Generating Computational Cognitive models using Large Language Models
Authors: Milena Rmus
Akshay Kumar Jagadish
Marvin Mathony
Tobias Ludwig
Eric Schulz
Venue: NeurIPS 2025 poster ()
Keywords: large language models
cognitive computational models
cognitive science
learning
decision making
neuroscience
Primary area: neuroscience_and_cognitive_science

Abstract: Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data. Traditionally, these models are handcrafted, which requires significant domain knowledge, coding expertise, and time investment. However, recent advances in machine learning offer solutions to these challenges. In particular, Large Language Models (LLMs) have demonstrated remarkable capabilities for in-context pattern recognition, leveraging knowledge from diverse domains to solve complex problems, and generating executable code that can be used to facilitate the generation of cognitive models. 
Building on this potential, we introduce a pipeline for Guided generation of Computational Cognitive Models (GeCCo). Given task instructions, participant data, and a template function, GeCCo prompts an LLM to propose candidate models, fits proposals to held-out data, and iteratively refines them based on feedback constructed from their predictive performance. We benchmark this approach across four different cognitive domains -- decision making, learning, planning, and memory -- using three open-source LLMs, spanning different model sizes, capacities, and families. On four human behavioral data sets, the LLM generated models that consistently matched or outperformed the best domain-specific models from the cognitive science literature. 
To validate these findings, we performed control experiments that investigated (1) the contribution of the different LLM features (model size, model family, capacities); (2) the causal role of different prompt components; (3) the effect of data contamination; (4) the ability to recover ground truth models from simulated data; and (5) the total explainable variance in human behavior captured by LLM-generated models. 
Taken together, our results suggest that LLMs can rapidly generate cognitive models with conceptually plausible theories that rival -- or even surpass -- the best models from the literature across diverse task domains.

Generating Computational Cognitive Models using Large Language Models
Milena Rmus f
Akshay K. Jagadish Princeton University
Helmholtz Munich
milena. romna.mmu@@lee www.mmmwssslllleeee.eeeme mmw.ltz-mmmiih..ee munich.de
akshay www.aajishppiinettton.oo p
Marvin Mathony
Tobias Ludwig
Helmholtz Munich
Tübingen University
marvin, marvin. hewvin.mathony@helmmoltzzz wwwttzzmmmnchhhecm de
tobias wwdwiguuniiii - de
Eric Schulz
Helmholtz Munich
www.llchellzeeeeee tz- ch.de
Milena Rmus and Akshay K. Jagadish contributed equally.
Abstract
Computational cognitive models, which formalize theories of cognition, enable re- searchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data. Traditionally, these models are handcrafted, which requires significant domain knowledge, coding expertise, and time invest- ment. However, recent advances in machine learning offer solutions to these challenges. In particular, Large Language Models (LLMs) have demonstrated remarkable capabilities for contont pattern recognition, leveraging knowledge from diverse domains to solve complex problems, and generating executable code that can be used to facilitate the generation of cognitive models. Building on this potential, introduce pipeline for Guided generation of Computational Cog- nitive Models (GeCCo). Given task instructions, participant data, and a template function, GeCCo prompts an LLM to propose candidate models, fits proposals to held-out data, and iteratively refines them based on feedback constructed from their predictive performance. We benchmark this approach across four different cognitive domains decision making, learning, planning, and memory using three open-source LLMs. spanning different model sizes, capacities, and fami- lies. On four human behavioral data sets, the LLM generated models consistently matched or outperformed the best domain-specific models from the cognitive sci- ence literature. To validate these findings, we performed control experiments that investigated (1) the contribution of the different LLM features (model size, model family, capacities); (2) the causal role of different prompt components; (3) the effect of data contamination; (4) the ability recover ground truth models from simu- lated data; and (5) the total explainable variance in human behavior captured by L..M-generated models. Taken together, our results suggest that LLMS can rapidly generate cognitive models with conceptually plausible models that rival or even surpass - the best models from the literature across diverse task domains. The code for GeCCo is available at httthtt:///// htpps:////tthtt com www.//iieeaCCClaa/eeegeoeeo gww.oo.geoooooooooooooooooo... .it
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Across the sciences, computational models provide a formal framework for understanding natural phenomena. In cognitive science, these models ground theories about the cognitive processes that underlie human behavior. In practice, researchers hand-craft these models, fit them to behavioral data, and iteratively refine them until they capture behavior sufficiently well (Polk and Seifert 2002). However, handcrafting cognitive models that accurately explain behavior can be timecconuming (Musslick et al.] 2024bla) a). It requires trained researchers to conduct lengthy literature reviews, formulate cognitively plausible theories, and implement computationally feasible algorithms to evaluate these theories. While assumptions are necessary constrain any model, researchers' own theoretical commitments and expertise may unintentionally narrow the range of models they consider Screeld-Schwalb et al. 2022 Taatgen et al.) 2016). potentially missing better explanations of the data (Frischkorn and Schubert 2018 Addyman and French 2012).
Recent progress in Large Language Models (LLMs) offers a practical route to addressing these challenges, tackling both efficiency and biases, while broadening the space of possible models (Wang) et al. 2023 Binz al., 2023 Musslick et al. [2024a] Li et al., (024a)). Specifically, LLMs exhibit at least three abilities that can facilitate the development of flexible, domain- domain-general general framework for
generating cognitive models. First, LLMS can process behavioral data formatted in natural language along with the corresponding task description (Schubert et al.) 2024] Jagadish et al. 2024), providing them with the flexibility to handle diverse task domains with varying levels of complexity (Binz et al., 2024). Second, they can identify patterns in complex problems using domain knowledge in-context, and generate hypotheses about the data-generating process (Xiao et al. 2024). Third, their ability to synthesize highly accurate programs (Austin el al., 2021 Perez et al., 2021 lends itself nicely to cognitive model generation and subsequent evaluation. Importantly, these three abilities have already shown promise in statistical modeling, where LLMs have been used to generate and evaluate models represented as probabilistic programs (Li et al. 2024a)..
Prompt
Guided generation of Computational Cognitive models (GeCCo) LLM model evaluation
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LLM
Model comparison
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Baseline
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Previous parametars
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Code template
Model fitting
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Deavvor
Human data LLM modal prediction
Optimize BIC
Jog.0 to 16 (6-000t0)
We prompt the LLM with task description, participant data, guardrails to constrain the format of L.LM responses, and the code template to generate cognitive models that offer different explanations of the underlying data as Python functions. Model generation evolves over 10 sampling iterations. During each iteration, three LLM-generated models are fitted offline to the held out data (not included in the prompt), and the fitness metric Bayesian Information Criterion (BIC: Watanabe 2013) is used to provide feedback to the LLM on the subsequent iteration. The best model across all 10 sampling iterations is used for evaluation. The LLM --M--eerrttedd models are evaluated by 1) fitting them to behavioral data and comparing the model fit to that of the baseline cognitive model (e.g. the best performing model from the literature) using BIC, and 2) running posterior predictive checks i.e. simulating the models and comparing simulated to ground truth data to further verify their validity. For full prompts, see the Appendix B
Figure I: Schematic of GeCCo:
In this work, we leveraged these LLM abilities to generate hypotheses about the cognitive processes underlying human behavior. We developed novel pipeline for Guided generation of Computational Cognitive Models (GeCCo) that helps synthesize cognitive models as Python functions using LLMMs, and iteratively refines them based on feedback their predictive performance. We evaluated

