Large Language Models as Model Organisms for Human Associative Learning
Authors: Camila Kolling
Vy A. Vo
Mariya Toneva
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Models
Associative Learning
In-Context Learning
Representational Change
Primary area: neuroscience_and_cognitive_science
TL;DR: LLMs show human-like representational change during associative learning, driven by pair similarity and modulated by prior knowledge.

Abstract: Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Testing hypotheses on how representational changes occur in biological systems is challenging, but large language models (LLMs) offer a scalable alternative. Building on LLMs' in-context learning, we adapt a cognitive neuroscience associative learning paradigm and investigate how representations evolve across six models. Our initial findings reveal a non-monotonic pattern consistent with the Non-Monotonic Plasticity Hypothesis, with moderately similar items differentiating after learning. Leveraging the controllability of LLMs, we further show that this differentiation is modulated by the overlap of associated items with the broader vocabulary--a factor we term vocabulary interference, capturing how new associations compete with prior knowledge. We find that higher vocabulary interference amplifies differentiation, suggesting that representational change is influenced by both item similarity and global competition. Our findings position LLMs not only as powerful tools for studying representational dynamics in human-like learning systems, but also as accessible and general computational models for generating new hypotheses about the principles underlying memory reorganization in the brain.

Large Language Models as Model Organisms for Human Associative Learning
Camila Kolling
Vy Ai Vo
Mariya Toneva
Max Planck Institute for Software Systems, Saarbrücken, Germany {ckolling, vyvo, mtoneva]@mpi-sse
Abstract
Associative learning-forming links between co-occurring items-is fundamental to human cogmition, reshaping internal representations in complex ways. Testing hypotheses on how representational changes occur in biological systems is chal- lenging, but large language models (LLMs) offer a scalable alternative. Building LLMs' in-context learning, adapt cognitive neuroscience associative learn- ing paradigm and investigate how representations evolve across six models. Our initial findings reveal non-monotonic pattern consistent with the Non-Monotonic Plasticity Hypothesis, with moderately similar items differentiating after learning. Leveraging the controllability of LLMs. we further show that this differentiation modulated by the overlap of associated items with the broader vocabulary factor we term vocabulary interference, capturing how new associations compete with prior knowledge. We find that higher vocabulary interference amplifies differentia- tion, suggesting that representational change is influenced by both item similarity and global competition. Our findings position LLMs not only powerful tools for studying representational dynamics human- learning systems, but also accessible and general computational models for generating new hypotheses about the principles underlying memory reorganization in the brain.
Introduction
Associative learning-ther ability to form links between co-occurring items-is fundamental mecha- nism that shapes how experiences are encoded, stored, and retrieved. Its ubiquity across species and associations are learned, the brain's internal representations of the associated items are altered a reflection of the central and ongoing question in cognitive neuroscience is how this self-supervised learning process reshapes representational structure, and why 46 tive learning alters representations in biological systems (see Figure A The classical Hebbian learning rule, where repeatedly associating items strengthens connections between shared features, predicts more integrated representations across learned items [d However, alternative theories suggest the opposite. For example, the hippocampus often exhibits pattern separation, where rapidly learned memories reduce representational overlap to minimize interference and facilitate retrieval 27 [ These opposing dynamics integration versus differentiation are both observed in human studies 77 3& 351. To reconcile this, the Non-Monotoni, Plasticity Hypothesis (NMPH) posits that representational change follows a U-shaped curve: highly similar or dissimilar items tend to integrate or remain stable, while moderately similar pairs differentiate
cognitive domains has made it core component theories of intelligence 47 [[C
neural plasticity that strengthens some connections while weakening others 34 36 I2
There are three main hypotheses for how associa-
[4]
Fully testing these hypotheses in biological systems inherently difficult
major challenge lies in precisely controlling the similarity between items before learning, prerequisite for detecting
Code available gggthub..cm/bridgeeeea.... hathab-coommiiddde-a--nnnnnnniie www.o//lluuusooeeeeoooa associativv-leemmngg
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: (A) Hypotheses about representational changes in humans. Competing theories propose different patterns of representational change as a function of pair similarity before learning: Hebbian learning predicts integration, pattern separation predicts differentiation, and the Non-Monotonic Plasticity Hypothesis (NMPH) predicts a U-shaped curve, with high differentiation at moderate similarity levels prior to learning.
(B) Schematic of adapted associative learning task for LLMs. Given repeated in-context presentations of token pair (z, y). the LLM learns to predict the associated token y. We measure representational change by computing the difference cosine similarity between hidden representations of the pair before and after learning. This setup, inspired by a neuroscience paradigm [46]. enables us to examine whether similar dynamics representational restructuring emerge during in-context learning. (C) Illustration of low and high vocabulary interference in the model's representational space. In the low vocabulary interference case (top, yellow), the target token is dissimilar to most other tokens, resulting in less competition from alternative completions when paired with x. the high interference case (bottom, orange), the pairing zy is highly similar to many other possible token pairings, increasing competition and representational pressure to differentiate the learned association from potential distractors during learning.
non-monotonic representational change. Moreover, the similarity level at which differentiation emerges can vary across tasks and stimuli. making it unclear in advance which mid-similarity range will reveal the effect. Capturing this requires dense sampling across the similarity spectrum, further increasing experimental complexity. Finally, human studies are constrained by cost, participant fatigue, and measurement noise, which limit the number of trials that can feasibly be conducted. To address these challenges, we propose using large language models (LLMs) as model organisms for human associative learning. LLMs exhibit complex cognitive behaviors R 48 49 including in-context learning (ICL) 53 19
rapidly forming associations without weight updates-making them promising for studying memory dynamics. Unlike hand-crafted neural models designed to replicate specific representational dynamics
33 LLMs offer a scalable, natural testbed for
uncovering emergent cognitive phenomena.
In this work, investigate whether LLMs exhibit representational dynamics akin to human as- sociative learning, and whether they can help disambiguate between competing hypotheses for representational change. We adapt cognitive neuroscience associative learning paradigm the ICL setting (Figure IB). repeatedly presenting token pairs in-context to induce associations. By system- atically controlling the similarity of token pairs before learning, we evaluate how representations evolve through learning across six open source, well-performing LLMss. Our initial findings support the NMPH: moderately similar pairs significantly differentiate after learning, mirroring human-like patterns of representational change.

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ICL has been We then leverage the controllability of LLMs to examine factor that may further contribute to representational change, and is difficult to isolate in biological systems: the similarity between each paired item and the model's prior knowledge. Because L.MSs are pre-trained to encode co-occurrence statistics across the entire vocabulary-simine introduced during ICL must compete with pre-existing patterns. We refer to this competitive influence as vocabulary interference (Figure [[C): the extent to which prior knowledge shapes the learning of new associations. In such cases, learning the correct pairing may require greater changes in the model's representations to distinguish it from competing associations. This phenomenon has long been studied in neuroscience and psychology [32 42), but empirical measurement in the brain is limited by the inability to access all competing representations. By contrast, LLMs provide a tractable framework for quantifying this effect, the entire distribution of token relationships is explicitly known. We find that, while pair similarity remains a key determinant of representational change, vocabulary interference modulates this effect-greater interference leads to stronger differentiation. These results position LLMs as valuable tools for probing associative learning principles, offering new insights into how both local and global associative structures influence representational change.
to how humans learn from experience new associations
2 Related work
2.1 Representational change in human memory and neural models
Integration vs. differentiation in the brain. Associative memory-related representational changes are primarily studied the hippocampus, a region of the brain thought to be most influential in memory-drived behavior [4] 25 Both integration and differentiation of memory representations have been shown to support distinct behavioral functions: differentiation reduces interference and enhances specific recall, while integration promotes generalization and inference across related experiences [6 4 These functional roles are thought to map onto distinct hippocampal subre- gions, e.g., integration with CA1 and differentiation with the dentate gyrus (DG), which shows sparse activity linked to orthogonalized representations tion-differentiation spectrum remains an open question.
46 5T. How LLMs align with this integra-
Non-monotonic plasticity in the brain. The NMPH proposes that representational change depends non-linearly on pair similarity before learning: moderate similarity leads to differentiation, whereas low or high similarity leads to stability or integration. Recently, Wammes et al. 46 provided empirical results supporting this effect by parametrically manipulating the visual similarity of
object pairs using CNN-derived 17 40 representations. Participants arranged images by perceived similarity, and the resulting pairwise distances correlated with model-based similarity estimates. During fMRL, repeated exposure to these pairs revealed significant differentiation for similiiiy pairs in the DG, but not for other parts of the hippocampus.
Computational accounts. To account for this variety of findings, 33 proposed an unsupervised recurrett network model in which partial activation of competing memories during retrieval induces representational differentiation, a dynamic linked to retrieval-induced forgetting and inhibitory oscillations [24 While such models are an important step towards a computational account, they rely on hand-crafted inputs and necessitate simplified settings, limiting scalability and behavioral richness. Our work complements prior computational efforts by investigating whether non-monotonic differentiation, previously observed in biological memory systems, emerges naturally in large-scale, general purposs LLMs trained on real-world fath-without an explicit separated memory system.
2.2 Associative learning and in-context dynamics in LLMs
LLMs are increasingly studied as systems capable of associative learning, rapidly forming token-level associations directly within the input context [3]. Recent work shows that LLMs can form stable in-context associations that shape future predictions
19 53 exhibiting behaviors consistent with 45 1 These findings suggest that transformer-baat
retrieval, interference, and generalization [
architectures support implicit memory mechanisms across attention and MLP layers, despite the absence of explicit memory modules. Several studies have also analyzed ICL as form of fast memory encoding or Bayesian inference [5). and have shown that attention layers can support long-range retrieval, stability, and structured generalization 50
26
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interpreted through the lens of both episodic memory, as models retrieve and reuse information based on context, and working memory, given that representations are updated dynamically across tokens
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without any parameter changes 5
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We build on this line of research by
shifting focus from behavioral outcomes to the internal representational dynamics underlying learning through repeated associative exposure.
3 Methods
3.1 Associative learning paradigm
Our associative learning paradigm is inspired by the experimental design of [46]. who investigated how repeated exposure to stimulus pairs with different visual similarity leads to non-monotonic changes in human hippocampal representations. We adapt this paradigm to LLMs using ICL B [ replacing visual stimuli with token pairs and modeling learning through repeated token co-occurrence. This setup allows us to examine whether similar non-monotonic representational shifts occur in LLMs, and to what extent LLM behavior parallels hippocampal learning dynamics. We focus on ICL rather than fine-tuning, LLMs are known to exhibit emergent associative abilities 48
9 making
ICL a natural fit for studying association tasks. It also provides a controlled and biologically plausible analogy to how humans acquire associations [53] 19 while enabling consistent comparisons across models of different sizes and architectures without introducing task-specific fine-tuning.
