Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space
Authors: Felipe Diego Toro-Hernández
Jesuino Vieira Filho
Rodrigo M. Cabral-Carvalho
Venue: ICLR 2026 Poster (2026)
Keywords: Semantic Navigation
Natural Language Processing
Human Cognition
Text Embeddings
Primary area: applications to neuroscience & cognitive science
TL;DR: We propose a natural language-based characterization of human semantic navigation in concept production as trajectories in embedding space through metrics to classify groups and concepts

Abstract: Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To investigate how humans traverse this geometry, we introduce a framework that represents concept production as navigation through embedding space. Using different transformer text embedding models, we construct participant-specific semantic trajectories based on cumulative embeddings and extract geometric and dynamical metrics, including distance to next, distance to centroid, entropy, velocity, and acceleration. These measures capture both scalar and directional aspects of semantic navigation, providing a computationally grounded view of semantic representation search as movement in a geometric space. We evaluate the framework on four datasets across different languages, spanning different property generation tasks: Neurodegenerative, Swear verbal fluency,  Property listing task in Italian, and in German. Across these contexts, our approach distinguishes between clinical groups and concept types, offering a mathematical framework that requires minimal human intervention compared to typical labor-intensive linguistic pre-processing methods. Comparison with a non-cumulative approach reveals that cumulative embeddings work best for longer trajectories, whereas shorter ones may provide too little context, favoring the non-cumulative alternative. Critically, different embedding models yielded similar results, highlighting similarities between different learned representations despite different training pipelines. By framing semantic navigation as a structured trajectory through embedding space, bridging cognitive modeling with learned representation, thereby establishing a pipeline for quantifying semantic representation dynamics with applications in clinical research, cross-linguistic analysis, and the assessment of artificial cognition. https://github.com/jesuinovieira/semtraj-iclr2026

Published as a conference paper at ICLR 2026
CHARACTERIZING HUMAN SEMANTIC NAVIGATION IN CONCEPT PRODUCTION AS TRAJECTORIES IN EM- BEDDING SPACE
Felipe D. Toro-Hernández! Jesuino Vicira Filho² Rodrigo M. Cabral-Carvalho"
'Center of Mathematics, Computing and Cognition, Federal University of ABC
*Department of Computer Science and Operations Research, Université de Montréal
([torooee,, rodri vodrigodanee godanot t acc gmail .com "jesuino, viei ualuumetteaa..
All authors contributed equally
ABSTRACT
Semantic representations can be framed as structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To investi- gate how humans traverse this geometry, we introduce framework that repre- sents concept production as navigation through embedding space. Using different transformer text embedding models, construct participant-specifi semantic trajectories based on cumulative embeddings and extract geometric and dynam- ical metrics, including distance to next, distance to centroid, entropy, velocity, and acceleration. These measures capture both scalar and directional aspects semantic navigation, providing computationally grounded view of semantic rep- resentation search as movement in geometric space. We evaluate the framework four datasets across different languages, spanning different property generation tasks: Neurodegenerative, Swear verbal fluency, Property listing task in Italian, and in German. Across these contexts, our approach distinguishes between clin- ical groups and concept types, offering mathematical framework that requires minimal human intervention compared to typical labor-intensive linguistic pre- processing methods. Comparison with non-cumulative approach reveals that cumulative embeddings work best for longer trajectories, whereas shorter ones may provide too little context, favoring the non-cumulative alternative. Critically, different embedding models yielded similar results, highlighting similarities be- tween different learned representations despite different training pipelines. By framing semantic navigation as a structured trajectory through embedding space, bridging cognitive modeling with learned representation, thereby establishing pipeline for quantifying semantic representation dynamics with applications in clinical research, cross-linguistic analysis, and the assessment of artificial cogni- tion. https: /ttpt/gttttttt//tt//////tt/thhtt con/ http///eiiiiiiiiaaeaaeeaaaaaaaea sawassiiiiieiirrrsstttsaaaaa semttajiiiilra0
INTRODUCTION
Semantic representations are the stored, structured traces of knowledge about the world (Hills et al., 2015). Retrieving a concept depends context and draws jointly on experiential details and abstract. shared knowledge, for "dog," this might range from memories of family pet generic category knowledge (Xie et al., 2024; Barsalou, 2023). Navigation in semantic representations involves searching within space that both dynamic and context-dependent, including features for sensorimotor representations, affective experiences, linguistic encoding, and contextual cues (Hills et al., 2015; Diveica et al., 2025).
Influential cognitive models of semantic navigation treat this search as foraging process, where humans balance exploitation and exploration (Hills et al., 2012), much like animals foraging for re- sources. Operationally, this dynamic has been measured using two primary components of semantic control: clustering and switching (Troyer et al., 1997). Clustering (exploitation) refers to the gen- eration of items within specific semantic subeategory (e.g., 'cat,' 'dog" within "house animals'),

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while switching (exploration) marks the transition to new subeategory (e.g., moving from 'house animals' to 'sea animals"). ) Crucially, this navigation is an inherently cumulative process. Successful semantic retrieval relies on executive functions, specifically working memory and inhibitory control, to monitor the search history and inhibit the repetition of previously generated items (Baddeley, 2000; Unsworth al., 2011). Consequently, the semantic representation of any given response is not isolated; rather, it is contextually bound to the sequence of preceding words.
Here, we extend this approach and adopt the view that semantic retrieval can be understood nav- igation through a multidimensional space in which multiple features jointly define each concept. From this perspective, we move beyond the binary distinction of clustering and switching. Instead, we aim to capture the step-by-step granularity of the search process itself by computing navigation metries. To capture this history-dependent dynamic, we propose a natural-language-bar based char- acterization of human semantic navigation using cumulative embeddings, modeling the search as trajectories in transformer-bant based emhedding space.