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this approach across four different cognitive domains: decision making, learning, planning, and memory, using three open-source LLMs, with different features including model sizes, capacities, and families. Specifically, we first compared L.M--genrraee models with the best domain-specifie model from the literature in terms of predictive performance and then validated the best performing L.M model using posterior predictive checks. Furthermore, we conducted control experiments to gain mechanistic understanding of this pipeline. These included: (1) linear mixed-effects models to isolate the contribution of different features of the base LLM; (2) LLM prompt ablations to test the causal role of instruction, data, iterative feedback, and template components; (3) LogProber (Yax et al. 2024) estimation of data contamination in prompts; (4) simulations to access recovery of ground truth models; and (5) a comparison with a foundation model of cognitive science (CENTAUR: Binz et al. 2024) to approximate the total explainable variance captured in human behavior. Across all domains, we found that L. L.g--eeerdtte models matched or surpassed the best domain-specific model from the literature, and captured as much explainable variance as CENTAUR, while remaining interpretable. The results from the control experiments revealed that the iterative feedback component in the prompt and the reasoning ability of the LLMs are the two main drivers of this performance, with no traces of contamination in the prompt. Together, these results position GeCCo as a practical, scalable approach for rapid cognitive-model generation.
2 Methods
Guided generation of Computational Cognitive Models (GeCCo). We implemented GeCCo, a structured guided sampling pipeline for generating cognitive models with LLMs. Each pipeline run consisted of 10 sampling iterations, repeated across 5 independent runs per domain to assess stability, On each iteration, the base LLM was prompted using fixed prompt structure (see Appendix (6) that included I) a natural language task description, 2) behavioral data from subset of participants (prompt data), 3) instructions and guardrails (e.g., write Python function with a specific name and defined inputs/outputs), 4) a domain-specinn and 5) feedback (included from iteration onward). Using this prompt, the LLM generated three distinct cognitive models with non-overlapping parameter sets. These models were parsed from the I.M response, converted into executable Python functions (e.g., via exec exuncciintion (eentiiionn_ttriinn)) and their parameter names were extracted. Fach model was then fit to second, held-out validation dataset (not shown in the prompt) using the minimize function from the SciPy optimization library (Virtanen et al. 2020), initialized from 20 random starting points to avoid local minima. Model fit was evaluated using the Bayesian Information Criterion (BIC), which was used to identify the current best rest-performing model. This best model, and a list of previously used parameter names were then included in the next prompt to guide model generation and avoid duplicates. If model generation failed (e.g., due to syntax or runtime errors), the iteration a run, the best --ggerrrte model compared against competing cognitive models by evaluating on third, held-out test set. See Figure [ for an overview of the pipeline and Appendix B for exact prompts that were used in each of the experiments.
template model used primarily for syntactic guidance,
automatically restarted. After completing all iterations in
LLM specifications. We used three open-source LLMs: Llama-3. 10amma-3.1-Inseee I--nstrrcct--00 (Llama: Meta Plat- forms 2024, --s-sel--RR--isssillaaaa----...
(R1; Guo et al. 2025). and Qwwen2.5-72B-nnsrmncc (Qwen: Yang et 2024). We chose these models as they span different sizes (70B, 72B), capabilities (with/without reasoning), and families (Qwen, Llama, and Deepseek). Importantly, all of our tests were performed in-context. We set the temperature to 0.2 for Llama models, 0.15 for Qwen and 0. for RI to encourage some exploration when generating models. In practice, our approach takes a maximum of 8 hours per task domain on four Nvidia A100s with 40GB memory each.
(R1;Guo
Evaluation. We applied GeCCo to four canonical cognitive domain paradigms: decision making, learning, planning, and memory. These paradigms include well-established baseline models that are easy to fit and support systematic increases in complexity, both in parameter count and in the cognitive processes they formalize. We began with a simple peciisonnmmainng task, progressed to sequential learning with trial dependencies, then examined planning task involving multi-step reasoning, and finally used a task that examines interactions between working memory and reinforcement learning. Evaluating L-ggerrrrreedd models on human behavioral datasets from these paradigms, where data are noisy and no single model fully captures the underlying processes, essential, as reflects the real-world settings in which cognitive scientists would use approach.
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Metrics. We evaluated the performance of the GeCCo generated model using the Bayesian Infor- mation Criterion (BIC; @ata:aae 2013), which balances goodness of fit and model complexity by penalizing the number of parameters. Because BIC is a relative metric, when models achieve similar fit, favors the less complex (fewer parameter) model. The pipeline agnostic to the model quality measure; for example, in [ppendix[]] we report results using the Akaike Information Criterion (AIC) as a metric. In the last stage of the evaluation, we additionally computed the exceedance probability (EXP: Stephan et al. 2009), which quantifies the posterior probability that one model explains the data better than its competitors. EXP expresses, in probabilistic terms, how confident we are that a given model provides the most prevalent explanation of the observed behavior.
3 Experiment 1: Decision making
Task. To understand the mechanisms underlying human decision making, researchers have com-- monly resorted to nulti-feature decision making tasks (Bogacz et al., 2006). such tasks, participants typically choose between multiple options, each defined by a distinct set of features. We considered the paradigm from Hilbig and Moshagen (2014), are defined by four feature values and their respective validities (Fig. 2 see Appendix for more details).
(2014)) where participants chose between two options that
Baseline cognitive model. The winning model in the Hilbig and Moshagen (014) study was a probabilistic weighted additive model (pWADD), which combines feature values and inferred feature weights (instead of feature validities) to predict human choices. Critically, the inferred weights can match completely differ from the feature validity provided by the experimenter; see Appendix F
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Human-Heursite choloe agreement
Figure 2: Experiment 1: Decision Making, A) Schematic of the decision task from Hilbig and Moshagen 2014), where participants were asked to choose between two options based on Tour binary features and their validities. Arrow thickness indicates validity value (i.e., thicker arrows mean higher validity). B) Model fit comparison: L.L--geneaale
models from RI and Llama outperformed the best literature model. C) Posterior predictive checks showed that proportions of choices accounted for by the canonical heuristics (Equal Weighting, Take The Best and Weighted Additive Heuristics) closely aligned between human data and respective LLM and PWADD model predictions. D) Code of the best I.I. -e..ereee model (Llama), which can arbitrate between three canonical heuristics mentioned above via discount factor.
Results. We found that both RI and Llama produced model that outperformed the best literature model (Ilama BICM+SEM vs. PWADD M+CM+ 39.40 + 4.54 vs. 51.97 + 4.52; t-test (pWADD > Llama BIC: 1(52) 41.8, p .001: see F The best LLM--gnerated model (by Llama) weighs the option features by the respective feature validities and a discount factor. The strength of this model is that can arbitrate between Take the Best, Weighted Additive, and Equal Weighting decision making strategies with a single parameter, see Figure 2p. Although the PWADD model can do the same using its inferred feature weights, it requires four parameters instead of one parameter used by the LLM-generated model. To validate the LLM- -e-eeeeteeedd model, we performed posterior predictive checks which typically includes simulating data based on the cognitive model, and checking whether the simulated data agrees with the human data (for details, refer to Appendix H.. We found that the data simulated from the best L.-M-geeerteed model aligned closely with human choices; see 2C.

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Scientific Insight. The parsimonious way in which the LLM- -e-eeetteeeed model selects between heuristics aligns with the continuum- of-models view, which interprets heuristics as Bayesian inference under extreme priors (Parpart et al. 2018). In fact, the discount term in the L.M-generrted model parallels the prior-strength parameter in the constraint-optmml regression and half-ridge formulations from Parpart et al. (2018), both of which enable smooth modulation of posterior feature weights- therefore the decision rule. Importantly, the LLM-generated model achieves this flexibility without adopting Bayesian framework.
4 Experiment 2: Learning
Task. Multi-armed bandit tasks are frequently used in the study of feedback-driven learning (Frank) et al. 2004) Pessiglione al. 2006 Wang et al. 2016). Generally, in a bandit task, agent chooses between N arms, often with a predefined reward contingency associated with each arm. The agent receives feedback for their chosen action and, over sequence of trials, learns to adjust action selection in a way that maximizes positive outcomes. For analysis, we considered the two-armed bandit task dataset from Chambon al. 2020). In addition to the classic condition (partial feedback), where only rewards for chosen options are observed (see Appendix and Fig. 12 for results), the authors include full-feedhack condition, where rewards for both unchosen and chosen options observable; see Appendix D for details.
Baseline cognitive model. The winning model from Chambon et al. (2020) is variant of the classic Rescorla-Wagner model that is commonly used to study learning dynamics in bandit tasks. This model (aW contains four learning rates: learning rates for positive and negative prediction errors for chosen actions, and the same for unchosen actions. In addition to using a delta rule for action-value updating, the RW model employs softmax policy for action selection, with an inverse temperature parameter that transforms values into probabilities and a perseveration parameter that increases the weight of previously chosen actions. For full model details, see Appendix F2
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Figure 3: Experiment 2: Learning. A) Schematic of the learning task from Chambon et al. 2020 where participants chose hetween two options and received feedback for both, but only got the reward for the chosen option. B) Model fit comparison: L.LM--enerated models from Llama on average fit better than RW 4cx C) Posterior predictive checks showing close alignment between human data and predictions of the best LLM model in both low and high reward blocks. D) Code of the best LLM-generated model (Llama), which displays asymmetric learning rates along with forgetting of values, and dedicated fictive trace for counterfactual outcomes.
Results. We found that Llama (BIC = 77.97 = 5.40) produced a model that performed slightly better than 40 (BIC 85.24 + 7.95); see Figure 3B. However, we found that, in terms of the exceedance probability (EXP), the LLM model (0.97) scored significantly higher compared the best model reported in the literature (0.03). In addition to differentiating learning rates based on the valence of the prediction error, the best I.I. ...---gnertted model included two key components: a forgetting parameter that decays the value of the unchosen action (potentially capturing working memory limits), and a separate meta-vale value function (and learning rate) that learns uniquely from forgone outcomes (capturing regret/relicf signals in a dedicated fictive trace); see Figure 3D. For posterior predictive checks, we assessed the agreement between model-predicted choices and actual humaa choices separately for high- and low-reward blocks. The LLM LLM-generated model matched human behavior as well as RW 4o (Figure 3C).
Scientific Insight. The GeCCo-generated model separates two sets of Q-values: one updated from chosen outcomes and another counterfactual set updated from forgone outcomes. Whereas
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standard models often merge these via asymmetric learning rates, GeCCo keeps them distinct, raising questions about how experienced and counterfactual values are weighted in decision-makeer The model also applies forgetting to unchosen actions, creating temporal asymmetry in value retention and eliminating the need for an explicit choice stickiness parameter- -ften included mainly for model fit rather than theory. This illustrates how GeCCo can inspire rethinking and improving cognitive models.
5 Experiment 3: Planning
Task. A widely used paradigm in the planning literature the two-stage task introduced by Daw al. (201T). The goal of this task is to investigate whether people learn action-reward contingencies using habitual, reward-driven learning (referred to as model-free), or they make decisions based on the underlying state-transition structure that they estimate from experience (model-basede We considered a planning dataset from Feheer da Silva and Hare (2020), which replicated the original study (Daw et al. 201 see Appendix for details of the experiment.
Baseline cognitive model. For baseline, we considered the Hybrid model from Daw et al. (2011) the best performing model from the literature, which effectively combines model-free action values from a SARSA(A) algorithm with model-based values (updated based on the transition probabilities), weighted by free parameter. For detailed equations, see Appendix F3
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Figure 4: Experiment 3: Planning. A) Schematic of the planning task from Feher da Silva and Hare (2020). where participants took two steps in stochastic environment with common and rare transitions and fluctuating rewards. B) Model fit comparison: the model generated by RI had a lower average BIC score compared to the Hybrid model. C) Posterior predictive checks showing the same pattern across humans, literature model and RI Ri-generated model of repetition of common (dark) and rare (bright) transitions depending if the previous action was rewarded. D) Code of the best LLM-generated model (RI), which uses separate learning rates for common and rare transitions and inverse temperature for exploration omitting discounting of rewards.
Results. As shown in Figure 4B, RI generated the best overall model that achieved lower mean BIC (432.47 + 19.31) than the hybrid (437.38 + 18.72). While the BIC difference was not significant (t-test (Hybrid RI BIC): t(15) 0.61; p = 0.27), the exceedance probability (EXP) favored the R1 model (R1 EXP = 0.85 versus Hybrid EXP 0.)).. Posterior predictive checks revealed that the LLM-generated model, like humans, made action selections consistent with more model-based strategy; see Figure/4C. The code generated by RI describes version of the transition dependent learning rate model that uses two separate learning rates for common and rare transitions, respectively (similar to one of the candidate models reported by Feher da Silva and Hare (2020)). Furthermore, learns the transition probabilities, indicating that it is not model-free and behaves similarly to humans. It also incorporates an inverse temperature parameter to regulate exploration, but notably omits a discounting parameter, which typically included in multi-step planning tasks to devalue distant rewards. Drawing on reinforcement learning literature, the model implements value iteration (Bellmann] 1958), taking the maximum over future value estimates (line of the code) to update the expected value for the first-stage actions; see Figure 4D