Formally, we present the token pair (Ir, y) total of 1 times, followed by one final presentation of r alone as a cue for predicting its paired token y. Given the input sequence
[21 V1, T2, y2....... -r 1r-1, 2,
(1)
the model's goal is to generate a prediction of the associated paired token, y. By default, we restrict the number of repetitions such that the total sequence length remains within each model's m maximum context length (L or the limits imposed by available GPU memory (Lmem 2 40k tokens), i.c., 1T min(L L mmem) In our setup. the sequence length L2 ~ (2 x -
We predict that the LLM's representations of these tokens will change through the course of ICL, phenomenon observed in prior studies analyzing ICL tasks 29 152 9. This prediction also aligns with findings from neuroscience, where repeated co-occurrence of stimuli is known to drive representational change in the hippocampus
(Pair) Representational change. For a given model m E M,, where M is the set of LLM models under study, we extract the hidden representations of token x. the last layer of the model, h We chose to examine the last hidden layer to more effectively control for representations that This choice also aligns with a sensory-inoormation processing hierarchy in which the hippocampus sits at the top of the memory stream [28 20). (Pair) Representational change across ICL is then defined as the difference in cosine similarity between representations at repetition r' and the first occurrence of the pair:
directly affect model behavior on the ICL task |
S cos cos(h",, h - cos( 2,: hm))
where the hidden representation is conditioned the whole sequence up until that point, e.g.. hTt = h I1, ./....,, -1 1)- Note that our ICL paradigm means that the first occurrence of y is always conditioned on x, hm = hh" xx paradigms, where pairs are presented sequentially [А]. Throughout the work, we refer to the hidden states from the first occurrence (h,,: h, as the representations obtained before learning occurs.
This design mirrors human associative learning
Token similarity groups. To examine whether LLMs exhibit representational dynamics consistent with those observed in humans, we sample token pairs across the similarity continuum. More specifically, we sampled evenly along the cosine similarity axis, defining 17 groups y that fall within the interval [0.1, 0.95). Each group is defined by a window [0min. 6maz) that spans 0.05 cosine similarity. The token pairs within each group are chosen such that their representational similarity before learning cos h", hy) lies within the group interval [@ (_
Details on our procedure to find these token pairs are given in the section below. We find 12 token pairs in each group to form set for each model, pm The token pair sets for each model are constructed independently due to differences in their vocabulary size and tokenization.
Preeliminary results for the other layers are shown in Appendix D

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3.2 Optimized search for pairs of tokens
To systematically find tokens whose pair similarity before learning falls within a given interval, we employ an efficient way for searching the large vocabulary space (between 10K2-72k² tokens, depending on the model). Inspired by recent work on prompt and input optimization (55 39 we
follow a two-step approximation strategy to identify suitable pairs. The Greedy Coordinate Gradient (GCG) algorithm (55), originally developed for optimizing sequences in adversarial settings (e.g.. minimizing next- next-token likelihood), provides framework for iteratively refining a sequence by making targeted, maddien--infomee edits to individual tokens. We repurpose the GCG method to minimize a loss defined over the cosine similarity of internal representations.
We start with duplicate token pair (1) with the goal of finding pair (I, v) that falls within the larget cosine similarity range min max)) We fix the first æ token, and iteratively replace the second token of this sequence by using gradient signals (without updating the model) identify vocabulary items that would bring the pair's cosine similarity closer to the target range. We select replacements from the top-k candidates that reduce the loss the most, repeating the process until the similarity falls within the desired range or a maximum number of steps is hit. This approach efficiently guides token selection in a controlled, representation -aware aware way. enabling the construction of token pairs with precise similarity properties. More information on this algorithm can be found in Appendix A
3.3 Estimating vocabulary interference
To estimate how given pair (zr, y) relates to the broader LLM vocabulary space, we fix and sample each alternative token from representative subset of the vocabulary, Dm C Vm, We then compute the similarity between the representation of the correctly associated token y and each alternative token / E 1 conditioned on x's presentation in context. This provides an estimate of how much y. when associated with z, competes with other pair completions in the vocabulary space, capturing the degree of the pair's vocabulary interference in the model's 's representational space. Due to the computational cost of exhaustively computing all possible pairwise combinations, we randomly sample 1, 000 tokens from y to form the representative subset Lmm resulting in 1 million pairwise combinations.
Concretely, for each pair (z, t) we extract its pair representation before learning, yielding the set H1r = (hT Vt E pmm). We then compare the representation of the pair (a, v) before learning to each alternative pair,
syn = {cos(h)) (h b1 hTT Vhg E H(T).
We can interpret Sy as a distribution showing how much interference the pair (z, y) receives from all competing associations (z, t) with t 1i. We define the vocabulary interference score for each (x, y) as the median of the set Sty".
All of the above has been described for a single token pair (2r, y). The analysis shown in Figure 3a depicts results for token pairs drawn from the original stimulus set described above, pm, optimized solely for token pair similarity before learning. We then extend the original set of (xr, 3) pairs. from pmm, to uniformly sample from the joint distribution of refore-learning pair similarity and vocabulary interference (Figure 3b.. That is, we use the million token pairs from our sub-sampled vocabulary 7m to yield a larger set Q" We aimed find least 10 pairs per similarity group and vocabulary interference group (details in Appendix A
3.4 Experimental setup
We analyze six recent open-source base LLMs: Llama2-7b, Llama3.1-80 Llama3.2-16 1b, Llama3.2-3 Gemma2-9b, and Mistral-7b 44! availability, and relatively small size within their respective families, providing a balance between computational efficiency and architectural representativeness. All experiments were performed on internal compute clusters, using two NVIDIA H100 PCIe GPUs with x 80GB GPU memory per device. The computation of the experiments took total of s 15 days.
16, 43 18). These models were selected for their recency, open
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4 Results
4.1 LLMs exhibit structured, multi-phase learning dynamics
As expected, LLMs are able to complete the in-context associative learning task with high accuracy (between 90 - 100%), though the number of repetitions required to reach peak accuracy varies across models. Figure 2a shows how overall prediction accuracy evolves as a function of the number of repetitions. We identified three distinct phases of learnig--Encoding. Consolication, and Forgetting- and we observed that their duration varied across models. To enable direct comparison across models, we normalized the number of repetitions in each phase by aligning phase boundaries: repetitions within each phase were linearly rescaled to fixed intervals (0- for Encoding, 1 2 for Consolidation, and 2--3 for Forgetting). This normalization preserves each model's internal dynamies while making phase-aligned trends directly comparable across models. The accuracy curves per model are provided Appendix B.2
Encoding phase (blue): This phase corresponds to the initial stage of learning, defined by a steep increase in accuracy as the model is repeatedly exposed to the token pair. We define this phase as the period during which accuracy continues to rise by more than 3% between consecutive repetitions, until the model reaches at least 97% of its peak performance.
Consolidation phase (red): This phase reflects stable performance regime, where the model has largely acquired the association and maintains high accuracy over repetitions. Accuracy remains within +.3% of the peak, indicating that learning has plateaued and performance is stabilized.
Forgetting phase (green): Surprisingly, in some models, accuracy begins to decline even though the number of repetitions remains within the model's maximum context window (Lg < _ We define the forgetting phase as the point where accuracy drops by more than 3% relative to the average of the two prior repetitions, marking the emergence of performance degradation.
While all models exhibited the Encoding and Consolidation phases, only two models (Llama2-7b and Mistral-7b) showed forgetting phase. For Llama2-7b, forgetting begins relatively early (r 40), whereas for Mistral-7b emerges much later (r = 3, 000). We speculate that the delayed forgetting in Mistral-7b may be related its use of sliding window attention (SWA). For Llama2-7b, we present preliminary analyses in Appendix [B.4] but the underlying cause of early forgetting is not yet well understood. More broadly, remains unclear how to predict if, and when, forgetting will occur. We leave this question to future work. Overall, these results demonstrate that LLMs can effectively acquire associations and maintain them for a sustained period before eventual degradation.
4.2 Moderately similar pairs significantly differentiate during consolidation
We next investigate how the representations of successfully associated token pairs evolve during learning, specifically focusing on identifying when integration or differentiation occurs. Figure 2b shows how the representational similarity between token pairs changes as a function of their similarity before learning across different phases of learning. We aggregate representational change values AS by collapsing across models and token pairs within each similarity group y and learning phase, and report the mean and standard error of the resulting values. To test for differentiation, we performed one-sided paired (-tests for each similarity group and learning phase, testing whether pair similarity after learning was significantly lower than pairsimilarity before learning. To account for multiple comparisons across the 17 similarity groups and 3 learning phases, applied the Bemjamini-Yekutiae (BY) procedure to control the false discovery rate under dependency among tests. Groups that remain significant after BY correction (p 0.05) are marked with asterisks.
During the Encoding phase, no significant differentiation is observed across groups that were highly similar before learning. Instead, models show consistent increase in pairwise similarity for low- to mid-similarity pairs (between 0. 0.160.6), reflecting early-stage representational integration: repeated co-occurrence leads these tokens to move closer together in representation space, supporting initial association formation. In contrast, mid- to high-smillarity pairs (0.65-0.95) exhibit little to no representational change at this stage.

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Figure 2: Accuracy and representational changes during learning- (a) Models generally show three
phases of learning: encoding, where accuracy steeply increases; consolidation, where accuracy stabilizes; and forgetting, where accuracy declines. To compare across models with different phase lengths, the x-axis is normalized: repetitions within each phase are linearly scaled to fixed intervals (0 - 1 for encoding, 1 - for consolidation, 2 3 for forgetting). allowing phase-aligned trends to be visualized despite variability in learning dynamics. (b) The U-shaped differentiation pattern, characteristic of the Non-Monotonic Plasticity Hypothesis, is observed only during consolidation (red). Asterisks (+) indicate groups that remain significant after Benjamini -ekuttill correction for multiple comparisons across similarity groups and phases (p < 0.05).
During the Consolidation phase, striking effect emerges for pairs that were moderately similar before learning (0.55 0.75y: these groups exhibit significant decrease in pairwise similarity during this phase of learning. This produces clear U-shaped pattern in representational change- conssistnt with predictions from the NMPH 34 26 Notably, this effect coincides with the stabilization of model performance, suggesting that LLMs undergo structured reorganization of internal representations to maintain high task accuracy. Otherwise, we find that lower similarity pairs still exhibit integration, although to a lesser extent than during Encoding. Higher similarity groups remain largely unchanged, suggesting that their representational similarity relatively stable across the first two learning phases.