Classical task paradigms in cognitive sciences such as semantic fluency and property listing provide behavioral windows into this search process (Canessa et al., 2024; Canessa & Chaigneau, 2020; Troyer et al., 1997), and formal models have described how people balance exploitation and explo- ration over time (Hills et al., 2012). Yet these approaches often rely on labor- intensive heteroge- neous pipelines that hinder comparability across studies (Chaigneau al., 2018). Natural Language Processing (NLP) weethods-especiall have already helped differentiate clinical groups and organize conceptual structure (Gareía et al., 2025); for example, word embedding metrics have separated Alzheimer's 's and Parkinson's 'ss patients from controls, and language model (LM) embedding trajectories have characterized psychosis and schizophrenia profiles (Toro-Hernández et al., 2024; Ferrante et al., 2025; He et al., 2024; Nour et al., 2023; Lopes da Cunha et al., 2024; Sanz al., 2022; Palominos et al., 2024).
embedding- based amalsses-fffe scalable alternatives that
Building on this foundation, our framework represents concept production as movement through transformer-basee spaces. For each participant, we construct semantic trajectories and extract geo- metric and dynamical metrics, distance to next, velocity, acceleration, entropy, distance to centroid, that capture both scalar and directional aspects of navigation. Critically, this trajectory- ap- proach offers continuous view of the search, complementing traditional metrics of semantic control by capturing a more fine-grained navigation dynamics. Furthermore, this computational approach minimizes manual intervention while preserving rich structure in the data, enabling principled tests of hypotheses about semantic meaning and search in humans and in artificial agents (Xu et al., 2025).
We demonstrate the effectiveness of our approach by applying it to datasets that specifically chal- lenge standard LM embeddings. Our evaluation probes: the clinical utility of embeddings for analyzing natural language in patients with Parkinson's 's disease (Linz et al., 2017) 0); the semantic consistency of multilingual embeddings across Italian and German (Conneau et al., 2020; Artetxe & Schwenk, 2019); and the atypical geometric properties of swear word embeddings as revealed through a verbal fluency task (Graumas et al., 2019). Critically, our metrics provide novel insights in each of these established research areas by isolating the specific trajectory features that differen- tiate between different groups and semantic categories. Notably. different embedding models yield essentially similar patterns, suggesting convergent geometry across learned representations despite distinct training pipelines and architectural differences (Valeriani et al., 2023; Doimo et al., 2024; Lee et al., 2025; Wolfram & Schein, 2025). By framing human semantic retrieval as structured trajectories in embedding space, we bridge cognitive modeling with learned representations and establish a pipeline for quantifying semantic dynamics with applications to clinical research and cross-linguistii analysis (Shakeri & Farmanbar, 2023). This approach holds promise for applica- tions, including the classification of brain disorders, the differentiation between concept types, and the testing of core hypotheses about search dynamies in artificial agents, models that compare human responses to linguistic data with L.LMs' generated responses.
METHODS
2.1 DATASETS
To evaluate our metrics, we use four open datasets that vary in language, population, and tasks. Table 1 provides an overview of their key characteristies and descriptive statistics.
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Overview of the four datasets used in experiments. We report the mean + standard deviation for the number of properties and words produced per concept-participaae pair.
Table 1:
Dataset
Language
Subjects
Comparisons
Properties
Words
Neurodegenerative
ES
76
3
19.53 + 12.39
19.79 + 12.64
Swear Fluency
EN
274
5
20.69 = 7.88
21.20 = 8.10
Italian German
IT
69
10
4.96 + 1.86
14.14
DE
73
10
5,49 = 1.82
14.31 + 6.00
Neurodegenerative dataset. Introduced in Toro-Hernández et al. (2024), consists of 76 Chilean Spanish-speaking participants divided into three groups: individuals with Parkinson's 's disease (PD), with the behavioral variant of frontotemporal dementia (bvFTD), and healthy controls (HC). Par- ticipants completed property listing task (PLT), in which they were asked to generate attributes as possible for 10 concrete concepts ("tree," "sun," "clown," "puma," "airplane," " "hair," "duck," "house," "shark," " and "bed"). Instructions emphasized the inclusion of "physical charac- teristics, internal parts, appearance, sounds, smells, textures, uses, functions, and typical locations" (Toro-Hemández al., 2024). Finally, the data in this paper was preprocessed by only extracting content words (nouns, verbs, adjectives, and adverbs).
many
Swear fluency dataset. Introduced by Reiman & Farleywine (2023), includes 274 undergraduate native speakers of U.S. English who performed verbal fluency tasks across domains. In this case, participants were instructed to generate as many items as possible within given category in one minute (e.g.. if the category was "animals," acceptable responses might include "dog." "cat," "lion," or "tiger"). The categories comprised animals, words beginning with F, A, and S, and swear words.
Italian and German datasets. Drawn from Kremer & Baroni (201 I), comprising 69 Italian and 73 German students, respectively. Participants were asked to generate descriptive properties for 50 concrete concepts, divided into 10 categories: "Bird," "Body Part," "Building." "Clothing," "Fruit," "Furniture," "Implement," "Mammal," "Vegetable," " and "Vehicle". The task has time limit of one minute per item. Participants were encouraged to provide at least four descriptive phrases per concept and were not allowed to return to previously described items once the time expired.
2.2 CHARACTERIZING NAVIGATION
Participants generate concept streams ordered lists of items (e.g., "cat," "dog," .d.og."....offf of length N. Let item denote the t-th entry. We map each stream to a trajectory in semantic space, X (II....,NN) NN), where Ie is the point associated with item t. The points are time-indexed (x1 is the first item, I2 the second, etc.). Rather than computing embeddings independently (Linz et al., 2017; Nour al., 2023), construct them cumulatively: 2; summarizes items 1:t. For ample, if the first two items are "cat" then "dog." 22 encodes "cat dog." This design captures depen- dencies among successive items, avoids independence assumptions, and yields distinet trajectory for each prrieipant-conceyy pair, enabling analysis of navigation dynamics (Figure I). Because H, conditions on the full prefix, this approach calls for more complex, sequence aware embedding representations capable of modeling memory dependent semantics.
Distance Next. To quantify noment-to-moment change in semantic state, we compute the cosine distance between each pair of successive unit- normalordd embeddings, yielding an N length series of step sizes ("semantic jumps") per trajectory. Larger values indicate bigger shifts in meaning from one item to the next. Because trajectories naturally differ in length, we also summarize each series with mean step size, the average cosine distance across step, as length-invariant indicator of average memory-search breadth. In cognitive terms, this metric assesses the local, step-by-step dynamic of the search. For example, the 'semantic jump" from 'dog' to 'shark' would yield larger distance than the jump from 'cat' to 'dog'.