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Scientific Insight. The CeCCoogeneratee model offers parsimonious account of planning, show- ing how behavior based on learned transition structures can emerge from general principles of transition learning and value updating, without explicitly separating model-based and model-free systems. This provides an alternative to traditional reinforcement learning frameworks that rely on multiple canonical components and aligns with recent calls to move beyond the strict MB/MF dichotomy (Collins and Cockburn 2020).
6 Experiment 4: Working memory
Task. The reinforcement learning-working memory (RL--WM) paradigm was designed disentan- gle reinforcement learning (RI.) and working memory (WM) contributions to the learning process (Collins and Frank 2012). In this paradigm, participants learn a varying number of stimulus-action (S-A) associations per block via feedback, receiving reward for correct responses to individual stimuli. Varying the cognitive load targets the WM capacity by increasing the number of associations and the delay between stimulus repetitions. We used data from the version reported in Rmus et al. with two set sizes: three (low) and six (high) S-As see Appendix for details.
(2023)
Baseline cognitive model. As a baseline, we used the RL-WM model, which consists indepen- dent but interacting reinforcement learning (RL) and working memory (WM) modules isolate their respective contributions to learning. The RL module captures incremental, capacity-unlimited learning, whereas the WM module supports one-shot learning with perfect retention from the previous trial but is capacity-liminn limited and subject to decay. For detailed equations, see Appendix
du 5. actionsttl, statesitl, reaarstt) aib.e)
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Set Size
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Figure 5: Experiment 4: Working Memory, A) Schematic of the reinforcement learning working memory task from Rmus et al. (2023), where participants learned a varying number of state-action associations. B) Model fit comparison: Llama-generated models outperformed the best literature model. C) Posterior predictive checks showing close alignment between human data and predictions of the best LLM model. D) Code of the best LLM- --M-generated model (Llama). which distinguishes between fast learning under low cognitive load (working memory) and slower learning under high cognitive load.
Results. We found that Llama (BICM+SEM 267.25 + 9.31) produced model that, average, had lower average BIC score than the LL model ( 275.2 + 9.21; t-test (RL-WM > Llama BIC): t(24) 14.2: Llama-genen namaa-geerraed model (0.99) scored higher than RL-WM (0.01). The LLM-generated cognitive model implemented differences in learning based low high cognitive load, but did in the RL module, rather than using the cognitive -eadddependent WM weight (as the case in the original RL-WM model); Figure 5P. Posterior predictive checks revealed that the LLM-generated model captured key patterns in human behavior reasonably well: faster, asymptotic learning in the low cognitive load condition typically associated with WM, and incrementai, RL-like learning in the high cognitive load condition; Figure
.01); Figure BB. In terms of exceedance probability (EXP), the
Scientific Insight. The LLM- -egeerertted model captured behavioral patterns and fit the human data well, even though the cognitive load manipulation primarily affected the RL rather than the WM component (Collins and Frank 2012). This raises questions about where cognitive load effects arise and challenges the assumption that RL processes are relatively insensitive to load. Notably, GeCCo's proposal aligns with evidence that RPE signals-central
central to reinforcement learning blunted
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under low cognitive load (Collins et al. 2017). suggesting that the RL system may adjust learning rates based on WM load.
7 Control experiments
Contribution of different model features. To examine the importance of LLM features-reasoning ability, base model family, and model size-we fit a mixed-effects regression predicting the BIC of the best ---eeerrrte models from these three factors, with participant-specifec random in- tercepts. Reasoning ability (as in R1) yielded slightly better models (3M+SEM = -0.266 1.029, p = 0.07), though the effect was not significant. In contrast, Llama outperformed Qwen as a base model (3M SEM = 5.624 + 1.029. p = 0.03), despite Qwen having more parameters- due to differences in training data and/or training protocols.
Causal role of different prompt components. We conducted an ablation study to assess the causal contribution of each prompt component-feedbacce code template, task description, and participant data sequences- by systematically ablating each element and rerunning the model generation pipeline (see Figure 6 for the full prompt structure). To estimate their joint influence, we fit a linear mixed- effects regression predicting post-ablation BIC scores from binary-coded prompt components, with random intercepts per participant. Feedback was the strongest predictor of model performance (8M SEM -1..400 + 2.287, p < 0.05), followed by participant data (8M+SEM = -12836 2.287, p < 0.05) and task description (3M -9.834 + 2.287, p 0.05). The code template had the weakest effect (3M4SEM = 99.359 + 2.287, < 0.05; see Figure 9
Data contamination analysis. We checked for data leakage in the prompts used to generate cognitive models using the LogProber method Yaax et al. (022).. Upon applying it to Llama (the base model that generated the pest-perfonming cognitive models most frequently), we found evidence of data contamination, with the acceleration term substantially below the threshold of I recommended by Yax al. (2024) for the four human experiments. Specifically, was 0.0171 for the prompts used for drecision-making 0. 147 (full feedback) and 0.14 (partial feedback) for learning, 0.1718 for planning, and 0.6481 for working memory; see Figure I0 for details.
Recovering ground truth models in synthetic datasets. Synthetic datasets generated through model simulations provide full control over task behavior and access ground truth, making them ideal for benchmarking. As a sanity check, we evaluated our approach on synthetic data from two domains: learning and decision making. In both cases, the LLM inferred models that closely matched the true data- generating processes, supporting the validity of our method. In decision making, the LLM recovered key heuristics: fenture focus for Take the Best and cross- cross-feature comparisons for Tallying. In learning, it correctly identified the Rescorla- Wagner (RW) model with two learning rates when fitted to corresponding data, and for data from an RW model with stickiness, it produced a similar value-decay formulation. See Appendix C Appendix D Figure 7 and Figure X for details.
Explainable variance captured in human behavior. To assess explainable variance in human behavior captured by ALM-generater models, we compared its negative log-likeling lig-likel to that of CENTAUR (Binz et al., 2024) a foundation model trained to predict human behavior across multiple cognitive domains on held-out data. As CENTAUR outperforms domain-specific models, it serves as a proxy for maximum explainable variance. The L.L. --gnerrate models matched or exceeded its performance across domains (see Table TT indicating that they captured most of the explainable variance while remaining interpretable.
Table 1: Mean negative log-likelihoods (M+SE) and paired 1-test results of CENTAUR vs. LLM- generated cognitive model across four task domains.
DOMAIN
CENTAUR (M+SE)
LLM (M=SE)
t
P
Decision- makiin
15.41 +: 1.99
15.08 +: 2.29
0.27
0.78
Leamingg
53.58 - 3.31
26.17 : 2.79
18.83
< 0.001
Planning
206.00 = 11.14
214.10 + 10.59
-1.42
0.18
Memory
127.44 + 4.68
120.67 = 4.68
5.75
< 0.001
8