During the Forgetting phase, the previously observed non-monotonic pattern disappears, and mid- similarity pairs no longer exhibit significant differentiation. Surprisingly, this is the only phase in which groups that were highly similar before learning show a notable change their representational structure, displaying clear signs of differentiation relative to their before-learning similarity. Low- similarity pairs, by contrast, undergo even stronger integration than during the Encoding phase. This results in a mild, approximately linear trend in representational change as a function of similarity before
the general trend of Encoding, but with greater integration at low similarity and stronger differentiation at high similarity. This trend indicates a loss of structured representational updates, aligning with the observed decline in accuracy. Further results of the evolution of these changes are presented in Appendix B.3
Taken together, our findings show that LLMs exhibit structured representational dynamics consistent with the NMPH. Interestingly, this non monoonno pattern is present only during the Consolidation phase, when behavioral performance is stably high, but absent during the Encoding and Forgetting phases marked by behavioral instability and less structured representational change. Importantly, unlike prior computational models explicitly designed to produce U-shaped dynamics [ the LLMs that exhibit this non-monotonic effect are general-purpose, pretrained models that were not architecturally constrained or fine-tuned to exhibit such behavior.
4.3 Pair similarity drives representation change, modulated by vocabulary interference
During the Consolidation phase-wher when models exhibited stable maintenance of learned associations we observed a non-monotonic pattern of representational change as a function of pairwise similarity before learning: low-similarity pairs (up to R 0.5) integrated, mid-similarity pairs (0.55-0.73) 75) differentiated, and high-similarity pairs (> 0.75) showed little to no representational change, aligning best with the NMPH. Building this analysis, next examine an additional factor that may
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contribute to this pattern and is difficult to isolate in biological systems: the similarity between each paired item and the model's prior knowledge. Because LLMs are pre-trained to encode co-occurrence statistics across the entire vocabulary, as humans are thought to do through learning, new associations introduced during ICL must compete with pre-existing patterns. We expect that this competition- seerference-iffleenne
what we refer to as vocabulary (i.e., higher similarity to other items in the vocabulary space) should impose stronger pressure for differentiation to support successful learning.
representational change: greater interference
We thus extend the representational similarity change analysis from Section 4.2 by systematically examining these changes across different levels of vocabulary interference. Specifically, using our original token pairs (x. y) in pm", we estimate their vocabulary interference with respect to alternative tokens in the set bm (see Section 3.3). To facilitate comparison across conditions, we categorize pairs into three equally sized groups based on their vocabulary interference scores: Low, Mid, and High (see Appendix A.4 for details).
Figure 3a shows how vocabulary interference modulates representational change, with the pattern of this effect varying depending on the pairs' similarity before learning. For low-similarity pairs (up to 0.5 1 0.6). we observe consistent integration at all levels of vocabulary interference. For mid- similarity pairs (0.6 0.7), differentiation emerges as the primary driver across interference levels. High-similarity pairs (above 0.7), however, display high variability and heterogeneous effects: lower interference tends to promote integration, whereas higher interference tends to yield differentiation. This heterogeneity may help explain the apparent U-shaped pattern in our earlier analysis: while low- and mid-similarity pairs exhibit seemingly consistent behavior across interference levels, the variability among high-similarity pairs can mask these opposing trends when averaged, leading to an apparent lack of representational change.
Sampling the full spectrum of vocabulary interference. These findings suggest an interaction between pairwise similarity and vocabulary interference, particularly in the highsimmllarity regime. To more directly test this interaction, we next control for both factors simultaneously by examining token pairs that span the full joint distribution of pairwise similarity and vocabulary interference (see Section 3.3 and Appendix A for details). To do this, we form extended set of (z, y) tokens pairs, Q"h by sampling additional token pairs uniformly across vocabulary interference values. Our approach ensured minimum of 10 representative pairs per model, similarity group and vocabulary interference level.
Figure 3 shows the results under this controlled sampling regime, where both pair similarity and vocabulary interference are explicitly balanced. As before, we observe a consistent pattern of integration for low-similarity pairs (up to 0.5 - 0.6). Yet now we observe robust effect of vocabulary interference: the curves for higher interference levels lie below those for lower interference, indicating reduced, but still present, integration. For mid-similarity pairs, we observe differentiation across all levels of vocabulary interference, and the effect stronger under higher interference. For high- similarity pairs, we observe distinct trend: under moderate or high interference, these pairs clearly differentiate, while under low interference, their representations remain relatively stable.
Importantly, across all similarity levels, we observe that higher vocabulary interference is consistently associated with reduced pairwise similarity after learning. One possible explanation for this pattern is that increased interference introduces greater competitive pressure: to reliably associate with each other, paired tokens must distinguish themselves from many similar distractors in the vocabulary. This competition drives the model to reshape representations not only to encode the intended association, but also to preserve distinctiveness within the broader context of the model's prior knowledge.
Representational change through the lens of vocabulary interference. The idea that increased interference introduces competitive pressure provides a useful lens for interpreting the distinct behaviors observed across different pair similarity regimes. For instance, one possible interpretation of the robust integration observed among cww-iimilarity pairs, regardless of vocabulary interference level, is that their representational distance before learning provides greater flexibility for alignment. Because these pairs begin far apart in the representation space, the model can bring them closer (i.e., integrate) without risking excessive overlap that would compromise their individual distinguishabiiity. In this regime, vocabulary interference may impose relatively weak constraints, since the updated representations remain unlikely to be confounded with each other-thur thus, integration can proceed even
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Figure 3: Effect of vocabulary interference on representational change across different pair similarity groups. (a) Results for our original token pairs, sampled uniformly with respect to pair similarity before learning (x-axis). We observe consistent integration trend for low-similarity pairs and shift toward differentiation for mid-similarity pairs (0.6-0.7). High-similaryy pairs show more heteroge- neous behavior, where low interference tends to promote integration, while higher interference tends to yield differentiation. (b) Results for extended token pairs, sampled uniformly over pair similarity before learning (X-axis) and vocabulary interference level (colored lines). Higher vocabulary interfer- ence consistently leads more differentiation, especially for mid- and high-similarity groups. These results suggest that while pairwise similarity is a key driver of differentiation, vocabulary interference amplifies this effect.
under high interference. We speculate that this relative freedom from competition allows the model to prioritize pairwise association without necessitating broader adjustments across the vocabulary space.
At the other extreme, high-similarity pairs begin very close in representational space. Under high vocabulary interference, the model must reshape these representations to prevent confusion with nearby distractons-yett increasing their similarity further could risk entanglement. As a result, differentiation becomes the most likely direction of change, consistent with the strong divergence we observe under high vocabulary interference. In contrast, when vocabulary interference is low, these pairs are already well isolated from the rest of the vocabulary. reducing the pressure for representational differentiation.
Mid-similarity pairs lie in a "sensitive zone" where both lary interferene-iinerrn most dynamically. They are similar enough to each other that further integration might risk overlap the point of risking their distinction, yet not similar enough to be clearly associated. Consequently, differentiation appears to be the primary orientation of change for mid-similarity pairs, intensifying with greater vocabulary interference to preserve separability. This suggests that mid mid-similarity pairs are especially vulnerable to representational reorganization, regardless of the specific interference level.
similarity and vocabu-
Therefore, the observed U-shaped curve in the previous analysis (Section may be partially explained by nuanced interaction between pairwise similarity and vocabulary interference. In the high-similarity range, pairs fragment into opposing hehaviors across levels of vocabulary interference, so that averaging over these heterogeneous effects mask systematic representational change and create an illusion of stability.
5 Discussion
In this paper, we investigate whether LLMs exhibit representational changes during associative learning that mirror those observed in humans, and whether they help disambiguate between com- peting hypotheses about how such changes unfold. Controlling for within pair similarity, we found non-monotonic pattern of representational change, consistent with the NMPH. This pattern is observed when models stabilize their learning, in what we name the Consolidation phase. The fact that LLMs naturally give rise to these dynamics without any task-specific optimization, and under

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conditions aligned with how humans learn associations suggests that they may serve as emergent, flexible model organisms for studying memory reorganization in the brain.
We then leverage the controllability of L.Mss to investigate how the vocabulary interference-deffned the interaction between token pair similarity and their similarity to the broader vocabulary-affects representational changes. By introducing this second dimension of analysis, we show that repre- sentational dynamics cannot be fully understood in terms of pairwise similarity alone. Instead, representational change reflects a joint influence of pairwise similarity and global contextual com- petition within the model''s s prior knowledge. This interaction is especially evident the extremes: low-similarity pairs integrate consistently across all interference levels, suggesting greater flexibility due to low risk of confusion. High-simi riggh-iiilartty pairs, by contrast, are already near each other in repre- sentational space and face stronger constraints: when vocabulary interference is high, differentiation the only viable way to maintain separability,, whereas under low interference, they remain relatively insulated from external competition, reducing the pressure for further differentiation. Mid-similarity pairs appear to lie a critical boundary-sinee enough to risk confusion, yet not similar enough to differenti-
form strong iooociation-maingg them particularly susceptible to interference-induee ation. This sensitivity highlights how small shifts in competitive context can alter the direction of representational change.
Our results show that, while pairwise similarity is a key determinant of representational change. vocabulary interference modulates this effect. This interaction between pair association strength and global contextual interference reveals richer representational dynamic than previously assumed, and may help reconcile diverging findings in the neuroscience literature, where such vocabulary -eeveel interference remains difficult to assess due to limited access to global representational structure. Critically, this kind of fin-grained, systematic manipulation is difficult to achieve in human studies, where both pairwise similarity and global interference are hard to quantify and control. LLMs thus serve powerful computational model organisms for testing hypotheses about memory dynamics, offering a level of scale and experimental control that is rarely achievable in biological systems.
Limitations and future work. Although LLMs differ mechanistically from human brains, they provide a valuable model system for generating and testing hypotheses that are otherwise challenging to examine in biological systems. Nonetheless, they are not direct stand-ins for humans, and empirical validation in human studies remains essential.
Our operationalizzatinn of vocabulary interference also has limitations. By design, it estimates representational competition from the broader vocabulary space, but this approximation may not fully capture the dynamics of interference in human memory, where similarity is shaped by experience, attention, and context. Furthermore, our measure relies on sampled subsets of tokens for tractability, which may underrepresent the true structure of competition across the full vocabulary.
Methodologically, our analysis focused on the final hidden layer of each model, with only preliminary exploration of carlier layers. Future work could systematically track representational change across layers. providing an extended analysis of how interference and differentiation emerge throughout the model hierarchy. Additionally, to ensure coverage across a wide range of similarity values, we used token pairs defined by geometric properties rather than naturalistic semantics. A preliminary analysis on WordNet stimuli is provided in Appendix C with further investigation left for future work.
