantic representations composed of these 13 dimensions32 (see Table S1 for example studies and evidence): sensorimotor dimensions of the human brain system (color, shape, taste, smell, sound, touch, and bodily motor) and core cognitive domains (time, space, number, mental-cognition, emotion, and social). Word representations as 13-dimensional vectors were tested across languages: how similar are the words for rose in different languages in terms of their loading (relation) patterns with color (e.g., red, blue), shape (e.g., round, square), emotion (e.g., happy, sad, angry ), etc.? This neurocognitive approach also offers methodological advantages for cross-linguistic comparison. By focusing on dimensions with established neural correlates, we create a controlled space where both universal patterns and meaningful variations can be understood against a consistent backbone of biological universality. Such dimensional structures not only reveal fundamental commonalities in semantic organization across languages due to our shared neural architecture, but also filter out potentially idiosyncratic semantic dimensions to highlight variations specifically along sensorimotor and core-cognitive channels.

The neurocognitive framework makes two key predictions (Figure 1): First, semantic representation derived from this neurocognitive framework better captures the universal structure of semantic representations compared to other distribution-based models (Figure 2a) that do not directly incorporate the neural architecture, including distributional semantic models12, psycholinguistic semantic featural models33,34, and randomized statistical control models. Second, regarding variations along this dimensional structure, the intrinsic assumption of the framework that semantic representation derives from sensory channels predicts that variability is the natural consequence of variables affecting these channel-mediated experiences, including those associated with both natural and cultural environments35. This perspective differs from those of previous studies that predominantly emphasized sociocultural factors, including geographic distance14, communication pressures36, linguistic history37, and cultural proximities12. These variables were mainly motivated by the significance of communication, word borrowing, and phylogenetic history in the cultural evolutionary process for cross-language alignment14,38. However, beyond such language-related experiences, it is possible that languages used in distant locations share similar conceptual meanings when they have comparable sensory signals and similar neurocognitive structures. One salient ecological variable worth highlighting is climate, which has been shown to be strongly associated with natural and cultural properties39-41. By systematically considering these macroscale environmental variables, we predict that climate, as an ecological factor shaping sensory environments, exerts independent effects on human semantic processes beyond previously tested sociocultural-centered variables (geographic, linguistic, and cultural distance).

Here, we demonstrate that the neurocognitive dimensional framework captures cross-language semantic commonalities more effectively than alternative distributional models, and that climate emerges as a significant ecological predictor of semantic variations across languages, independent of sociocultural factors. Converging evidence from a series of studies employing computational, behavioral, and neural approaches supports these conclusions (Figure 1). Study 1 involved language computational analyses on large-scale multilingual pre-trained word embedding data42. For the universality prediction, we compared the extent of commonalities captured by the target model (with the 13 neurocognitive dimensions as anchor words) and alternative models (different types of anchor words). For the variation prediction, we examined the relationship between variations along this neurocognitive dimensional structure and ecological variables across languages. Such models have the advantage of analyzing large-scale language patterns, but the relationship of ecological variables with the human mind/brain is approached indirectly through computational relationships. Thus, in Study 2, we collected human individual semantic behavioral ratings on these 13 dimensions, and in Study 3, we analyzed brain activities during language comprehension, offering preliminary insights into linking cognition and biology variations more directly with the ecological variables of interest. Figure 1 (bottom panel) illustrates the geographic distribution of language samples across three studies, with detailed language information provided in Table S2.

Results
Commonalities on the neurocognitive semantic structure

We examined cross-language semantic commonalities through computational analysis of word embeddings across 53 languages (Study 1a). We focused on the NorthEuralex (NEL) wordlist, which provides 1,016 concepts across 107 languages. Among them, 53 languages (spanning 10 language families) were well covered in the recently developed word embedding databases (fastText multilingual word vectors42). These databases provided 300-dimensional (hidden) dimensional word embedding vectors for a large number of languages, pre-trained on vast corpora comprising billions of words for each language. For each target concept in each language, we projected its embedding onto different sets of anchor words based on different models (see below), i.e., computing the target-anchor cosine distances (see an illustration of construction processes in Figure 2a; see also the anchor word approach43-45). Distances to different model-driven anchor sets were taken as the model-based semantic representations for the target concepts, based on which cross-language comparisons were performed.

We performed cross-linguistic comparisons of semantic representations obtained based on different model-anchor-based semantic models from word embedding data. All semantic representations were obtained using an anchor word approach (i.e., essentially all are distributional semantic methods), and the key difference is what types of words were used as anchors for representations. Different anchor words were selected based on different model principles:

Distributional models represent concepts by their usage contexts, a prevalent approach in cross-linguistic semantic comparisons represents12. Following this approach, we represented the 1,016 concepts using their local patterns (i.e., anchor words were semantic neighbors, N = 100) and global patterns (i.e., anchor words were the entire native word space).

Semantic feature models in the psycholinguistic tradition are based on human-generated decompositional features (e.g., has fur, is round), representing concepts as binary or weighted vectors indicating the presence/absence or strength of each feature33,34. To adapt this approach for cross-linguistic comparison, we represented the 1,016 concepts using their embedding distance patterns with the 100 most frequent feature words from Buchanan et al.33.

Neurocognitive semantic models, like semantic feature models, assume decompositional representation but highlight a set of underlying primitive dimensions constrained by the human brain. We identified 13 primary structural dimensions from neurocognitive semantic research (see Introduction and Table S1 for a list of example empirical references). Anchor words corresponding to these dimensions were manually selected from the NEL concept list (see Methods for details; anchor word list shown in Table S3). Concepts were projected onto these 13 neurocognitive salient dimensions by averaging semantic distances of anchor words within each dimension. Our text-derived dimension loadings significantly correlated with the human rating data reported in Binder et al.32 (Pearson r (6914) = 0.42, P < 0.001, 95% confidence interval (CI) [0.40, 0.44]; see Figure S1 for details), consistent with previous findings on subjective-rating validity for the dimension computation45.

To assess the explanatory powers of the above candidate theoretical models for cross-linguistic semantic universality, we further computed two types of theory-free benchmark distributions. One type is random word models, where 100 randomly selected words from the original NEL list served as anchors, and concepts were projected onto these anchors’ embedding vectors, with 10,000 iterations. The other is the random dimension model (dimensionality-matched control), where 100 randomly selected words were grouped into 13-dimensional anchors using K-means clustering, and concepts were then projected onto these pseudo dimensions, with 10,000 iterations. The statistical confidence for each theoretical model’s universality was established by comparing its position with these two models’ distributions.

To quantify and compare the universality of these semantic models (both candidate theoretical models and random control models), we used two methods for convergence: inter-language correlation analysis (Figure 2b) and principal component analysis (PCA; Supplementary Note 1, Figure S2). For inter-language correlation analysis, we computed Fisher z-transformed Pearson correlation coefficients between model-based semantic representations for each language pair. As shown in Figure 2b (left panel), the neurocognitive-dimensional semantic representation had greater cross-language similarity compared to the other representation types (mean Fisher z-transformed correlation: neurocognitive (M = 0.63, SD = 0.19); distributional (global) (M = 0.42, SD = 0.17); distributional (local) (M = 0.33, SD = 0.10); semantic feature (M = 0.35, SD = 0.13). Two-sided paired Wilcoxon signed-rank tests with false discovery rate (FDR)-correction for 3 comparisons revealed all adjusted Ps < 0.001: neurocognitive vs. distributional (global), V = 950123, n = 1378, median difference = 0.21, 95% CI [0.20, 0.21]; neurocognitive vs. distributional (local), V = 950131, n = 1378, median difference = 0.30, 95% CI [0.29, 0.31]; neurocognitive vs. semantic feature, V = 750925, n = 1225, median difference = 0.30, 95% CI [0.30, 0.31]). These results were robust when we varied the number of anchor words in the distributional (local) and feature models (see Figure S3). Figure 2b (right panel) illustrates that the mean inter-language correlation of neurocognitive models (red vertical line) exceeded the upper ends of distributions of random word models (light gray area; P < 0.001, one-tailed) and the dimensionality-matched random dimension models (dark gray area; P = 0.005, one-tailed), whereas the other types of theoretical models did not demonstrate this pattern. These results demonstrated that the model’s performance stems from the specific neurocognitive dimension rather than anchor word selection and dimensionality reduction procedure per se.

We also employed a complementary PCA on 204 concepts common to all 53 languages, treating languages as features and semantic patterns as samples. This approach assumes that a universal semantic structure would manifest as a relatively high first component (PC1), with the variance explained by PC1 reflecting the degree of universality46,47. The neurocognitive-dimensional representation of concepts again showed a greater amount of universality (neurocognitive: 44.31%; global-distributional: 34.16%; local-distributional: 36.38%; feature-based: 34.45%). Similar to the inter-language correlation findings, in comparison with statistical random models, the neurocognitive model, not the other models, was positioned at the upper bounds of their distributions, signifying a relatively universal semantic structure (random word models: P < 0.001, one-tailed; random dimension models: P = 0.009, one-tailed). Detailed results, including the scree plot and neurocognitive structure PC1 matrix, are presented in Supplementary Note 1 and Figure S2. We further validated the main analyses above using text embedding data derived from different corpora (Supplementary Note 2, Figure S4).

To test the generalizability of neurocognitive semantic structures beyond these initial 53 languages, we investigated whether the neurocognitive semantic model could predict word relational patterns in a larger, more diverse language sample. Given the challenges of obtaining semantic representations for low-resource languages, we utilized the database of Cross-Linguistic Colexifications (CLICS), which contains colexification patterns from 2,681 languages48. We employed common topological network metrics, including edge, common neighbors, and two community-based measures (Louvain and Infomap algorithms), and used the average distance across these four metrics as the word distance in the topological graph space. The hypothesis here is that if the neurocognitive semantic model outperforms statistical control models in predicting word topological patterns in the colexification network, it would indicate the generalizability of these structures to a broader range of languages. Specifically, we examined whether concepts closer in the 13-dimensional neurocognitive space (derived from embedding data of the original 53 Eurasian languages) were more likely to be topologically proximate in the colexification network.

We conducted Spearman correlations between 165 NEL concepts in the embedding-constructed neurocognitive semantic space and the CLICS topological graph space (Figure 2c, left panel). For statistical comparison, we generated random models (word and dimension) and correlated them with the CLICS graph space 1,000 times to establish null distributions. These tests were performed on the following samples: 1) the entire language sample (N = 2,681) and 2) non-Eurasian languages (South America, North America, Africa, Papunesia, and Australia; N = 1,813, excluding unclassified languages). The second test specifically targeted other language samples that were not included in our initial word embedding analysis.

As shown in Figure 2c (right panel), the neurocognitive dimensional representation derived from the original 53 languages significantly explained topological similarities in the colexification network for both the 2,681 languages (random word: P = 0.006, one-tailed; random dimension: P = 0.009, one-tailed) and the non-Eurasian subset (both baselines: Ps < 0.001, one-tailed). In both cases, the neurocognitive model's performance was at the upper bound of the random model distributions, providing positive evidence for the cross-language generalizability of these semantic structures. Separate density plots for each non-Eurasian area's language samples yielded similar patterns to the entire sample (Figure S5).

In summary, the 13-neurocognitive-dimensional structure, obtained from brain studies in a few industrialized languages (see Table S1), captures cross-language similarities better than control models in the word embedding spaces of 53 languages, which further generalizes to explaining word similarities across a larger and more diverse set of languages in the CLICS networks (colexification data).

Variations associated with environmental variables

Having established the superiority of the neurocognitive semantic model in capturing semantic universality, we then investigated variations across languages along this universal structure. Based on the shared assumption of semantic neurocognitive theories that semantic representations are derived from sensorimotor (and language) experiences, we hypothesized that variations in the model stem from variables affecting these channels of experience, including both natural and cultural environmental factors. The following variables were considered: climate, geographic, linguistic history, and cultural variables. The question under scrutiny was whether and how variations across languages along the universal neurocognitive semantic structures could be explained by these external variables (i.e., whether language pairs with similar environmental characteristics exhibit similar semantic representations).

To this end, we implemented a consistent analytical framework across all three studies (i.e., studies with word embeddings, behavioral ratings, and neural activity data). At the language level (aggregating participants where applicable), we constructed linear mixed models that included all four variables of interest simultaneously – geographic, cultural, linguistic history, and climate variables – with random intercepts for language families to account for phylogenetic relatedness12,49. This approach allowed all factors to compete within the same model, revealing their unique contributions to semantic variations. For Studies 2 and 3, we additionally conducted predictions at the individual participant level, confirming that patterns remained consistent across analytical scales.

Variations in word embeddings associated with climate

We first applied this framework to computational analysis of word embedding data across 53 languages (Study 1b). We employed representation similarity analysis (RSA)50 to quantitatively model cross-language variations in semantic representational geometries by regressing the semantic dissimilarity representational matrix (RDM; Figure 3a) on the RDMs of our selected environmental variables. Data on environmental variables for 53 languages were extracted from various official public databases using latitude and longitude provided by Glottolog 4.651. The sample sizes (i.e., language pairs) varied depending on the availability of the environmental data.

Correlation analyses first revealed moderate associations between these four environmental RDMs and the semantic RDM (Spearman ρs = 0.39 – 0.53, all Ps < 0.001; Table S4). Within a sample of 29 languages (406 language pairs) where all four environmental variables were available, they collectively explained 37.99% of the semantic (neurocognitive) space variations (Spearman ρ = 0.52, P < 0.001, 95% CI [0.43, 0.60]; Figure 3b).

We then employed a linear mixed regression model to assess the unique effect of each environmental factor. As indicated in Figure 3c, climate showed the strongest unique explanatory effects (β = 0.28, P < 0.001, 95% CI [0.19, 0.37]). Linguistic history also showed significant effects (β = 0.22, P < 0.001, 95% CI [0.16, 0.28]). Cultural and geographic distance did not show significant unique contribution to semantic variations (culture: β = 0.07, P = 0.097, 95% CI [-0.01, 0.15]; geography: β = 0.05, P = 0.237, 95% CI [-0.03, 0.13]). Our focus on sensorimotor-related influences led us to further investigate climate effects across semantic dimensions. Further analyses revealed that climate contributed significant effects across 12 of the 13 semantic dimensions (all FDR-corrected Ps < 0.05), with the exception of the shape dimension (FDR-corrected P = 0.118; Figure 3c, right panel). The climate effects were robust across multiple validation analyses, including those utilizing alternative pre-trained word embeddings and different random effect structures controlling for non-independence (see Supplementary Note 2, 3; Figure S4).

We further tested whether the observed cross-linguistic associations between semantic variation and climate were specific to the neurocognitive semantic structure. Linear mixed regression models were employed with climate and other semantic structures as regressors to predict neurocognitive semantic variations and vice versa. Results in Table 1 indicated that the relationship between climate and neurocognitive semantic structure remained significant even when controlling for distributional and feature-based models. Specifically, climate maintained a significant association with the neurocognitive semantic structure after controlling for the distributional global (β = 0.03, P < 0.001, 95% CI [0.02, 0.05]), distributional local (β = 0.11, P < 0.001, 95% CI [0.09, 0.13]), and feature norm (β = 0.12, P < 0.001, 95% CI [0.10, 0.15]) models. Conversely, when predicting these alternative semantic structures while controlling for the neurocognitive structure, climate showed significant associations with distributional global (β = 0.04, P < 0.001, 95% CI [0.02, 0.06]), and not with distributional local (β = -0.01, P = 0.271, 95% CI [-0.03, 0.01]) or feature norm (β = -0.02, P = 0.220, 95% CI [-0.05, 0.01]). These findings suggest a specific relationship between climate and neurocognitive semantic organization that cannot be fully explained by other semantic representation frameworks.

Variations in behavioral ratings associated with climate

To investigate whether the principles of commonalities and variations observed in language records (Study 1) are reflected in language speakers’ semantic behaviors, we conducted a behavior rating study with participants from 8 languages (Study 2). We recruited participants from 58 city sites representing 8 languages (a subset of 53 languages in Study 1) with broad coverage of geographic, linguistic, and cultural diversity: Arabic (Egypt), Chinese (China), English (USA), Hindi (India), Japanese (Japan), Korean (South Korea), Russian (Russia), and Spanish (Spain). A total of 253 participants from these countries were included in the final sample (sampling procedures and geographical information are provided in Methods, Figure S6 and Table S5). Participants were asked to rate the Swadesh concepts (N = 207, Table S6) on their associations with the 13 neurocognitive dimensions, resulting in individual semantic spaces comprising 2,691 ratings per participant (207 Swadesh concepts × 13 neurocognitive dimensions). We computed Pearson correlations between the rating vectors (207 concepts × 13 dimensions) for each participant pair, resulting in an intersubject correlational matrix (Figure 3d). The matrix showed a substantial semantic component shared across participants (65.78% of variance), with much smaller language-specific effects (4.20% of variance), validating the commonality of such neurocognitive semantic structures across languages (see Supplementary Note 4 for details).

We first computed average ratings for each concept-dimension pair within each language to create language-level semantic representations. To assess the relationship between neurocognitive semantic space variation and environmental variables, we collected macroenvironmental variables (identical to those in Study 1) for each language and constructed language-level environmental RDMs for climate, culture, geography, and linguistic history (Figure 3e). We also included a demographic RDM to account for variations in participants' age, gender, education level, and socioeconomic status across language groups.

Hierarchical regression revealed that, in addition to the explanatory power of demographic distance (R2 = 0.12), the four environmental variables collectively accounted for an incremental 47.01% variance in the neurocognitive semantic structural variations (Spearman ρ = 0.74, P < 0.001, 95% CI [0.50, 0.88]). In alignment with our analytical approach in Study 1, we performed analyses at the language level, with all four environmental variables as simultaneous predictors and random intercepts for language families to account for phylogenetic relationships. Beta estimation (Figure 3f) showed that climate had unique effects on cross-language semantic variation (climate: β = 0.53, P < 0.001, 95% CI [0.26, 0.80]). The other three variables showed no or negative effects (culture: β = -0.07, P = 0.640, 95% CI [-0.36, 0.25]; geography: β = -0.22, P = 0.025, 95% CI [-0.40, -0.04]; linguistic history: β = 0.13, P = 0.336, 95% CI [-0.17, 0.39]). When analyzing each neurocognitive dimension separately (Figure 3f, right panel), all dimensional variations, except for number, continued to be significantly modulated by climate (number: q = 0.098).