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8 Discussion
We developed GeCCo, a novel pipeline that uses open-source LLMS to generate cognitive models as Python functions and iteratively refine them via feedback. We applied GeCCo paradigms from four cognitive domains and found that LLM- -ggeerrtt models matched or outperformed Nomain-specifie baselines in all cases. Control experiments showed that 1) the base LLM model family was an important determinant of performance, 2) all prompt components contributed meaningfully, but iterative feedback was causally the most important, and 3) No significant data leakage occurred in task prompts. We also demonstrated that GeCCo recovered ground truth models from simulated data and produced interpretable models that outperformed CENTAUR in predictive power, indicating that LLM-generated models captured most of the explainable variance in human behavior.
8.1 Related work
Model discovery with LLMs. The mingsitanding goal of automated model discovery holds promise of accelerating and democratizing scientific discovery by reducing dependence on prior expertise. Until recently, such efforts relied on domain- specific languages and hand-crafted search algorithms
to explore predefined model spaces (Kemp and Tenenbaum 2008 [loyd et al.) 2014,, Musslick et al.)
2024a| Gulwani 2011; Steinruecken et al. 2019; Hewson et al.
2023). Recent advances in LLMs
have overcome many of these limitations. Researchers have used LLMs to automate statistical model discovery (Li et al., 2024a), solve classic MI tasks (Xiao et al. 2024), support hypothesis generation and refinement (Zhou et al. 2024), propose valid domain rules (c.g., chemistry) (Zheng et al., 2023b), and even automate the entire scientific pipeline, from experiment design to peer review, in machine learning (Lu et al. 2024). In cognitive modeling, LLMs have been used to translate human strategy descriptions into executable code (Xie et al. 2024). and to generate cognitive models from task descriptions and data using evolutionary search (Castro et al. 2025). Our approach complements these concurrent efforts by offering lightweight open-source method that relies purely on in-context learning, enabling much faster convergence (hours instead of days).
Code writing abilities of LLMs. The promise of LLMs in model search lies not only in their broad knowledge but also in their ability interpret natural language and synthesize executable code. Using LLMs to generate models in general-purpose purpose languages like Python provides a path beyond handcrafted, domain-specific languages for automating model discovery (Austin al. 1 Ni et al.
2024). Prior work has shown that LLMS excel at mathematical and programming tasks (Austin et al.
2021 Ni et al. 2024 Perez et al. 2021) and can generate mathematical functions and probabilistic Python programs that model input data (Li et al., 2024a; Xiao et al. [024] Zheng et al.) (023b))
More recently, LLMs have advanced program induction, with successes domains such as abstract
reasoning (Li et al. 2024b) and graphical function induction (Li and Ellis 2024).
8.2 Discussion and Limitations
A key advantage of our approach is that all results were obtained purely in-context using open-source LLMs, without any fine-tuning. This drastically reduces the barrier to entry, as researchers could use our pipeline off-the-shelf to generate alternative models for their behavioral data. GeCCo implements a hybrid optimization loop, where the LLM generates candidate models that are fit to the data offline via traditional optimization tools. This ensures that model selection remains data-driven, with the LLM acting as a proposal engine rather than an unverified model generator. Finally, our pipeline generalizes well across diverse task domains, making it adaptable to other researchers. These advantages suggest that our approach can help cognitive scientists explore a broader model space more efficiently.
Despite its success, our approach has some limitations. Our approach uses a simple domain-specific model as a template in the prompt. Although this could bias the LLM toward a particular model class, an ablation analysis showed that removing the template had the smallest impact on downstream model performance. To further reduce dependency on handcrafted templates, we also generated the template with the LLM itself, which produced models that fit the data just as well (see Appendix [). Currently, our approach uses offline-computed model fit metrics (e.g., BIC) to generate feedback, consisting of the current berf-pern model and previously tested parameter combinations. This could be enhanced by incorporating feedback from another LLM based on predefined criteria such as

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theoretical plausibility, parsimony, alternative hypotheses, code quality, which may further reduce the need for human oversight. also important note that the domains examined this paper do not represent the full scope of cognitive modeling. Future work should extend our approach to other areas such as language processing and perception. Applying GeCCo to nigher-dimensional data (e.g., vision or language) and generating models with many parameters may be challenging, but it represents an exciting direction for future research. Furthermore, to make our approach valuable beyond cognitive science, it should be evaluated more naturalistic datasets that capture human behavior real-world settings.
Overall, our findings suggest that LLMs can significantly advance computational modeling in cognitive science by democratizing access complex model discovery and accelerating the pace of research.
Acknowledgements
We thank all reviewers for their constructive and thoughtful feedback. We also thank the authors of Chambon al. (2020), Rmus al. (2023), Feeeer da Silva and Hare (020), and Hilbig and Moshagen (2014) for making the data from their study available. Furthermore, we thank the members of the "Human-Centered Artificial Intelligence" (HCAI Lab) for their comments, discussions, and support. This work was supported by the Volkswagen Foundation, Princeton Al Lab, Helmholtz Munich and the Deutsche Forschungsgermeinschaat (DFG, German Research Foundation) under Germany's Excellence Strugg--DXCC26441139077666555555
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A Prompt structure
Task description
In a 2-armed bandit task, a participant chooses between two actions. The participant's action selection is followed by an outcome: positive (1) if reward is received, negative (-1) if not.
Data
Here task dataset from several participants:
Data from participant XX:
Trial: action: [a0], reward: [rO]
Trial: action: [a1], reward: [r1]
LLM guardrails
Your task is to propose 3 unique cognitive models that could explain the observed behaviors in this dataset.
Make sure all of the model parameters are actually being used. Each model should be implemented as Python function called: "ignitive_model1"
"cognitive_mode"
and cognitive_model3
Each the functions should accept the following lists as arguments: actions, rewards, and model parameters. Each function should return negative log likelihood of observed actions given the model parameters.
Ensure your models have distinct assumptions and parameter sets. Avoid repeating ideas used in previous iterations.
When you write the function, also include the description of each parame- ter and what it does in the function's meta commented section.
Make sure you write functions that are free of bugs, and can be executed.
Code template
Here is a function template:
def cognitive__odel(aatie rewards, parameters):
parameter_P parameter_22 parameter_n parameters log_ likelihood for in range(len(actions))
...
log_likellhood np.log(p(actions(t))
return -log_ jikellhiooooo
Feedback
Best model you generated far
[Model code]
Parameters have used so far are:
Keep trying!
Figure 6: General structure of the LLM prompt we used across all of the domains. The prompt generally consisted of the paradigm description, data the text format, Python function specifications (including the name of the function, required arguments and output), code function template, and the feedback (on iterations after the first one) established following offline evaluation of LLM-generated models.
14

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B Prompts
B.1 Decision making
Decision making prompt
This is multi- multi-attribute decision-makee task, where participants have to choose the superior product between two options, labeled A and Each option represented fictitious product and they had to infer which product was superior in terms of quality in every trial. For each option, they were provided with four expert ratings (with representing a positive and representing negative rating). The four experts differ in their validity. The ratings of experts were given in descending order of their validity (having validities of 0.9, 0.8, 0.7, and 0.6 respectively). Participants selected product by selecting the corresponding option, i.c. A or B. Here a task data set from several participants:
Data from participant 1:
Trial 1: Product A ratings: [1 1 1]. Product B ratings: [0.0 1]. Chosen option: A
Trial 2: Product A ratings: [1 OJ. Product B ratings: [0 0.0 1]. Chosen option: A
Trial 3:
Product A ratings: [0 1]. Product B ratings: [1 1 Oj. Chosen option: B.
Propose three unique cognitive models that could explain the observed behavior in the dataset. Each model should have distinct assumptions and parameters. Avoid repeating ideas used in previous iterations. Think step by step: How do participants use features or expert validites make their decisions?
Each model should be implemented as Python function named 'cognitive modell ; 'cognitive model2's and 'cognitive model3' Each function should take following inputs: list of choices, list of option A feature lists, a list of option B feature lists and a list of model parameters. Each function should return the negative log likelihood of the observed choices given its parameters.
Clearly define each model parameter and explain role in the function's 's commented section. All parameters (except inverse temperature) should have values between 0 and 1. Ensure the equations do not result in nonsense values (e.g., avoid division by zero). Make sure your Python functions are executable and bug-free.
Consider the following code as a function template:
def cognitive_monee (choices, optionAs, optionBs, validities, params):
w1, w3, w3, w4, temperature params
v1, v2, v3, v4 validities
likellood
for t in range (len (choices)):
option_A, option_B optionAs [t], optionBs [t]]
value_A np. np. (option_A)
value_B np. mp. array (option_B)
scale_ value_aleeeeeeee sale_valle___eeeee diteerefffeeeeeeeeeee = temperature spvvuvvel value_A)
choice.probabilityy.
1.0
(1.0 + ex. expscaee value )
# compute log probability for actual choice
choices [t] np. lp.hogcchoice. probability
(1 choices [t]) lp choice_ brooce_probability..B
log_likelihon +=
return Llog_ikkeihhor
Your function:
15

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B.2
Learning
Learning prompt
This is aultt--rmed bandit reinforcement learning task, where participants were asked to choose one of the two actions to gain monetary rewards. The task included two possible actions, and on each trial participants had to choose between the two.
They observed the reward for both their chosen and unchosen actions. Each action led to reward according to an underlying probability distribution, which remained the same within block but could change between blocks. Here a task data set from several participants:
Data from participant 1:
Block: 1, Trial: 1, Chosen action: 1, Reward for the chosen action: -1, Reward for the
unchosen action: 1
Block: 1, Trial: 2, Chosen action: 1, Reward for the chosen action: -1, Reward for the
unchosen action: -1
Block: 1, Trial: 3, Chosen action: 1, Reward for the chosen action: -1, Reward for the
unchosen action: -1
Your task is to propose unique cognitive models that could explain the observed behaviors in this dataset. When generating the models think in steps for example: if on trial participant chose specific action and observed given feedback for the chosen action AND the non-chosen action both, what is their subsequent action choice?
Ensure your models have distinet assumptions and parameter sets. Avoid repeating ideas used in previous Kerations.Maks sure all of the model parameters are actually being used. Each model should be implemented as a Python function called Lognitive_modell ' segnittve_model2,, and "cognitive_modees Each of the 3 functions should accept the following lists as arguments: actions, rewards, forgone rewards, and model parameters. Each function should return the negative log likelihood of observed actions given the model parameters.
When you write the function, also include the description of each parameter and what it does in the function's 's meta commented section.
Make sure the equations do not lead to nonsense values (e.g. watch out for division by 0). Make you write functions that are free of bugs, and be executed. Here is initial model guess of how participants solve the task: def cognitive_modee modell (actions, rewards, porgone_rewwrds, parameters)
1r, neg_lr, inverse tetrrseettmperatuure = parameters
values mp. ap.array array([O.5, ([0.5, 0.5])
log_ likelihod 0
for in range (len (actions)):
p0 = ep. p 1ppp)())+p0(1))
(raeeeerrttueeealleeee-aaaaaa (values-values [actions [t]]))
log likelihood np. op)
delta rewards [t] values [actionss if delta >= 0: values [actions [t]] values [actions[t]]] (1r delta)
else: values [actions [t]] values(actions [t]] (neg_ delta)
return -log_ likelihod
16