Entropy. We also summarize the information contained of the distance to next with a scale-free approximate Shamnon entropy. Distances are split at their within- trajenttt median into "high" versus "low." forming a binary sequence whose Shannon entropy is computed and then normalized by the number of valid steps (Pineus et al., 1991). This value is to zero when all steps fall
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trial Injoctoriis subject
Oxp Np.. X3
Characterization of semantic trajectory
Cat Dog. Dog Bird,
Enceder
Distance Next
Velocity (x))
Cat Bird Elephant
Acceleration (x") Entropy
By N trials trajectorics (same subjoct)
Distance Centroid
N trials embeddings
Figure 1: A schematic of the semantic trajectory analysis. (A) In single trial, participant generates a cumulative word list. A text encoder then maps each sequential step to a vector embedding, creating trajectory in semantic space. This path characterized using dynamical metrics like velocity (x'), acceleration (x"), and entropy. (B) Across multiple trials for the same subject, the dispersion of the resulting cloud of embeddings trajectories is summarized by measuring the distance of each point to the collective centroid.
on single side the median and is only estimated when at least three valid steps are available, ensuring stability for short sequences. Let 0 denote distance next within-trajectory median. We first binarize the sequence:
1,
Tt 2 0, St < 0
b
t L......,
(1)
0,
Let n Ei= by be the fraction of ones. The Shannon entropy of the binarized sequence is H = 1-==plogee plog2
(1 - p) log2(1 pp),
(2)
be interpreted as the information richness of fluctuation around a typical step size in a time series (Gao et al., 2008). Theoretically, high entropy indicates less predictable, more variable search dynamic, that may be consistent with higher cognitive effort or reduced executive control. This suggests disorganized or erratic search process (e.g., the sequence "cat', 'shark', 'dog' would be more entropic than 'cat', 'dog", 'shark') ), potentially reflecting a failure to maintain consistent search strategy.
with the convention H O when p (0, 1). This measure
Velocity and Acceleration. Beyond scalar cosine distances, we characterize the semantic direc- tional dynamics by computing discrete derivatives of the embeddings themselves, similarly to Nour al. (2025). It is important to remark that we are assuming Fuclidean dynamics for simplicity, which overlooks the real anisotropic nature of the embedding spaces (Nickel & Kiela, 2017; Etha- yarajh, 2019), Velocity defined as the vector difference between consecutive embeddings, yielding both direction and a magnitude for each step; the final row has no velocity. Since in the datasets tasks don't ' have a time stamp, then a = At
ax xt), (I=1...,,T
Acceleration is defined as the difference between successive velocity vectors and quantifies changes in direction or speed from one step to the next; the final two rows have no acceleration. These kinematic quantities retain information about where the trajectory is heading the high-dimensionall innce-information that step-wise scalar distances alone cannot convey. By default, derivatives assume unit time step between items; if timestamps available, magnitudes be rescaled accordingly with a.
a, a(V141 = a 2x.11 + xx).
1,
.
(4)
Crucially, velocity quantifies the magnitude and direction of a single step (e.g., 'dog' to 'shark' yields high velocity). Acceleration, in turn, quantifies the change in this movement. cognitive terms, sta- ble "exploitation" (clustering) involves minimal changes in velocity, thus yielding low acceleration.
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Conversely, a process characterized by 'unstable exploitation' (a series of erratic 'switches') would involve constant, significant changes in magnitude, thus resulting in a high average acceleration as physics inspired characterization.
Distance to Centroid. To capture how individual properties relate to the overall semantic context, computed centroid-based measure. When categorical property labels and their embeddings were available, repeated occurrences of the same property were collapsed to a single instance, en- suring that redundancy did not overweight specific properties. For each unique property, we retained only its first embedding and constructed a centroid vector representing the average position of all unique property embeddings over N trials in each specific concept and unique subject. Each item in the sequence was then assigned the cosine distance between its embedding and this centroid. This measure quantifies how far each produced property lies from the central tendency of the partici- pant's semantic exploration, providing an index of dispersion that complements step-wise trajectory distances. A high average distance to centroid indicates a more dispersed search, suggesting that the participant explored wider, more varied semantic space. Conversely, low distance to centroid would suggest more focused or "centered' search, confined to a tighter semantic neighborhood.
In sum, these five metrics are complementary, each characterizing distinct aspect of the navigation process inspired by physics. Together, they provide a granular view of a person's 's semantic orga- nization and executive control. Specifically, Distance to Next, Velocity, and Acceleration quantify the local kinematics of the search; Entropy quantifies the global variability and predictability of the trajectory; and Distance Centroid captures the global dispersion of the entire search relative to central meaning. This multi-metric approach allows us to answer how the search unfolds.
2.2.1 BASELINE AND EMBEDDING MODEL COMPARISON
Each trajectory is time-ordered sequence of dense multilingual embeddings. Unless otherwise noted, all results are reported using OpenAI's lext-embeeding----laree results with alternative en- coders (Google's 's rext-embedding-000 To benchmark our approach against traditional static representations, we computed all metrics us- ing non-cumulative fastText embeddings, serving as a baseline for standard semantic organization models (Mikolov et al., 2018). Moreover, the model choice uses causal attention, in case of Qwen3 (Zhang et al., 2025) and bidirectional-encddee Gecko in Google's text-embedding--0 (2024); however, OpenAl's model doesn't 't have any documentation on the training method. Finally, to address the potential confound of embedding anisotropy, mation to our embeddings and re-calculated the metrics to assess the robustness of group discrim- ination under approximated isotropic conditions (Bell & Sejnowski, 1997; Zhuo et al., 2023; Su et al., 2021). These results are included in the Appendix A.4 due to only minor differences between groups. Moreover, Qwen3, Google's text-embedding- and fastText models are also reported in the Appendix A.
and
0.66,, and using fastText as baseline).
Lee et al.
applied ZCA-whitening transfor-
2.2.2 STATISTICAL ANALYSIS
For each metric, we evaluated group- and concept-level effects using generalized linear mixed mod- els (GLMMs), with each metric a fixed factor, including participants and concept as random factors to account for repeated measures and individual variability. Models were fitted according to the most appropriate distribution. Based on this procedure, a lognormal distribution was applied to distance to next, entropy, velocity, and acceleration, while Gaussian distribution was used for the distance-to-ceenrrod metric. Post-hoe pairwise comparisons were adjusted with Tukey's 's HSD to control the family-wise error rate. Visualizations combine raw distributions (boxplots with jittered points) with model-estimated marginal means and 95% confidence intervals, annotated with signif- icance levels from the Tukey tests. This approach highlights both the variability in the data and the inferential estimates used for statistical testing. All statistical analyses were conducted in R (v.4.3.1) using the glmm'MBB package (Brooks et al., 2017).