To examine whether these climate effects extend beyond language-level variation to individual semantic representations, we conducted supplementary analyses while controlling for non-independence (see Supplementary Note 3). These analyses confirmed that climate effects remain robust across different random effect structures and are observable at both language and individual participant levels.

Variations in language evoked brain activity patterns associated with climate

To investigate whether the principles of commonalities and variations observed in language records (Study 1) are reflected in language speakers' neural responses during language comprehension, we analyzed a multi-language functional magnetic resonance imaging (fMRI) dataset comprising 86 individuals recruited from the United States, whose native languages spanned 45 languages across 12 language families (Study 3)52. We focused on neural activity during native language processing, specifically examining the contrast between intact and acoustically degraded language conditions in the 12 language-responsive regions (6 left, 6 right) in the language network53 (Figure 4a).

Following our consistent analytical approach across studies, we aggregated individual neural responses at the language level. We first investigated whether neural activity patterns in these regions reflected cross-language alignment in the 13-neurocognitive-dimensional semantic space (based on embedding data in Study 1; Figure 3a). To this end, for overlapping language samples in two studies (65 participants, 33 languages; Figure 4b), we computed language-level neural RDMs for each brain region by averaging t-value maps from speakers of the same language. We used linear mixed regression models to assess the relationships between these neural RDMs and the 13-dimensional neurocognitive semantic model RDMs from Study 1, while including random intercepts for language families to account for the phylogenetic structure. Significant beta coefficients were found only in the right anterior temporal lobe (r-ATL; β = 0.44, FDR-corrected P = 0.016, 95% CI [0.17, 0.71]). That is, the more closely aligned two languages are for the 13-neurocognitive dimensional semantic representation, the more similar their speakers’ brain activities are in the r-ATL when processing language. Given the r-ATL’s role in semantic processing and the significant relationships with our semantic space representation, we identified this region as our primary region of interest for subsequent analyses (Figure 4c). We also found that r-ATL showed a certain degree of cross-language commonalities, validating our commonality results (Supplementary Note 4).

To maintain analytical consistency with Studies 1 and 2, we constructed language-level environmental RDMs based on climate, cultural, geographic, and linguistic history distances (Figure 4c; see Methods), analyzing 25 languages for which all environmental measures were available. Linear mixed regression models revealed that climate distance significantly predicted neural pattern dissimilarities in the r-ATL (β = 0.12, P = 0.010, 95% CI [0.03, 0.22]), while the other three distances did not reach significance (all FDR-corrected Ps > 0.05). This climate effect remained significant (β = 0.13, P = 0.007, 95% CI [0.03, 0.22]) even when controlling for differences in peak activation location (calculated as Euclidean distances between individual peak cluster centroids), indicating that the effect reflects distributed activation patterns across r-ATL voxels rather than simple spatial displacement. To assess robustness, we conducted additional analyses with alternative random effect structures (see Supplementary Note 3), which consistently showed that climate effects remain significant across different model specifications.

Importantly, when using environmental RDMs to predict the r-ATL's neural activity during non-linguistic tasks (e.g., Spatial Working Memory -- Hard vs Easy; Math -- Hard vs Easy), the climate effects were no longer significant, suggesting that these effects are unique to language-related processing (Supplementary Note 5; Figure S7). To further characterize the relationship between semantic and climate effects in the r-ATL, we conducted a commonality analysis to partition the r-ATL variance explained by different components (Figure 4c, right panel). This analysis revealed unique contributions from both semantic factors (6.26%, 95% CI [1.00%, 37.46%]) and climate factors (55.58%, 95% CI [16.83%, 85.80%]), as well as substantial common variance shared between climate and semantic effects (38.20%, 95% CI [14.75%, 48.30%]). The common variance suggests that climate effects on neural activities in the r-ATL partially reflect semantically related neural activation patterns.

In summary, cross-language variations on the 13-neurocognitive-dimension structure obtained from language computation data (Study 1), human behavior rating data (Study 2), and multi-language fMRI data (Study 3), were all significantly predicted by climate, the effects of which were also robust across all validation analyses (see Table 2). Our neural findings in Study 3 revealed that climate-related semantic variations were specifically associated with activity patterns in the r-ATL, a critical region for semantic processing. Given the limited sample size per language, these findings warrant replication with larger cohorts.

Exploratory analyses on the associative patterns between climate and semantic space

Having identified that climate has robust and unique effects on variations in semantic structures across large text models (Study 1b), subjective ratings (Study 2), and brain activity patterns (Study 3), we aimed to elucidate the semantic profiles that are associated with the major climate groups. We carried out the following analyses based on the embedding data from Study 1 given that this study covers the largest language sample and concept set. We performed a PCA on climate data consisting of 19 biologically relevant climate variables related to temperature and precipitation across 53 languages. The results revealed two primary climate-related principal components (PCs): Climate-PC1, accounting for 42.3% of the variation, and Climate-PC2, accounting for 30% (Figure 5a). The contributions of specific climate variables to these PCs (Table S7) led us to characterize the Climate PC1 as representing cold/temperate vs. tropical climates and the Climate PC2 as representing oceanic vs. continental climates (more precipitation and low seasonality vs. less precipitation and high seasonality).

To project the semantic space along each PC axis, we scaled the semantic space for each language and multiplied it by their loadings on the two climate PCs, resulting in semantic spaces along the Climate PC1 and Climate PC2 axes (Figure 5b). Higher values along a particular direction of PCs indicate that the given climate type tends to have stronger semantic relations. The associated semantic space for Climate PC1 and Climate PC2 is visualized for each concept (Figure S8) and summarized by domains (Figure 5b).

For Climate PC1 (cold/temperate vs. tropical) dichotomy, concepts in general tend to exhibit higher intensity on emotional and sensorimotor dimensions (touch, motor, shape, color) in the cold/temperate zones and higher intensity on social-cognitive (social, space, number, cognition) and smell dimensions in tropical zones. For Climate PC2 (oceanic vs. continental) dichotomy, concepts tend to exhibit higher intensity on the smell, cognition, and time dimensions in oceanic zones and higher intensity on the social and sound dimensions in continental zones.

One data pattern emerged from this visualization (Figure 5b): The dimensional difference patterns associated with climate groups are overall coherent across various concepts (and concept domains). For Climate PC1, cold/temperate climate is associated with higher loading on emotion (and sensorimotor) not only for specific concepts relating to temperature, such as sun or warm, and tropical climate is associated with higher loading on olfactory not only for flower and on social not only for father, but for all domains of concepts in general. This pattern of association with dimensions instead of concept domains is also present for Climate PC2, although less clear-cut (see examples in Figure 5b, lower panel).

Methods
This research complies with all relevant ethical regulations. Study 2 involved an online study conducted via a crowdsourcing platform and was approved by the Institutional Review Board of the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (approval number: IRB_A_0202_2021001). Studies 1 and 3 involved computational analyses of publicly available data and did not require additional ethical approval. No statistical methods were used to predetermine sample sizes for Studies 1 and 2. Study 3 analyzed an existing dataset with sample size predetermined by the original study. Investigators were not blinded to group assignments, as language identity was a key variable of interest across all studies. Unless otherwise specified, all statistical tests were two-sided.

Study 1: Language computational study using word embedding data in 53 languages

Language samples and concept list: We investigated 1,016 concepts across 53 languages from 10 distinct language families. The concept list and languages were sourced from the NorthEuralex (NEL) dataset62. The NEL dataset provided translational word forms for these concepts, which have enduring and relatively consistent word representations in history and cover important semantic fields12. Language sample selection proceeded in two steps: First, we identified 61 languages with translated word forms available in the multilingual pre-trained word embedding models (see below). Second, we excluded 8 languages missing over 25% vector representations for NEL concepts to ensure adequate shared concept coverage between language pairs. Analyses on all 61 languages yielded the same results. Detailed language information can be found in Table S2, and the distribution of concept numbers for each language is illustrated in Figure S9.

Multi-language pre-trained word embedding model: We obtained semantic representations of concepts using 300-dimensional word vectors from pre-trained embedding models available through fastText (https://fasttext.cc/docs/en/crawl-vectors.html). These models were trained on vast corpora from Wikipedia and Common Crawl, with the training data containing hundreds of millions to billions of word tokens for each language, resulting in improved performance for low-resource languages compared to Wikipedia-only models (see model details42). FastText represents words as a combination of whole-word and subword (character n-gram) embeddings, which allows for better handling of morphologically rich languages and out-of-vocabulary words64. To validate our results against different corpora types, we also ran replication analyses using additional pre-trained models based on Wikipedia and Open Subtitles corpora65, which incorporate speech transcriptions from television shows and movies (Supplementary Note 2).

Construction of different semantic representation models: To compare semantic representations across languages, we employed an anchor word approach43,44. This approach involves projecting the original 300-dimensional embedding vector representations of 1,016 NEL concepts onto various anchor vectors to construct different semantic models (i.e., computing cosine similarities between NEL concept vectors and different anchor vectors). We excluded words appearing in both target concepts and anchor dimensions to avoid overestimating commonalities. We constructed the following types of models:

Distributional semantic models (local and global). Following Thompson et al.12, we projected concepts onto their relationships with other words. We considered two types of measures: distributional local models, using varying numbers of semantic neighbors as anchors and distributed global models, using all the other 1,015 concepts as anchors. We computed cosine similarities between each NEL concept vector and these anchors to obtain distributional semantic representations. This approach preserves the complete representational geometry within each language's native embedding space. For local models, results using the top 100 closest neighbors are reported in the main text.

Semantic feature model. We projected concepts onto human-generated descriptive feature words from the Semantic Feature Production Norm33. This norm includes approximately 4,000 feature words used to describe 4,436 concepts, representing various types of semantic knowledge (e.g., 'is red', 'is mammal', 'lay eggs', and 'live in the water'). As the norms were primarily generated by native English speakers, we translated key feature words into other languages in our samples using Google Cloud Translation API (https://cloud.google.com/translate), excluding Bashkir, Breton, and Sakha due to resource limitations. The semantic feature representation was obtained by computing cosine similarity between each NEL concept vector and the feature word vectors. We conducted analyses using varying numbers of the most frequently nominated feature words (top 100, 200, 500, and 1,000) as anchors, with results from the top 100 feature words reported in the main text.

Neurocognitive semantic model. The neurocognitive semantic model consisted of 13 primary structural dimensions gleaned from neurocognitive semantic research (see Introduction and Table S1 for a list of example empirical references). These dimensions include seven sensory-motor domains (color, shape, taste, smell, sound, touch, and bodily motor) and six core cognitive domains (time, space, number, mental-cognition, emotion, and social). The anchor words for each dimension were selected from the NEL concept list by three native Chinese speakers, who first manually selected words defining or corresponding to each dimension and then reached consensus on a final anchor word list. A total of 122 anchor words were used (see Table S3), with approximately 2-14 anchor words for each dimension. We computed the averaged cosine similarity between each NEL concept vector and the vectors of anchor words for each dimension to obtain a 13-dimensional vector as the neurocognitive semantic representation. Using the original anchor concepts from Binder et al.32, which were selected by English speakers, yielded the same result patterns.

Statistical control models. To assess the explanatory power of the above candidate theoretical models for cross-linguistic semantics commonalities, we constructed two statistical control models: a) Random word model. We randomly selected 13 words from the original 1,016 NEL concepts as anchor words and calculated their cosine similarity with the target concepts. b) Random dimension model. We randomly selected 100 anchor words, grouped them into 13 dimensions (corresponding to the number of dimensions of the neurocognitive model) using K-means clustering, and calculated their cosine similarity with the target concepts. This random dimension model provides a dimensionality-matched control, demonstrating that larger cross-language alignment is not because simply having fewer dimensions. Each random model was repeated 10,000 times to obtain the cross-language commonality distributions. We then established the statistical confidence of each theoretical model by comparing the model’s cross-language commonality scores with the distribution from the statistical control models.

Estimation of cross-language commonalities: To assess the degree of cross-language commonality across different semantic models, we employed two complementary measures of convergence. Note that these measures also differ in the number of concepts analyzed and their treatment of between-concept variations.

Inter-language correlation analysis. For each semantic representation model, we calculated the Pearson correlation coefficient (r) between the semantic representations of each language pair for all 1,016 concepts. The resulting inter-language correlation (ILC) was then Fisher’s r-to-Z transformed and averaged across concepts. To compare the ILCs between different types of theoretical semantic representations, we employed the Wilcoxon signed-rank test (two-sided) for paired samples.

Principal component analysis. To further estimate shared variances across all languages, we conducted principal component analysis (PCA) for 204 concepts shared across all 53 languages. For each semantic representation model, we represented each language as a matrix of semantic relations (e.g., for the neurocognitive model, the matrix dimensions were 204 × 13). We then treated languages as features and semantic relations (reshaped from a matrix into a vector) as samples, and performed PCA across languages. The proportion of variance explained by the first principal component (PC1) was used as an estimate of shared variance for cross-language semantic alignments.

Prediction of colexification network topologies: To investigate whether the neurocognitive semantic models could predict word relations in more diverse language samples, we employed the Database of Cross-Linguistic Colexifications (CLICS version 3.0), a comprehensive cross-linguistic resource containing colexification patterns of approximately 3,000 concepts across 2,681 languages in 180 language families48, capturing instances where two or more concepts are expressed by the same wordform within a language. The hypothesis here is that if the neurocognitive semantic model outperforms statistical control models (random word models and random dimension models) in predicting word topological patterns in the colexification network, it would indicate the generalizability of these structures to a broader range of languages.

We conducted separate analyses using colexification networks derived from: a) The entire CLICS language sample (N = 2,681); b) A subset of non-Eurasian languages from the CLICS database. For 165 of the 204 concepts used in the pre-trained embedding analyses, we extracted the following network properties from the CLICS database: edge weight, weighted common neighbors, and community structures based on Louvain and Infomap algorithms. These measures capture first-order, second-order, and global information in the network, respectively, and are widely used to quantify semantic similarity across languages14,66,67. We computed an overall pairwise similarity score as the average of these four metrics and then correlated these network similarities with the neurocognitive semantic relations from the 53 languages in our embedding data using Spearman correlation. The correlations were averaged across the 53 languages to obtain a mean correlation for each network (full sample and non-Eurasian subset). The statistical confidence of the neurocognitive models' predictability for colexification network properties was established by comparing the model's Spearman correlation coefficients with the distribution of correlation coefficients from the statistical control models (N = 1,000 times).

Prediction of inter-language semantic variations: To understand what environmental factors may account for the variations in the universal semantic structure, we carried out representational similarity analysis (RSA) between inter-language semantic variation and the distances of the four salient ecological variables, including the geography, climate, linguistic history, and culture.

Environmental variables. We obtained pairwise distances on these four environmental variables based on the languages’ coordinates provided by Glottolog 4.651. The geographic distances were calculated as the geodesic distances between the locations of languages on the Earth's surface. The climate distances were calculated as the scaled Euclidean distance based on the estimates of 19 bioclimate variables from WorldClim68 (see Table S7 for the full list). The 19 bioclimate variables were derived from the monthly temperature and precipitation, which are often used in species distribution and related ecological modeling. The distances of linguistic history were estimated based on cognates of the NorthEuralex wordlist, using LingPy69 with the LexStat method70. The cultural distances were retrieved from Thompson et al.12, which were calculated based on the eco-cultural traits in the D-place dataset71, including societies’ housing, labor institutions, marriages, and political systems.

Representational similarity analysis. The inter-language RDMSs were first constructed for semantic variations and for each environmental variable. The semantic RDM was constructed by computing the distance (i.e., 1 – Pearson correlation, with Fisher’s r-to-Z transformed) of averaged concepts’ neurocognitive semantic vectors between language samples. After obtaining these inter-language RDMs, we performed nonparametric Spearman correlations for their raw associations and built multiple regression models to estimate how the semantic variations across languages could be explained by the environmental variables. Specifically, we tested the association between the lower triangle of the semantic representational dissimilarity matrix (RDM) across languages and the lower triangle of RDMs for each or composite of these environmental variables (excluding the main diagonal elements). The linear mixed regression model was further conducted to estimate the fixed effects of each environmental variable with a crossed random-effects structure that nested language pairs within language families considered, to account for potential non-independent language sampling12,72. The model can be formally expressed as:

Semantic_Distance = 𝛽 +𝛽 Climate + 𝛽 Culture +𝛽 Geography + 𝛽 Linguistic_History +(1 | Family )+(1| Family)+𝜀 (1)

, where i and j index the language pairs, β₀ represents the intercept, β₁ through β₄ represent the fixed effects for each environmental variable, (1|Family) and (1|Family) represent the random intercepts for the language families of languages i and j respectively, and ε represents the residual error ij term (see Supplementary Note 3 for details and other ways to control non-independence). Effects of environmental factors on cross-linguistic semantic variations at the single dimension level were also assessed through linear mixed regression models.

Study 2: Human participants’ behavioral ratings in 8 languages

Participants: We recruited a diverse sample of 272 online participants from 58 city sites across 8 countries, representing distinct languages: Arabic (Egypt), Chinese (China), English (USA), Hindi (India), Japanese (Japan), Korean (South Korea), Russian (Russia), and Spanish (Spain). Participants were recruited through the Appen crowdsourcing platform (http://www.appen.com) and provided informed consent prior to participation. Participants were compensated for their time through the Appen platform according to standard platform rates. All participants were confirmed to be native speakers residing in their respective targeted sites. To ensure geographical diversity while minimizing intercorrelations among environmental variables of interest, we strategically selected 6-8 geographically dispersed sites within each country (Figure S6; Table S5). To ensure data quality, we implemented a stringent inclusion criterion. Participants whose rating vector correlated less than 0.5 with the averaged group vector within their respective language were excluded from the main analyses. This resulted in the exclusion of 19 participants. The final sample size was 253 participants (self-reported gender: 113 male, 139 female, 1 other/preferred not to say; age range: 18-83 years), with a balanced distribution of 30 to 34 participants per country. Gender and age were included as covariates.