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Your functions:
B.3 Planning
Planning prompt
In this task participants were instructed to obtain as many gold coins as possible by flying one the two magic carpets (carpets carpets[0] and carpets[1]). After choosing one of the carpets, participants would fly to one of the two mountains (Pink or Blue) where they would encounter one
two genies. Pink Mountain has genies genies[O) and genies[1] [1 and Blue Mountain has genies genies[2] and genies[3].
Participants then chose one of the two encountered genies to ask for gold coins and was either given gold coin not. Different genies gave gold coins with varying probability. Therefore, this was a two-stage decision process: first, participant chose the flying carpet; second, after ending up
of the mountains they chose the genie they would ask for gold coins.
In the task, the magic carpet carpets[O] generally flew the Pink Mountain, and magic carpet carpets[1] generally flew to the Blue Mountain. However, on rare occasions a strong wind would send the magic carpet to less likely destination (e-g., magic carpet carpets[O] would end up flying to the Blue Mountain). The participants might leverage their knowledge of the transition structure in the task (e.g., which carpet likely goes which mountain) to maximize the chance of encountering the genie that on average yielded more gold).
Here is a task dataset from several participants: Participant
Trial O: The participant chose magic carpet A and ended up on the Blue Mountain. The participant rubbed the lamp and received 0 coins.
Trial 1:The participant chose magic carpet and ended up on the Pink Mountain. The participant rubbed the lamp W and received coin.
Your task is propose 3 unique cognitive models that could explain the observed behaviors in this dataset. Think in steps for example: if on trial participant won gold coin after choosing magic carpet that took them to mountain where they asked a genie for gold coins, what their subsequent carpet choice? Do they consider how often they end up on that mountain after choosing that specific carpet?
Ensure your models have distinct assumptions and parameter sets. Avoid repeating ideas used in previous iterations. Make sure all of the model parameters are actually being used. Each model should be implemented as a Python function called 'cognitive_moon modell "cognitive_modde" mociiiee__mmeee_...__ and cognitive_modell
Each of the 3 functions should accept the following lists as arguments: action 1, state, action 2, reward, parameters. Each function should return the negative log likelihood of observed actions given its parameters.
When you write the function, also include the description of each parameter and what it does in the function's meta commented section. Note that for each parameter except the beta, the plausible bounds are between 0 and 1. Make sure the equations do not lead to nonsense values (e.g. watch out for division by 0).
Make sure you write functions that are free of bugs, and can be executed. Here is an initial model guess of how participants solve the task:
17

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def Lognitive_modelle (action_1, state, action_2, reward, parameters);
learning_rate, beta model_ parametrs n_trials len (en(action_)
transition_matric np.array( [[.7, .3,, [.3, .71]
P_choice_1 = mp. (n_tials) p_choice_2 2 zeros (n_ (rrrrsls))
Q up. ((3 2))
for trial in range (n_ trialsil::
max_q = np np.max axis=1) q_stage1 = tramsition_matriy @ max_q
# Compute probability for choice exp_values np. expex * q_stage1)
p_ __choice_[[trial] mp. expexp ebptteta * q_stagel ( np. pp.sumexp__alusee
# Compute probability for choice
state_ide _ix state [trial] +
Ensure correct state indexing
exp_ exp_ values__aas _ff exp (beta * Q[state_ixx, :])
p_choice_2 2[trial] np exp (beta * (istateiixx, action_ {
delta = reward[trial] - Q[state_iat _ixx action_2[triat)
[[taateidx, action_2 [[tria]] learning_ delta
eps = 1e-10
loss (np. sum www.:nppp.ppppooooo 1 eps)
np. sum (np log (p_ccccce__ + eps)))
return log_loss
Your functions:
B.4
Working memory
Working memory prompt
In this task participants are presented with a varying number of states (either 6), and asked to select one of the possible actions. For each state there is a fixed correct action.
Following the action selection, participants observed feedback (0 or 1). Possible states reset the start of each block (that is, there were no overlapping states between the blocks).
The objective the experiment was to understand differences in learning across different cognitive load conditions (3 vs 6 state-action pairs).
Here is task dataset from several participants:
Data from participant 1:
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Block: 0, Set size:3, Trial: 0, State: O, Chosen action: 0, Reward: 1
Block: 0, Set size:3, Trial: 1, State: 1, Chosen action: 1, Reward:
Block: 0, Set size:3, Trial: State: 2, Chosen action:
Reward: 0
Ensure your models have distinct assumptions and parameter sets. Avoid repeating ideas used in previous iterations. Make sure all of the model parameters are actually being used. Each model should be implemented as a Python function called gignitive_modes serrnitvve_mdeel22 and gegnitive_model.. following arrays as arguments: states, actions, rewards, blocks and model parameters. Each function should return the negative log likelihood of observed actions given its parameters.
Each of the 3 functions should accept the
When you write the function, also include the description each parameter and what it does in the function's 's meta commented section. Note that for each parameter except the inverse temperature, the plausible bounds between and 1. Make sure the equations do not lead to nonsense values (e.g. watch out for division by 0).
Make sure you write functions that are free of bugs, and can be executed. Here is an initial model guess of how participants solve the task:
def cognitive m_mmeldell (states, actions, rewards, blocks, parameters): 1r, wm_weight, softmax_bed parameters sof tofat_beeawa 50
nd =
nS = len (np. unique
q (1 nd) op.ones (nS, nA))
w = (1 / nA) * op.ones (nS, nA))
w_o (1 / nA) mp. ones (nS, nA))
log_p = 0
for in range (1en range (ccbboooocttttc))
= block_actions [t]
block_states [t]
= block_reward [t]
Q_s q[s,:]
W_s [[a,:]
p_rl / np. (np. (eppssoftmaxxbeeta((.... [a])))
p_wm = / np. np. sup exp (st_/fffmaabbtaaamm betawm * (W_s V_s[a])))
p_total p- p_wm=m__weight + <1-wm_weight)*pp +p_rl
log-p += np.1og(p_total)
delta r-]_s[a]
q[s] [a] += 1r*delta
r 0:
w[s] [a]
else:
w[s] [a] += lr (0-w[s) [a])
return -log_p
Your function:
C
Decision making: Sanity checks using synthetic data
Task. We designed a task in which decision making agents chose between two options (A and B). Each option is characterized by three features, represented as integers ranging from 0 to 100.
Heuristics. We specifically examined two: the Take the Best and the Tallying heuristic. The Take the Best heuristic selects an option based solely on single prioritized feature, ignoring comparisons across other features (Gigerenzer and Goldstein 1996). Specifically, only the prioritized feature is
19