Finally, this work focuses on hypotheses tested in mature adult brains, leaving open the question of how these processes emerge during development. Promising future directions include exploring curriculum-learnin setups that more closely mirror human developmental trajectories, analyzing at- tention patterns to identify circuit-leve mechanisms involved in resolving interference, and examining how representational dynamics evolve during fine-tuning and longer-term learning.
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Acknowledgments
This work was partially funded by the German Research Foundation (DFG) DFG Research Unit FOR 5368 and by the Max Planck Institute for Software Systems graduate center. We thank Omer Moussa and Mathis Pink for providing valuable feedback on earlier versions of this work.
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15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsoureing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [Yes]
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Guidelines:
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Please refer to L.LM policy hstt::///eerrpsssc.c////rreereee for what should or should not be described.
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Reviews:
[{"review_id": "VB87XYuvhY", "reviewer": "Reviewer_ctgT", "summary": "The paper studies how Large Language Models (LLMs) learn associations between word pairs through repeated exposure in their input context. The authors test if LLMs show the same learning patterns as human brains, specifically the Non-Monotonic Plasticity Hypothesis (NMPH). They find that when token pairs are shown many times, moderately similar pairs become less similar while very different or very similar pairs stay the same. They test this on 6 different LLMs and introduce a new factor called \"vocabulary interference\" that affects learning.", "questions": "Why does the U-shaped pattern only appear in the final layer when you test representations from earlier layers? This seems to contradict your claim that LLMs demonstrate general associative learning principles similar to the human brain. If this is truly a fundamental learning mechanism, please provide a mechanistic explanation for this layer-specific effect and discuss whether this limits your biological parallels.\nCan you validate your findings using semantically meaningful word pairs from established human association databases? Your current approach uses tokens selected purely for their geometric properties in embedding space, which may not reflect natural language associations. \nWhat do the attention patterns look like during the learning process? Since LLMs implement associative learning through attention mechanisms rather than weight changes, analyzing how attention weights shift across repetitions could show the actual mechanism behind your observed representational changes. Do high-interference tokens show more distributed attention patterns?\nCould you please standardize the y-axis scales across all panels in Figure 7? The current presentation with different scales makes it very difficult to assess whether the effects in earlier layers are genuinely absent or just smaller in magnitude.\nHow do you distinguish your \"forgetting phase\" from catastrophic interference effects in neural networks? The pattern you observe, where only two models show forgetting at very different repetition counts, seems more consistent with capacity limitations or positional encoding artifacts than a genuine memory consolidation process. \nAdditionally, could you provide the actual token examples used in your experiments? The paper mentions using gradient-optimized tokens but never shows what these look like, making it impossible to assess their linguistic validity.", "limitations": "The authors acknowledge some limitations like focusing only on the final layer and using artificial token pairs, but they fail to address the most critical limitation revealed by their own data. The fact that the non-monotonic plasticity pattern completely disappears when examining earlier layers fundamentally undermines their comparison to biological memory systems. \nThe authors also fail to discuss whether their experimental setup reflects real learning situations. In their experiments, they repeat the same two tokens together hundreds or thousands of times in a row, which is nothing like how people actually learn word associations in everyday life. When humans learn that two concepts are related, it happens through varied contexts, conversations, and experiences over time, not through endless mechanical repetition. This extremely artificial learning environment might create patterns that tell us nothing about how human memory actually works in natural settings.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe authors present a creative and novel approach by using large language models as experimental subjects to examine theories about human memory. This is an innovative bridge between cognitive neuroscience and AI research that opens new ways to study learning at scale. The authors demonstrate good scientific rigor by testing their hypothesis across six different models, including various versions of Llama, Mistral, and Gemma, which strengthens the generalizability of their findings. One particularly valuable contribution is the introduction of \"vocabulary interference\" - a measure of how many similar tokens compete with the target word. This concept is difficult to measure in human studies but easy to calculate in LLMs, providing new insights into how global similarity affects learning. The technical execution is also solid, with the authors developing a gradient-based search method to find token pairs with precisely controlled similarity levels, ensuring systematic coverage across the similarity range rather than relying on random sampling.\nWeaknesses:\nThe paper has a critical concern in Appendix B that undermines its entire premise. When the authors test their hypothesis using representations from earlier layers of the models, the U-shaped learning pattern completely disappears. This means their main finding only exists in the final layer of the models, not throughout the learning process, yet they never properly explain why this happens or what it means for their theory. The statistical analysis also has serious issues - they perform different tests across similarity groups and learning phases but never correct for multiple comparisons, which means some of their \"significant\" results could just be random chance. Moreover, the authors only use 12 token pairs in each similarity group, which is a very small sample size for making broad claims about learning patterns.\nAnother major concern is that they select token pairs based purely on mathematical similarity in the model's representation space, not on meaningful semantic relationships. While the paper doesn't give specific examples of the tokens used, they admit to using an artificial selection process that may not reflect how humans actually learn associations between words. Perhaps most fundamentally, the paper tries to draw parallels between how LLMs process information through attention mechanisms and how the human brain forms memories through synaptic changes. These are completely different processes : one is a mathematical operation that happens instantly without changing the model, while the other involves physical changes in brain connections over time. The authors don't adequately address why this comparison is valid, which calls into question the entire premise of using LLMs as \"model organisms\" for human memory.", "was_revised": true, "final_justification": "I increased the quality score and the rating score after reviewing the author's responses.", "rebuttal": "We thank the reviewer for the valuable feedback.\n\n**Summary of main points:**\n- Q1: motivated the parallels between LLM and human associative learning by drawing on a line of prior works showing a functional equivalence between ICL and SGD.\n- Q2: applied Benjamini-Yekutieli corrections across similarity groups and learning phases. Differentiation in mid-similarity range remains significant.\n- Q4: showed that WordNet-restricted tokens exhibit monotonic representational change, consistent with high global interference. Strengths the point that NMPH depends on global similarity structure.\n- Q6: fixed Fig 7a analysis; now shows monotonic differentiation across intermediate and late layers. Added new analysis using last-layer-optimized pairs (in progress).\n\n**Q1 [Parallels between human and LLM associative learning]:** Thank you for raising this fundamental point.  While it may appear unintuitive on the surface, we were motivated to investigate the possible parallels due to some recent successes in interpretability work on ICL. Several papers demonstrated that ICL performs updates similar to gradient descent [38, Reb2, Reb3, Reb4], suggesting that it may be closer to ‘synaptic weight updates’ than previously expected. Another paper drew a deep parallel between the mechanisms of induction heads in ICL to a well-validated model of human episodic memory (i.e. fast learning over a stream of episodes) [19].  We also do not think the field widely agrees that attention mechanisms are “completely different processes” than human learning for long-term information storage. Some work has drawn an analogy between attention mechanisms and the older concept of ‘fast weights’ in machine learning [Reb1][Reb2]. That is, one can view ICL as using attention layers to establish temporary weights that allow the LLM to flexibly learn patterns on a fast, short-term basis. While ICL does not directly modify the synaptic weights of the LLM, we do not think this invalidates the comparison with human learning. Rather, we believe the conceptual analogies of ‘synapse’ and ‘neuron’ break down when comparing *very short-term learning* between humans and LLMs.\n\n**Q2 [Statistical tests]:** Thank you for raising this point. In our revised analysis, we apply Benjamini-Yekutieli correction to account for multiple comparisons across the 17 similarity groups and 3 learning phases. The findings remain significant, showing differentiation in the mid-similarity range (0.55-0.75). This is consistent with the original nonmonotonic pattern.\n\n**Q3 [Small sample size]:** We extended the number of pairmates for 2 models (due to time constraints), sampling 100 pairs per similarity group, and found similar non-monotonic trends. We will add this to the revised paper.\n\n**Q4 [Semantically meaningful tokens]:** Our study intentionally used token pairs selected for their pre-learning similarity, regardless of semantic meaning. This mirrors the use of synthetic stimuli in [39], which highlights the need to sample across the full similarity spectrum to test the NMPH effectively. Real-word tokens are unevenly distributed across this space, making precise control difficult. Our focus is on the structural dynamics of representational change during learning, not semantic meaning.\nThat said, we agree that using meaningful tokens is important to assess whether NMPH applies in real-world contexts.  To this end, we sampled new token pairs restricted to single-token words from WordNet, reducing the vocabulary to ~20% of the original token set in the two models tested so far.\nWe first examined how pairwise similarity and vocabulary interference were distributed in this constrained space, anticipating that the limited vocabulary might bias the pairs toward narrower interference ranges. Indeed, real-word token similarity more closely aligned with the vocabulary interference distribution. As outlined in Sec. 3, this suggests that most of these pairs face high competition during prediction. Based on this, we hypothesized that representational change would shift from a U-shaped to a linearly decreasing pattern, as in the orange and blue curves of Fig 3b. Our results confirmed this: post-learning similarity decreased monotonically with pre-learning similarity, supporting the idea that highly similar pairs are influenced by vocabulary interference.\nWe’ll include this new analysis in the revised paper and update our claims. Specifically, we’ll emphasize that NMPH is observed only under specific global interference conditions. Our findings suggest that to fully distinguish between competing hypotheses, global similarity structures, like vocabulary interference, must be considered as modulating factors.\n\n**Q5 [Examples of token pairs]:** Here are randomly selected examples optimized for llama-7b. Similar examples for other models will be included in the appendix. Note: WordNet includes abbreviations and acronyms.