Concept list: For this study, we used the Swadesh 207-Word list, which has been extensively studied in historical and comparative linguistics and is considered to represent the core basic vocabulary of human languages73. The 207 words also sufficiently overlapped with the NEL concept list used in Study 1. To obtain translated word forms of these concepts across our target languages, we sourced initial word forms from the PanLex Swadesh database74 and asked professional translators to review them. In cases where PanLex provided multiple word forms for a single concept, our translators were instructed to select the most commonly used ones. To mitigate potential ambiguity, particularly for polysemous words, we accompanied them with contextual specifications (e.g., lie (as in a bed)). The final word forms are provided in Table S6.

Rating instructions: To evaluate the 13-dimensional neurocognitive semantic structures, participants were instructed to rate concepts on the extent of association with specific dimensions. Our approach was adapted from Binder et al.32, providing some forms of association for each dimension (e.g., for color, the association could be “This word refers to something that has a characteristic color (e.g., eggplant)”, “This word describes the change in color (e.g., fade)”, or “this word directly refers to a particular color (e.g., red)”). To support participants’ understanding, we included example words with high and low loadings from Binder et al.’s questionnaire. To mitigate potential cross-cultural biases, we excluded example words with below-average frequency based on word frequency data in Binder et al.32 and example words that could introduce cultural biases. The English rating instructions were translated into 7 other languages using multi-proofreading processes (Supplementary Note 6). The rating instructions for each language can be found at the link (https://osf.io/suyeb).

Online rating procedures: Participants completed a prescreening process followed by three sessions of the conceptual rating task.

Prescreening. To ensure native speaker selection, we employed a rigorous prescreening process comprising self-reports and a 5-minute audio transcription test in the target language. We collected detailed language background and demographic information, including age, gender, education level, ethnicity, subjective socioeconomic status75 (SES), second language proficiency, country of upbringing before age of 7, and current region of residence. Participants who were nonnative speakers, failed the transcription test, had lived in a foreign country before the age of 7, or were currently residing in foreign countries were not invited to the rating task. Moreover, those who self-reported high proficiency in a second language (5 on a 5-point Likert scale, indicating “upper intermediate and above”) were also excluded. For Indian participants, criteria were adjusted due to the country's multilingual nature, excluding only those with high proficiency in languages of interest (e.g., English).

Rating Task. Eligible participants completed a rating task comprising three 50-minute sessions. Each session involved rating approximately 70 words on 13 neurocognitive dimensions. The 207 concepts were pseudo randomly shuffled into 30 wordlists, which were then randomly assigned across the three sessions. To ensure quality control during the process, three catch trials using high-loading examples from the instructions were randomly inserted into each wordlist. The task automatically terminated if erroneous (low) ratings were detected on control questions. Pearson correlations were computed between each participant's rating vector and the averaged group vector within each language. Participants with correlations below 0.5 were excluded from further analysis. The final sample consisted of 253 participants. Analyses including all participants yielded similar results.

Prediction of cross-language speakers’ semantic variations: An inter-subject correlation matrix was constructed by calculating Pearson correlation coefficients between each participant pair's ratings on 207 concepts across 13 dimensions (2,691 ratings per participant). We first analyzed the variance components shared across individuals (universal), across languages (language-specific), and individual errors to validate the commonality results (see Supplementary Note 4). To investigate the explanatory power of macro environmental variables in understanding cross-language (neurocognitive) semantic variations, we then shifted our analysis to the language level and employed RSA to predict language-level semantic variations using the distance of the four environmental variables, including climate, geography, linguistic history, and culture.

Environmental variables. We obtained environmental variables for each language by aggregating information from participants’ geographic locations. Specifically, we first collected city/county location data from all participants within each language group. The site-level geographic coordinates were obtained using the Bing Maps API (https://www.bingmapsportal.com/). For each language, we then calculated the average coordinates to represent its geographic center. The climate profile for each language was derived by extracting the 19 bioclimate variables from WorldClim68 at these averaged coordinates. The geographic distances between languages were calculated as the geodesic distances between these averaged coordinates. The linguistic history and cultural distances were taken from the language/country level distance measurements from Study 1. Additionally, we constructed a measure of demographic distances based on scaled Euclidean distances to account for potential confounds (age, gender, education level, and SES) between language groups.

Representational similarity analysis. For language-level analysis, we first computed average ratings for each concept-dimension pair within each language to create language-level semantic representations. The semantic RDM was constructed by computing the distance (1 - Pearson correlation, with Fisher’s r-to-Z transformed) between these language-level semantic representations. We then constructed language-level environmental RDMs for climate, culture, geography, and linguistic history, as well as a demographic RDM.

We first assessed raw association patterns between the semantic RDM and environmental RDMs using nonparametric Spearman correlations. We then conducted hierarchical regression with demographic distance entered as the first step, followed by the four environmental variables in the second step to evaluate their collective contribution beyond demographic differences in explaining semantic variations. Following the same procedures as in Study 1, we conducted linear mixed regression models to estimate the unique fixed effects of each environmental variable while controlling for the others. This model included all environmental variables as simultaneous predictors with a random effects structure that included random intercepts for language families to account for phylogenetic relationships:

Semantic_Distance = 𝛽 +𝛽 Climate + 𝛽 Culture +𝛽 Geography + 𝛽 Linguistic_History +(1 | Family )+(1 | Family )+𝜀 (2)

Effects of environmental factors on cross-language semantic variations at the single dimension level were also assessed.

Study 3: Human participants’ brain activity patterns in 45 languages

fMRI dataset: This study utilized an open-access multi-language fMRI dataset52 available at the OSF repository (https://osf.io/cw89s). The dataset comprises neural activity measurements during a language comprehension task performed by 86 native speakers across 45 languages from 12 language families. Eighty-six participants (43 reported as males; age range: 19-45 years) were recruited from Boston, the United States. Sex/gender was balanced, and no explicit sex- or gender-based contrasts were performed in our secondary analyses of this dataset. All participants were proficient in English in addition to their native language. Participants underwent a passive listening task during fMRI scanning. They were presented with auditory stimuli consisting of passages from “Alice in Wonderland” translated into their native languages. The stimuli were presented in two conditions: intact native-language condition and acoustically degraded-language condition (control condition). This contrast between the two conditions is commonly used to obtain neural activities that are related to high-level language processing, including semantics. We obtained the preprocessed whole-brain contrast maps (t-value maps comparing intact native-language and acoustically degraded-language conditions) for all 86 participants from the OSF repository for our analysis. Detailed information on participant characteristics, imaging acquisition procedures, task designs, data preprocessing and first-level modeling are referred to Malik-Moraleda et al.52.

Prediction of cross-language speakers’ neural variations: We investigated whether environmental effects were reflected in cross-language speakers' neural activity patterns during language processing using RSA methods. Our analysis proceeded in the following steps:

Identification of brain regions of interest (ROIs) encoding cross-language semantic alignment: For each of the 12 language-responsive regions, we first aggregated individual neural responses at the language level by averaging t-value maps from speakers of the same language. We then assessed whether neural activity patterns in these regions reflected cross-language alignment in the 13-neurocognitive-dimensional semantic space derived from Study 1. For this analysis, we constructed neural RDMs for each brain region and compared them with semantic RDMs using a linear mixed regression model that included random intercepts for language families:

Neural_Distance = 𝛽 +𝛽 Semantic_Distance +(1 | Family )+(1 | Family )+𝜀 (3)

Construction of environmental variables. We obtained environmental variables for each language based on Glottolog 4.6 coordinates51. For these coordinates, the climate distances were calculated as the scaled Euclidean distance based on the 19 bioclimate variables from WorldClim68. The cultural distance measures were taken from Study 1. The geographic distances were calculated as the geodesic distances. The language history distances were obtained from lang2vec (https://github.com/antonisa/lang2vec), which calculates the distance between two languages by counting the number of upward steps required on the tree until both languages converge at a common node, circumventing the need for cognate calculations on specific wordlists.

Representational similarity analysis. To assess environmental influences on neural activity patterns in the r-ATL, we constructed language-level environmental RDMs for climate, culture, geography, and linguistic history. We then used a linear mixed regression model to test whether these environmental variables predicted neural pattern dissimilarities:

Neural_Distance = 𝛽 +𝛽 Climate + 𝛽 Culture +𝛽 Geography + 𝛽 Linguistic_History +(1 | Family )+(1 | Family )+𝜀 (4)