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used to evaluate the two options, with the option that has the higher value for the prioritized feature winning in the comparison. The Tallying heuristic instead compares the two options based on all three features, counting the number of features for which one option has a higher value than the other, and favoring the option that has a higher number of superior features.
Synthetic data generation. We generated 80 decision problems (each problem including two sets of three features), in accordance with the task specification. During task generation, we ensured that the number of times option A or B is superior is balanced. For Take the Best simulations we deliberately prioritized the second feature to avoid the LLM making a potentially misleading assumption that the first feature should be prioritized. We simulated 40 decisions based each of the two heuristics. Importantly, we initially ensured that our examples were unambiguous -avoiding cases where both heuristics would lead to the same decision. This approach guaranteed the identifiability of decision patterns unique to each heuristic. To further test the robustness of our approach, we simulated second data set that introduced noise to the data simulation. We did this by increasing the proportion of decisions in which the final output of the heuristic was flipped, resulting in the opposite choice than the heuristic would actually make. We considered three noise levels: 0, 0.25, and 0.5. This progressively increased the level of confusion between the two heuristics (Figure B). At a noise level of 0.5, the decision patterns for both heuristies are indistinguishable and are equivalent to random guessing.
LLM prompting We queried the LLM perform two tasks: (1) match the data the source model (model identification) and (2) generate a cognitive model based on the observed data for both decision making and learning experiments (model generation). In the model identification task, the LLM prompt included the code for model candidates provided as Python strings along with simulated decisions. For the model generation task, the prompt included description of the desired structure of the Python function (e.g., the function name, input arguments, and expected output). We considered three different prompting strategies: vanilla (containing only the description of the task setup), Step- Back (Zheng et al., (2023a), and Chain of Thought (CoT: Wei et al. 20.2). This comparison aimed to identify the most effective prompting strategy for subsequent tests.
C.1 Results
Model identification. Model identification enabled us to evaluate the LLM's ability to reason through decision making strategies and map the data to the underlying function. Consistent with previous results (Wei et al.) (022), CoT prompting led to the best L.LM performance (Figure 77) therefore, we used CoT prompting for all subsequent experiments. We found that in the noise-free data set, the LLM was perfect at identifying the source model based the data (Figure 7 When evaluated on the noisy data set, the LLM robustly identified the ground truth heuristic at noise level of 0.25 (Take the Best mean accuracy: 0.86 (SEM = 0.02); Tallying mean accuracy: 0.71 (SEM 0.06)). At a noise value of 0.5 corresponding to random guessing in the data generating process (Figure 7B) the LLM's heuristics predicted decisions at chance level (Take the Best accuracy >> 0.5 test: : t( 1100, p=0.10; Tallying accuracy > 0.5: t( t(9)=0.27, p00.99).
Model generation. Next, we tested whether the LLM could generate decision making algorithm that aligned with the observed strategy patterns, simulated using either the Take the Best or Tallying heuristic. We evaluated the LLM-generatee algorithms on unseen decision tasks, comparing their outputs to the predictions of the ground truth heuristic.
Our analysis of L.M--geeerted functions revealed that the LLM could successfully identify the two underlying heuristics: prioritizing a single feature for Take the Best simulations and performing across-feature comparisons for Tallying simulations (Figure 7C.. The choices generated by the LLM- proposed models matched the ground truth heuristic choices with perfect accuracy in the evaluation tasks. It is notable, however, that for the Tallying heuristic there was slight departure from the ground truth in the LLM --g-errrrteed code - using the total sum of features instead of a tally of superior features. There feature values are not normalized / in the same range). Nevertheless, the LLM proposed an equally valid alternative to the true data generating process.
corner cases where these strategies would make diverging predictions (e.g.. g., if the
To account for the noise the noisy data set, the LLM -.M-ggeeedtedd heuristics deviated more from the underlying heuristics. For noisy Take the Best data, the LLM still prioritized the second feature but
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A
B
1.0
generated
Vanilla
Step-Bace BackBack
Chain of Thought
0.8
heuristic(A, B):
retur 'A' if A[1] B[1] else
0.69
0.31
0.7
0.3
Tallying
0.5
LLM
Tallying
LLM generated code for Tallylnn
T15 Tallynng
Tt8 Tailying
Tte Taillying
0.2
0.5
def heuristic(A, Bt:
return (tsum(A) sum(B]
Decision
Figure 7: A) Identifying the source decision making heuristic by using the LLM to relate data to the source simulation code. We prompted the LLM to identify which the two heuristics (Take the Best or Tallying) underlies the behavioral data from the two-alternative forced choice task, where the agent chooses between two options defined by three features. While exploring the LLM's capacity to perform this task, tried different prompting techniques. B) We tested the LLM with noisy decision making data, with injected noise increasing the confusion between the two heuristics. The LLM shows robustness to noise in the data performance decreases proportionally with noise, but only reaches chance level when the heuristics are indeed indistinguishable (noise level of 0.5). Error bars represent standard error of the mean (SEM) across 10 runs. C) LLM-generated Python function heuristics closely align with the ground truth. The LLM-generated functions remained the same across 10 separate experiment runs.
modified the heuristic to apply only when specific criterion (e.g., feature value differences above certain threshold) was met. For noisy Tallying data, the LLM generated various strategies such as choosing based on the highest overall or minimum feature value.
D Learning: Sanity checks using synthetic data
Task. We implemented a two-armed bandit task, with each of the two options associated with fixed probability of receiving a reward if selected (e.g. p(r = 1|a1) = 0.20; p(r = 1/az) = 0.80). The rewards in our task were binary (r E {0, 1}).
Learning models. The Rescorla- Rescorla-Wagner (RW) model (Rescorla 1972) is commonly used to study learning dynamics the bandit tasks. In the experiment, we considered the vanilla RW model and two variants of it RW with two learning rates (RW + a-+) and RW with stickiness (RW + k).
The RW model posits that the value of each action (V) is determined by the history of rewards obtained from selecting that action. According to the RW model's learning rule, the value of the selected action a (V²) is updated on each trial follows:
V4 = V a (a)
where r /a is the reward prediction error a learning signal that drives the adjustment of the selected action value, and a represents learning that captures the extent which the action value is modified by the prediction error.
Learning models commonly rely on the softmax policy in conjunction with the RW learning rule, providing a way to transform action values into probabilities. The softmax policy introduces the exploration parameter B, which controls the degree to which action selection is deterministic:
P(a) exp(3 Vi) Σ exp(3 Vi
The Rescorla- Rescorla-Wagner model with two learning rates (RW + a differentiates between outcomes that are better/worse than expected. More precisely, the model uses two distinet learning rates for action value updating, contingent the valence of the prediction error:
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Ve + + (r- Vi if V, 20 (ee + a (r V if - Ve <0
t+1
The Rescorla- Saccorla-aagner model with stickiness (RW + K) has the same learning rule the vanilla RW but its policy differs in that the additional weight x is applied to the value of the action that was selected during the previous trial, resulting in greater tendency to choose the previously selected action:
P(a) & exp (BV + +II(a= at-1))
Synthetic data generation. To test how well we recover the ground truth learning model, we simulated 100 agents from each of the two above-mentioned models on a two-armed bandit task, with each task comprising of 150 trials. The simulation parameters were randomly sampled for each agent a range defined by plausible parameter bounds.
A
B
C
1.0
Ground truth agent
1.00
220
Proportion of choosing rewarding action
RW+* RW++*
chance
0.95
0.05
200
ground truth
0.50
180
0.5
0.15
0.85
0.25
160
0.00
R
RW+*
140
0.0 RW
Vanilla RW
LLM
LLM
prediction
model
model
Figure 8: A) Model identification task. The LLM-ggerrated "Modelddntiffiationn function utilizes the SciPy differential evolution method to successfully differentiate between the two learning models. B) Evaluation of the LLM-generated cognitive model based on the data simulated from the RW + revealed that it captured behavior better than the random guessing model and the vanilla RW. C) Simulation of the LLM-generated model showed that it captured the underlying propensity for choosing more rewarding actions. Error bars represent standard error of the mean (SEM) across simulated agents.
LLM Prompting. Identifying the source model by reasoning through the long sequences of learning data, which consisted of 150 actions and rewards, is much more challenging than identifying the underlying decision making heuristic. Additionally, differently configured models can produce similar action/reward trajectories common challenge in models of bandit tasks (Wilson and Collins 2019). As result, we modified the prompts we had developed for the decision making experiment.
Function generation for model identification. Unlike in the decision making case where the LLM directly returned the model identity. the learning task the LLM instead generated function for model identification. The function's 's arguments were predefined (e.g., lists of actions and rewards). The prompt encouraged the L.LM to propose a method for matching the source model to the data without requiring step-by-sted reasoning. The generated function was then manually evaluated to determine its accuracy in identifying the correct model.
Evaluation of LLM-generated cognitive models. The best LLM-generated cognitive models were evaluated in two steps the end of the final sampling run. First, we compared the Bayesian Information Criterion (BIC; Watanabe 2013) of the best model to the ground truth (or the best model from the literature for human data), random and a competing model. Second, we manually implemented simulation script based on the equations of the LLM LLM-ggnnrated model. Using the best-fit parameters from the first step, the script simulated action choices according to the model's equations and parameters, with rewards determined by the probabilistic action-reward contingencies of the task. This step allowed us to assess how well the model captured behavioral patterns by comparing the simulated data with the ground truth.
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Table Examples of parameters in LLM--poposed cognitive models across various sampling runs. Modeling of these mechanisms is documented in previous research (Wilson and Collins 2019))
MODEL PARAMETER
EXPLANATION
Decay
Forgetting mechanism [Paskewitz et al. 2022
Random lapse
Random action-executions (Nassar and Frank 2016)
Bias
Preference for a particular action (Balcarras et al,
2016
Dynamic scaler
Parameter learning rate) adjustment based on the trial numher (Diederen and Schultz (2015)
Exploration bonus
Directed exploration (Wilson et al.
2021
D.I Results
Model identification. We prompted the LLM write a function, called MfdelIdentif cation that would identify the source model based on the underlying data in the learning experiments. We found that the LLM generated function that performed an optimized search over possible model parameter values to find optimal log-likelihood, leveraging the differential evolution algorithm for optimization (Storn and Price 1997) from the SeiPy library. The proposed function returned the identity of the model associated with the smaller negative log-likelihood. We executed this LLM- generated function offline to determine the identity of the model. As shown in Figure SA, we found that the RW + a= model can be successfully identified 95% of times (SEM 4) and the RW model could be identified about 85% of times (SEM= = 7).
Model generation. For model generation, we run GeCCo for 10 iterations where the LLM generated three cognitive models that were fitted to the data offline on each run. The feedback was automatically constructed based on the model fits, and included as the part of the prompt in the subsequent iterations. Note that we only considered the best LL LLM--ennrated models across all runs for model fitting and comparison.
We found that the LLM recovered the RW + a model correctly from its simulated data. That is, the best generated model was the RW model with two learning rates, based on positive and negative feedback (ground truth model BIC: 78.77 78 (SEM = 5.3), LLM-generated model BIC: 78.40 (SEM 1.3).
For the RW + x model, the LLM did not discover the ground truth model. Instead, the best-fitting model contained value-decaying mechanism, which lowered the value of the non-selected action on each trial. This can be viewed as a way to model the forgetting, or the information decaying mechanism, the learning process. The LLM-generated model fitted better than the random guessing model (Figure 83: (99) 7.44, p < 0.001) and the vanilla RW (Figure 3: (999) = 2.36, p = 0.01). As sanity check, we also compared the data simulated based on the LL.-ggnneated model the ground truth by quantifying the proportion of trials in which the simulated agent selected the more rewarding option (Figure 8C). The results showed that the data simulated from the LLM-ddrived model approximated the ground truth data reasonably well (Figure &B, proportion of selecting the more rewarding action: ground truth: 0.69 (SD 0. LLM-generated model: 0.68 (SD 03).
Additionally, for both data sets, we checked which cognitive model parameters the LLM proposed across other sampling runs, beyond quantitatively studying only the best model. We found that the parameters were reasonable and among those commonly cited the cognitive modeling literature.
E
Human experiments
E.1
Decision making
In Hilbig and Moshagen (2014), 79 participants were instructed to repeatedly choose one of two options, labeled A and B, as shown in Figure AA. They were told that these represent fictitious products and that they should infer which product is superior terms of quality. In each trial, they were openly provided with the values of four binary features, explained to them as fictitious expert
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110
60
280
105
270
95
760
240
804
10
No
10 dota
No fendback
No
No
datt
No foodBbokk
abtatio
ooie
Iaak
abration
code
feedbock
ablation
coce
123k
bemplate
desoflation
template
deserppiin
tomplttte
descrptiion
Figure Ablation experiments. A) Ablation experiment in the decision making domain revealed that all components of the prompt were significant for contributing to performance of the L.M--eeerrtte models, with feedback removal from the prompt having the biggest effect. Results of the ablation experiment in the working memory domain B) and learning domain C) also support the impact of feedback.
Data contamination analysis
los18)
0.0
2-armed bandit partial feedback (Chamben al. 2020)
2-armed bandit full feesback (Chambon et al. 2020)
2-step task (da Silva et al. 2020)
multi-ate aib--attribnn bute decsion-making (Hilbig et al. 2014)
reinforcement learning with korking memory (Rmus al. 2023)
datasets
Figure 10: Data contamination analysis using the LogProber method (Yax 2024). LogProber fits a two-parameter exponential model to the cumulative likelihodo of each sequence being checked for contamination: f(x) -A(1 exp Bar). Prompts that are memorized from the pretraining data will show a high acceleration (log B). Following the results presented in (Yax et al., 2024), we set threshold for possible contamination to log 1. We considered prompts up to the point of the choices of human participants, which contained the task instruction, instruction given to help with code generation, and the code template, for the four human datasets on the Llama-3.1-700 Instruct (base model for all winning LLM). The analysis revealed no evidence of contamination, with the acceleration term being substantially below for four human experiments investigated. Specifically, it was 0.1409 for partial feedback condition (Chambon et al. 2020). 0. 0.111 147 for full- feedback condition [Chambon et al. 2020). 0.0171 for multi attrrbbtt decision-making (Hilbig and
Moshagen 2014), 0.1718 for two-step task (Feher da Silva and Hare 2020), and 0.6481 for memory
lask (Rmus et al. 2023).

