Title: Large Language Models as Model Organisms for Human Associative Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Large Language Models

Associative Learning

In-Context Learning

Representational Change
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n* 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nI 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.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nWhy 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-06"}