Relationship between semantic and environmental effects. To further characterize the relationship between neural activity, semantic and environmental variations, we conducted a commonality analysis. This analysis partitioned the variance in neural pattern dissimilarities into components uniquely attributable to semantic factors, uniquely attributable to environmental factors, and shared between them. This analysis provided insight into how environmental and semantic factors might jointly influence neural representations during language processing.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I have seen previous versions of this manuscript, and I approved them, as far my expertise is concerned, so I also approve this version. Data and code are much clearer then before, minor points from my side were addressed. Bigger concerns regarding the approach and conclusions that are beyond my expertise may be raised, but it is beyond my abilities to bring them up here. So I agree with the publication of the manuscript.\n(Remarks on code availability)\nAfter previous discussions with the authors, they have tried to comply with my requirements to provide replicable code, specifically providing an extensive README file, but also program requirements, and instructions of how to run the experiments on one's own machine. I am happy with what I see here, but I have to admit I did not actively RUN the code for time constraints, as Nature Comms only gives me two weeks for the reviews and sends annoying emails to remind me to make them earlier.", "reviewer_4": "Summary:\nFu et al.’s study tackles two main questions:\nHow well do different semantic frameworks capture cross-lingual alignment in meaning?\nWhat variables affect inter-language differences in alignment?\nTo address question (1), the authors compare their primary candidate, i.e., the “neurocognitive framework” (henceforth NF, which represents meaning as loadings onto 13 cognitively relevant semantic dimensions, such as color, shape, time, space), with a) distributional semantic models, b) semantic feature models, and c) statistical control models. They find that the NF better captures cross-lingual alignment in semantics, as shown by analyses on word embeddings in multiple languages and a colexification network.\nTo address question (2), the authors use representational similarity analysis, assessing the second-order similarity between “semantic” RDMs (based on word embeddings, human ratings, and fMRI data) and RDMs based on geographic, linguistic, cultural, and climatic information. The core finding is that climate-based RDMs explain cross-lingual variation in semantic structure.\nEvaluation:\nWe find the hypothesis that climate might modulate cross-lingual differences in semantics intriguing and the motivation for investigating this question is convincing: climate strongly modulates sensory signals across modalities, and given that meaning is tied (at least in part) to sensory information, it follows that climate is a reasonable candidate for modulating semantic idiosyncrasies. We also commend the methodological breadth of the article, spanning computational modelling, behavioral experimentation, and neuroimaging; and we find the baseline conditions to be strong (with two complementary statistical control models). Nonetheless, we have serious concerns about the methodological approaches adopted in the various studies, which need to be addressed.\nThe main concern\nRepresentational similarity analyses. The most substantial concern we have is with how representational similarity analyses are conducted across the studies. In the three studies assessing the impact of climate on semantics (Study 1B, 2, and 3), the authors adopt ad-hoc statistical testing where each dependent variable is modeled with a different statistical approach:\nIn Study 1B, the authors predict NF-based RDMs for individual languages with RDMs based on climate, geographic, linguistic history, and culture variables. The analysis involves a crossed random-effects structure that nests language pairs within language families.\nIn Study 2, the authors construct RDMs for individual participants. Unlike in Study 1B, they convert the climate RDMs to ranked data. The mixed effects models involve subject pairs nested within language families (but not languages).\nIn Study 3, the authors use a hierarchical approach by which they compare nested models: first, a “baseline” model incorporating geographic distance and linguistic family as phylogenetic controls, and then a target model that additionally includes climate and cultural distances. The RDMs are constructed at the level of individual participants. The random effect structure only includes random intercepts for each participant dyad. The nested models are compared by means of ΔAIC.\nThe three approaches are very different: sometimes the distance matrices are converted to ranked data (Study 2), the random effect structure of the mixed models varies from study to study, and while Study 1B and Study 2 simply examine the joint influence of several variables on the outcome variable, Study 3 adopts a model comparison approach. Most critically, the paper makes inferences about inter-language differences, but only Study 1B analyzes the data at the level of individual languages, whereas both Study 2 and Study 3 analyze the data at the level of individual participants.\nIf these studies are to be combined in a single study, the analytical approach should be consistent across them. One convincing approach would be to analyze the data at the level of individual languages (aggregating across participants) within a model-comparison framework, where a baseline model (including geographic, cultural, and linguistic history distances) is compared with a model that additionally includes climate, with random effects (ideally intercepts and slopes) for the languages in the pair. The key advantage of this analytic approach (where the analyses are conducted at the level of individual languages) is that it would most directly support the key claims of the paper about inter-language differences. Moreover, the approach where multiple participants from the same language are included in the RDMs may inflate the second-order similarity between climate RDMs and semantic RDMs simply because speakers of the same language have the same climate and possibly similar semantics. However, we acknowledge that other analytic solutions are possible; the only critical conditions are: (i) using the same analytic approach across studies and (ii) providing theoretical reasons why there might be (small) differences in the analyses across experiments. If the findings prove robust and come out reliably when the same analytic procedure is applied across studies, this would make us (and likely other readers) more confident that the findings are real and meaningful, and would likely increase the long-term impact of this work.\nAdditional major concerns\nBrain activity patterns. In Study 3, the authors correlate RDMs based on the t-maps for the intact-degraded contrast (fMRI data from Malik-Moraleda, Ayyash, et al., 2022) with RDMs based on climate, geographic, linguistic history, and culture variables. We don’t think that the authors characterize this result properly. For instance, in their response to Reviewer #1 (from the earlier round), they say: “We found that in the right ATL brain region, languages associated with similar climates showed similar semantics-related neural activities”. However, this is not what the t-maps for the intact-degraded contrast reflect; this contrast indicates, for each voxel, how much stronger it responds to intact vs. degraded language stimuli (this contrast is often used to identify brain areas that respond to language). This contrast does not isolate semantic processing, instead including many other aspects of language processing (e.g., phonology, morphology, syntax). What these results (if real) would indicate is that climate modulates the localization of the language areas across languages. This is a very different (and very strong!) claim. This claim is barely related to the conclusions drawn from Study 1B and Study 2, which are clearly about semantics. Moreover, the results seem highly surprising given the substantial interindividual variability in the topography of the language areas; finding meaningful interlinguistic variability on top of this interindividual variability is generally challenging, especially given that the dataset the authors draw on has only two individuals from each language.\nRepresentational similarities and differences within the NF. The NF in Study 1 is based on projecting word embeddings onto a lower-dimensional space of 13 dimensions/features. The authors found that this lower-dimensional space better reflects semantic similarities across languages. This makes sense, as this space consists of a possibly universal set of conceptual dimensions and filters away variance along other potentially relevant semantic axes that might be more idiosyncratic and language-specific. Yet, they use this space to study cross-lingual differences and how those are modulated by climate. There appears to be a contradiction here—if the authors want to model cross-lingual differences in semantics, why choose a representational projection that minimizes those differences? One motivation for restricting the feature space to those semantic dimensions could be that, in principle, climate could be expected to modulate semantic differences tied to the sensory information conveyed by words, and for this reason, it would make sense to restrict the focus to those features. If this is the case, this line of reasoning should be clearly formulated in the article (see also the following point “Sensorimotor and cognitive dimensions”), because the original line of reasoning put forward by the authors (i.e., adopting the NF because it better identifies cross-lingual consistencies in semantics) is not convincing. More generally, the authors should emphasize the point we made above, namely that the NF restricts semantic variation by projecting meaning onto a small set of semantic dimensions. Relatedly, it would be interesting to see how much information is lost in this projection (partialling out information beyond the 13 NF axes); this could be achieved by doing RSA over the native word embedding space and the NF space in the various languages.\nCharacterization of the alternative models. The authors present three alternatives to the NF: a) distributional semantic models, b) feature-based psycholinguistic models, and c) statistical control models. However, we believe that the authors’ operationalization of feature-based psycholinguistic models (calculating the cosine similarity between a target word and the 100 most frequent English features in the Buchanan et al. (2019) norms) mischaracterizes how feature models are intended to be used (where each word is represented as a set of discrete features that characterize a concept, like “is red”, “is mammal”, etc.). After all, all the models they consider in their first study are distributional semantic models, where word embeddings are projected onto lower-dimensional spaces which consist of neighbouring words (a), frequent features (b), or randomly sampled words (c). Even the NF consists of a projection of word embeddings onto the 13 cognitively relevant features. Since all the considered models are ultimately projections of a distributional semantic model (DSM), we believe it is warranted to include the “native” representations of the DSM itself. This would require:\nEvaluating how similar the different languages are in the native fastText word embeddings space (without projecting embeddings onto anchor words), and\nEvaluating the similarity between the native fastText-based RDM and the RDMs based on climate, geographic, linguistic history, and culture.\nSensorimotor and cognitive dimensions. The NF uses 13 dimensions; 7 are sensorimotor (color, shape, …), while the remaining 6 are called “cognitive domains” (time, space, number, …). While it is rather clear why climate should have an effect on sensorimotor representations, the authors need to elaborate why climate would affect the “cognitive domains” dimensions as well.\nMinor points:\nSemantic projection. The authors say they employ the “semantic projection” method to project word embeddings onto the 13 dimensions of the NF. Nonetheless, their approach only consists in calculating the cosine between each word and a few anchor words for each dimensions, which does not correspond to the semantic projection method proposed by Grand et al. (2022)—in fact, it was found to perform worse than the actual method, which involves calculating a dimension vector as the difference between two “poles” (for the sound dimension they could be something on the lines of “noisy” vs. “silent”), and then projecting individual words onto these dimensions with the dot product. We understand that it might be difficult to define the poles for some features which are not intrinsically unidimensional (e.g., shape), but this should then be clarified.\nLanguage and thought. From the abstract: “languages are used worldwide as the primary means of human thought”. This is a rather controversial statement, we’d recommend the authors to tone it down in light of evidence that language and thought appear to be largely dissociable (see Fedorenko, Piantadosi, Gibson, 2024 for a recent review).\nLanguage family distance. The language family distance metric (1 if two languages belong to the same family, 0 otherwise) is a coarse metric. The authors could consider using a distance metric based on a language tree. For instance, the genetic distance calculated by the Python package lang2vec calculates the distance between two languages by counting the number of upward steps required on the tree until both languages converge at a common node (Littell et al., 2017).\nTypos and writing suggestions:\nThe core hypothesis of the paper (that climate might modulate cross-lingual differences in semantics because of general perceptual differences in different climatic environments) becomes clear only at ll. 130-137; we recommend to clearly specify this point earlier in the introduction so that readers can understand from the get-go what the article is about.\nThroughout the article, the authors consistently refer to word embedding models as “language models”; consider rephrasing as word representation models or word embedding models. Also, fastText (the word embeddings model used in the study) is a peculiar word representation model in that it represents words as a sum of word embeddings plus subword (n-gram) embeddings. This should be mentioned somewhere in the paper.\nThe theoretical basis of the NF is not very clear: it starts by positing that language and sensory experience form the basis of conceptual representations (ll. 77-99) and then concludes that an appropriate way to represent semantics is by using 13 dimensions (sensorimotor dimensions and “cognitive domains”), without mentioning how language information fits within this model and where the dimensions from the “cognitive domains” come from.\nl. 63: “beauty in English, bella in Italian” – it should either be “beautiful in English, bella in Italian” or “beauty in English, bellezza in Italian”.\nll. 119-120: “These alternative models [distributional semantic models, feature models] are prevalent in cognitive disciplines without explicit considerations of the neurobiological basis and have been shown to be less correlated with word semantic brain activities than the neurocognitive dimensional model [...]” – distributional semantic models and their successors (e.g., language models) are often linked to neurobiological principles, and obtain great fit to brain activity patterns (see e.g., Goldstein et al., 2022; Shrimpf et al. 2021).\nl. 299: It would be great if the authors could write the lmer formulas to better understand the random effect structure of the mixed models (also, l. 347).\nl. 352: The p-value is missing for the coefficients of “linguistic history”.\nll. 211-212: In the context of the finding that the “global” distributional space finds more cross-lingual commonalities than the “local” distributional space, consider citing Lewis et al. (2023), who found the opposite pattern.\nl. 596: “billions to hundreds of millions” → “hundreds of millions to billions”\nl. 713: “with Fisher-Z transformed” → “with Fisher’s r-to-z transformation”\nQuestions:\nDistributional local vs. global models. It is unclear how the distributional local models work. The authors say that in the local condition, each word is projected onto its immediate neighbors. We do not understand how this might work—if each word is projected onto its neighbors and the neighbors change from word to word, this would create a dimensionality mismatch (e.g., w1 would be projected onto w2, w3, w4 etc., while say w123 would be projected onto w124, w125 etc., so the dimensions of the projection of w1 would be different from the dimensions of the projection of w123). Could you please clarify how this approach works? Relatedly, the global models use as anchors all the words but the target word itself. This would once again cause a dimensionality mismatch, as, for instance, the anchor w1 would be absent from the embedding projection of w1, and the anchor w123 would be absent from the embedding projection of w123.\nReferences\nLewis, M., Cahill, A., Madnani, N., & Evans, J. (2023). Local similarity and global variability characterize the semantic space of human languages. Proceedings of the National Academy of Sciences, 120(51), e2300986120.\nLittell, P., Mortensen, D. R., Lin, K., Kairis, K., Turner, C., & Levin, L. (2017). Uriel and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 8–14.\nFedorenko, E., Piantadosi, S. T., & Gibson, E. A. (2024). Language is primarily a tool for communication rather than thought. Nature, 630(8017), 575-586.", "reviewer_5": "Fu et al. address an important yet neglected topic--namely, crosslinguistic similarities and differences in meaning, and their implications for cognition--by combining computational linguistics and neuroscience. Their methods are innovative and their results are interesting and valuable. I do, however, have some concerns.\nLike the study by Thompson et al. (2020), this one relies heavily on Dellert et al.'s (2020) NorthEuralex database, but this database has several limitations that Fu et al. should explicitly acknowledge. Two major limitations are as follows.\n1. Fu et al. state that the \"target concepts\" and \"anchor terms\" they draw from this database are \"translation equivalents\" across the 53 languages they focus on. But this is not true. Even Dellert et al. are quite open about this. They state: \"For instance, many languages lexically distinguish the substance one breathes from the space in which birds move, making it necessary to further specify the English equivalent “air”. For some concepts (especially kinship terms), the specification also defines which term is to be mentioned first in our list of equivalents if a more fine-grained distinction is made in the target language, e.g. ‘grandfather (e.g. father’s father)’.\"\nAll of the information in the database was compiled from dictionaries, without extensive validation from native speakers. This is another shortcoming that Dellert et al. (2020) admit and that Fu et al. should make clear too.\nCrosslinguistic lexical-semantic studies that HAVE included native speaker judgments have revealed nontrivial differences in how even closely related languages partition the same conceptual domains, and this has implications for Fu et al.'s study. As described below, two examples involve color terms and body part terms, but comparable results have been reported for many other domains.\nFor instance, Fu et al. use lots of basic color terms (e.g., blue, red, yellow, etc.) as \"anchors\" for their neurocognitive color dimension. But Majid et al. (2015) show that there are not straightforward translation equivalents for these terms across 12 Germanic languages. Here's a snippet of their discussion: \"Within the colour domain, English and Danish differed most from one another (see Table 2 with correlations). English red is very localised: it was the dominant response for only one colour chip. In contrast, Danish rød was a dominant response for 11 different chips. In English, speakers preferentially called most of those chips pink, which was then also extended into an area that Danish speakers called lilla. Similarly, English orange was smaller in extension than its Danish equivalent orange. English yellow and Danish gul are approximately the same size, but because Danish orange has a larger extension gul seeps into territory that English speaker preferentially name green.\"\nThe body part domain is relevant to Fu et al.'s study because they used lots of body part terms as \"anchors\" for their neurocognitive motor dimension. But Majid et al. (2015) report nontrivial lexical-semantic differences between 12 Germanic languages for such terms. Here's part of their discussion: \"...there is no single cognate set used in the Germanic languages for ‘leg’, and we see a more divergent pattern of naming. Participants saw red dots on the upper and lower leg, on the right and left side of the body, viewed from front and back. This lets us examine how they chose to refer to these distinct images. English speakers predominantly used leg for all such stimuli; similarly Dutch, Flemish and Luxembourgish used a single term been/Been. Frisian speakers also used been, but not as frequently. However, the speakers of the other languages tended to use distinct terms for the upper and lower leg. German speakers used Oberschenkel and Unterschenkel, literally ‘upper-leg’ and ‘lower-leg’, but the root *Schenkel alone was never attested. Schwyzerdütsch speakers used the cognate terms, obèrschènku and ungèrschènku, although bare schènku was also used (but only twice, and only with reference to upper leg stimuli). The Scandinavian languages make sporadic use of bein/ben, but for the most part other terms were more frequent. Like German speakers, the participants preferentially distinguished upper and lower leg. This discussion illustrates that even in a domain with much shared structure, there are still many intricacies to be observed.\"\nSo the point here is that many of the \"target concepts\" and \"anchor words\" that Fu et al. take from the NorthEuraLex database are not really translation equivalents across the 53 languages in their study, contrary to their initial assumption. This matters for their investigation of \"commonalities\" because, despite the initial non-equivalence of the word meanings, their still found that the neurocognitive model disclosed greater crosslinguistic similarities than the other models. And it matters for their investigation of \"variation\" because the initial non-equivalence already embodies some variation.\n2. Another limitation of the NorthEuraLex database is that it excludes LOTS (literally thousands) of languages. MANY lexical-semantic similarities and differences have been found in studies that had more globally distributed language samples, and while some of these discoveries seem to fit Fu et al.'s climate-based account, others don't.\nHere's an example that's compatible with the climate-based account. Brown (2005) found that in a global sample of 617 languages, only 389 (63%) have separate terms for arms and hands. Consistent with a previous study by Witkowski and Brown (1985) that involved a smaller sample, he also found that languages that do make this distinction tend to be located in non-equatorial zones where cold weather is frequent. Given this correlation with lattitude and, by extension, temperature, he proposed that because societies in these regions are more likely to wear clothes that increase the salience of the arm-hand boundary (e.g., gloves, mittens, and long sleeves ending at the wrist), they may be more inclined to develop separate terms for arms and hands.\nAn example that seem to not fit the climate-based account involves spatial frames of reference. This issue is discussed in Majid et al.'s (2004) Box 1.