\n\nSynthetic token pairs:  \n| Similarity Range | Pair 1 | Pair 2 | Pair 3 |\n|------------------|--------|--------|--------|\n| 0.1–0.15 | (Liter, CLARE) | (artifactId, gew) | (emat, SOUR) |\n| 0.15–0.2 | (Ste, UITableView) | (Pers, pmatrix) | (ries, pragma) |\n| 0.2–0.25 | (it, Autres) | (bt, Autres) | (Bad, tf) |\n| 0.25–0.3 | (VD, Autres) | (Vertical, ierte) | (coordinate, gesch) |\n| 0.3–0.35 | (elf, ScrollView) | (Tr, named) | (Else, newcommand) |\n| 0.35–0.4 | (DER, stackexchange) | (ific, ently) | (von, trightarrow) |\n| 0.4–0.45 | (uk, ThreadPool) | (vez, ISBN) | (under, rov) |\n| 0.45–0.5 | (illet, cially) | (icio, atr) | (ptop, Wikimedia) |\n| 0.5–0.55 | (bootstrap, rach) | (utes, Vorlage) | (iveau, tersuch) |\n| 0.55–0.6 | (mittel, umbn) | (Series, notify) | (Problem, emptyset) |\n| 0.6–0.65 | (fte, zott) | (Length, TRUE) | (elve, PDF) |\n| 0.65–0.7 | (nings, setAttribute) | (isen, issenschaft) | (ouv, schluss) |\n| 0.7–0.75 | (ru, occup) | (result, utzt) | (aka, rola) |\n| 0.75–0.8 | (cock, eland) | (hib, heast) | (prepare, Once) |\n| 0.8–0.85 | (relation, emptyset) | (reen, bmatrix) | (uliar, ienn) |\n| 0.85–0.9 | (Italie, urre) | (cement, cement) | (onna, onna) |\n| 0.9–0.95 | (aped, aped) | (loster, loster) | (lict, lict) |\n\nSemantic token pairs:  \n| Similarity Range | Pair 1 | Pair 2 | Pair 3 |\n|------------------|--------|--------|--------|\n| 0.1–0.15 | (mix, loaded) | (defined, standard) | (pub, any) |\n| 0.15–0.2 | (identifier, astern) | (layout, eclipse) | (defined, slash) |\n| 0.2–0.25 | (online, pop) | (suite, abb) | (pub, format) |\n| 0.25–0.3 | (absolute, pus) | (round, pa) | (annotation, hum) |\n| 0.3–0.35 | (series, math) | (black, roc) | (gas, bat) |\n| 0.35–0.4 | (spec, cock) | (information, leg) | (argument, lear) |\n| 0.4–0.45 | (contra, architecture) | (dictionary, ike) | (rooms, ho) |\n| 0.45–0.5 | (gen, nil) | (factory, acre) | (shadow, nih) |\n| 0.5–0.55 | (gen, raise) | (time, eb) | (zero, iga) |\n| 0.55–0.6 | (dale, person) | (dawn, esp) | (irs, ante) |\n| 0.6–0.65 | (any, essen) | (final, mission) | (gi, dim) |\n| 0.65–0.7 | (cap, bind) | (mus, skim) | (dd, safe) |\n| 0.7–0.75 | (bye, anas) | (izar, through) | (lined, click) |\n| 0.75–0.8 | (replace, stock) | (unction, week) | (execution, frame) |\n| 0.8–0.85 | (geometry, list) | (locale, embed) | (partition, brand) |\n| 0.85–0.9 | (opacity, fragment) | (render, inflate) | (analysis, section) |\n| 0.9–0.95 | (gable, board) | (volution, ship) | (slider, simple) |\n\n**Q6 [Intermediate layer analysis]:** We appreciate you raising this concern. We will revise Fig. 7 to fix an analysis error and to add a new analysis. Since NeurIPS does not support figures or PDFs during the rebuttal, we summarize changes below:\n1. Using token pairs optimized for similarity in earlier layers, similar to Fig 7a. Here we discovered an error in the code that indexed the wrong layers for averaging. When fixed, we saw:\n- Intermediate and late layers show a monotonic pattern (e.g. similar to Fig 2b, green line), with significant differentiation for high similarity pairs (>0.7 pre-learning similarity.\n- Intermediate layers show stronger differentiation (greater decreases in similarity) than late layers.\n- Early layers (1 & 2) behave more erratically, lacking a clear trend.\n2. New analysis using the same token pairs as Fig 2b (optimized for last layer similarity), replacing Fig 7b. This is still running, but we will update the rebuttal when our results come in.\nWe note that in our setup, the learning process happens across repetitions, not across layers, so representational dynamics may change along the layer depth.\n\n**Q7 [Forgetting phase vs catastrophic interference]:** While the \"forgetting phase\" in ICL might look like catastrophic interference, it's not as there are no weight updates. Instead, we attribute the accuracy drop to model-specific and reversible effects such as sliding window attention or vocabulary interference.\n\n**Q8 [Attention pattern]:** While this is an important direction, we excluded it due to time constraints and our focus on representational dynamics. We see it as a valuable direction for future work.\n\n[Reb1] J. Ba, G. E. Hinton, V. Mnih, J. Z. Leibo, and C. Ionescu, “Using Fast Weights to Attend to the Recent Past,” in Advances in Neural Information Processing Systems 29, 2016, pp. 4331–4339.\n\n[Reb2] I. Schlag, K. Irie, and J. Schmidhuber, “Linear Transformers Are Secretly Fast Weight Programmers,” in Proceedings of the 38th International Conference on Machine Learning, PMLR, Jul. 2021, pp. 9355–9366.\n\n[Reb3] Dherin, Benoit, et al. \"Learning without training: The implicit dynamics of in-context learning.\" arXiv preprint arXiv:2507.16003 (2025).\n\n[Reb4] Dai, Damai, et al. \"Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers.\" arXiv preprint arXiv:2212.10559 (2022).\n\nWe hope we have addressed the reviewers' main concerns and we would happily go into more detail if there are any remaining questions."}, {"review_id": "XBiESRhlgh", "reviewer": "Reviewer_5X6h", "summary": "This paper uses a transformer language model as a stand-in for a human subject to discover how the perceived similarity between two items changes as they are seen together. In particular, it attempts to figure out which hypothesis of “Hebbian Learning,” “Pattern Separation,” and “Non-Monotonic Plasticity” is true. The paper measures interference between two tokens in terms of the median pairwise similarity to other tokens, and change in similarity using the difference in cosine similarity before and after in-context learning. The results show that items that are perceived to be different become more similar after association, the items that are somewhat similar grow more apart, and items already thought to be similar remain at the same level of similarity. The paper identifies this result with the prediction of the NMP hypothesis.", "questions": "- Can you explain why (ref. Weakness 2) you take your definition of vocabulary interference to be what you have in your paper? \n- Do you think that the “forgetting” phase is just an artifact of older/weaker models, and is not canonically a part of the learning process? Do you have any hypotheses as to why other models don’t show this behavior?\n- Can you clarify what role interference plays in the original NMP hypothesis?", "limitations": "I think it bears mention in the paper that Language Models may not necessarily be faithful stand-ins for humans in cognitive neuroscience. Although I still would find work with LMs useful in formulating and refining hypotheses, one eventually needs to actually study humans to make conclusions about humans. This point should be mentioned in the paper.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 1, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\nI like the idea of using LMs to test hypotheses on human learning. In effect, this assumes that various artifacts of human learning are attributes more of the “learning” aspect, not the “human” aspect. Being more controllable and interpretable, LMs are good candidates.\nThe method is adequately described. I was mostly able to follow the methodology of the paper without much difficulty.\n\n**Weaknesses**\n\n- I am confused by the given definition of vocabulary interference. I was unable to understand where the three hypotheses say anything about interference (I thought they only talk about similarity). \n- The given definition of interference is confusing for several reasons.\n    - It is not symmetric between *x* and *y*, both in its formulation and the fact that the hidden representation of *y* is conditioned on *x*.\n    - I am not convinced that the cosine similarity between two embeddings should be interpreted as the interference between the corresponding tokens\n    - Even if we ignore the above point, the average similarity between *y* and other tokens t in context would tell us how much *y* interferes with these other tokens on average, not how much it interferes with *x*\n- I don’t think that the results necessarily support the NMP hypothesis. My reading of Figure 2 is that low-similarity pairs become more similar, whereas mid-high similarity pairs remain largely the same; the decrease for all except the 0.65-0.7 bracket seem rather small to me. This would then contrast with the NMPH which instead would suggest (1) that mid-similarity pairs differentiate, and (2) high similarity pairs become even more similar. Basically, the curve does not look U-shaped as it should.", "was_revised": true, "final_justification": "The authors have sufficiently clarified the role of interference in the NMPH, and that the change they observe is consistent with the changes that other works exploring the NMPH have observed. Therefore, I will raise my score to \"3: Borderline reject.\" The reason for still erring on the rejection side is that I still feel that the results are not entirely convincing. Granted that other works have also not found high correlations for the NMPH predictions, but I believe that also speaks to the NMPH not being a highly predictive theory to begin with. On the other hand, I'd like to stress that I am not very knowledgeable in the area of cognitive neuroscience and that my expertise lies squarely with LLMs. Therefore, if any conflict of opinions were to arise, other reviewers' points should supersede mine.", "rebuttal": "We thank the reviewer for the valuable feedback.\n\n**Summary of main points:**\n - Q1: we clarify that our vocabulary interference metric quantifies how densely a token is represented in embedding space. This measure is grounded in cognitive theories of memory interference. We find that higher vocabulary interference is associated with reduced pairwise similarity after learning, across all pre-learning similarity levels. We also find that NMPH-like effects fade under higher vocabulary interference, which may help account for some of the divergent patterns observed in neuroscience studies (differentiation, integration, non-monotonicity).\n\n- Q2: while the NMPH provides a theoretical account of how representational change should vary with memory coactivation, it lacks quantitative predictions—making it difficult to define how pronounced a U-shape must be to support the hypothesis. Our findings nonetheless align with its core idea: we observe statistically significant differentiation for mid-similarity pairs across six models, particularly in the low vocabulary interference range. Although integration is weaker for high-similarity pairs, the overall pattern supports NMPH’s central claims and is consistent with prior empirical work [39].\n\n**Q1 [Clarifying vocabulary interference]:**  We appreciate the reviewer’s comments and the opportunity to clarify. \nIn our paper, we considered the possibility that some of the divergent or controversial findings in the neuroscience literature could be due to a variable difficult to measure in human studies: the global associative structure between stimuli, which in our work we call vocabulary interference. This is intended to measure representational competition from the entire stimulus space – that is, how many other tokens in the vocabulary are highly similar to a given token x. This link between similarity and long-term memory interference is supported by decades of psychology and neuroscience literature [Reb1-Reb5, 9, 23], leading to an influential computational model [23] showing how the hippocampus can  resolve interference by disambiguating similar representations. This perspective suggests that representational similarity beyond the immediate pair (i.e., within the model’s broader representational space) could play a key role in modulating learning dynamics, and may help explain variability in empirical outcomes.\nWe operationalize this idea  by computing the average cosine similarity between x and all other vocabulary tokens prior to learning. Regarding the conditioning: in our experimental design, we analyze the final $x,y$ pair in a stimulus stream such as $[x_1, y_1, x_2, y_2]$. The representations we use $h(x_2 | x_1, y_1)$ and $h(y_2 | x_1, y_1, x_2)$ are both contextualized within the same prompt. We compute similarity between these two vectors to capture how much the model binds $x$ to $y$ after learning. The vocabulary interference measure is computed before learning, and is not used to define the similarity, but to assess whether regions with higher representational density tend to show weaker learning (i.e., smaller representational change).\nImportantly, we find that when we control for vocabulary interference, the post-learning similarity curves change systematically: higher vocabulary interference is associated with reduced pairwise similarity after learning, across all pre-learning similarity levels.\nWe will revise the text to clarify the motivation, definition, and limitations of this interference measure. If further concerns remain, we welcome continued discussion as we aim to make the paper as clear and rigorous as possible.