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3 RESULTS
3.1 NEURODEGENERATIV DATASET
There was a significant effect of category across all metrics. For distance to next, healthy controls showed lower values than both bvFTD) and PID, while bvFTD and PD did not differ. A similar pattern emerged for velocity and for acceleration: HCs were lower than both patient groups, with bvFTD) and PD comparable. For entropy. HC again showed reduced values compared both groups, with no difference between bvFTD and PD. In contrast, distance-to-centroid showed the opposite pattern: HC exhibited greater distances than both patient groups, which did differ from each other. These results indicate that semantic navigation in patient groups is characterized by greater spread, higher variability, increased entropy, and more compact clustering relative to controls (see Figure 2).
Moc.
Coundi
Figure 2: Boxplot of Neurodegenerative metries by category. Matrices display pairwise statistical comparisons.
3.2 SWEAR FLUENCY DATASET
For distance to next, all letter categories and swear words were higher than animals, with swear words eliciting the longest distance next and animals the shortest; letters occupied an intermediate range. Velocity showed the same pattern, with navigation being faster for letters and especially for swear words than for animals. Acceleration mirrored velocity, with letters and, most prominently, swear words exceeding animals. For entropy, animals showed the lowest values; letters were higher, and swear words the highest. Finally, distance-to-cen to-entrooo reversed the pattern. with animals being farther from the centroid than letters, whereas swear words were markedly closer. Overall, swear words consistently drove the strongest responses across metrics, animals the lowest, and letters clustered in between (see Figure 3).
3.3 ITALIAN DATASET
Relative to Bird (reference group), most categories showed shorter distance to next, with Building and Vehicle the least separated from Bird. Velocity followed the same ordering, with Bird exceed- ing most categories and Building and Vehicle only weakly or not separated. Acceleration mirrored velocity, again showing Bird higher than the bulk of categories. Entropy differences were selec- tive: several categories had lower entropy than Bird, whereas many contrasts were not significant. showed partially reversed structure: some categories were farther from the centroid than Bird, while others (c.g.. Body Part, Clothing, Implement) were closer; several cate- gories showed no difference from Bird (see Figure 4).

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Nett
Selurty
stie
Tietarro to Gentrrd
0.0x
10.
Grie
Late
fends
Anwal
fewils
Letee
Lette
Leter
Bluse Wi+
Beni St
Near W
Lanc
Sonar
Some Rt
Lnow
Lowe
Lake
Figure 3; Boxplot of Swear metrics by category. Matrices display pairwise statistical comparisons.
D2T
Zeraton
Monrall
PC3--3
pate-2
Figure 4: Boxplot of Italian metries by category. Matrices display pairwise statistical comparisons.
Datanon to Nott
Vincky
Acedeaatios
Distare to Catoral
22-
Sepicaad
Ina
Bar
bo
Bads
Baaly Muat
Dark Per.
F
Dalillac
Belllace
Chalting
That
Viunadle Valuk
Vauriater Inlade
Velad
Bahk Patk
The
Figure 5: Boxplot of German metrics by category. Matrices display pairwise statistical comparisons.
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3.4 GERMAN DATASET
For distance next, most categories produced shorter values than Bird, with Vehicle and several others most distinct from Bird, and Vegetable showing little no separation. Velocity showed Bird higher than nearly all categories, with the largest gap against Vehicle. Acceleration followed the same pattern, with Bird exceeding most categories and the clearest separation against Vehicle. Entropy differences were selective: several categories were lower than Bird, while Implement was higher; many others showed no differences. Finally, sitancce-o--entroii revealed a different structure: some categories (e.g., Fruit, Mammal, Vegetable) were farther from the centroid than Bird, whereas others (e.g., Body Part, Building, Clothing, Furniture, Implement, Vehicle) were closer, with the most pronounced gaps involving Fruit compared to clothing and tool-like categories (see Figure 5),
3.5 MODEL COMPARISON
To ensure our findings were not dependent on specific text encoder, we compared the trajec- tory metrics from three different models. The non-cumulative fastText baseline is reported in Ap- pendix A.5, and cumulative vs. non-cumulative comparisons for all models are provided in Table 2. Figure shows the Pearson correlation matrices for five metrics across all four datasets, revealing generally high degree of robustness. The results exhibit a block-diagonal structure, indicating strong positive correlations for each metric across models. Kinematic measures-velocity, accel- highly correlated across models and moderately correlated with one another, consistent with their shared capture of step-wise trajectory dynamics (except the base- line; fastText is its non-cumulative version). Two metrics diverge: distance to centroid and entropy. The first shows the weakest inter-model correlation, we attribute the low inter-model correlation of distance-to-cen teaeeettcccenneeee to-centroid to its sensitivity to embedding geometry. Phenomena such anisotropy (Etha- yarajh, 2019) and 'rogue dimensions' (Timkey & van Schijndel, 2021) result in unique, model- specific centroids that distort global statistics. Whereas entropy shows near-perfect inter-model correlation because it depends on rank ordering rather than absolute distances; median binarization further stabilizes it when models agree on the relative size of semantic jumps.
eration, and distance-to-nexte
09
19 10
dew
ale
Figure 6: Pearson correlations of metrics across datasets. From left right: Neurodegenerative, Swear Fluency, Italian, and German. Each heatmap reports pairwise correlations between Distance to Next, Velocity, Acceleration, Entropy, and Distance to Centroid, computed with three embed- ding models (O OpenAl coxt-embedding----aae G = Google text-embedding- Q Qwen3- Embedding-O05 F fastText). Color indicates correlation strength (blue negative, red = positive).
DISCUSSION
We aimed to characterize human semantic navigation through fine-grained analysis of step-by-step dynamics. Our results indicate that this approach captures granular aspects of the search process that are complementary to traditional clustering and switching dynamics (Troyer et al., 1997). By successfully differentiating between clinical groups and concepts, framework offers highly scalable, automated technique. This addresses critical challenges the field regarding automation, reliability, and the limitations of time-consuming manual pipelines (Buchanan et al., 2020; Ramos
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et al., 2024; 2025). Crucially, our work builds upon previous literature that successfully leveraged embedding spaces for semantic characterization (Linz et al., 2017; Froo-eenández et al., 2024; Sanz et al., 2022; Zhang et al., 2022). We further extend these approaches by explicitly modeling the search trajectory, using step-by-step geometric and kinematic metrics, inspired by physics, as a dynamic proxy for human semantic navigation. Our cumulative transformer-basee characterization approach enhances prior NLP-based methods for human semantic navigation (Linz et al., 2017), particularly for tasks involving large sequences, as shown in A.3.