\nAnother concern of mine has to do with the fMRI results. Fu et al. found that crosslinguistic semantic alignment in the neurocognitive model was associated with neural activity in bilateral ATLs and left angular gyrus. But each target concept in each of the 53 languages was represented by a 300-dimensional vector, and in Fu et al.'s own previous experiment (Fu et al., 2023), vector-based representations of word meanings didn't correlate with activity patterns in ANY left perisylvian cortical areas; instead, only graph-based representations led to significant correlations. Now, other fMRI studies have in fact found significant mappings between vector-based representations of word meanings and left perisylvian cortical areas, but the details of these mappings have been inconsistent across models and studies (e.g., Carota et al., 2017, 2021, 2024; Liuzzi et al., 2023). This variability reduces confidence in Fu et al.'s new findings.\nBrown, C.H. (2005). Hand and arm. In M. Haspelmath, M.S. Dryer, D. Gil, & B. Comrie (eds.), The world atlas of language structures (pp. 522-525). Oxford, UK: Oxford University Press.\nCarota, F., Kriegeskorte, N., Nili, H., & Pulvermüller, F. (2017). Representational similarity mapping of distributional semantics in left inferior frontal, middle temporal, and motor cortex. Cerebral Cortex, 27, 294-309.\nCarota, F., Nili, H., Pulvermüller, F., & Kriegeskorte, N. (2021). Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: Evidence from RSA of BOLD signals. NeuroImage, 224, 117408.\nCarota, F., Nili, H., Kriegeskorte, N., & Pulvermüller, F. (2024). Experientially grounded and distributional semantic vectors uncover dissociable representations of conceptual categories. Language, Cognition, and Neuroscience, 39, 1020-1044.\nDellert, J., Daneyko, T., Münch, A., Ladygina, A., Buch, A., Clarius, N., et al. (2020). NorthEuraLex: A wide-coverage lexical database of Northern Eurasia. Language Resources and Evaluation, 54, 273-301.\nFu, Z., Wang, X., Wang, X., Yang, H., Wang, J., Wei, T., Liao, X., Liu, Z, Chen, H., & Bi, Y. (2023). Different computational relations in language are captured by distinct brain systems. Cerebral Cortex, 33, 997-1013.\nLiuzzi, A.G., Meersmans, K., Storms, G., De Deyne, S., Dupont, P., & Vandenberghe, R. (2023). Independency of coding for affective similarities and for word co-occurrences in temporal perisylvian neocortex. Neurobiology of Language, 4, 257-279.\nMajid, A., Bowerman, M., Kita, S., Haun, D.B.M., & Levinson, S.C. (2004). Can language restructure cognition? The case for space. Trends in Cognitive Sciences, 8, 108-114.\nMajid, A., Jordan, F., & Dunn, M. (2015). Semantic systems in closely related languages. Language Sciences, 49, 1-18.\nThompson, B., Roberts, S. G., & Lupyan, G. (2020). Cultural influences on word meanings revealed through large-scale semantic alignment. Nature Human Behaviour, 4(10), 1029-1038.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to Reviewers\nWe sincerely thank the reviewers for their thoughtful and constructive feedback on\nour manuscript. These comments and suggestions have greatly helped us improve the\nstudy. We have carefully revised the manuscript accordingly. Below, we explain how\neach point was addressed and the corresponding revisions.\nReviewers' comments:\nReviewer #1 (Remarks to the Author):\nI have seen previous versions of this manuscript, and I approved them, as far my\nexpertise is concerned, so I also approve this version. Data and code are much clearer\nthan before, minor points from my side were addressed. Bigger concerns regarding the\napproach and conclusions that are beyond my expertise may be raised, but it is beyond\nmy abilities to bring them up here. So, I agree with the publication of the manuscript.\nReviewer #1 (Remarks on code availability):\nAfter previous discussions with the authors, they have tried to comply with my\nrequirements to provide replicable code, specifically providing an extensive README\nfile, but also program requirements, and instructions of how to run the experiments\non one's own machine. I am happy with what I see here, but I have to admit I did not\nactively RUN the code for time constraints, as Nature Comms only gives me two weeks\nfor the reviews and sends annoying emails to remind me to make them earlier.\nResponse: We sincerely thank Reviewer #1 for their continued support of our\nmanuscript and for acknowledging the improvements made to our data and code\navailability. We are pleased that our README file, program requirements, and\ninstructions meet their standards for reproducible code.\nReviewer #4 (Remarks to the Author):\nSummary:\nFu et al.’s study tackles two main questions:\nHow well do different semantic frameworks capture cross-lingual alignment in\nmeaning?\nWhat variables affect inter-language differences in alignment?\nTo address question (1), the authors compare their primary candidate, i.e., the\n“neurocognitive framework” (henceforth NF, which represents meaning as loadings\nonto 13 cognitively relevant semantic dimensions, such as color, shape, time, space),\n1\nwith a) distributional semantic models, b) semantic feature models, and c) statistical\ncontrol models. They find that the NF better captures cross-lingual alignment in\nsemantics, as shown by analyses on word embeddings in multiple languages and a\ncolexification network.\nTo address question (2), the authors use representational similarity analysis, assessing\nthe second-order similarity between “semantic” RDMs (based on word embeddings,\nhuman ratings, and fMRI data) and RDMs based on geographic, linguistic, cultural, and\nclimatic information. The core finding is that climate-based RDMs explain cross-lingual\nvariation in semantic structure.\nEvaluation:\nWe find the hypothesis that climate might modulate cross-lingual differences in\nsemantics intriguing and the motivation for investigating this question is convincing:\nclimate strongly modulates sensory signals across modalities, and given that meaning\nis tied (at least in part) to sensory information, it follows that climate is a reasonable\ncandidate for modulating semantic idiosyncrasies. We also commend the\nmethodological breadth of the article, spanning computational modelling,\nbehavioral experimentation, and neuroimaging; and we find the baseline conditions to be strong\n(with two complementary statistical control models). Nonetheless, we have serious\nconcerns about the methodological approaches adopted in the various studies, which\nneed to be addressed.\nResponse: Thank you for the positive evaluation on our study and the constructive\nsuggestions!\nThe main concern\nRepresentational similarity analyses. The most substantial concern we have is with\nhow representational similarity analyses are conducted across the studies. In the three\nstudies assessing the impact of climate on semantics (Study 1B, 2, and 3), the authors\nadopt ad-hoc statistical testing where each dependent variable is modeled with a\ndifferent statistical approach:\nIn Study 1B, the authors predict NF-based RDMs for individual languages with RDMs\nbased on climate, geographic, linguistic history, and culture variables. The analysis\ninvolves a crossed random-effects structure that nests language pairs within language\nfamilies.\nIn Study 2, the authors construct RDMs for individual participants. Unlike in Study 1B,\nthey convert the climate RDMs to ranked data. The mixed effects models involve\nsubject pairs nested within language families (but not languages).\nIn Study 3, the authors use a hierarchical approach by which they compare nested\nmodels: first, a “baseline” model incorporating geographic distance and linguistic\nfamily as phylogenetic controls, and then a target model that additionally includes\nclimate and cultural distances. The RDMs are constructed at the level of individual\nparticipants. The random effect structure only includes random intercepts for each\nparticipant dyad. The nested models are compared by means of ΔAIC.\nThe three approaches are very different: sometimes the distance matrices are\nconverted to ranked data (Study 2), the random effect structure of the mixed models\nvaries from study to study, and while Study 1B and Study 2 simply examine the joint\ninfluence of several variables on the outcome variable, Study 3 adopts a model\ncomparison approach. Most critically, the paper makes inferences about inter-\nlanguage differences, but only Study 1B analyzes the data at the level of individual\nlanguages, whereas both Study 2 and Study 3 analyze the data at the level of individual\nparticipants.\nIf these studies are to be combined in a single study, the analytical approach should\nbe consistent across them. One convincing approach would be to analyze the data at\nthe level of individual languages (aggregating across participants) within a model-\ncomparison framework, where a baseline model (including geographic, cultural, and\nlinguistic history distances) is compared with a model that additionally includes\nclimate, with random effects (ideally intercepts and slopes) for the languages in the\npair. The key advantage of this analytic approach (where the analyses are conducted\nat the level of individual languages) is that it would most directly support the key\nclaims of the paper about inter-language differences. Moreover, the approach where\nmultiple participants from the same language are included in the RDMs may inflate\nthe second-order similarity between climate RDMs and semantic RDMs simply\nbecause speakers of the same language have the same climate and possibly similar\nsemantics. However, we acknowledge that other analytic solutions are possible; the\nonly critical conditions are: (i) using the same analytic approach across studies and (ii)\nproviding theoretical reasons why there might be (small) differences in the analyses\nacross experiments. If the findings prove robust and come out reliably when the same\nanalytic procedure is applied across studies, this would make us (and likely other\nreaders) more confident that the findings are real and meaningful, and would likely\nincrease the long-term impact of this work.\nResponse: We greatly appreciate this suggestion. Indeed, we had chosen analysis\nmethods considering the data properties for each study separately. We fully agree with\nyou that having a more coherent analysis scheme across studies would make the work\nstronger. Following this recommendation, we have updated the analyses to use a\nconsistent analysis scheme across studies. The key findings remain robust (see Table\nR1 below).\nWe have made the following revisions to reflect this updated analysis scheme and\ncorresponding results.\nFor the analytical procedures, we have added an overall paragraph explaining our\nunified analytical framework. In the Results section “Variations associated with\nenvironmental variables”: “To this end, we implemented a consistent analytical\n3\nframework across all three studies (i.e., studies with word embeddings, behavioral\nratings, and neural activity data). At the language level (aggregating participants\nwhere applicable), we constructed linear mixed models that included all four variables\nof interest simultaneously - geographic, cultural, linguistic history, and climate\nvariables - with random intercepts for language families to account for phylogenetic\nrelatedness (Jackson et al., 2023; Thompson et al., 2020). This approach allowed all\nfactors to compete within the same model, revealing their unique contributions to\nsemantic variations. For Studies 2 and 3, we additionally conducted predictions at the\nindividual subject level, confirming that patterns remained consistent across analytical\nscales.” (p.8–9). We have also substantially revised the Methods (p.19-20; p.22; p.23)\nto present these analytical procedures in greater detail.\nFor the updated results, we revised the Results sections (p.9-12). To summarize briefly\nhere, the consistent analytical approach reveals remarkable convergence across all\ndatasets (summarized in Table R1). Climate distance emerges as a significant predictor\nof semantic similarity in each dataset, with standardized beta coefficients ranging from\nβ = 0.12 to β = 0.53, even when controlling for cultural, geographic, and linguistic\nhistory distances. The association patterns between climate and neurocognitive\nsemantic variations were also robust across different model specifications (see\nSupplementary Text S3).\ndatasets\nData Types Climate Cultural Geographic Linguistic History\nWord embedding data 0.28 (0.20, 0.37) 0.07 (-0.01, 0.14) 0.05 (-0.03, 0.13) 0.22 (0.16, 0.28)\n(wiki + CC) P = 1.50 × 10-9*** P = 0.10 P = 0.24 P = 1.29 × 10-13***\nWord embedding data 0.44 (0.33, 0.58) 0.22 (0.12, 0.33) -0.18 (-0.29, -0.06) 0.28 (0.20, 0.35)\n(wiki + Subs) P = 1.39 × 10-10*** P = 4.64 × 10-5*** P = 0.002** P = 7.86 × 10-12***\n0.53 (0.26, 0.80) -0.07 (-0.36, 0.25) -0.22 (-0.40, -0.04) 0.13 (-0.17, 0.39)\nBehavioral data\nP = 0.0006*** P = 0.64 P = 0.02 P = 0.34\nNeural data 0.12 (0.04, 0.22) 0.11 (0.008, 0.21) -0.07 (-0.15, 0.002) -0.02 (-0.08, 0.04)\n(R-ATL) P = 0.01* P = 0.047* P = 0.06 P = 0.61\nNote: Values represent standardized beta coefficients with 95% confidence intervals in parentheses. Significance levels: *** P < 0.001, ** P <\n0.01, * P < 0.05.\nBrain activity patterns. In Study 3, the authors correlate RDMs based on the t-maps for\nthe intact-degraded contrast (fMRI data from Malik-Moraleda, Ayyash, et al., 2022)\nwith RDMs based on climate, geographic, linguistic history, and culture variables. We\ndon’t think that the authors characterize this result properly. For instance, in their\nresponse to Reviewer #1 (from the earlier round), they say: “We found that in the right\nATL brain region, languages associated with similar climates showed similar semantics-\nrelated neural activities”. However, this is not what the t-maps for the intact-degraded\ncontrast reflect; this contrast indicates, for each voxel, how much stronger it responds\nto intact vs. degraded language stimuli (this contrast is often used to identify brain\nareas that respond to language). This contrast does not isolate semantic processing,\ninstead including many other aspects of language processing (e.g., phonology,\nmorphology, syntax). What these results (if real) would indicate is that climate\nmodulates the localization of the language areas across languages. This is a very\ndifferent (and very strong!) claim. This claim is barely related to the conclusions drawn\nfrom Study 1B and Study 2, which are clearly about semantics. Moreover, the results\nseem highly surprising given the substantial interindividual variability in the\ntopography of the language areas; finding meaningful interlinguistic variability on top\nof this interindividual variability is generally challenging, especially given that the\ndataset the authors draw on has only two individuals from each language.\nResponse: We appreciate this important comment about our interpretation of the\nfMRI data in Study 3. Indeed, we fully agree and are aware that the “intact-degraded”\ncontrast in this large-scale cross-linguistic fMRI study captures multiple aspects of\nlanguage processing, which include but are not restricted to, semantics. In our original\nsubmission we tried to indicate that this result by itself is suggestive rather than\nconclusive -- the relation to semantics was inferred from the functionality of the ATL\nbased on literature findings and not from the current analyses. However, we agree that\nthis is rather indirect and have thus made the following improvements for clarification,\nconsideration of caveats, and additional analyses, inspired by this important comment.\nExplicit reasoning about the semantic relationship with added analyses\nWe explicitly caution against characterizing the intact-degraded contrast as\nspecifically or exclusively semantic, and further discuss how several lines of evidence\nconvergently point to a semantic component in our findings:\n1. Connection to the independently derived cross-language semantic structure:\nThe cross-language variations of the language-task neural activity pattern in\nthe right ATL correlated with the cross-language semantic variations\nindependently derived in Study 1. That is, the neural variation pattern at least\npartly contains variance explained by the semantic variations.\n2. The results of a commonality analysis: We added a new commonality analysis\n6\n(see Figure R1), demonstrating significant shared variance between climate\nand semantic factors in explaining the right ATL brain activation pattern\nvariations (38.20%, 95% CI [14.75%, 48.30%]). Of course, this does not mean\nthat climate does not affect other aspects of language processes supported by\nthe ATL, as there were also significant unique climate effects (55.58%, 95% CI\n[16.83%, 85.80%]) in the commonality analysis.\n3. The results of the language specificity for the climate effect: We added\nanother new control analysis, examining neural activity during non-linguistic\ntasks (Spatial Working Memory and Math). Using environmental factors to\npredict the r-ATL’s neural activity, we found that climate effects are specific to\nlanguage processing and are not present during non-linguistic cognitive tasks\n(see Figure R2).\n4. Evidence from multiple lines of research in the literature that highlights ATL as\na semantic hub: Our findings were localized to the ATL, which has been\nestablished as a semantic hub by extensive literature (e.g., Lambon-Ralph et\nal., 2017; Patterson et al., 2007). This reasoning by itself suffers from the risk\nof reverse inference and is considered in light of the empirical results above.\nClarifying our interpretation of climate effects\nRegarding the interpretation of the results – “climate modulates the localization\nof the language areas across languages.” We wish to clarify that this is not our intended\ninterpretation:\n1. Our analyses focus on neural pattern similarities within a functionally defined\nregion (r-ATL) rather than shifts in the anatomical localization of language areas.\nWe are examining how patterns of activation within this region vary across\nlanguages, not claiming that the region itself is relocalized based on climate.\n2. The commonality analysis is particularly informative here - the shared variance\nbetween climate and semantic factors (38.20%) suggests that climate partially\ninfluences the pattern in which semantic information is processed within this\nregion, rather than determining where language is processed in the brain.\n3. Our interpretation aligns with a more conservative view: that climate may\nshape aspects of language processing (particularly semantic representations)\nwithin established language regions, without altering the neuroanatomical\norganization of language areas. This interpretation is more consistent with our\nfindings across all three studies, which focus on climate's influence on semantic\ncontent implementation supported by a universal neuroanatomical\norganization.\nThis clarification helps bridge our neural findings with the behavioral and\ncomputational results in Studies 1 and 2, providing a more cohesive framework that\nfocuses on how climate shapes aspects of language processing within a universal\nneural architecture.\nMethodological strengthening\nWe agree with you that finding meaningful cross-linguistic differences amid\n7\nsubstantial individual variability presents a significant challenge, especially with only\ntwo participants per language. We carefully carried out analyses also at the individual\nlevel that take into consideration individual variations while considering cross-\nlanguage patterns:\nAs the reviewer suggested, for the main results we report language-level results\nby averaging neural activity patterns within each language, allowing us to isolate\nmeaningful interlinguistic patterns.\nAdditionally, we have implemented individual-level analyses that account for the\nhierarchical structure of our data. As detailed in Supplementary Text S3, we employed\na nested random effects model:\n𝑆𝑒𝑚𝑎𝑛𝑡𝑖𝑐_𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝛽 +𝛽 𝐶𝑙𝑖𝑚𝑎𝑡𝑒 +\n𝑖𝑗 0 1 𝑖𝑗\n(1 | 𝐹𝑎𝑚𝑖𝑙𝑦 /𝐿𝑎𝑛𝑔𝑢𝑎𝑔𝑒 /𝑆𝑢𝑏𝑗𝑒𝑐𝑡 ) + (1| 𝐹𝑎𝑚𝑖𝑙𝑦 /𝐿𝑎𝑛𝑔𝑢𝑎𝑔𝑒 /𝑆𝑢𝑏𝑗𝑒𝑐𝑡 )+ε\n𝑖 𝑖 𝑖 𝑗 𝑗 𝑗 ij\nThis model addresses the concern about having only two individuals per language\nby explicitly modeling the variance components at multiple nested levels (subjects\nwithin languages within families). By implementing this three-level nested random\neffects structure, we account for variance at the subject, language, and language\nfamily levels.\nImportantly, these individual-level analyses yielded significant climate effects\n(Study 2: β = 0.18, 95% CI [0.17, 0.18], P < 1 × 10-16; Study 3: β = 0.07, 95% CI [0.03,\n0.11], P = 0.0003), confirming that our findings remain robust despite the challenges\nof inter-individual variability highlighted by the reviewer.\nManuscript revision\nWe have made the following revision accordingly:\n1. Reporting the semantic-neural associations in the r-ATL: “We then used linear\nmixed regression models to assess the relationships between these neural\nRDMs and the 13-dimensional neurocognitive semantic model RDMs from\nStudy 1, while including random intercepts for language families to account for\nthe phylogenetic structure. Significant beta coefficients were found only in the\nright anterior temporal lobe (ATL; β = 0.44, 95% CI = [0.17, 0.71], FDR-corrected\nq = 0.016). That is, the more closely aligned two languages are for the 13-\nneurocognitive dimensional semantic representation, the more similar their\nspeakers’ brain activities are in the r-ATL when processing language.” (p.11)\n2. A newly added section to characterize the effects using control analysis and\ncommonality analysis: “Importantly, when using environmental RDMs to\npredict the r-ATL's neural activity during non-linguistic tasks (e.g., Spatial\nWorking Memory -- Hard vs Easy; Math -- Hard vs Easy), the climate effects\nwere no longer significant, suggesting that these effects are unique to\nlanguage-related processing (Supplementary Text S5; Figure S7). To further\ncharacterize the relationship between semantic and climate effects in the r-\nATL, we conducted a commonality analysis to partition the r-ATL variance\nexplained by different components (Figure 4c, right panel). This analysis\nrevealed significant unique contributions from both semantic factors (6.26%,\n8\n95% CI [1%, 37.46%]) and climate factors (55.58%, 95% CI [16.83%, 85.80%]),\nas well as substantial common variance shared between climate and semantic\neffects (38.20%, 95% CI [14.75%, 48.30%]). The significant common variance\nsuggests that climate effects on neural activities in the r-ATL partially reflect\nsemantically related neural activation patterns.” (p.11)\n3. Revised interpretation in the Discussion and relationships with Studies 1 and\n2: “This broad dimensional effect is also in line with our intriguing observation\nof climate-brain activity associations in the r-ATL in the human brain. Note that\nwe analyzed brain activity for the “intact vs. degraded” contrast, which\ncaptures broad linguistic processes including, but not limited to, semantics.\nRegarding the ATLs’ role in language processing, accumulating research has\nestablished them as key regions for processing higher-order semantics, binding\nthe distributed semantic dimensional representations and word relational\nstructures in language (see reviews in Bi, 2021; Lambon-Ralph et al., 2017;\nMiyashita, 2019). Indeed, the commonality analyses revealed significant\nshared components between climate and semantics, linking ecological\nvariables and language neural processing that converges with our behavioral\nand computational findings. We do not exclude other potential language\nprocesses that may also mediate the climate-ATL association -- our\ncommonality analysis indeed also revealed significant variance shared by\nclimate and neural activities that is not shared with semantics.” (p.15).\n4. Further consideration of caveats in the Discussion. We further specified in the\nDiscussion that: “Future studies should employ tasks specifically designed to\nisolate semantic processing across diverse languages to more precisely\ncharacterize climate-semantic neural relationships.” (p.16).