\n\n**Q2 [Validity of NMPH results]:** The work that proposed the NMPH [28] provides a theoretical account of how representational change should vary with memory coactivation, but it does not specify quantitative predictions, making it difficult to define how pronounced a U-shape must be to support the hypothesis. For empirical context, [39] report a differentiation effect of approximately -0.1 (Pearson correlation) for mid-similarity pairs in human fMRI data. While this offers a useful benchmark, our analysis uses cosine similarity and is conducted on LLM representations -- thus, direct comparisons of effect size are not possible. Nevertheless, the ~0.15 change we observe is robust and statistically significant across all 6 models (after multiple comparisons correction; see reply to reviewer ctgT). While we do not observe clear integration for high-similarity pairs, a factor that contributes to the U-shape being less pronounced, the differentiation in the mid-similarity range remains consistent with core predictions of the NMPH, and we believe this provides meaningful support for the hypothesis as illustrated in Fig. 2b.\n\n**Q3 [Forgetting phase]:** While a detailed analysis of the forgetting phase is beyond the scope of this paper, we provide initial observations and hypotheses in lines (208-215) and Appendix C.1. Specifically, we note that only two models exhibit this behavior. In one case, we suspect it may be related to the model’s use of a sliding window attention (SWA) mechanism, which could limit its ability to integrate information over longer repetition streams. In the other, we hypothesize that high vocabulary interference may play a role in suppressing learned associations. While we do not claim that forgetting is a canonical part of the learning process across all LLMs, we believe these preliminary findings highlight interesting model-specific dynamics that could lead to further investigation in future work.\n\n**Limitations:** We will add a statement in the limitations section acknowledging that LLMs are not direct stand-ins for humans, and that empirical validation in human studies remains essential.\n\nReferences:\n\n[Reb1] A. D. Baddeley, “The Influence of Acoustic and Semantic Similarity on Long-term Memory for Word Sequences,” Quarterly Journal of Experimental Psychology, vol. 18, no. 4, pp. 302–309, Nov. 1966.\n\n[Reb2] M. C. Anderson and B. A. Spellman, “On the status of inhibitory mechanisms in cognition: Memory retrieval as a model case,” Psychological Review, vol. 102, no. 1, pp. 68–100, 1995.\n\n[Reb3] M. C. Anderson, C. Green, and K. C. McCulloch, “Similarity and inhibition in long-term memory: Evidence for a two-factor theory,” Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 26, no. 5, pp. 1141–1159, 2000.\n\n[Reb4] J. B. Caplan, M. Rehani, and J. C. Andrews, “Associations Compete Directly in Memory,” Quarterly Journal of Experimental Psychology, vol. 67, no. 5, pp. 955–978, May 2014.\n\n[Reb5] Y. Zhao, A. J. H. Chanales, and B. A. Kuhl, “Adaptive Memory Distortions Are Predicted by Feature Representations in Parietal Cortex,” J. Neurosci., vol. 41, no. 13, pp. 3014–3024, Mar. 2021.\n\nWe hope we have addressed the reviewers' main concerns and we would happily go into more detail if there are any remaining questions."}, {"review_id": "7LGNawzc1J", "reviewer": "Reviewer_Sfgb", "summary": "The authors in this paper are asking if LLMs also similarly associate words and concepts to the Non-Monotonic Plasticity Hypothesis (NMPH), as observed in humans. The objective of this study is to use in-context learning (no finetuning, to avoid any changes to learned weights) and examine if the model starts predicting y when it sees x, just from seeing them co-occur after r repetitions. This study offers a unique way of not only understanding the internal dynamics and the representational space of pre-trained LLMs but also offers a novel way of using LLMs as a computational tool to understand associative learning in humans. After performing experiments across a wide range of LLMs, the authors report that LLMs do show the pattern of NMPH, akin to humans. \n\nSpecifically, the authors first compute the cosine similarity between tokens (x,y) before and after r repetitions to observe how closely these tokens are integrated or differentiated over time. Finally, the authors perform one more experiment to check for vocabulary inference by sampling random tokens from the model's vocabulary. This is done to find the similarities between lookalikes to token y in the (x,y) token pair, where x is fixed.\n\nIt is important to note that the authors conduct all these experiments by using the representations from the last layer of the models.\n\nThis is a novel, clean, and direct way of testing cognitive theories of the human brain, such as NMPH.", "questions": "1. In Figure 1B, the token pairs (x,y) that are passed as input to the model are shown in different colors. What's the significance of using different colors for the same input? Is it done to show the time scale? If yes, then I would recommend using a single color and then showing the time scale with an arrow.\n\n2. I am curious if the authors found any LLMs that did not align with either of the three conditions (encoding, consolidation, and forgetting). If so, the authors can consider adding that to the appendix. Reporting such models won't necessarily weaken the claim in the main text, but rather provide insights about models that don't have human-like learning.\n\n3. What would happen if the models are given some noise in between the repetitions? For instance, how would an input like xy,xy,xy,yx,xy,xy,av,xy,xy,... affect the results shown in the graphs in Fig. 2?\n\n4. Did the authors observe any pattern between successful and failed LLMs, such as architecture size, vocabulary size, or anything else?\n\nSuggestions:\n\n1. Please focus on the points that I have given under the 'weakness' title above to improve the quality of the paper. Those are my main concerns and suggestions to improve the paper.\n\n2. I would be very interested to see how the results turn out in case of representations from the intermediate layers. I highly encourage authors to probe the intermediate layers for future work.\n\n3. While I have already noted this under the \"Weaknesses\" section above, I would like to emphasize it again. The authors should consider including results from a randomly initialized LLM (i.e., without loading pretrained weights) to test whether the architecture alone is sufficient to produce some of the patterns observed in the trained models. Additionally, a promising direction for future work would be to experiment with training data that follows a developmental curriculum, mimicking how human infants learn progressively over different age stages.", "limitations": "The authors have addressed some limitations of the paper. However, since the study draws parallels between the learning mechanisms of the human brain and LLMs, the following limitation should also be acknowledged:\n\nLLMs are pre-trained on billions to trillions of tokens, whereas humans, especially infants, are exposed to a significantly smaller amount of data during early development. This stark difference in training exposure raises questions about the direct comparability of learning dynamics between the two systems. A solution to this limitation is to train a model from scratch on a curriculum (developmental) data of different age groups and evaluate the model after each stage.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\n1. The experiments are conducted across six LLMs of varying architecture sizes, and the results are consistent across the models reported in the main text. I don't think the authors need to include more LLMs to check for generalization; however, I do think that some additional evaluations in the form of visualizations will increase the quality of this paper. I will add those additional evaluations in the weakness section. Please look at those points.\n\n2. The results are very clearly plotted, and a consistent pattern is observed in all the graphs.\n\n3.  The authors build this research by shifting their focus from behavioral outcomes to the internal representational dynamics, an attempt to understand associative memory in LLMs. I think this is an important line of research and has a lot of potential and research gaps to address in the future.\n\n4. The authors use In-Context Learning (ICL), which I found to be an optimal choice for probing the LLMs, as it allows the authors to be consistent across different models of varying architecture sizes. This also prevents the need for fine-tuning models on task-specific datasets, which could introduce biases.\n\n5. A follow-up experiment is also done to check for the similarities between the y token from the (x,y) pair with likewise tokens to y from the model vocabulary. \n\nWeakness:\n\n1. The foundation of this research is built upon the fact that associative memory in the human brain utilizes NMPH to associate words and concepts. However, I find no concrete evidence in the paper suggesting or explaining why this is tricky/difficult to perform with human subjects. What are the core problems of conducting such research with human subjects? Why is this study not possible to do with biological systems? What behavioral evidence is already present, and why is it not sufficient to explain this hypothesis? Why is the use of LLMs the only alternative option (if that's the case)? To strengthen the claims made in this study, the authors should address these points early in the paper. If the authors think they have already addressed all these questions, then please highlight the line numbers, and I will recheck if they answer these points\n\n2. I agree with the statement in lines 37-41 where the authors highlight the unique property of ICL in LLMs; however, I would like to add that this is highly dependent on how the LLMs were pre-trained. Biological systems, especially human infants, don't have access to billions or trillions of word tokens like LLMs. The authors should consider adding that they are mapping the results of the LLMs to those of mature human brains. This requires a small tweak in the language used in the paper, but it is an important one as the study compares learning mechanisms of human brains and LLMs.\n\n3. The authors don't show whether the claims made in this study are a result of the training data, model architecture, or a combination of both. This is important to understand why LLMs show this human-like behavior and how this behavior even emerges in the first place. If the authors can evaluate an untrained (or a random) LLM architecture and re-run the same experiments on it, it will increase the quality of this paper. I will increase a point in the quality assessment if the authors can report the performance of an untrained network.\n\n4. I did not find any example of the (x,y) token pair in the manuscript or the appendix. I request that an example be given in the main manuscript to better understand the input type. The input pair (x,y) can be as simple as (\"UK\", \"London\") or much more complex. Therefore, an example prompt should be given in the paper. On a similar note, did the authors try different types of prompts when running the experiments? If yes, did they observe any difference in results?\n\n5. The study has shown that the internal dynamics of LLMs change over time with repetitive cues. This is shown using cosine similarity (manuscript) and heatmaps (appendix). To further increase the quality of this study, I suggest including a visualization using either tSNE or PCA, showing how the internal representations of the tokens shift before and after training.", "was_revised": true, "final_justification": "The authors have addressed all my concerns in the rebuttal, and I am convinced by their response. Therefore, I am increasing my score.", "rebuttal": "We thank the reviewer for the insightful feedback.\n\n**Summary of main points:**\n- Q1: studying NMPH in humans is challenging due to limited control over pre-learning similarity, task-dependent effects, and constraints like noise and cost. Human studies also struggle to account for global knowledge structure. We argue that LLMs, though biologically distinct, offer a valuable model system for testing representational-level hypotheses under controlled and fully observable conditions.\n- Q4: we considered this, but untrained LLMs are unsuitable for our paired-associate task due to the lack of ICL abilities.\n- Q5: we used synthetic token pairs selected by pre-learning similarity, independent of semantics, to ensure full coverage of the similarity spectrum, especially the mid-similarity range critical for testing NMPH [39]. Token pair examples per group are now included. We also explored real-word tokens and found their similarity aligns with vocabulary interference, shifting the representational change curve from U-shaped to monotonic. This supports our claim that global interference is a key modulating factor.