To test our approach, we tested 4 datasets. First, the analysis of the neurodegenerative dataset reveals a semantic navigation profile primarily characterized by disorganization, reflecting the exec- utive dysfunction typical of these clinical groups. While healthy navigation relies on strategic con- trol to balance clustering and switching, patients with PD and bvFTD often suffer from executive constraints that disrupt this regulation (Birba et al., 2017; Cousins & Grossman, 2017). Our metrics capture this deficit as a specific kinematic signature: rather than focused exploitation, patients exhib- ited significantly higher distance to next, velocity, and acceleration. These elevated values indicate a volatile trajectory, marked by erratic 'semantic jumps' and abrupt changes in direction, consistent with an inability to sustain stable semantic clusters, or effectively inhibit irrelevant associations. Crucially, this disordered traversal is further confirmed by higher entropy, quantifying a search pro- cess that is significantly less predictable and routine than that of controls, thereby extending the semantic variability findings of the original work (Toro-Hernández et al., 2024). Finally, distance to centroid results showed that, despite their kinematic volatility, patient searches were spatially more constricted, remaining closer to a central meaning. This likely reflects a diminished seman- tic space, prompted by the loss of sensorimotor traces required to evoke rich content (Cousins & Grossman, 2017; Fernandino et al., 2013; Boulenger et al., 2008), which, combined with the afore- mentioned executive dysfunction, restricts navigation to a narrower semantic neighborhood (Zhang et al., 2022).
Conversely, the swear fluency dataset revealed a distinct navigational profile characterized by a highly variable semantic exploration within a constrained space. Navigation for curse words was marked by significantly larger 'semantic jumps' among successive items, reflected in higher ve- locity and acceleration dynamics. This kinematic volatility, combined with higher entropy, aligns with the unique organization of taboo lexicons: unlike taxonomic categories (e.g., animals) which allow for structured exploitation of sub-categories, the structure of swear words -and differential search strategies- may lead to more erratic retrieval (Jay & Jay, 2015). Regarding global geometry, swear-word fluency exhibited lower distance centroid than animal and letter tasks. This higher centrality is consistent with the structural nature of the taboo category, a lexical neighborhood that spatially compact but explored with high variability and directional unpredictability.
Furthermore, the analysis of the Italian and German datasets revealed that kinematic patterns con- sistently exposed an informative structure regarding how retrieval unfolds within specific linguistic contexts across models. Since data acquisition followed an identical protocol for both languages (Kremer & Baroni, 2011), observed differences cannot be ascribed to task administration artifacts. While our results successfully discriminated specific semantic categories. differences in category effects emerged between the Italian and German datasets. As noted in psycholinguistic research, variables such as concreteness and specificity are encoded differently depending on linguistic struc- ture (Bolognesi et al., 2020; Montefinese et al., 2023). This reflects the flexible nature of lcxico- semantic representations, where cultural conventions shape how meaning is accessed and organized during semantic search Vigliocco et al. (2009); Barsalou (2023); Kemmerer (2023). Thus, different transformer- based models, trained on distinct corpora, may be expected to capture specific manifes- tations of semantic structure in divergent ways (Conneau et al., 2020; Artetxe & Schwenk, 2019). Our results open new avenues in less-explored domains as a general approach, such as the semantic navigation underlying the production of swear words. Since swear-word fluency has been linked to substance use (Reiman & Earleywine, 2023) and differential brain activity patterns in schizophre- nia (Lee et al., 2019), analyzing its semantic dynamics could provide novel insights into behavioral regulation and inhibitory control.
Crucially, our cross-model analyses revealed that trajectory metrics were highly correlated across the three different embedding models, despite their training differences (i.e., causal and bidirectional- encoder attention). indicating that the observed dynamics are not an artifact of single model, as agrees with previous literature as they generate similar representations (Lee et al., 2025; Wolfram & Schein, 2025). This consistency was particularly strong for metries capturing the local, -ye-bby-ttep

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evolution of the trajectory, such as velocity, acceleration, and entropy. In contrast, the distance-to- centroid metric consistently showed the lowest inter-model correlation, revealing that while models agree trajectory's 's local dynamies (its shape and variability)), they differ significantly in its global positioning. This sensitivity arises because the metric uses a static, global average rather than suc- cessive states, making it dependent the unique high-level geometry of each model's embedding space. It might be possible tool for comparing how different models structure knowledge. Notably, this dissimilarity between models was most pronounced in the neurodegeneratiie dataset, potentially reflecting a more complex disruption in semantic navigation.
CONCLUSION
In sum, applying five cumulative embedding-base trajectory metrics, inspired by physics, to flu- ency and property listing tasks data are complementary to previous NLP methods, reveling signa- tures of semantic navigation: distance to next, velocity, and acceleration distinguished neurode- generative groups from healthy controls in their semantic search; entropy captured irregularity in search (notably higher for swear-word fluency); and distance to centroid indexed positional cen- trality orthogonal to the other measures, exposing category and language-specifie structure. These effects were broadly consistent across three multilingual, causal and bidirectional, transformer em- bedding models, indicating robustmess to the choice of encoder for local trajectory dynamics, while lower cross-model agreement for distance-to-centree Together, these results show geometrically grounded NLP framework for characterizing human semantic retrieval, through across tasks and languages, and open avenues for clinical stratification and model comparisons of how humans and LMs navigate semantic space.
highlights model-deeendent global geometry.
LIMITATIONS AND FUTURE WORK
Although fluency and property-listing tasks are useful across range of applications, they capture only a partial view of human semantic navigation, in this specific case the tasks didn't contain the time step of the words. This could contribute more temporal meaningful dynamics. Developing richer speech-based protocols may help probe semantic search and representation more directly, es- pecially when linked to learned representations from language models (LMs). We acknowledge that our assumption of Euclidean dynamics is simplification that overlooks the anisotropic structure of embedding spaces (Nickel Kiela, 2017; Ethayarajh, 2019) and the tests with ZCA- -hitehitening showed only little difference between groups, indicating low effect of the anisotropy A.4. More mathematically robust, potentially Fuclidei,,,,,, metrics can be used to better characterize these trajectories. Furthermore, we used basic information measure (Shannon entropy); future work should broaden the information-theoreti toolkit for semantic navigation to assess complexity in systems with many interacting variables that jointly shape human semantic representation and trieval. Finally, future work could apply a similar framework to characterize different LLMs and assess their generative semantic navigation across tasks. The goal is to develop unified account of trajectories in semantic space that encompasses both humans and generative language models.