\neffects on language processing (Study 3). a. Methodology for constructing neural\nRDMs from 86 subjects across 45 languages within the high-level language processing\nnetwork (Fedorenko et al., 2010). Inter-language neural RDMs were derived from the\ncontrast between intact and degraded native language processing (Malik-Moraleda et\nal., 2022), with data averaged for each language. b. Linear mixed regression analysis at\nthe language level, using semantic relation patterns (from the 13-neurocognitive\ndimensional space in Study 1) to predict cross-language neural variations. The bar plot\ndepicts regression coefficients across all 12 brain regions. The right ATL is the only\nregion showing significant beta values after multiple corrections (FDR-corrected p <\n0.05), indicating that this region encodes cross-language semantic alignments in the\n13-dimensional space derived in Study 1. c. Climate effects on neural activity patterns\nin the right ATL. Environmental factor RDMs were obtained for participants’ native\nlanguages. The regression coefficient plot (left panel) illustrates significant climate\neffects in the right ATL. The commonality analysis (right panel) quantifies the\nproportion of variance explained by different components: unique semantic effects\n(6.26%, 95%CI [1%, 37.46%]), unique climate effects (55.58%, 95% CI [16.83%, 85.80%])\nand common variance shared between climate and semantic factors (38.20%, 95% CI\n[14.75%, 48.30%]). The significant common variance indicates that climate effects on\nneural activities in the right ATL partially reflect semantically related neural activation\npatterns. Abbreviations: ATL, anterior temporal lobe; IFG, inferior frontal gyrus; IFGorb,\ninferior orbital frontal gyrus; MFG, middle frontal gyrus; MTG, middle temporal gyrus;\nFigure R2 [Figure S7 in the Revised Manuscript]. Beta coefficients from linear mixed-\neffects regression models predicting neural pattern dissimilarities in the right\nanterior temporal lobe (r-ATL) during nonlinguistic tasks. (a) Spatial Working Memory\ntask (Hard vs Easy condition), showing significant effects only for cultural distance (β =\n0.13, 95% CI [0.02, 0.24], P = 0.016) but not for climate distance (β = 0.06, 95% CI [-\n0.05, 0.16], P = 0.213). (b) Math task (Hard vs Easy condition), showing no significant\neffects for any environmental variable (all Ps > 0.05). Error bars represent 95%\nconfidence intervals; * indicates P < 0.05.\nRepresentational similarities and differences within the NF. The NF in Study 1 is based\non projecting word embeddings onto a lower-dimensional space of 13\ndimensions/features. The authors found that this lower-dimensional space better\nreflects semantic similarities across languages. This makes sense, as this space consists\nof a possibly universal set of conceptual dimensions and filters away variance along\nother potentially relevant semantic axes that might be more idiosyncratic and\nlanguage-specific. Yet, they use this space to study cross-lingual differences and how\nthose are modulated by climate. There appears to be a contradiction here—if the\nauthors want to model cross-lingual differences in semantics, why choose a\nrepresentational projection that minimizes those differences? One motivation for\nrestricting the feature space to those semantic dimensions could be that, in principle,\nclimate could be expected to modulate semantic differences tied to the sensory\ninformation conveyed by words, and for this reason, it would make sense to restrict\nthe focus to those features. If this is the case, this line of reasoning should be clearly\nformulated in the article (see also the following point “Sensorimotor and cognitive\ndimensions”), because the original line of reasoning put forward by the authors (i.e.,\nadopting the NF because it better identifies cross-lingual consistencies in semantics)\nis not convincing. More generally, the authors should emphasize the point we made\nabove, namely that the NF restricts semantic variation by projecting meaning onto a\nsmall set of semantic dimensions. Relatedly, it would be interesting to see how much\ninformation is lost in this projection (partialling out information beyond the 13 NF\n11\naxes); this could be achieved by doing RSA over the native word embedding space and\nthe NF space in the various languages.\nResponse: We greatly appreciate this comment! Indeed, our writing invited this\nconfusion—after showing that the representation based on the neurocognitive\nframework (NF) shows less variance in the commonality analyses in Study 1, it was not\noptimal to study variation if the goal was to model variation maximally.\nIn fact, our rationale here is not to exhaustively model all cross-lingual differences in\nsemantics (it’s not clear how this would be done, see next paragraph). Rather, the goal\nis to understand the mechanisms underlying variations along the NF -- if this structure\nis neurobiologically universal, how does it adapt to different ecological environments?\nThat is, the motivation for restricting the focus to the NF is to understand variations\nmeaningful to these dimensions. We have made careful revisions to make this\nmotivation explicit.\nFurthermore, it is not clear how to model the “maximum” semantic variations. The\nreviewer suggested an interesting approach that we also considered initially -- doing\nRSA over the native word embedding spaces and the NF space in the various languages.\nBut from a methodological perspective, cross-linguistic semantic alignments require\ncommon reference systems. Native word embedding spaces are trained independently\nwithin each language, creating non-isomorphic spaces that lack shared reference\npoints (as discussed in Artetxe et al., 2018; Mikolov et al., 2013; Thompson et al., 2020).\nThis is why our approach, like related work, relies on anchor word methodologies.\nThe following specific revisions were made to more clearly explain our rationale:\n1. Emphasized that NF frameworks intrinsically accommodate variations when\nfirst introducing the NF model: “We propose that the intrinsic way in which the\nhuman brain represents semantic knowledge offers a strong candidate\nframework for understanding both cross-linguistic semantic universality and\nvariation. The biological constraints of the human brain are the result of the\nbiological evolution of homo sapiens and lay the foundation for universality\n(see similar arguments for color space in Berlin & Kay, 1991; emotion space in\nJackson et al., 2019), and such a biological structure would respond to different\nenvironmental inputs (e.g., naturally and culturally varied), resulting in\nphenomenal variations.” (p.3-4)\n2. Added methodological justification for using the NF framework to study\ndifferences: \"This neurocognitive approach also offers methodological\nadvantages for cross-linguistic comparison. By focusing on dimensions with\nestablished neural correlates, we create a controlled space where both\nuniversal patterns and meaningful variations can be understood against a\nconsistent 'backbone' of biological universality. Such dimensional structures not\nonly reveal fundamental commonalities in semantic organization across\nlanguages due to our shared neural architecture, but also filter out potentially\n12\nidiosyncratic semantic dimensions to highlight variations specifically along\nsensorimotor and core-cognitive channels.\" (p.4)\n3. Highlighted an explicit prediction about non-linguistic variations within the\nframework: “The neurocognitive framework of semantic representation\nintrinsically makes the following predictions regarding commonalities and\nvariations (Figure 1)...Second, regarding variations along this dimensional\nstructure, the intrinsic assumption of the framework that semantic\nrepresentation derives from sensory channels predicts that variability is the\nnatural consequence of variables affecting these channel-mediated\nexperiences, including those associated with both natural and cultural\nenvironments (Kemmerer, 2023).” (p.4-5)\nReferences:\nArtetxe, M., Labaka, G., & Agirre, E. (2018). A robust self-learning method for fully\nunsupervised cross-lingual mappings of word embeddings. arXiv preprint\narXiv:1805.06297.\nMikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting similarities among languages for\nmachine translation. arXiv preprint arXiv:1309.4168.\nThompson, B., Roberts, S. G., & Lupyan, G. (2020). Cultural influences on word\nmeanings revealed through large-scale semantic alignment. Nature Human\nBehaviour, 4(10), 1029-1038.\nCharacterization of the alternative models. The authors present three alternatives to\nthe NF: a) distributional semantic models, b) feature-based psycholinguistic models,\nand c) statistical control models. However, we believe that the authors’\noperationalization of feature-based psycholinguistic models (calculating the cosine\nsimilarity between a target word and the 100 most frequent English features in the\nBuchanan et al. (2019) norms) mischaracterizes how feature models are intended to\nbe used (where each word is represented as a set of discrete features that characterize\na concept, like “is red”, “is mammal”, etc.). After all, all the models they consider in\ntheir first study are distributional semantic models, where word embeddings are\nprojected onto lower-dimensional spaces which consist of neighbouring words (a),\nfrequent features (b), or randomly sampled words (c). Even the NF consists of a\nprojection of word embeddings onto the 13 cognitively relevant features. Since all the\nconsidered models are ultimately projections of a distributional semantic model (DSM),\nwe believe it is warranted to include the “native” representations of the DSM\nitself. This would require:\nEvaluating how similar the different languages are in the native fastText word\nembeddings space (without projecting embeddings onto anchor words), and\nEvaluating the similarity between the native fastText-based RDM and the RDMs based\non climate, geographic, linguistic history, and culture.\nResponse: Thank you for this very helpful suggestion. Indeed, the original writing\n13\ncreated confusion and your suggested changes would be much clearer.\nWe have thus revised the corresponding section accordingly (Constructing different\nmodel-anchor-based semantic representations, p.6): “We performed cross-linguistic\ncomparisons of semantic representations obtained based on different semantic models.\nAll semantic representations were obtained using an anchor word approach (i.e.,\nessentially all are distributional semantic methods), but the key difference is what\ntypes of words were used as anchors for representations. Different anchor words were\nselected based on different model principles:”\nFor the feature-based approach specifically, we now elaborate: “The psycholinguistic\ntradition employs semantic feature models based on human-generated\ndecompositional features (e.g., 'has fur', 'is round'), representing concepts as binary or\nweighted vectors indicating the presence/absence or strength of each feature\n(Buchanan et al., 2019; McRae et al., 2005). To adapt this approach for cross-linguistic\ncomparison, we represent the 1,016 concepts using their embedding distance patterns\nwith the 100 most frequent feature words from Buchanan et al. (2019).” (p.6)\nRegarding your suggestion to evaluate native fastText representations without\nprojection, direct comparison in the original embedding space is challenging due to\nthe non-isomorphic nature of embedding spaces across languages (see also the\nresponse to the previous comment above). However, we fully agree that considering\nvariation patterns along other semantic structures is also valuable.\nTo this end, we have added analyses testing whether climate effects on cross-linguistic\nsemantic alignment are specific to neurocognitive semantic models. As shown in Table\nR2 (Table 1 in the revised manuscript, p.39), climate maintains a significant association\nwith neurocognitive semantic structure even when controlling for distributional global\n(β = 0.03, p = 0.02), distributional local (β = 0.11, p < 0.001), and feature-based (β =\n0.13, p < 0.001) models. Conversely, when predicting these alternative semantic\nstructures while controlling for neurocognitive structure, climate showed either\nweaker or non-significant associations (distributional global: β = 0.03, p = 0.005;\ndistributional local: β = -0.02, p = 0.37; semantic feature: β = -0.03, p = 0.15).\nThese findings suggest a specific relationship between climate and neurocognitive\nsemantic organization that cannot be fully explained by other semantic representation\nframeworks. These analyses provide additional support for our central claim that\nneurocognitive semantic structure captures meaningful cross-linguistic variation\nassociated with non-linguistic environmental factors like climate. We sincerely thank\nthe reviewer for these suggestions to strengthen our findings.\nModel Type Climate Effect (β) 95% Confidential Intervals\nModel 1: Neurocognitive as DV\nNeurocognitive ~ Climate + Global Dist. 0.03* [0.01, 0.05]\nNeurocognitive ~ Climate + Local Dist. 0.11*** [0.09, 0.13]\nNeurocognitive ~ Climate + Feature 0.13*** [0.10, 0.14]\nModel 2: Alternative Models as DV\nGlobal Dist. ~ Climate + Neurocognitive 0.03*** [0.02, 0.06]\nLocal Dist. ~ Climate + Neurocognitive -0.02 [-0.03, 0.01]\nFeature ~ Climate + Neurocognitive -0.03 [-0.04, 0.00]\nNote: *** P < 0.001, ** P < 0.01, * P < 0.05; DV, dependence variable; Global Dist., global distributed model; Local Dist., local distributed model;\nFeature, semantic feature model; All models control for random effects of language families; All variables were standardized.\nsensorimotor (color, shape, …), while the remaining 6 are called “cognitive domains”\n(time, space, number, …). While it is rather clear why climate should have an effect on\nsensorimotor representations, the authors need to elaborate why climate would affect\nthe “cognitive domains” dimensions as well.\nResponse: Indeed, the mechanisms of climate’s effect on the core-cognitive domains\nwere not as transparent as its effects on the sensorimotor dimensions. We have now\nmade this more explicit and added discussions: “Notably, our results from both word\nembedding and behavioral rating experiments demonstrate that climate exerts a\nrobust effect across all semantic dimensions, encompassing not only sensorimotor\nproperties but also higher-order domains such as mental-cognition, time, space,\nnumber, social, and emotion (Figure 3c and 3f). Sensorimotor dimensions are expected\nto be directly shaped by environmental inputs. While higher-order cognitive\ndimensions are further abstracted from immediate perceptual input, they arguably\nremain partially grounded in sensorimotor experience (Sigismondi et al., 2024). These\nfindings are consistent with prior evidence that environmental conditions can\nmodulate temporal perception (Li et al., 2022), and that climate-linked subsistence\npatterns shape social behaviors and emotional tendencies (Talhelm et al., 2014; Van\nde Vliert, 2013), and they clearly invite further investigations for the underlying\nneurocognitive mechanisms” (p.14-15)\nMinor points:\nSemantic projection. The authors say they employ the “semantic projection” method\nto project word embeddings onto the 13 dimensions of the NF. Nonetheless, their\napproach only consists in calculating the cosine between each word and a few\nanchor words for each dimensions, which does not correspond to the semantic\nprojection method proposed by Grand et al. (2022)—in fact, it was found to perform\nworse than the actual method, which involves calculating a dimension vector as the\ndifference between two “poles” (for the sound dimension they could be something on\nthe lines of “noisy” vs. “silent”), and then projecting individual words onto these\ndimensions with the dot product. We understand that it might be difficult to define\nthe poles for some features which are not intrinsically unidimensional (e.g., shape),\nbut this should then be clarified.\nResponse: Thank you! We have revised our terminology to accurately reflect our\nmethodology: We now use the term “anchor word approach” rather than “semantic\nprojection” throughout the manuscript to accurately describe our method and avoid\nconfusion with Grand et al. (2022)'s semantic projection technique.\nLanguage and thought. From the abstract: “languages are used worldwide as the\n16\nprimary means of human thought”. This is a rather controversial statement, we’d\nrecommend the authors to tone it down in light of evidence that language and thought\nappear to be largely dissociable (see Fedorenko, Piantadosi, Gibson, 2024 for a recent\nreview).\nResponse: Thank you. We have toned down to languages being “the primary means\nof human thought communications”.\nLanguage family distance. The language family distance metric (1 if two languages\nbelong to the same family, 0 otherwise) is a coarse metric. The authors could\nconsider using a distance metric based on a language tree. For instance, the genetic\ndistance calculated by the Python package lang2vec calculates the distance between two\nlanguages by counting the number of upward steps required on the tree until both\nlanguages converge at a common node (Littell et al., 2017).\nResponse: Thank you! Following this helpful suggestion, in Study 3 we now use this\nmore fine-grained language family distance metric based on a language tree using the\ngenetic distance calculated by the Python package lang2vec (see p.23). Results\nremained robust.\nTypos and writing suggestions:\nThe core hypothesis of the paper (that climate might modulate cross-lingual\ndifferences in semantics because of general perceptual differences in different climatic\nenvironments) becomes clear only at ll. 130-137; we recommend to clearly specify\nthis point earlier in the introduction so that readers can understand from the get-go what\nthe article is about.\nResponse: Thank you. We now made this point earlier in the introduction: “We\npropose that the intrinsic way in which the human brain represents semantic\nknowledge offers a strong candidate framework for understanding both cross-\nlinguistic semantic universality and variation. The biological constraints of the human\nbrain are the result of the biological evolution of homo sapiens and lay the foundation\nfor universality (see similar arguments for color space in Berlin & Kay, 1991; emotion\nspace in Jackson et al., 2019), and such a biological structure would respond to\ndifferent environmental inputs (e.g., naturally and culturally varied), resulting in\nphenomenal variations.” (p.3)\nThroughout the article, the authors consistently refer to word embedding models as\n“language models”; consider rephrasing as word representation models or word\nembedding models. Also, fastText (the word embeddings model used in the study) is a\npeculiar word representation model in that it represents words as a sum of word\nembeddings plus subword (n-gram) embeddings. This should be mentioned\nsomewhere in the paper.\n1. We have replaced all instances of “language models” with more precise\nterminology such as “word representation models” or “word embedding models”\nthroughout the paper.\n2. We have explicitly described the unique characteristics of fastText, noting its\nsubword mechanism in Method section: “FastText represents words as a\ncombination of whole-word and subword (character n-gram) embeddings, which\nallows for better handling of morphologically rich languages and out-of-vocabulary\nwords (Bojanowski et al., 2017).” (p.17)\nThe theoretical basis of the NF is not very clear: it starts by positing that language and\nsensory experience form the basis of conceptual representations (ll. 77-99) and then\nconcludes that an appropriate way to represent semantics is by using 13 dimensions\n(sensorimotor dimensions and “cognitive domains”), without mentioning how\nlanguage information fits within this model and where the dimensions from the\n“cognitive domains” come from.\nResponse: We appreciate the reviewer's thoughtful comment about the theoretical\nfoundation of the NF. We have clarified this in our revised manuscript.\nThe theoretical basis of our NF is grounded in converging evidence from cognitive\nneuroscience showing that semantic representation in the brain derives from both\nsensory (and language) experiences through specific neural processing architectures.\nAs we explain in our manuscript, “Neurocognitive research reveals a consensus\nframework that semantic representation in the brain is derived from sensory (and\nlanguage) experiences in ways that respect the specific information processing\narchitectures of the brain: Brain responses to word meanings are distributed along\nsensorimotor and related associative cortical networks, respecting domains of\nevolutionary saliency, with activity strength modulated by the meaning’s loading on\ncorresponding attributes/domains (Fernandino et al., 2016; Fernandino et al., 2022;\nHe et al., 2013; Martin et al., 1995).” (p.4)\nl. 63: “beauty in English, bella in Italian” – it should either be “beautiful in English, bella\nin Italian” or “beauty in English, bellezza in Italian”.\nResponse: Thank you! Corrected (see p.3: beautiful in English and bella in Italian).\nll. 119-120: “These alternative models [distributional semantic models, feature models]\nare prevalent in cognitive disciplines without explicit considerations of the\nneurobiological basis and have been shown to be less correlated with word semantic\nbrain activities than the neurocognitive dimensional model [...]” – distributional\nsemantic models and their successors (e.g., language models) are often linked to\nneurobiological principles, and obtain great fit to brain activity patterns (see e.g.,\nResponse: Agreed. We now removed the statement which might introduce\ncontroversial and more accurately characterize models as “other distribution-based\nmodels (Figure 2a) that do not explicitly incorporate the neural architecture…” (p.4)\nl. 299: It would be great if the authors could write the lmer formulas to better\nunderstand the random effect structure of the mixed models (also, l. 347).\nResponse: Thank you! Following the recommendation, we have now added the lmer\nformulas in the main texts (p.19; p.22; p.23).\nl. 352: The p-value is missing for the coefficients of “linguistic history”.\nResponse: Added.\nll. 211-212: In the context of the finding that the “global” distributional space finds\nmore cross-lingual commonalities than the “local” distributional space, consider citing\nLewis et al. (2023), who found the opposite pattern.\nResponse: Thank you for the valuable suggestion to reference Lewis et al. (2023). We\nhave added an explanation about the seemingly different findings (due to different\nmethods) and implications.\nIn our revised discussion, we explain that our definition of “global” differs from Lewis\net al. (2023): Our “global” includes all word relationships in the vocabulary space (i.e.,\ngrand global), while Lewis et al. specifically separated cross-domain (“global”) from\nwithin-domain (“local”) word relations, i.e., excluding near neighbor relations from\ntheir global computations. The finding that including all words as anchors (in our\nresults) shows greater cross-linguistic commonality, while including only distant words\nas anchors dose not (Lewis et al., 2023), indicates that preserving domain structure is\nimportant in capturing cross-linguistic semantic universals.\nWe have incorporated this point into our Discussion section (p.13-14): “Note that\ncompared to Lewis et al (2023), which also studied distributional models and showed\nthat global distribution space (excluding within-domain neighbors as anchors) showed\nless universality compared to local space (within-domain neighbors as anchors), our\nanalyses of global distribution space included all word relationships in the vocabulary\nspace (i.e., grand global), and showed greater universality. These results, when\nconsidered together, indicate that preserving domain structure is important in\ncapturing cross-linguistic semantic universals.”\nl. 596: “billions to hundreds of millions” → “hundreds of millions to billions”\nl. 713: “with Fisher-Z transformed” → “with Fisher’s r-to-z transformation”\nResponse: Revised.