\n\n**Q1 [Foundation of the research]:** Studying the NMPH in humans presents several challenges. First,  it is extremely difficult to precisely control the pre-learning similarity between associated items – a core requirement for detecting nonmonotonic representational change, as emphasized in [39]. Defining what counts as “0.1” vs. “0.5” similarity in humans is nontrivial, costly, and time-consuming. For this reason, [39] relied on representational inversions of DNNs to generate visual stimuli with controlled perceptual similarity. Second, the similarity level at which differentiation emerges varies by task and stimulus set, making it unclear in advance which mid-similarity range will reveal the effect. This requires dense sampling across the similarity continuum, adding further complexity. Finally, as with any human behavioral experiment, results are constrained by cost, limited trials, participant fatigue, and noise.\nBeyond pairwise similarity, additional factors (such as global (vocabulary-level) interference) may also influence learning dynamics. Measuring this in humans would require estimating an individual’s lifetime exposure to words and concepts, which is inherently difficult and practically infeasible. As a result, human studies typically do not account for how prior knowledge structure might modulate representational change.\nThere is empirical support for all three hypotheses discussed in our paper [6, 9, 10, 28, 30, 32]. However, these findings are often empirically inconsistent, and it remains unclear when each dynamic arises, under what conditions, and which factors drive the observed differences. We propose that global interference may be one such factor influencing these outcomes. For instance, in memory systems such as the hippocampus, similar representations are thought to compete during retrieval, and the system works to disambiguate overlapping information. [9] note, “The hippocampus is believed to reduce memory interference by disambiguating neural representations of similar events.”. This suggests that similarity beyond just the target pair (i.e., relative to the broader knowledge space) may play an important role in shaping representational dynamics.\nLLMs offer a tractable tool for testing representational-level hypotheses. Unlike biological systems, they provide full access to internal representations, explicit control over exposure, similarity, and repetition, and complete observability of prior knowledge.This enables precise computation of local and global similarity, systematic testing of learning dynamics, and exploration of how broader structure shapes outcomes. While we do not claim that LLMs are mechanistically equivalent to human brains, we view them as complementary model systems – ideal for generating and testing hypotheses that are difficult or impossible to probe directly in human subjects.\n\n**Q2 [Comparison with mature human brains]:** We agree that LLM pretraining differs from human developmental learning. Our comparisons focus on mature human brains, aligning with the existing neuroscience literature on representational change. We will revise the paper to make our focus more clear, and note this in the limitations section.\n\n**Q3 [Factors influencing results]:** We tested 6 LLMs with diverse architectures and pretraining data and observed consistent trends across them, suggesting that the representational changes we study reflect a broader pattern not tied to any one model or dataset.\n\n**Q4 [Evaluating untrained LLM]:** While testing untrained models is a useful control in some contexts, our task requires models to be able to effectively learn paired associates. This is not possible with untrained LLMs. Still, we agree it would be valuable future work to study how these representational dynamics emerge during training, especially under a curriculum that mimics human developmental learning.\n\n**Q5 [Examples of token pairs]:** Below we provide randomly selected examples per similarity group, based on optimization for llama2-7b. Similar examples for other models will also be included in the appendix.\n\nOur study intentionally used token pairs selected for their pre-learning similarity, regardless of semantic meaning. This approach is similar to the use of synthetic stimuli in [39], which highlights the need to sample across the full similarity spectrum (especially the mid-similarity range) to test the NMPH effectively. Real-word tokens are unevenly distributed across this space, making precise control difficult. Our main goal in this paper is not to study meaning, but the structural dynamics of representational change in response to learning. \nWe agree that using meaningful tokens is important for assessing how NMPH applies in real-world learning. To explore this, we sampled token pairs restricted to single-token WordNet words, reducing the usable vocabulary to ~20% in the two models tested. We found that real-word token similarity closely aligns with vocabulary interference, implying that most pairs face high competition during prediction. Based on this, we hypothesized – and confirmed – that the representational change curve would shift from a U-shape to a linearly decreasing pattern (similar to orange and blue curves in Fig 3b). We will include this analysis in the revised paper and clarify that while NMPH remains a useful framework, its predictions may depend on the similarity structure of real-world stimuli. Our findings suggest that global interference should be considered as a modulating factor in naturalistic learning settings. See reply to reviewer ctgT for more details and semantically-meaningful token pair examples.\n\nSynthetic token pairs:  \n\n| Similarity Range | Pair 1 | Pair 2 | Pair 3 |\n|------------------|--------|--------|--------|\n| 0.1–0.15 | (Liter, CLARE) | (artifactId, gew) | (emat, SOUR) |\n| 0.15–0.2 | (Ste, UITableView) | (Pers, pmatrix) | (ries, pragma) |\n| 0.2–0.25 | (it, Autres) | (bt, Autres) | (Bad, tf) |\n| 0.25–0.3 | (VD, Autres) | (Vertical, ierte) | (coordinate, gesch) |\n| 0.3–0.35 | (elf, ScrollView) | (Tr, named) | (Else, newcommand) |\n| 0.35–0.4 | (DER, stackexchange) | (ific, ently) | (von, trightarrow) |\n| 0.4–0.45 | (uk, ThreadPool) | (vez, ISBN) | (under, rov) |\n| 0.45–0.5 | (illet, cially) | (icio, atr) | (ptop, Wikimedia) |\n| 0.5–0.55 | (bootstrap, rach) | (utes, Vorlage) | (iveau, tersuch) |\n| 0.55–0.6 | (mittel, umbn) | (Series, notify) | (Problem, emptyset) |\n| 0.6–0.65 | (fte, zott) | (Length, TRUE) | (elve, PDF) |\n| 0.65–0.7 | (nings, setAttribute) | (isen, issenschaft) | (ouv, schluss) |\n| 0.7–0.75 | (ru, occup) | (result, utzt) | (aka, rola) |\n| 0.75–0.8 | (cock, eland) | (hib, heast) | (prepare, Once) |\n| 0.8–0.85 | (relation, emptyset) | (reen, bmatrix) | (uliar, ienn) |\n| 0.85–0.9 | (Italie, urre) | (cement, cement) | (onna, onna) |\n| 0.9–0.95 | (aped, aped) | (loster, loster) | (lict, lict) |\n\nWe did not use alternative prompt templates, opting for a minimal prompting setup to isolate learning effects and reduce confounds from prompt structure.\n\n**Q6 (intermediate layers):** We agree this is an interesting point. We examined intermediate layers in an appendix figure, which we later found to have an error. Since NeurIPS does not support figures or PDFs during the rebuttal, we summarize the results below for two different experimental settings:\n1. Using token pairs optimized for similarity in earlier layers, (similar to Fig 7a, but fixing a coding error). Our corrected results show:\n    - Intermediate and late layers show a monotonic pattern (e.g. similar to Fig 2b, green line), with significant differentiation for high similarity pairs (>0.7 pre-learning similarity.\n    - Intermediate layers show stronger differentiation (greater decreases in similarity) than late layers\n    - Early layers (1 & 2) behave more erratically, lacking a clear trend\n2. Using token pairs optimized for last layer similarity (same tokens as Fig 2b). This analysis is still running, but we will update the rebuttal when our results come in.\nOther comments:\n\n**[Learning phases across LLMs]:** All models exhibited encoding and consolidation phases; only two showed a forgetting phase. No models failed to show encoding or consolidation.\n\n**[Noise between tokens]:** We intentionally avoided adding noise (e.g., distractors or varying context) to isolate learning-related representational changes between specific token pairs. This controlled setup minimizes confounds and aligns with our goal of probing structural learning dynamics rather than robustness to noise.\n\n**[Successful and failed LLMS]:** If the reviewer refers to whether models exhibit forgetting, we address this in Appendix C.1, which summarizes model-specific behaviors.\n\nWe believe we have addressed the main points and welcome any follow-up questions."}, {"review_id": "MONcdXjrdk", "reviewer": "Reviewer_nm9c", "summary": "The main objective of this paper is to assert wether the computational model of LLMs are useful for testing hypotheses about human brain memory dynamics, featuring scale and control in experimental design that is rarely achievable in real-life biological studies. More precisely, the question is whether LLMs learn token representations that evolve during associative learning, mimicking those observed in humans, with emphasis on the Non-Monotonic Plasticity Hypothesis (NMPH).\n\nTo do so, the authors establish a simple and clear experimental protocol, that does not involve fine-tuning LLMs, as it operates “in context”. Associative learning is simulated by presenting LLMs with token pairs, repeated a certain number of times within the context (thus, in context learning). Token pair representation is measured in the last layers of the LLM architecture, before the output logits are produced. Representation dynamics is measured by the difference between the cosine similarity of the initial pair, and the pair after a number $r$ of in context repetitions.\n\nResults indicate: 1) representation dynamics exhibit a non-monotonic pattern of representational change, consistent with the NMPH, 2) that such dynamics are influenced by vocabulary interference, reflecting a joint influence of pairwise similarity and global contextual competition within the model’s prior knowledge.", "questions": "* In Fig. 2(a), can you clarify the x-axis? Given the comments in the forgetting phase paragraph (lines 208-210), I think there has been some normalization, for otherwise the ticks are not consistent with the explanations.\n* In Fig. 2(b), at which repeated co-occurrence $r$ is $\\Delta S^m_r$ computed? If I understand lines 220-222 correctly, this is the average value aggregated across models, and similarity group, but nothing is said about $r$. Are you aggregating also along the number of repeated co-occurrences?\n* In Fig. 2(b) you claim that with moderate pre-learning similarity (0.6, 0.75) there is a substantial decrease in pairwise similarity, during consolidation. I see a delta of approximately 0.15 and I cannot wrap my head around the idea that this is *substantial*. I also read the reference [28] and there is no easy quantitative estimation of what is a substantial deviation from a “pattern separation” hypothesis. This observation, combined to the doubts I have from the previous question, makes me wonder if the U-shape we observe is significant or not.\n* Despite you acknowledge it as a limitation of your work, I wonder. What exactly are the token pairs you selected in your study? How would your result change if you considered token pairs that would be more natural, real-world examples?\n* As a follow up of the previous question, how would the conclusion of this work change if we considered more realistic patterns with more than token pairs, for example considering sentences? In this case, we would need a method to “mangle” sentences in a controlled manner to gauge dissimilarity.\n* A final technical question: in equation (2) and in line 129 you specify how to compute $\\Delta S^m_r$, and how $h^m_{x_r}$ is obtained. However, I am not sure to get a grip on how $h^m_{y_r}$ is obtained. If the model task was to predict $y_r$, then you would not have access to its latent representation right? So are you actually feeding the model $y_r$, such that you can measure the *contextualized* latent representation of $y_r$? Any clarification on this point would be very useful to understand the nitty gritty details of your method.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n* In my personal opinion, I believe this kind of measurement studies are very important and very interesting. Explaining LLMs behavior through the lenses of neuroscience is informative, but it is also very promising to understand whether LLMs could be thought of as “surrogate human brains”, to test hypotheses and replicate experiments that would otherwise be cumbersome if applied to real biological systems.