REPRODUCIBILITY STATEMENT
The code and data required reproduce the findings of this study are openly available. The source code for all analyses and figure generation is accessible at https://githot con/ jesui novi e r semmtraa----eee..e j- cl r2026. The datasets are from public sources, which are de- tailed with their respective access links in Appendix A.2.
ACKNOWLEDGMENTS
Felipe Diego foro-Hemánnez acknowledges financial support from the São Paulo Research Foun- dation (FAPESP), grant 2025/04243-2.
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A APPENDIX
Figures in Appendices A.5, A.6, and A.7 share a common format: boxplots with jittered observations and Tukey HSD pairwise comparison matrix. maintain clarity without overloading the appendix with redundant details, captions specify only the dataset and embedding model.
A.1 USE OF LARGE LANGUAGE MODELS DISCLOSURE
We used large language models (LLMs) code assistant and text editor to refine implementation details, improve manuscript clarity and grammar, and identify relevant literature. All LLM outputs were thoroughly reviewed and verified by the authors. The conceptual framework and methodology contributions presented in this work are entirely our own.
A.2 DATA AVAILABILITY
The datasets used in this study are publicly available. The neurodegenerative dataset can be found at https: https://ostt //t:://...i.. //ossiio//////// /ook/p. and the swear words dataset is located at ht https: // //sff io/w8drt// The Italian and German datasets were sourced from the appendices of Kremer & Baroni (201 available at attpp:://iime springer. com/ art httpm//aa.....e....3////////3/a.
A.3 NON-CUMULATIVE EMBEDDING RESULTS
Table 2 compares the cumulative and non-cumulative versions of each embedding model across the four datasets. Overall, we observe a consistent pattern that aligns with the structure and length of the underlying trajectories.
For the Neurodegenerative dataset, all models clearly benefit from the cumulative representation: the cumulative variants yield more significant pairwise differences and higher effect sizes. This suggests that longer trajectories allow cumulative embeddings to capture broader semantic patterns that are not as evident when looking only at point-to-point changes. The Swear Fluency dataset shows milder version this effect. The cumulative models still identify more significant differences, but the non-cumulative variants produce slightly larger Cohen's d.
In contrast, the Italian and German datasets favor the non-cumulative variants, which achieve the highest number of significant differences and the strongest effect sizes across models. This pattern consistent with the much shorter trajectories these datasets (Table 1). When sequences contain very few observations, there is less contextual information average over, and the geometric metrics
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Table 2: Summary of significant differences across embedding backends. For each dataset, report the number of significant difference pairs (# Pairs; binary count, with the weighted count-rangunn from 4 depending on the significance level-in parentheses) and the mean Cohen's across metrics. Rows marked with (nc) indicate non-cumulative variants. Bold values indicate the highest result each column.
Neurodegenerative
Swear Fluency
Italian
German
Model
# Pairs
d
# Pairs
d
Pairs
d
Pairs
d
openai 003
10 (27)
0.30
46 (185)
0.59
63 (203)
0.18
80 (284)
0.21
google 004 qwen
7 (21)
0.28
45 (182)
0.58
81 (306)
0.22
110 (413)
0.28
9 (25)
0.26
43 (162)
0.40
84 (304)
0.22
112 (413)
0.24
fasttext
8 (22)
0.23
46 (182)
0.72
81 (311)
0.22
105 (397)
0.23
openai 003 (nc)
4 (10)
0.19
46 (182)
0.64
81 (281)
0.24
111 (413)
0.28
google 004 (nc)
4 (13)
0.19
40 (161)
0.76
107 (395)
0.29
137 (521)
0.37
qwen (nc)
4 (11)
0.15
40 (148)
0.39
92 (353)
0.28
114 (427)
0.27
fasttext (nc)
(100)
0.17
37 (143)
0.86
70 (277)
0.25
122 (471)
0.31
naturally exhibit greater point-to-point variation. Non-cumulative embeddings retain this variability, which helps them expose stronger cross-model differences.
Taken together, these results suggest that the choice between cumulative and non-cumulative embed- dings depends on trajectory length: longer sequences benefit from cumulative aggregation, whereas shorter sequences retain more discriminative signal under a non-cumulative representation.
A.4 RESULTS ZCA- HEA-WHITENED EMBEDDING SPACE
We conducted preliminary analysis using the OpenAl embedding model to assess the impact of correcting for anisotropic geometry via ZCA-wnitening (Bell & Sejnowski, 1997; Zhuo al., 2023; Su et al., 2021). Before computing metrics, verified the transformation by checking the covari- ance matrix: diagonals were near and off-diagonals near O. For the Parkinson's and Swear Fluency datasets, the metrics derived from whitened embeddings were nearly identical to those obtained from the original cumulative embeddings. In contrast, the Italian and German datasets exhibited slight, non-directional fluctuations. This variation is likely explained by the significant difference in trajec- tory lengths across datasets. As shown in Table 1, the mean number of properties per trajectory is relatively high for the Parkinson's (19.53) and Swear Fluency (20.69) datasets, whereas the Italian and German datasets feature much shorter trajectories (4.96 and 5.49, respectively). These shorter sequences inherently yield higher metric variability, rendering them less stable than the robust esti- mates derived from longer trajectories.
Figure/ summarizes the impact of whitening on the correlation structure. ZCA introduces only minimal changes, indicating that the relative relationships between metrics remain stable despite transformation. This supports robustness of our semantic trajectory analysis under both raw and whitened embedding spaces, though further investigation is needed to determine when explicit anisotropy correction is beneficial.
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Impact of ZCA Whitening metric correlation structure. Heatmaps showing the dif- ference in Pearson correlations (ZCA Raw) between semantic trajectory metrics computed from ZCA-whitenee versus raw embeddings across four datasets (Neurodegenerative, Swear Fluency, Italian, German) and four embedding models (O OpenAl sett-ebbedding-O00 Q= Qwen3 Eeeeediieeeedn...... 0.6B, fastText). Each cell represents the change in correlation between two metric-model pairs, where red indicates increased correlation after whiten- ing and blue indicates decreased correlation.
Figure 7:
wwwemmbdddng---arrree G = Google
17

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Published as a conference paper at ICLR 2026
A.5 FASTTEXT RESULTS (BASELINE)
All metrics here are computed using the non-cumulative fastText embeddings, which we adopt as baseline. For each dataset, employ the corresponding language-specife fastText model.