---

A)
Learning: Full feedback condition
Multi-attribute decision making
100
120
98
96
100
min BIC
94-
min BIC
60
90
88
40
86-
20-
1
3
5
6
7
8
10
4
7
10
Iteration
Iteration
Figure 11: Minimum BIC scores decrease across sampling iterations. A) BIC improvement across iterations in the learning experiment. B) BIC improvement across iterations in the decision making experiment. Shaded area represents variability (SEM) across 5 different independent runs of GeCCo.
ratings. Furthermore, it explained that the four ratings corresponded to ratings from four experts who differ in how well they typically predict product quality (i.e. feature validity), with their validities being 90, .80, .70, and .60, respectively. The participants performed 96 trials in total.
E.2 Learning
In the Chambon et al. (2020) study, 24 participants performed two-armed bandit task designed to disentangle the effects of prediction-error valence on learning (see Appendix Efor additional study details). The task consisted of 16 blocks. The action reward probabilities varied across blocks and were sampled from high-probatee probability reward values (0.9, 0.6) or fow-probabiligg reward values (0.4, 0.1). The rewards in this task were binary, with participants received feedback solely based on their selected action (partial feedback condition; see Figure A2. the other half, the participants received feedback based on their selected action, as well as counterfactual feedback from the alternative action they did not select (full feedback condition; see Figure BA). We focused on the counterfactual condition, presents a broader space of possible data explanations (e.g. differences in learning based on actual and forgone rewards).
(-1,1}), In half of the blocks, the
E.3 Planning
We used data from the "magic carpet" variant of the Daw two-step decision task, implemented by Feher da Silva and Hare (2020).In this version, participants were told they were embarking on a series of magic carpet rides. On each trial, they first chose between two magic carpets (first-stage choice), each which probabilistically transported them to one two mountains (second-stage states). Each mountain housed two genies, of which participants could sclect to potentially receive a reward (second-stage choice). The reward probabilities associated with each genie drifted slowly over time, encouraging ongoing exploration.
The task structure designed to differentiate between model-free and model-based learning strategies. First-stage transitions are probabilistic: chosen carpet leads to its associated mountain with 70% probability (common transition) and the other mountain with 30% probability (rare transition). Second-stage (genie) choices are deterministic.
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A model-free learner reinforces actions that previously led to reward, regardless of the transition type. In contrast, a model-based planner takes into account the transition structure: if reward was obtained following rare transition, the optimal strategy would be to switch strst-stage choices on the next trial to more reliably reach the same second-stage state via common transition.
E.4 Working memory
Participants were instructed to learn stmulus-responee associations in order to earn much reward as possible (Rmus et al. 2023). The number of stimulus-action associations varied across different task blocks or 6), with new stimuli being introduced in each block. Participants encounter one stimulus at the time on the screen, pressed corresponding key and were subsequently provided with deterministic feedback. The rewards in this task were binary, with (r {0, 1)). Each stimulus was shown nine times within block and stimulus order was pseudo-rande randmmlly shuffled.
Cognitive models
F.1 Decision making: Take The Best (TTB), Equal weighting (EQW), Weighted sadditive hueristic (WADD)
Heuristics are simple, resource-cfficient approaches that individuals use to navigate the decision making process effectively (Tversky and Kahneman 1974] Gigerenzer and Goldstein 1996). Assume X, = (Tol.- ................................ .oo) to denote the feature vector for options o {A, B3, w (Wi,... wy) be the inferred feature weights and (W1..... f2) be the feature validities, where J is total number of features. As potential models (Gigerenzer and Goldstein 1996), Hilbig and colleagues considered three heuristics apart from the winning model (pWADD), namely. Take the Best (TTB), Equal Weighting (EQW), and Weighted Additive (WADD), to explain human choices. The Take the Best (TTB) heuristic selects option based solely on single prioritized feature, ignoring comparisons between other features (Gigerenzer and Goldstein) 1996). EQW heuristic instead compares options based all four features, favoring the option that has higher sum of superior features. WADD heuristic is similar to EQW, but weighs the four features differently as per the provided feature validities. Finally, pWADD is the probabilistic variant of the WADD heuristic that uses inferred feature weights instead of using the feature validities (note is the temperature term). The equations for each of the models can be found in the following.
TTB B) arg Tost. oE(A,B)
3 arg jE (1........JJ)
Uj
A
Σ
[AA - HBB) 00
EQW(A, B)
1=1
B, otherwise.
(1)
A,
Σ 12(0 xmn) >0, 0,
WADD(A, B)
j=1
B, otherwise.
((A),
PWADD(A, B)
+ exp[- Ey uy (oaas-a TBJ)]'
p(B), 1 1 ((A(.).
F.2 Learning: Rescorla Wagner model with 4 learning rates, softmax policy and perseveration
The winning model from Chambon et al. 2020 is variant of the RW model with four learning rates ( S a a ut a chosen, unchosen). Thus, the model updates values of actions
26