\n\n* The article is very clear, the editorial quality meets high standards, and the experimental protocol is sound from the statistical point of view.\n\n* Results corroborate many findings and discussions that have been presented in the workshop on Representational Alignment (https://representational-alignment.github.io/2025/)\n\n* The idea of studying vocabulary interference as a mechanism modulating representational change is a nice contribution of this work, which cannot be achieved on a real biological system, yet it provides some explanations about such dynamics.\n\n* The modified greedy coordinate gradient technique to find candidate token pairs is smart!\n\n\nWeaknesses:\n\n* The main figure of the paper, Fig. 2(b) indicates a very weak U-shape, in my opinion. Based on reference [28], it is difficult to quantitatively compare results of the “strength” of misalignment between representations. Despite extremely low standard errors, the NMPH hypothesis appears only during the so called “consolidation” phase, but it is kind of hard to understand at which number of repetitions this really happens. Indeed, Fig. 2(a) has an x-axis that does not report the exact numerical value of $r$.\n\n* Fig. 2(b) presents (if I understood correctly) an aggregate rendering across all tested models. This hinders an appropriate understanding of the phenomenon and its relation to a given LLM behavior.\n\n* The interpretation of Fig. 3(b) is somehow speculative. Also, the values in the interval (x-axis) [0.9-1] are in contrast with the explanation given.\n\n* Finally, as noted by the authors, one big limitation of the current work is that token pairs do not represent “real-world” natural semantics. Given the main paper and the appendix, it was hard for me to really understand exactly what were the selected token pairs.", "was_revised": true, "final_justification": "I was convinced by the authors' rebuttal, both to my review, and to others reviews (irrespective of their rating).\nI think this is a very interesting article, and I hope the authors will prepare a new version of the paper including all the necessary \"disclaimers\" they discussed in answering my questions.", "rebuttal": "We thank the reviewer for the insightful feedback.\n\n**Summary of main points:**\n- Q1: we clarify Fig. 2a and 2b and show that mid-similarity pair differentiation remains statistically significant after correction for multiple comparisons. While [28] does not provide quantitative predictions, our results are consistent with its core theoretical claims, even though we agree that integration effects for high-similarity pairs are weaker.\n- Q3: we clarify Fig 3 and our proposed vocabulary interference measure as a plausible factor influencing representational dynamics. This interpretation remains exploratory, as global stimulus interference has not, to our knowledge, been systematically studied in neuroscience studies. Estimating such effects in humans would require modeling an individual’s lifetime exposure to concepts, a challenging task. Nonetheless, by leveraging the fully observable space of LLMs, we are able to investigate this underexplored factor, grounding our interpretation in neuroscience principles.\n- Q4: we tested WordNet-based token pairs and found that high vocabulary interference led to a shift from U-shaped to monotonic representational change. This supports our claim in Sec 3 that NMPH predictions depend on global similarity structure.\n\n**Q1 [Fig 2b & significance of NMPH]:** \nIn Fig 2b we aggregate the representational change across models, similarity groups, and repetition counts that correspond to the consolidation phase (as defined per model in Tab 3 Appendix C). We will further include per-model and per-repetition plots in the appendix so readers can assess the consistency and robustness of the U-shape across settings across conditions.\n\nThe work that proposed the NMPH [28] provides a theoretical account of how representational change should vary with memory coactivation, but it does not specify quantitative predictions, making it difficult to define how pronounced a U-shape must be to support the hypothesis. For empirical context, [39] report a differentiation effect of approximately -0.1 (Pearson correlation) for mid-similarity pairs in human fMRI data. While this offers a useful benchmark, our analysis uses cosine similarity and is conducted on LLM representations–direct comparisons of effect size are not possible. Nevertheless, the ~0.15 change we observe is robust and statistically significant across all 6 models (after multiple comparisons correction; see reply to reviewer ctgT). While we do not observe clear integration for high-similarity pairs, a factor that contributes to the U-shape being less pronounced, the differentiation in the mid-similarity range remains consistent with core predictions of the NMPH, and we believe this provides meaningful support for the hypothesis as illustrated in Fig. 2b.\n\n**Q2 [Fig 2a]:** We normalize the x-axis to allow alignment of different models based on their phase transitions (from encoding to consolidation to forgetting). Each model enters and exits these phases at different repetition counts due to a slight variability in their learning dynamics (as can be observed in Tab. 3 of Appendix C). To enable visual comparison across models, we map the repetitions for each phase into fixed intervals on the x-axis: (0,1) for encoding, (1,2) for consolidation, and (2,3) for forgetting. Within each model, the actual repetition counts in a phase are linearly scaled to fit within these ranges. This approach ensures that phase-aligned trends are visible despite underlying differences in repetition scale. We will revise the figure caption and text to more clearly state this normalization and add the original accuracy per model in the Appendix. Please let us know if further clarification is necessary.\n\n**Q3 [Fig 3]:**  We agree that the interpretation of Fig 3b involves a degree of speculation. This is, in part, due to the fact that, global stimulus interference has not been systematically studied in memory neuroscience, to the best of our knowledge. Estimating such global effects in humans would require modeling an individual’s prior exposure to all words and concepts, which is extremely challenging and remains an open problem in cognitive neuroscience. For example, [1] explicitly discusses the difficulty of estimating and accounting for the impact of priors in human memory studies. By leveraging the fully observable representational space of LLMs, we are able to explore this under-investigated factor and propose vocabulary interference as a potential mechanism influencing the different and seemingly contradictory findings in humans. While our interpretation is necessarily exploratory, we believe it offers a plausible explanation grounded in principles from neuroscience – specifically, that memory systems must balance the competing demands of integration and interference resolution. We will revise the main text to (1) make the speculative nature of the explanation more explicit, and (2) more clearly connect it to the underlying theory and the observed behavior in Fig. 3(b).\n\n**Q4 [Semantically meaningful tokens]:** Our study intentionally used token pairs selected for their pre-learning similarity, regardless of semantic meaning. This approach is similar to the use of synthetic stimuli in [39], which highlights the need to sample across the full similarity spectrum (especially the mid-similarity range) to test the NMPH effectively. Real-word tokens are unevenly distributed across this space, making precise control difficult. Our main goal in this paper is not to study meaning, but the structural dynamics of representational change in response to learning. \nHowever, we do agree that using meaningful tokens is an important analysis to understand how the NMPH applies in real-world learning.  We sampled new token pairs, restricting the search space to  single-token words from WordNet. This filtering step reduced the usable vocabulary to approximately 20% of the original token set in the two models tested by the time of this reply.\n\nWe first examined how pairwise similarity and vocabulary interference were distributed within this space, anticipating that the limited vocabulary might bias the pairs toward a narrower range of interference values. We found that real-word token similarity more closely reproduces the vocabulary interference distribution. According to our framework in Sec. 3, this implies that most of these pairs would experience high vocabulary competition during prediction. Based on this observation, we hypothesized that the representational change curves would no longer exhibit a U-shaped trend but instead resemble a linearly decreasing pattern, as seen in the orange and blue curves of Fig. 3b. This prediction was confirmed in our results: post-learning similarity decreased monotonically with pre-learning similarity, supporting the idea that highly similar token pairs undergo representational changes influenced by the degree of vocabulary interference. \n\nWe will include this new analysis in the revised paper and revise our claims accordingly. Specifically, we will clarify that while the NMPH provides a useful theoretical framework, its behavioral predictions may depend on the distributional properties of real-world stimuli. Our findings suggest that to fully explain representational change in naturalistic learning contexts, NMPH-like theories may need to account for global similarity structure, such as vocabulary interference, as a modulating factor.\n\n**Q5 [h_y]:** In our setup, the model is prompted to predict a two-token sequence, where the first predicted token should be $y$ for the prediction to be considered correct. We obtain $h_x$ as the final hidden state of the input tokens, and $h_y$ as the hidden state of the first predicted token $y$ (that is, the first token generated by the model, which is not part of the input). Although $y$ is not explicitly fed into the model, we can still access its contextualized representation through the generation process. To validate the robustness of our method, we also conducted a control experiment in a non-predictive setting, where both $x$ and $y$ were given to the model as input, and $h_y$ was extracted directly from the input sequence. The results, including the U-shaped pattern, were qualitatively similar in both predictive and non-predictive settings. We will revise the main text to clarify this distinction between $h_x$ and $h_y$.\n\n**Q6 [Extension to sentences]:** We appreciate the reviewer’s question. Extending this work to sentence-level inputs is an interesting direction, but it introduces several challenges. First, it is nontrivial to construct sentence pairs with controlled and quantifiable pre-learning similarity, which is essential for testing nonmonotonic representational change as predicted by the NMPH framework. Second, it is unclear what specific patterns one should expect from sentence-level associations, given the added complexity of syntax and semantics. Our study intentionally focused on a simplified associative learning task using token pairs, in line with the experimental design used in neuroscience (e.g., [Reb1]), to evaluate whether theoretical predictions about hippocampal learning dynamics extend to LLMs. This controlled setup allowed us to isolate and investigate representational changes in a targeted and interpretable way.\n\n\n[Reb1] Stark, Shauna M., et al. \"What’s in a context? Cautions, limitations, and potential paths forward.\" Neuroscience letters680 (2018): 77-87.\n\nWe believe we have addressed the main points and welcome any follow-up questions."}]

Author rebuttal:

Meta-review: This paper presents a novel and creative approach for  using LLMs as model organisms to investigate the Non-Monotonic Plasticity Hypothesis (NMPH) from cognitive neuroscience. The work is interdisciplinary and all four reviewers consider the approach novel and original. The paper introduces a few new analysis approaches (e.g. gradient based) and the reviewers generally highlight the technically solid execution of the described experiments. A few concerns about the statistical analysis were raised during the review and discussion period, and additional analysis were discussed. The authors promised that the camera ready revision would include some improvements (listed below) including an updated statistical analysis. 
Overall, the the reviewers agree that the paper presents a valuable contribution, that the manuscript is well written and that the study is well executed with no major concerns. I think we can thus recommend this for to be accepted.
Decision: Accept (poster)