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Figure 8: Neurodegenerative dataset, TestText (baseline; non-cumulative) model.
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Figure 9: Swear Fluency dataset, fastText (baseline; non-cumulative) model.
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Published as a conference paper at ICLR 2026
16-
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Figure 10: Italian dataset, fastText (baseline; non-cumulative) model.
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Figure 11: German dataset, fastText (baseline; non-cumulative) model.
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Published as a conference paper at ICLR 2026
A.6 OWEN3-EMBEDDING RESULTS
All experiments were reproduced for open-source embedding model (Zhang al., 2025).
which is a small high-performance
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Figure 12: Neurodegenerative dataset, Qwen3-Embedding--000 model.
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Figure 13: Swear Fluency dataset, Qwen3- Seee----mmeddddnnnnne 0.6B model.
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Published as a conference paper at ICLR 2026
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Figure 14: Italian dataset, Qwen3-Embeddingg0000 model.
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Figure 15: German dataset, Qwen3-Eimbedding-0.ff model.
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Published as a conference paper at ICLR 2026
A.7 GOOGLE'S TEXT-EMBEDDING-OO RESULTS
All experiments were reproduced for Google's text-embedding-004.
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Figure 16: Neurodegenerative dataset, Google's text-embedding-OOl model.
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Figure 17: Swear Fluency dataset, Google's text-embedding-004 model.
22

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Published as a conference paper at ICLR 2026
12
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Figure 18: Italian dataset, Google's 's text-embedding-O0k model.
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Figure 19: German dataset, Google's text-embedding-004 model.
23


Reviews:
[{"review_id": "dIDgP0M49V", "reviewer": "Reviewer_xS4F", "summary": "This paper proposes a framework for characterizing human semantic navigation by representing concept production tasks (semantic fluency and property listing) as trajectories through transformer-based embedding spaces. The authors extract geometric and dynamical metrics including distance-to-next, velocity, acceleration, entropy, and distance-to-centroid from cumulative word sequences. They evaluate their approach on four datasets spanning clinical populations (Parkinson's, frontotemporal dementia), different languages (Italian, German), and semantic categories, showing that these trajectory-based metrics can distinguish between clinical groups and concept types across different transformer models.", "questions": "- Centroid computation: The distance-to-centroid shows lowest inter-model correlation. Could you elaborate on why this metric is particularly sensitive to model-specific geometry? How does collapsing repeated properties affect this measure?\n- Category effects: The category-specific patterns differ between Italian and German datasets. Do these differences reflect linguistic/cultural variations or task administration differences? This needs deeper analysis.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Novel computational framework: The trajectory-based approach to semantic navigation is creative, moving beyond static embedding analyses to capture dynamic aspects of semantic search. The use of cumulative embeddings (where x_t encodes items 1:t) is particularly interesting as it captures sequential dependencies.\n- Robust empirical validation: The evaluation across four diverse datasets (clinical, multilingual, different task types) demonstrates broad applicability. The consistency of findings across three different embedding models (OpenAI, Google, Qwen) strengthens the results.\n- Clinical relevance: Successfully differentiating neurodegenerative groups from healthy controls using distance-to-next and other metrics provides potential clinical utility. The finding that patient groups show greater variability and entropy aligns with executive dysfunction literature.\n- Minimal preprocessing: The approach requires less manual intervention compared to traditional linguistic preprocessing methods, making it more scalable and reproducible.", "weaknesses": "- Missing baselines and comparisons: The paper lacks comparison to previous computational methods for analyzing semantic fluency data. No baselines using simpler embeddings (e.g., Word2Vec, GloVe) or traditional NLP metrics are provided. Prior work like Linz et al. (2017) used word embeddings for similar tasks but isn't compared against.\n- Limited theoretical grounding: While the authors claim semantic retrieval can be \"understood as navigation through a multidimensional space\" (Hills et al., 2015), this theoretical framework needs stronger support. The connection between observed metrics and established cognitive theories (e.g., clustering-switching models by Troyer et al., 1997) is underdeveloped.\n- Interpretation of results lacks depth: The clinical findings (e.g., \"greater spread, higher variability, increased entropy\" in patient groups) are presented without sufficient discussion of whether these align with expected patterns from cognitive neuroscience literature. Are these results validating known theories or revealing new phenomena?\n- Euclidean assumption: The authors acknowledge but don't address their \"assumption of Euclidean dynamics\" which \"overlooks the anisotropic nature of embedding spaces\" (citing Nickel & Kiela, 2017; Ethayarajh, 2019). This could significantly impact the validity of velocity and acceleration metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UCLLIqbclE", "reviewer": "Reviewer_6H8S", "summary": "This paper proposes a framework to quantify human semantic navigation during concept production using trajectory-based metrics in transformer embedding spaces. It represents sequential concept generation (e.g., verbal fluency or property listing tasks) as a path through semantic space and computes geometric and dynamical metrics—distance to next, entropy, velocity, acceleration, and distance to centroid—to characterize this navigation. The method is applied to four datasets (neurodegenerative, swear-word fluency, and Italian/German property listing), showing that these metrics distinguish clinical groups, semantic categories, and languages. Results are robust across embedding models (OpenAI, Google, Qwen). The authors argue this approach bridges cognitive modeling and learned representations, offering potential applications in clinical and cross-linguistic research.\n\nOverall, the approach is interesting. However, its mathematical grounding is limited, and comparisons to prior methods are absent. It is unclear whether this paper is primarily a methodological contribution, or an empirical contribution. While it seems to be a mixture of both, neither is particularly compelling. This work may be more suitable as an expanded manuscript for a cognitive science journal venue, where the results can be more effectively situated within the broader relevant literature, and targeted towards a more appropriate community.", "questions": "1. How does this work improve upon prior baselines? \n2. Why are all of the metrics needed? What are the main distinctions between them? \n3. Do you agree with weakness 3? Can you perform another analysis with a causally masked transformer?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Potentially novel conceptual framing: the paper presents a method for modeling semantic search as trajectories in embedding space, bridging computational linguistics and cognitive science. \n2. Methodological simplicity and reproducibility: the framework requires minimal manual annotation and is implemented with publicly available datasets and embeddings, making it scalable and easy to replicate.\n3. Comprehensive empirical validation: the authors test across multiple datasets (languages, clinical populations, and semantic domains), providing strong evidence of the method’s generality.