\nQuestions:\nDistributional local vs. global models. It is unclear how the distributional local models\nwork. The authors say that in the local condition, each word is projected onto its\nimmediate neighbors. We do not understand how this might work—if each word is\nprojected onto its neighbors and the neighbors change from word to word, this would\ncreate a dimensionality mismatch (e.g., w1 would be projected onto w2, w3, w4 etc.,\nwhile say w123 would be projected onto w124, w125 etc., so the dimensions of the\nprojection of w1 would be different from the dimensions of the projection of w123).\nCould you please clarify how this approach works? Relatedly, the global models use as\nanchors all the words but the target word itself. This would once again cause a\ndimensionality mismatch, as, for instance, the anchor w1 would be absent from the\nembedding projection of w1, and the anchor w123 would be absent from the\nembedding projection of w123.\nResponse: Thank you for this important clarification question about our distributional\nmodels. We indeed maintained consistency in cross-linguistic comparisons by using\nthe same set of anchor concepts (all words other than the target word) across\nlanguages.\nThe cross-language similarity calculation was performed first for each word. Using your\nexample, in global models for a given language pair, the embedding projection of Wx\nwould use all words except for Wx, i.e., the same anchor words for each target word,\nyielding a correlation value for Wx for that language pair. The following analyses were\ncomputed from these correlation values (e.g., averaging the correlation coefficients of\nall target words to obtain the correlation coefficient for the language pair).\nIn the local distributional models, we chose the shared neighbors across each language\npair. Specifically, for any given word (e.g., “dog”), we identified the nearest semantic\nneighbors (K=100) in the embedding space in each language (e.g., English “dog” vs.\nChinese “狗”), then took the intersection of these neighbor sets across each language\npair. This ensures that we only used anchors that had correspondences in both\nlanguages, maintaining consistent dimensionality in the cross-linguistic comparisons.\nReferences\nLewis, M., Cahill, A., Madnani, N., & Evans, J. (2023). Local similarity and global\n20\nvariability characterize the semantic space of human languages. Proceedings of the\nNational Academy of Sciences, 120(51), e2300986120.\nLittell, P., Mortensen, D. R., Lin, K., Kairis, K., Turner, C., & Levin, L. (2017). Uriel and\nlang2vec: Representing languages as typological, geographical, and phylogenetic\nvectors. Proceedings of the 15th Conference of the European Chapter of the\nAssociation for Computational Linguistics: Volume 2, Short Papers, 8–14.\nFedorenko, E., Piantadosi, S. T., & Gibson, E. A. (2024). Language is primarily a tool for\ncommunication rather than thought. Nature, 630(8017), 575-586.\nReviewer #5 (Remarks to the Author):\nFu et al. address an important yet neglected topic--namely, crosslinguistic similarities\nand differences in meaning, and their implications for cognition--by combining\ncomputational linguistics and neuroscience. Their methods are innovative and their\nresults are interesting and valuable. I do, however, have some concerns.\nResponse: We greatly appreciate the positive evaluations of our study.\nLike the study by Thompson et al. (2020), this one relies heavily on Dellert et al.'s (2020)\nNorthEuralex database, but this database has several limitations that Fu et al. should\nexplicitly acknowledge. Two major limitations are as follows.\n1. Fu et al. state that the “target concepts” and “anchor terms” they draw from this\ndatabase are \"translation equivalents\" across the 53 languages they focus on. But this\nis not true. Even Dellert et al. are quite open about this. They state: “For instance,\nmany languages lexically distinguish the substance one breathes from the space in\nwhich birds move, making it necessary to further specify the English equivalent “air”.\nFor some concepts (especially kinship terms), the specification also defines which term\nis to be mentioned first in our list of equivalents if a more fine-grained distinction is\nmade in the target language, e.g., ‘grandfather (e.g., father’s father)’.”\nAll of the information in the database was compiled from dictionaries, without\nextensive validation from native speakers. This is another shortcoming that Dellert et\nal. (2020) admit and that Fu et al. should make clear too.\nCrosslinguistic lexical-semantic studies that HAVE included native speaker judgments\nhave revealed nontrivial differences in how even closely related languages partition\nthe same conceptual domains, and this has implications for Fu et al.'s study. As\ndescribed below, two examples involve color terms and body part terms, but\ncomparable results have been reported for many other domains.\nFor instance, Fu et al. use lots of basic color terms (e.g., blue, red, yellow, etc.) as\n“anchors” for their neurocognitive color dimension. But Majid et al. (2015) show that\nthere are not straightforward translation equivalents for these terms across 12\nGermanic languages. Here's a snippet of their discussion: “Within the colour domain,\nEnglish and Danish differed most from one another (see Table 2 with correlations).\ncontrast, Danish rød was a dominant response for 11 different chips. In English,\nspeakers preferentially called most of those chips pink, which was then also extended\ninto an area that Danish speakers called lilla. Similarly, English orange was smaller in\nextension than its Danish equivalent orange. English yellow and Danish gul are\napproximately the same size, but because Danish orange has a larger extension gul\nseeps into territory that English speaker preferentially name green.”\nThe body part domain is relevant to Fu et al.'s study because they used lots of body\npart terms as “anchors” for their neurocognitive motor dimension. But Majid et al.\n(2015) report nontrivial lexical-semantic differences between 12 Germanic languages\nfor such terms. Here's part of their discussion: “...there is no single cognate set used in\nthe Germanic languages for ‘leg’, and we see a more divergent pattern of naming.\nParticipants saw red dots on the upper and lower leg, on the right and left side of the\nbody, viewed from front and back. This lets us examine how they chose to refer to\nthese distinct images. English speakers predominantly used leg for all such stimuli;\nsimilarly Dutch, Flemish and Luxembourgish used a single term been/Been. Frisian\nspeakers also used been, but not as frequently. However, the speakers of the other\nlanguages tended to use distinct terms for the upper and lower leg. German speakers\nused Oberschenkel and Unterschenkel, literally ‘upper-leg’ and ‘lower-leg’, but the\nroot *Schenkel alone was never attested. Schwyzerdütsch speakers used the cognate\nterms, obèrschènku and ungèrschènku, although bare schènku was also used (but only\ntwice, and only with reference to upper leg stimuli). The Scandinavian languages make\nsporadic use of bein/ben, but for the most part other terms were more frequent. Like\nGerman speakers, the participants preferentially distinguished upper and lower leg.\nThis discussion illustrates that even in a domain with much shared structure, there are\nstill many intricacies to be observed.”\nSo the point here is that many of the “target concepts” and “anchor words” that Fu et\nal. take from the NorthEuraLex database are not really translation equivalents across\nthe 53 languages in their study, contrary to their initial assumption. This matters for\ntheir investigation of “commonalities” because, despite the initial non-equivalence of\nthe word meanings, their still found that the neurocognitive model disclosed greater\ncrosslinguistic similarities than the other models. And it matters for their investigation\nof “variation” because the initial non-equivalence already embodies some variation.\nResponse: We greatly appreciate this comment. We fully agree that the proxy term\n“translation equivalent” is misleading: methodologically the imperfect translations in\nNorthEuralex should be noted; theoretically, the whole point of the study is exactly to\naddress cross-linguistic similarity/differences. As presented in our Introduction, we\nexplicitly acknowledge the cross-linguistic lexical-semantic differences highlighted by\nthe reviewer: “Ethnographic descriptions have identified common concepts across\nsemantic domains such as color (Berlin & Kay, 1991) and emotion (Ekman, 1992;\nWierzbicka, 1992), while also revealing different words for the same perceptual\nreferents, culturally bound words resistant to translation, and different categorization\nsystems (Majid et al., 2004; Majid et al., 2015; Majid et al., 2018; Passmore & Jordan,\n2020).” (p.3)\nWe have deleted the term “translation equivalent” to avoid confusion, which did not\naffect the data description. The related critical issue is whether the empirical findings\nand implications are affected by the fact that the target words tested and the anchor\nwords we chose are not fully translation equivalent across languages.\nRegarding commonalities, the key comparison was the magnitude of the commonality\nacross different models, using the same set of concept translations. That is, the robust\ncross-linguistic convergence in neurocognitive dimensions, relative to other models,\nwas not attributable to word selection choices.\nRegarding variations, the reviewer's concern that “the initial non-equivalence already\nembodies some variation” is particularly insightful. To address this directly, we\nconducted new analyses to determine whether the climate-related effects we\nobserved might be artifacts of lexical-semantic differences inherent in the database. If\nour climate findings were merely reflecting these pre-existing lexical differences\n(which would still be interesting), then climate should show similar associations with\nall semantic models, since all models use the same imperfect translation equivalents.\nHowever, our new analyses (Table R2; Table 1 in the revised manuscript, p.39) show\nthat climate maintains a significant association with neurocognitive semantic structure\neven when controlling for other semantic models (distributional global: β = 0.03, p =\n0.02; distributional local: β = 0.11, p < 0.001; feature-based: β = 0.13, p < 0.001).\nConversely, when predicting alternative semantic structures while controlling for\nneurocognitive structure, climate showed either weaker or non-significant\nassociations (distributed global: β = 0.03, p = 0.005; distributed local: β = -0.02, p =\n0.37; semantic feature: β = -0.03, p = 0.15). This pattern suggests that the climate\neffects we observe are specifically related to neurocognitive semantic dimensions\nrather than being artifacts of the imperfect translation equivalents in the NorthEuralex\ndataset.\nFurthermore, our complementary Studies 2 and 3, which use different methodologies\nthat do not rely on the anchor word approach, yield similar climate-related effects,\nproviding converging evidence that our findings reflect cognitively related semantic\npatterns rather than methodological artifacts from the NorthEuralex database.\nIn the Revision, we have added these additional validation analyses (p. 9). We have\nalso added a specific caveat consideration section to bring attention to this issue:\n“Second, true translation equivalence across languages is impalpable given theoretical\nand empirical considerations. We tested words from the NorthEuralex database, which\ncontains dictionary-compiled translations. As Dellert et al. (2020) noted, many\nlanguages make lexical distinctions absent in English (e.g., differentiating “air” as a\nbreathable substance versus a space for flight), and these mappings lack extensive\nnative speaker validation. Despite these inherent limitations, our study focuses on the\n23\nunderlying principles governing such cross-linguistic (mis)alignments.” (p.15).\nThank you for prompting us to strengthen the paper with these important\nconsiderations.\nModel Type Climate Effect (β) 95% Confidential Intervals\nModel 1: Neurocognitive as DV\nNeurocognitive ~ Climate + Global Dist. 0.03* [0.01, 0.05]\nNeurocognitive ~ Climate + Local Dist. 0.11*** [0.09, 0.13]\nNeurocognitive ~ Climate + Feature 0.13*** [0.10, 0.14]\nModel 2: Alternative Models as DV\nGlobal Dist. ~ Climate + Neurocognitive 0.03*** [0.02, 0.06]\nLocal Dist. ~ Climate + Neurocognitive -0.02 [-0.03, 0.01]\nFeature ~ Climate + Neurocognitive -0.03 [-0.04, 0.00]\nNote: *** P < 0.001, ** P < 0.01, * P < 0.05; DV, dependence variable; Global Dist., global distributed model; Local Dist., local distributed model;\nFeature, semantic feature model; All models control for random effects of language families; All variables were standardized.\n25\n2. Another limitation of the NorthEuraLex database is that it excludes LOTS (literally\nthousands) of languages. MANY lexical-semantic similarities and differences have\nbeen found in studies that had more globally distributed language samples, and while\nsome of these discoveries seem to fit Fu et al.'s climate-based account, others don't.\nHere's an example that's compatible with the climate-based account. Brown (2005)\nfound that in a global sample of 617 languages, only 389 (63%) have separate terms\nfor arms and hands. Consistent with a previous study by Witkowski and Brown (1985)\nthat involved a smaller sample, he also found that languages that do make this\ndistinction tend to be located in non-equatorial zones where cold weather is frequent.\nGiven this correlation with lattitude and, by extension, temperature, he proposed that\nbecause societies in these regions are more likely to wear clothes that increase the\nsalience of the arm-hand boundary (e.g., gloves, mittens, and long sleeves ending at\nthe wrist), they may be more inclined to develop separate terms for arms and hands.\nAn example that seem to not fit the climate-based account involves spatial frames of\nreference. This issue is discussed in Majid et al.'s (2004) Box 1.\nResponse: We thank the reviewer for raising this important point about the\ngeographical limitations of the NorthEuralex database and for suggesting relevant\nresearch from more globally distributed language samples.\nWe fully acknowledge that the NorthEuralex database excludes thousands of\nlanguages, with a clear Eurasian bias that limits the global representativeness of our\nprimary analyses. We have now addressed this limitation in our revised manuscript,\nincorporating the very helpful points noted by the reviewer:\n1. We expanded our analysis beyond the 53 languages in NorthEuralex in the\n“Generalization to colexification networks with 2,681 languages” section (p.7),\nwhere we generalize the examined patterns to a more diverse set of languages.\n2. In our revised Discussion, we've added a specific section addressing how a\nmore globally distributed language sample might affect our findings: “…while\nour language samples included in the environment-semantic association\nanalyses cover a substantial portion (approximately 1/2 - 1/3) of the world's\nmajor populations across the three studies, they have all undergone varying\ndegrees of modernization. This may lead to an overestimation of the climate\neffects and an underestimation of the effects of cultural and linguistic factors\non semantic structures, which warrants further investigation. Future research\nwith more globally representative language samples is necessary to extend the\ngeneralizability of our findings.” (p.16)\nWe have also incorporated both types of examples provided by the reviewer in our\nrevised Discussion. While our study aims to explore similarities and variations at the\nstructural level, we now point out that whether climate influences within-domain\nvariations remains unresolved and that future studies are needed: “However, evidence\nregarding climate's influence on within-domain semantic variations shows mixed\npatterns - Brown (2005)’s findings on arm-hand lexical distinctions in colder regions\n26\nsupport climate-based accounts, while spatial reference frame variations (absolute vs.\nrelative) may be more influenced by a complex set of factors rather than by a single\nfactor such as climate (Majid et al., 2004). More generally, having identified the robust\nassociation between climate and a central cognitive component — semantics, future\nstudies elucidating the detailed neurocognitive mechanisms would be critical for\nunderstanding the intricate interplay between our living environment and diverse\nhuman behaviors, especially in the era of profound climate change.” (p.15)\nAnother concern of mine has to do with the fMRI results. Fu et al. found that\ncrosslinguistic semantic alignment in the neurocognitive model was associated with\nneural activity in bilateral ATLs and left angular gyrus. But each target concept in each\nof the 53 languages was represented by a 300-dimensional vector, and in Fu et al.'s\nown previous experiment (Fu et al., 2023), vector-based representations of word\nmeanings didn't correlate with activity patterns in ANY left perisylvian cortical areas;\ninstead, only graph-based representations led to significant correlations. Now, other\nfMRI studies have in fact found significant mappings between vector-based\nrepresentations of word meanings and left perisylvian cortical areas, but the details of\nthese mappings have been inconsistent across models and studies (e.g., Carota et al.,\n2017, 2021, 2024; Liuzzi et al., 2023). This variability reduces confidence in Fu et al.'s\nnew findings.\nResponse: We appreciate the reviewer for highlighting the seemingly inconsistency\nbetween our current findings and our previous work (Fu et al., 2023). First, we would\nlike to clarify that, in fact, Fu et al. (2023) reported the significant effect of word\nembeddings for word processing in language regions, largely consistent with other\nstudies, although specific regions varied, which might be related to the types of words\nbeing analyzed (Carota et al., 2017; Carota et al., 2024; Carota et al., 2021; Liuzzi et al.,\n2023). Fu et al. (2023) went further to see if such embedding representations account\nfor neural activities specifically, beyond a set of graph-based word distances, and\nreported no unique effects of the embedding space.\nThe current study, while also starting with embedding representations, measures\nspecifically the embedding space distance to theoretically driven anchor words,\nhighlighting the importance of the neurobiologically related anchor words in capturing\nthe cross-linguistic similarities. Thus, the results across studies are not in conflict.\nThis comment, nonetheless, prompted us to appreciate that other ways of computing\ndistances to the anchor words, including those graph-based distances tested in Fu et\nal. (2023) and Mao et al. (2024), could serve as additional candidate semantic\ncomputation approaches. We have now added this point in the Discussion. Thank you\nfor this insightful comment.\nBrown, C.H. (2005). Hand and arm. In M. Haspelmath, M.S. Dryer, D. Gil, & B. Comrie\n27\n(eds.), The world atlas of language structures (pp. 522-525). Oxford, UK: Oxford\nUniversity Press.\nCarota, F., Kriegeskorte, N., Nili, H., & Pulvermüller, F. (2017). Representational\nsimilarity mapping of distributional semantics in left inferior frontal, middle temporal,\nand motor cortex. Cerebral Cortex, 27, 294-309.\nCarota, F., Nili, H., Pulvermüller, F., & Kriegeskorte, N. (2021). Distinct fronto-temporal\nsubstrates of distributional and taxonomic similarity among words: Evidence from RSA\nof BOLD signals. NeuroImage, 224, 117408.\nCarota, F., Nili, H., Kriegeskorte, N., & Pulvermüller, F. (2024). Experientially grounded\nand distributional semantic vectors uncover dissociable representations of conceptual\ncategories. Language, Cognition, and Neuroscience, 39, 1020-1044.\nDellert, J., Daneyko, T., Münch, A., Ladygina, A., Buch, A., Clarius, N., et al. (2020).\nNorthEuraLex: A wide-coverage lexical database of Northern Eurasia. 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Nature Communications, 15(1), 3476.\nTalhelm, T., Zhang, X., Oishi, S., Shimin, C., Duan, D., Lan, X., & Kitayama, S. (2014).\nLarge-scale psychological differences within China explained by rice versus\nwheat agriculture. Science, 344(6184), 603-608.\nThompson, B., Roberts, S. G., & Lupyan, G. (2020). Cultural influences on word\nmeanings revealed through large-scale semantic alignment. Nature Human\nBehaviour, 4(10), 1029-1038.\nVan de Vliert, E. (2013). Climato-economic habitats support patterns of human needs,\nstresses, and freedoms. Behavioral and Brain Sciences, 36(5), 465-480.\nWierzbicka, A. (1992). Semantics, culture, and cognition: Universal human concepts in\nculture-specific configurations. Oxford University Press, USA.\n32"}, {"round": 2, "reviewer_comments": {"reviewer_4": "We were very pleased to read a revised version of the article, and we think the paper is now in good shape. The authors took our comments seriously and implemented all the control analyses we asked for.\nThe biggest issue in the previous round was the inconsistency across analyses. The authors fixed that in the revision: the paper now uses a single approach across studies 1B/2/3 (language-level RSA with mixed-effects models that include climate, culture, geography, and linguistic history in the same model, plus random intercepts for language families). This makes the comparisons across studies clean and interpretable. Importantly, the results appear to be robust under this analysis: climate remains a reliable predictor of cross-lingual semantic alignment across studies.\nOverall, we think that this is a strong revision. From our side, this looks publishable.\nStrengths\nThe authors implemented several changes that improved the paper, including (but not limited to):\nThe analytic procedures and exact model specifications are now comparable across experiments. This was the main concern with the previous version of the manuscript and now the authors have fixed it. The authors now also show lmer formulas that make the random effects structure clear.\nThe link between the “neurocognitive approach” and cross-lingual similarities is now clear (ll. 108-115).\nLanguage relatedness uses a tree-based distance (lang2vec), not a 0/1 dummy (p. 23).\nThe terminology is now more precise (rephrased “language models” to “word embeddings” for FastText; FastText is now described as word + subword embeddings; the descriptions of the different semantic models in p. 6 are clearer; the term “semantic projection” is now used correctly).\nWeaknesses\nWe disagree with the authors’ interpretation of the brain results (Study 3). As we elaborated in the previous round of review, the intact vs. degraded contrast does not indicate “neural pattern similarities” (from the authors’ response), but rather, where the peak responses to language are located within the region of interest—in other words, the topographical organization of the language-selective voxels in the right ATL. The paper argues that climate modulates these topographies.\nThis result is surprising given that meaningful cross-lingual differences in neural activations are difficult to find on top of the substantial inter-individual variability in these patterns (especially when the numbers of data points are so low for each language). Also, we cannot fathom any sensible explanation for how climate could affect the language areas’ topography.\nWe are therefore concerned that this result is spurious—similar to the finding that cultural (as opposed to climatic) factors influence the location of domain-general reasoning voxels (Figure S7). That said, these concerns do not, in our view, preclude publication. (If the authors did want to understand this effect a little better—at least to see whether the effect has to do with the peak location of the language region, or with some features of the topography surrounding the peak—they could e.g., remove the peak cluster from each participant’s fROI and see if the effect holds, or conversely, remove information from anything except for the peak cluster location to see if the effect holds.)