\n4. Cross-model robustness analysis: the inclusion of multiple embedding models (OpenAI, Google, Qwen) and the correlation analysis (Figure 6) convincingly demonstrate stability of local trajectory measures across model architectures.\n5. Interdisciplinary contribution: the work effectively connects semantic cognition, NLP, and neuropsychology, potentially valuable for both cognitive modeling and clinical diagnostics.", "weaknesses": "1. Limited theoretical grounding for metrics: while the chosen metrics (velocity, acceleration, entropy) are intuitive, their psychological interpretation is underspecified. The link between these geometric quantities and cognitive mechanisms of search (e.g., clustering/switching, semantic control) could be better formalized.\n2. Simplistic dynamics assumption: the framework assumes Euclidean dynamics, even though embeddings are anisotropic and often non-Euclidean. The authors acknowledge this multiple times, but do not explore or justify why Euclidean treatment suffices.\n3. Use of non-causal embeddings. It is a reasonable first step to use cumulative text embeddings, rather than independent word embeddings. However, by using non-causal encoder models for embeddings, for a sequence \"A B C\", the representation of token B has access to token \"C\", whereas in the earlier part of the sequence it does not. Rather than acquiring embeddings, the authors should use a causally-masked model, such that the token representation for \"B\" is the same across both \"A B C\" and \"A B\". This would likely make the trajectories smoother and more amenable to the kinematic metrics employed. \n4. Figures are dense: Figures 2–5 (and corresponding appendices) show many small boxplots and correlation matrices. It may be helpful to use comparison lines above the scatter plots rather than the correlation matrices, if the goal is merely to show which effects are significant. This is a more standard approach and would take less space, improving readability. \n5. No comparison to traditional linguistic baselines: the framework is presented as superior to “labor-intensive linguistic pre-processing,” yet there’s no quantitative comparison to classical measures (e.g., clustering, switching, word frequency, semantic similarity). A comparison with these approaches would strengthen the claim of added value.\n6. Missing temporal information: since no timestamp data are used, “velocity” and “acceleration” are only metaphorical. The interpretation of these measures as cognitive dynamics rather than geometric derivatives is thus limited.\n7. The work only characterizes semantic trajectories, it does not model them through some sampling from the latent space of a transformer. This makes the theoretical contribution weaker.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "klmDI1pYKz", "reviewer": "Reviewer_Xfqo", "summary": "*Concept production* refers to the task of a person listing as many words as they can within a given category in a short time period. This paper proposes a method to quantify the semantic dynamics of concept production by measuring dynamic properties of Language Model embeddings of the produced words.\n\nThey authors propose 5 summary statistics of the embedding trajectories: distance-to-next, entropy, velocity, acceleration and distance-to-centroid. They employ four datasets of to evaluate:\n - a neurodegenerative dataset containing participants with Parkinson's disease, frontotemporal dementia and healthy controls,\n- a swear fluency dataset, with three categories control categories and I swear word category,\n - Italian and German datasets, where particants were asked to produce words from a variety to categories in either Italian or German\n\nQualitative results in the form of plots, and quantitative statistical test are performed to assess the variability between categories of the embedding dynamics within datasets.\n\nThe authors perform their experiment using three different embedding models and find similar results.", "questions": "- Does the findings here align with other research on human semantic cognition?\n- What other methods exist to measure the semantic dynamics of words produced in these experiments? For example. what metrics are used in the papers originally proposing the datasets? You mention that your finding corroborate theirs, but how?\n- What are the advantages of this method over other methods?\n- In the definition of entropy, the time series $\\{x_t\\}_{t=1}^n$ is a vector time series, so what is the median of a set of vectors?\n- Similarly, the velocity and acceleration are vectors. Do you report their magnitude as the metric, or something else?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is clearly written and the methodology is novel, providing a means to quantify properties of the semantic dynamics of words produced during these experiment using Language model embeddings as a proxy.\n\nThe paper considers a variety of tasks, datasets and models and the results are clearly explained.", "weaknesses": "While this work clearly shows how the metrics they propose vary across groups in their datasets,  it's not clear to me what this actually tells us about human semantic navigation.\n\nI think there is some value in the proposed metrics, but the experiments only provide a weak indication that they are useful for the classification tasks described.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AUtVa8gRiL", "reviewer": "Reviewer_GZZr", "summary": "The paper models human concept production as trajectories in embedding space, extracting many geometric/dynamical markers from participant-specific paths built with cumulative text embeddings. The framework differentiates groups and concept types and appears robust across encoders for local trajectory metrics, while centroid-based dispersion shows model-dependent geometry. The authors discuss clinical and cross-lingual applications and note limitations.", "questions": "1. Do your main findings persist under whitened cosine, Riemannian distances, or other nonlinear metrics? A small ablation would help separate method from metric.\n2. If inter-response times become available, do velocity/acceleration still distinguish groups once scaled by real time?\n3. How do your metrics correlate with clustering/switching scores on the same sessions, and do they add incremental predictive value?\n4. Given stable local dynamics across encoders, can you leverage multilingual alignment (e.g., shared subspaces) to standardize centroid-based dispersion across languages/models?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Originality. Recasts verbal fluency/property listing as geometry + dynamics in learned representation spaces, bridging cognitive foraging accounts with modern NLP embeddings. The cumulative-embedding design captures history dependence rather than treating items independently.\n\nQuality. Clear metric definitions (including a binarized entropy proxy) and mixed-effects modeling (GLMM via glmmTMB) are appropriate for repeated-measures data; cross-encoder replication is a strong robustness check.\n\nClarity. The paper’s pipeline is easy to follow; per-dataset result summaries and heatmaps for cross-model correlations aid interpretation.\n\nSignificance. Demonstrates clinically and linguistically informative signals with minimal manual annotation, and highlights encoder-agnostic local dynamics vs model-dependent global geometry, aligning with known cross-lingual structure and anisotropy issues in embeddings.", "weaknesses": "1. The analyses only adopt Euclidean differencing despite acknowledged anisotropy in contextual embeddings; consider non-Euclidean or locally whitened metrics (e.g., hyperbolic/Riemannian, subspace-projected velocities) to test robustness will be helpful.\n2. Velocity/acceleration use implicit $\\Delta t=1$ because timestamps are missing. Include more results to show where real inter-response times modulate the dynamics will be helpful.\n3. Provide confidence intervals, seed/split variability, and multiple-comparison controls for pairwise tests in the figures; this will improve clinical interpretability.\n4. Writing/format nits. A few typos (“Neurodegerative”), minor grammatical errors like “This approach hold…”; ensure all acronyms expand on first use.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)