\nMore minor issues\nWe disagree with the authors’ response that evaluating fastText embeddings in the native space is “challenging due to the non-isomorphic nature of embedding spaces across languages” (p. 14 of the response). This is not an issue for RSA, since it does not require direct alignment of embedding spaces. What matters is the relative structure of pairwise distances within each language’s embedding space. RSA evaluates whether the representational geometry / similarity structure is comparable, independent of absolute coordinate systems. It might be valuable (though not strictly necessary) to include an additional analysis using fastText embeddings in the native space.\nIn Experiment 1, the authors implement two controls: the random words model (projecting embeddings onto 100 random words) and the random dimensions model, where embeddings are projected onto 13 pseudo-dimensions. They found that the latter, with 13 dimensions, is more aligned across languages than the former. Thus, it seems that having fewer dimensions may improve the alignment. The “neurocognitive” model, which is shown to outperform other models in alignment across languages, has fewer dimensions than the alternatives. We would recommend that the authors more directly address this issue of dimensionality. A stronger demonstration of the model’s strength would involve showing that its performance cannot be explained solely by dimensionality reduction.\nSuggestions\nWe think the article now reads well, but there is room for improvement. A suggestion for the authors to consider is to bring climate in more prominently in the introduction and in the abstract—to us, the core contribution of the paper is showing how climate modulates semantic representations across languages, but it is only mentioned at the end of the abstract (l. 33) and the introduction.\nIn sum, the paper is much improved and addresses the main concerns from the first round. The results are now presented in a consistent way, and the findings on climate and semantic alignment appear to be robust. A few open questions remain, but in our view, they do not stand in the way of publication.\n(Remarks on code availability)\nn/a", "reviewer_5": "The authors adequately addressed my concerns.\n(Remarks on code availability)", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)"}, "author_response": "Response to Reviewers\nWe sincerely thank the reviewers for their thoughtful and constructive feedback on\nour manuscript. We have carefully revised the manuscript accordingly. Below, we\nprovide point-by-point responses to explain how each point was addressed and the\ncorresponding revisions.\nReviewer #4 (Remarks to the Author):\nWe were very pleased to read a revised version of the article, and we think the paper\nis now in good shape. The authors took our comments seriously and implemented all\nthe control analyses we asked for.\nThe biggest issue in the previous round was the inconsistency across analyses. The\nauthors fixed that in the revision: the paper now uses a single approach across studies\n1B/2/3 (language-level RSA with mixed-effects models that include climate, culture,\ngeography, and linguistic history in the same model, plus random intercepts for\nlanguage families). This makes the comparisons across studies clean and interpretable.\nImportantly, the results appear to be robust under this analysis: climate remains a\nreliable predictor of cross-lingual semantic alignment across studies.\nOverall, we think that this is a strong revision. From our side, this looks publishable.\nResponse: Thank you!\nStrengths\nThe authors implemented several changes that improved the paper, including (but not\nlimited to):\nThe analytic procedures and exact model specifications are now comparable across\nexperiments. This was the main concern with the previous version of the manuscript\nand now the authors have fixed it. The authors now also show lmer formulas that\nmake the random effects structure clear.\nThe link between the “neurocognitive approach” and cross-lingual similarities is now\nclear (ll. 108-115).\nLanguage relatedness uses a tree-based distance (lang2vec), not a 0/1 dummy (p. 23).\nThe terminology is now more precise (rephrased “language models” to “word\nembeddings” for FastText; FastText is now described as word + subword embeddings;\nthe descriptions of the different semantic models in p. 6 are clearer; the term\n“semantic projection” is now used correctly).\nResponse: Thank you for the positive comment!\nWeaknesses\nWe disagree with the authors’ interpretation of the brain results (Study 3). As we\nelaborated in the previous round of review, the intact vs. degraded contrast does not\nindicate “neural pattern similarities” (from the authors’ response), but rather, where\nthe peak responses to language are located within the region of interest—in other\nwords, the topographical organization of the language-selective voxels in the right ATL.\nThe paper argues that climate modulates these topographies.\nThis result is surprising given that meaningful cross-lingual differences in neural\nactivations are difficult to find on top of the substantial inter-individual variability in\nthese patterns (especially when the numbers of data points are so low for each\nlanguage). Also, we cannot fathom any sensible explanation for how climate could\naffect the language areas’ topography. We are therefore concerned that this result is\nspurious—similar to the finding that cultural (as opposed to climatic) factors influence\nthe location of domain-general reasoning voxels (Figure S7). That said, these concerns\ndo not, in our view, preclude publication. (If the authors did want to understand this\neffect a little better—at least to see whether the effect has to do with the peak\nlocation of the language region, or with some features of the topography surrounding\nthe peak—they could e.g., remove the peak cluster from each participant’s fROI and\nsee if the effect holds, or conversely, remove information from anything except for\nthe peak cluster location to see if the effect holds.)\nResponse: Thank you for raising this concern about the power of current sample sizes\nfor each language and the interpretation of Study 3. We have made thorough revisions\nto address these issues from three angles: (1) reframing Study 3 as exploratory\nthroughout the manuscript, (2) clarifying our conceptual interpretation of what the\nneural patterns reflect, and (3) conducting the robustness analyses you suggested.\n(1) Reframing Study 3 as exploratory: We agree that the limited sample size per\nlanguage, despite covering multiple languages, warrants a more cautious\ninterpretation.\nWe have therefore reframed Study 3 as exploratory throughout the manuscript in the\nfollowing locations:\nAbstract (page 2): “…Exploratory neural data (86 participants, 45 languages)\nshowed consistent climate-related activation patterns in the right anterior temporal\nlobe…”\nIntroduction (page 5): “…in Study 3, we analyzed brain activities during language\ncomprehension, offering preliminary insights into linking cognition and biology\nvariations more directly with the ecological variables of interest…”\nResults (page 12): In summary section, we added a sentence that acknowledge\nthe sample size issue: “…Given the limited sample size per language, these findings\nwarrant replication with larger cohorts…”\nDiscussion (page 15-16): “The neural findings warrant replication with larger-\nsamples, given the limited sample size per language in the current dataset… Future\nstudies should employ tasks specifically designed to isolate semantic processing across\ndiverse languages to more precisely characterize climate-semantic neural\nrelationships.”\n(2) Conceptual clarification: We appreciate the reviewer's point about terminology.\nTo be precise, what we measured in Study 3 is the spatial pattern of neural\nactivation magnitudes across r-ATL voxels under the “intact vs. degraded” contrast.\nThis captures both which voxels are activated (topographic organization) and how\nstrongly they respond (activation magnitude). We view this pattern variation as\nfunctionally meaningful because different ATL subregions show preferential\nengagement for different semantic dimensions (e.g., concrete objects in ventral\nATL, emotional information in medial ATL, social information in dorsolateral ATL; Visser et al., 2010; Hung et al., 2020; Wang et al., 2019). Thus, variations in the\ndistributed activation pattern—not just peak location—could reflect differential\nrecruitment of these semantic components.\nWe explicitly incorporated this clarification into the Result section when first\nintroducing the methodology of constructing neural RDMs (page 11): “These neural\nRDMs capture the spatial pattern of activation magnitudes across voxels, reflecting\nboth which voxels are activated and how strongly they respond. Given that different\nATL subregions show preferential engagement for different semantic dimensions54-56,\nvariations in these distributed activation patterns may reflect differential recruitment\nof semantic components.”\n(3) Robustness analyses: Following your suggestion, we examined whether the\nclimate effect on neural patterns could be attributed to differences in peak\nactivation location versus broader distributed activation patterns. Given that peak\ncluster locations vary across individuals, we identified each participant's peak\nactivation cluster (centroid of the top 10% most activated voxels within r-ATL) and\ncalculated pairwise Euclidean distances between these centroids across all\nlanguage pairs. This peak distance metric captures variation in the spatial location\nof maximal language-selective activation. We then tested:\nDoes peak location vary with climate? Peak cluster distance showed no significant\ncorrelation with climate distance (β = -0.08, 95% CI [-0.22, 0.06], p = 0.267),\nindicating that climate is not associated with systematic shifts in where peak\nactivation occurs within r-ATL.\nDoes the broader activation pattern matter beyond peak location? When\nadditionally controlling for peak cluster distance, climate continued to significantly\npredict neural activation patterns (β = 0.13, 95% CI [0.03, 0.22], p = 0.007).\nThese results demonstrate that climate-related neural variation reflects\ndistributed activation patterns rather than peak location displacement.\nImportantly, our commonality analysis revealed significant shared variance\n(38.20%, 95% CI [14.75%, 48.30%]) between climate and neurocognitive semantic\nrepresentation, suggesting that climate influences which semantic dimensions are\npreferentially recruited during language comprehension, engaging partially\ndistinct functional subregions within ATL.\nWe have revised the main analysis to include peak cluster location as a control variable\n(page 11): “This climate effect remained significant (β = 0.13, 95% CI [0.03, 0.22], p =\n0.007) even when controlling for differences in peak activation location (calculated as\nEuclidean distances between individual peak cluster centroids), indicating that the\neffect reflects distributed activation patterns across r-ATL voxels rather than simple\nspatial displacement.”\nMore minor issues\nWe disagree with the authors’ response that evaluating fastText embeddings in the\nnative space is “challenging due to the non-isomorphic nature of embedding spaces\nacross languages” (p. 14 of the response). This is not an issue for RSA, since it does not\nrequire direct alignment of embedding spaces. What matters is the relative structure\nof pairwise distances within each language’s embedding space. RSA evaluates\nwhether the representational geometry / similarity structure is comparable,\nindependent of absolute coordinate systems. It might be valuable (though not strictly\nnecessary) to include an additional analysis using fastText embeddings in the native\nspace.\nResponse: We thank you for this clarification and completely agree that doing RSA\ndoes not require direct alignment of embedding spaces. We would like to clarify that\nour “global distributional model” is precisely the native-space fastText analysis the\nreviewer describes. Specifically, for each language, we used the full 300-dimensional\nfastText embeddings in their native space to construct each concept’s\nrepresentational dissimilarity vector based on pairwise cosine distances with all other\n1,015 concepts. These vectors capture the relational structure of concepts within each\nlanguage's distributional semantic space — this is what we understand the reviewer\nrefers to as “native space”.\nFor the results of universal comparisons, cross-language similarities of native-space\ndistributional models are more variable than neurocognitive semantic models (shared\nPC1 component across languages: global-distributional = 34.16%, neurocognitive =\n44.31%, page 7), indicating more language-specific information is contained in the\noriginal native space. For explaining cross-language variations, climate shows effects\non both types of semantic representations: (1) climate influences the native-space\ndistributional representations themselves when controlling neurocognitive semantic\nstructure (i.e., variations that are not captured by neurocognitive models; Table 1 and\npage 9: β = 0.04, 95% CI [0.02, 0.06], P < 7.57 × 10-7), and (2) climate shows unique\neffects on neurocognitive semantic structure even when controlling for the native-\nspace distributional model (Table 1 and page 9: β = 0.03, 95% CI = [0.02, 0.05], P = 5.53\n× 10-5).\nWe have clarified this terminology and link directly to this model with “native space”\nin the Results (page 6): “A prevalent approach in cross-language semantic\ncomparisons represents concepts by their usage contexts12. Following this approach,\nwe represented the 1,016 concepts using their … global patterns (i.e., anchor words\nwere the entire native word space)” and Methods (page 17): “distributed global\nmodels, using all the other 1,015 concepts as anchors. We computed cosine\nsimilarities between each NEL concept vector and these anchors to obtain\ndistributional semantic representations. This approach preserves the complete\nrepresentational geometry within each language's native embedding space.”\nIn Experiment 1, the authors implement two controls: the random words model\n(projecting embeddings onto 100 random words) and the random dimensions model,\nwhere embeddings are projected onto 13 pseudo-dimensions. They found that the\nlatter, with 13 dimensions, is more aligned across languages than the former. Thus, it\nseems that having fewer dimensions may improve the alignment. The “neurocognitive”\nmodel, which is shown to outperform other models in alignment across languages,\nhas fewer dimensions than the alternatives. We would recommend that the authors\nmore directly address this issue of dimensionality. A stronger demonstration of the\nmodel’s strength would involve showing that its performance cannot be explained\nsolely by dimensionality reduction.\nResponse: We thank the reviewer for this important point about dimensionality. We\nagree this needed to be controlled for, which is why we specifically designed the\nrandom dimension model as a dimensionality-matched control. This model has the\nsame 13 dimensions as the neurocognitive model, yet the neurocognitive model still\nsignificantly outperforms it (P = 0.005, Figure 2b).\nWe revised the description of this model to more directly in the following section:\nResults (page 6): “The other is the random dimension model (dimensionality-matched\ncontrol), where 100 randomly selected words were grouped into 13 dimensional\n“anchors” using K-means clustering, and concepts were then projected onto these\npseudo dimensions, with 10,000 iterations.”\nResults (page 7): “Figure 2b (right panel) illustrates that the mean inter-language\ncorrelation of neurocognitive models (depicted by red vertical line) exceeded the\nupper ends of distributions of random word models (depicted by light gray area; P <\n0.0001) and the dimensionality-matched random dimension models (depicted by dark\ngray area; P = 0.005), whereas the other types of theoretical models did not\ndemonstrate this pattern. These results demonstrated that the model’s performance\nstems from the specific neurocognitive dimension rather than anchor word selection\nand dimensionality reduction procedure per se.”\nMethod section (page 18): “This random dimension model provides a dimensionality-\nmatched control, demonstrating that larger cross-language alignment is not because\nsimply having fewer dimensions.”\nSuggestions\nWe think the article now reads well, but there is room for improvement. A suggestion\nfor the authors to consider is to bring climate in more prominently in the introduction\nand in the abstract—to us, the core contribution of the paper is showing how climate\nmodulates semantic representations across languages, but it is only mentioned at the\nend of the abstract (l. 33) and the introduction.\nResponse: We appreciate your suggestion to bring climate in more prominently and\nthank you for highlighting the contribution of climate results.\nWe have made targeted revisions to bring the introduction of the climate factor earlier:\nAbstract (page 2): We strengthened the presentation of climate findings in the\nabstract. “…with variations along this structure being significantly and uniquely\nexplained by climate, beyond sociocultural-centered variables. Exploratory brain\nactivity data (86 subjects, 45 languages) showed consistent climate-related activation\npatterns in the right anterior temporal lobe. These results present a universal,\nbiologically constrained semantic structure that is adaptive to climate, reconciling the\nclassical universality and relativity debate.”\nIntroduction (page 3): We introduced climate much earlier in the manuscript—in the\nthird paragraph when establishing the theoretical framework. “The biological\nconstraints of the human brain are the result of the biological evolution of homo\nsapiens and lay the foundation for universality (see similar arguments for color\nspace4,emotion space14), and such a biological structure would respond to different\nenvironmental inputs (e.g., climate), resulting in phenomenal variations.”\nIn sum, the paper is much improved and addresses the main concerns from the first\nround. The results are now presented in a consistent way, and the findings on climate\nand semantic alignment appear to be robust. A few open questions remain, but in our\nview, they do not stand in the way of publication.\nResponse: We sincerely thank Reviewer #4 for their constructive engagement across\nboth review rounds, which significantly strengthened our manuscript's analytical rigor,\ninterpretive precision, and clarity.\nReviewer #4 (Remarks on code availability):\nn/a\nReviewer #5 (Remarks to the Author):\nThe authors adequately addressed my concerns.\nResponse: Thank you!\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse: Thank you.\nResponse to Reviewers\nReviewer #4 (Remarks to the Author):\nWe were already pleased with the previous version of the article, and we confirm the paper is in\nvery good shape. In the previous round of review, the authors had already addressed and\nresolved all the substantive issues with the article. Our main point of disagreement concerned\nthe interpretation of the neural data (Experiment 3).\nThis latest revision:\n-Clearly states that the brain results are exploratory (throughout the paper: abstract,\nintroduction, results, discussion).\n-Includes robustness checks showing that the location of the peak cluster (centroid of top 10% of\nthe voxels) is not what is modulated by climate, and the effects remain significant controlling for\nthat.\n-Provides a conceptual clarification on their interpretation of the r-ATL findings. (We still have\nquestions concerning this interpretation; see below).\nAgain, we think this paper is a strong contribution and could be published in its current form. We\nhave, however, one comment regarding the interpretation of the findings (in particular, two\nsentences added in this latest revision). We believe those considerations have big implications,\nand we suggest either elaborating on that or removing those considerations altogether.\nAt p. 11, ll. 391–395, you have added the following text to clarify your interpretation of the r-ATL\nfindings:\n(a) “These neural RDMs capture the spatial pattern of activation magnitudes across voxels,\nreflecting both which voxels are activated and how strongly they respond. Given that different\nATL subregions show preferential engagement for different semantic dimensions, variations in\nthese distributed activation patterns may reflect differential recruitment of semantic\ncomponents.”\nAnalogously, in the response letter, you write:\n(b) “We view this pattern variation as functionally meaningful because different ATL subregions\nshow preferential engagement for different semantic dimensions (e.g., concrete objects in\nventral ATL, emotional information in medial ATL, social information in dorsolateral ATL; Visser et\nal., 2010; Hung et al., 2020; Wang et al., 2019). Thus, variations in the distributed activation\npattern—not just peak location—could reflect differential recruitment of these semantic\ncomponents.”\nThe t-map being used is a contrast obtained between intact and degraded speech. It tells how\nstrongly each voxel responds to language vs. perceptually matched controls, and not in a\nparticular sentence versus another, but across multiple sentences (diverse in semantic content).\nFollowing the considerations listed above, we believe the (semi-implicit) interpretation of the\nfindings is:\n(1) In the r-ATL, some voxels are preferentially tuned to some semantic dimensions (say,\nemotional information). (We don’t know the location of those putative meaning-tuned voxels in\nthe dataset you are using.)\n(2) Climate modulates the extent to which language comprehension engages those voxels.\nOur understanding is that from these points, it follows that:\n(3) Climate modulates the extent to which a given language expresses certain semantic\ndimensions in general—not when referring to a particular concept or when processing a\nparticular sentence, but when processing any linguistic input.\n(4) Climate does not modulate the location of the voxels tuned to semantic dimensions. The\nlocation of those meaning-tuned voxels would be assumed to be relatively stable across\nlanguages and individuals, and what changes is the extent to which those voxels are recruited by\nlanguage processing—which in turn, is determined by climate.\nPoint (3) is not a problem per se, but it is a very strong conclusion. If this is indeed the\nimplication you intend to make, we’d recommend stating this more clearly.\nPoint (4), however, seems in tension with the rest of the manuscript. The paper’s main claim is\nthat climate modulates semantic structure across languages. However, following the\ninterpretation from the new sentences added in (a), the semantically-tuned voxels would be\nassumed to be stable across individuals and languages, and what would instead vary with\nclimate is the precise topography of language-selective voxels identified by the localizer. This\ninverts the logic of the paper: semantic organization becomes fixed, and the organization of the\nlanguage system depends on climate.\nGiven this, we would recommend removing (a) altogether. As we noted before, there is currently\nno convincing mechanistic account of the neural finding in Study 3, and this attempt to hint at\none introduces conceptual inconsistencies with other arguments laid forth in the paper.\nBesides this very specific, conceptual point, we believe the paper can be published and no\nfurther review is necessary. All other issues raised in the previous round(s) have been addressed.\nThe analyses are fully consistent across studies, the terminology is precise, the writing is clear,\nand the findings are interesting. This is a very nice piece of work, and we appreciate the\nopportunity to read it and the care in responding to our feedback.\nResponse: We sincerely thank you for the thorough feedback and for recognizing the strengths\nof our work. To address your concerns, we have removed the sentence originally added to clarify\nr-ATL interpretation and ensured the neural results remain presented as exploratory evidence\nthroughout the manuscript."}]

Decision letter