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differently, depending on whether the outcome of the action is better/worse than expected, as well as whether the action was chosen by the participant or not:
Vi a (r- V(() VV a= (r- ( i Vi
i V 2
( V1-- + at (r'- V) (V. + a² (r' Vi--)
if V2 V,- V, V <
Vi+
V
if -
where -- V is the reward prediction error (RPE) for the chosen action, and V,l-a is the RPE for the unchosen action based the forgone reward. RPEs drive value updates, with a and a** denoting learning rates for positive prediction errors of chosen and unchosen actions, respectively, and af and a= for negative prediction errors.
The softmax policy introduces the exploration parameter 3, which controls the degree to which action selection is deterministic:
exp(B V22)
P(a) = softmax(a)
ΣN exp(3 Vt))
We have also included the perseveration parameter which assigns higher value to the action selected on the previous trial and thus captures the human tendency to repeat previously selected actions: P(a) x exp (8V [a a1-1)).
F.3 Planning: The Mode-baase/Moodelffree Hybrid model
The value of the selected first-stage action in state is updated based on the reward prediction errors from both the first and second stages, as follows:
( (81, a1) = =
(8,, (81,01) a1) + on [Q (82, 02) - ($1, a1)] + an [r+
Q (82, 02)]
Model-free values the second-stage action a2 performed in the second-stage state 82 are updated at the end of the trial (e.g., when the reward is observed) based on the reward prediction error-defined as the difference between the received reward r; and the current value the chosen action:
MP (82, a2) = Q (s2, 02) a2 [rr Q (52, a2)]
The model-based and model-free value of second-stage actions state s2 are equal:
MB (s2, a2)
Q. (s2, a2)
The model-based value of each first-stage action is computed at the time of decision-making based on the values of the second-stage actions, as follows:
Q MB (81.
Σ P(s2 81, a1) max QM $1ES
(s2, .2)
First-stage choices are determined by combining model-free and model-based value estimates for each state-action pair. These are integrated using a weighting parameter w (where 0 S w < 1), which reflects the relative influence of model-based planning. The resulting action values are then passed through softmax function to generate choice probabilities:
P1(s1,a1)= = Ense @i [M (11)) ("xx)
a2( Exea (xQ2()))
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F44
Working memory: The Reinforcement Learning Working Memory model
The learning dynamics in the RL module of this model are the same in the RW model, see Appendix
dRL = Qe(s,a)
[Q( a) at δRL. if ÒRI. >> 0 Q++1(s,a) Qe(ss 0) + a ÓRL. if ÒRL. S 0
where Q denotes the action value, the presented stimulus and the chosen action.
The WM module, rather than integrating information over time, memorizes information from the previous trial. It also allows to encode WM stimulus action weights differently for positive and negative reward prediction error. the case of positive reward prediction error,
Ws,1 a) = (s,1(s,a) = (s(s, + dwm
if бwm >
if 8Wm S 0
where v can be seen as the strength of the imperfection of the association and identical to relative neglect of negative feedback from the RL module a. Additionally, to model forgetting of the stimulus-action associations that are not seen in current trial, the WM module has decay parameter & which pulls the WM weights to their initial values Wo-
W1+1(s:: = (iss;; a²) + o (Wo(s;, a,) - Wi(s,, a,)) vs, Vaj + a)
To make choices, the Q-values and WM weights are transformed into choice probabilities using softmax policy:
exp(BRLLr(ss,d) En Exp(BBLQQiS exp( (x,
PRt((ass)
Pww(als) D (Xp(BWMW.ss..))
The RL and WM policies are then combined using WM weight parameter w, determining the relative reliance WM in taking action.
(als) = " (aw(als) + -w=*) (R(()()
w is dependent on the set size of stimulus-response association pairs na. because as WM's capacity exceeded with increasing set size, individuals should rely more RL. The model thus predicts learning to be fast when set size is small due to high reliance on WM and learning to be more incremental when set size is high due to increased reliance on RL. Finally, the policy also captures value-independent random lapses:
P (1 P
PRIWM +
Y
where n
denotes the uniform random policy and the noise parameter.
G Partial feedback (learning) experiment
As part of the learning experiment we considered two datasets by Chambon et al. (020)) learning in partial feedback condition and full feedback condition. the partial feedback condition participants only observed the outcome of the actions they chose (as opposed to also observing forgone rewards
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A
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pertiel feedback
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detta revards[t] valurstaction)
rp sumtno wep.simdoo-epppeeeeeee exppeeperreeee...e tenparatare velues])
wapt (inverse, pureatuur oslues)
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Figure 12: Learning experiment: partial feedback A) Schematic of the learning task from Chambon al. (020), where participants chose between two options and received feedback only for the option they chose. B) Model fit comparison: I.M --.eerrrrteee model from R1 on average fit as well as RW C) Posterior predictive checks showing close alignment between human data and predictions of the best LLM model. D) Code of the top LLM-generated model (R1), differentiating learning based on positive and negative feedback.
based on unchosen actions, Fig. A2 Chambon et al. (2020) reported a two-learning rate RW model (RW22) the best model in their experiment. We applied the GeCCo to the partial feedback dataset using a vanilla RW model (with a learning rate and an inverse softmax temperature) as the function template. We found that the R1 proposed the best cognitive model (Fig. B2 which closely aligned with RW2a in terms of containing two learning rates, with a notable difference that the learning rates differentiated learning based positive/negative feedback, rather than the prediction error valence; Fig. D2P. This model fits neatly into current debates about learning from valenced rewards, providing valid alternative hypothesis differentiating learning rate based on valence of reward prediction error. Posterior predictive checks revealed close agreement between the LLM-generated model and human data; Fig. 12C.
H Posterior predictive checks
Posterior predictive checks are crucial tool for evaluating model's generative validity- is. its ability to reproduce patterns observed in real behavior-thus offering insight into how well the model captures the underlying cognitive processes. To perform posterior predictive checks, used ChatGPT to convert the participant behavior and parameter values as input and return negative log likelihood. into simulation functions that generate behavior from parameter values based on the model equations. Using the fist-fitting parameters for each participant, we simulated behavior and compared it to the actual human data through domain-specifi informative analyses applied to both simulated and real datasets.
model-fitting functions Junctions--orginnallly designed to take
In the decision making experiment, we computed the proportion of option choices that explained by three different heuristics: Equal Weighting (EQW), Take The Best (TTB). and Weighted Additive (WADD); see section F.] for details about the heuristics. We conducted this analysis on human data, 2) data simulated from the best LLM-generated model, and 3) data simulated from p pWADD the best performing model from the literature. We then examined correlation between the heuristic-spects proportions on participant-level as the data was simulated using the actual decision tasks (e.g. options and features) from the experiment participants experienced and best-fit fit model parameters for each participant.
In the learning experiment, we simulated data for each participant using best fitting parameters, and block-specific reward probability contingencies specified in the Chambon et al. (2020) (high or low). We computed alignment with human choices for both the best performing LLM model, and RW in high/low reward blocks separately, as well as for carly/late trials in blocks to test whether the models capture temporal learning dynamics.
In the planning experiment we performed the canonical analysis from Daw et al. (201)), which looks at the likelihood of participants repeating the same stage 1 response, contingent whether 1) participants received reward at the end of the previous trial and 2) experienced a common or a rare transition en route to feedback.
In the RL- LL- experiment, we computed prrticipant-specifee learning curves showing the proportion of correct choices a function of the stimulus iterations the number of times participants
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countered the stimulus. We used participant-spese stimulus sequences and correct stimulus-action mappings, to ensure that the behavior between simulated and real data comparable.
I Using AIC for the best model selection in the GeCCo pipeline
Decision making
Planning
480
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BIC
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pWADD
R1
Llama
Qwen
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Hybrid
R1
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Qwen
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(AIC-based
(AIC-based feedback)
feedback)
Figure 13: Comparison of the best GeCCo-generated models in the decision making and planning domains, including models from a pipeline variant using AIC-based feedback. This demonstrates that the pipeline adaptable different fit metrics and that AIC-based results closely replicate those obtained with BIC-based feedback.
J Using LLM-generated template in the prompt
Decision making
Learning
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pWADD
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Liama with LLM-generated
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Llama with LLM- -MM-geeeeteed template
template
Figure 14: Comparison of the best DoCCoogeneratee models in the decision decision-making and learning domains, including models from pipeline variant using an LLM-generated (rather than hand-crafted) template. The results show performance comparable to using hand-crafted template.
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Reviews:
[{"review_id": "oXINuDNrbZ", "reviewer": "Reviewer_MXWn", "summary": "This paper introduces the Guided generation of Computational Cognitive Models (GeCCo), a novel pipeline that leverages Large Language Models (LLMs) to automate the discovery of cognitive models. The framework prompts an LLM with task instructions, participant data, and a code template to generate candidate models in the form of Python code. These models are then fitted to data, and their predictive performance (measured by BIC) is used to provide iterative feedback to the LLM, guiding it toward better solutions. The authors test this approach across four distinct cognitive domains (decision-making, learning, planning, and memory), demonstrating that the LLM-generated models often match or outperform state-of-the-art, human-handcrafted models from the literature.", "questions": "Q1: Does achieving a better fit (e.g., lower BIC score) equate to a better scientific theory? How does the GeCCo pipeline ensure that the generated models represent genuinely new, theoretically insightful cognitive hypotheses, rather than just being more complex or cleverly parameterized functions that find statistical regularities in the data without corresponding to a plausible cognitive process?\n\nQ2: The prompt includes a Code template that often contains a baseline model from the literature. How can the authors be certain that the LLM is exploring a diverse model space, rather than being heavily anchored to the provided template? While the ablation study showed this was the 'weakest' predictor of performance, a weak effect is not a null effect.\n\nQ3: The paper presents a data contamination analysis using LogProber and finds no evidence of leakage from the prompts. However, many of the discovered model components are well-established concepts in cognitive science (e.g., forgetting parameters, separate learning rates for positive/negative errors etc). How do the authors disentangle genuine, data-driven model discovery from the LLM simply reconstructing known modeling components from its vast pre-training data, which almost certainly includes the cognitive science literature?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n\n- Significance and Originality: The paper addresses a significant and challenging problem in cognitive science: the time-consuming and expertise-dependent nature of computational model development. The proposed GeCCo framework is an original and innovative approach to this problem, effectively demonstrating how modern AI can be used as a tool for scientific discovery. The work is a compelling example of AI-assisted science and is likely to have a strong impact on the field.\n\n- Quality and Rigor: The methodological quality of the paper is significantly higher than your average \"we prompted some LLMs to do stuff\" papers. The GeCCo pipeline is well-designed, and the iterative feedback loop is a sound mechanism for guiding model discovery. The authors rigorously test their framework across four canonical and diverse cognitive paradigms, showing its versatility. The inclusion of multiple control experiments is a good too. The prompt ablations, data contamination analysis, and ground-truth recovery simulations provide strong evidence for the pipeline's robustness and help isolate the key drivers of its success. The comparisons are made against strong, well-established baseline models from the literature as well as CENTAUR, a foundation model of cognition, which provides an estimate of explainable variance.\n\n- Clarity: The paper is very well-written, clear, and easy to follow. The introduction does an excellent job of motivating the problem and situating the work within the existing literature. The GeCCo framework is explained clearly, and the schematic in Figure 1 provides an intuitive overview of the entire process. Providing the full prompts in the appendix significantly enhances the paper's reproducibility.\n\n\n# Weaknesses and Areas for Improvement\n\nWhile the paper is strong overall, there are several areas where clarification and more nuanced framing would strengthen it.\n\n- Clarity of \"Democratization\" Motive: The paper suggests that a primary motivation is to \"democratize and scale cognitive model development\". This is an interesting goal, but its meaning and scope are not fully clarified. Cognitive modeling is a highly specialized task, and it is unclear who the target audience for this democratization is. This point would benefit from a more concrete explanation of what a \"democratized\" modeling ecosystem would entail and why it is a necessary or desirable goal for the scientific community.\n\n- Distinction Between Models and Theories: The paper occasionally makes strong claims about LLMs broadening the space of possible \"theories\" and generating \"conceptually plausible theories\". While the work clearly demonstrates the ability to generate novel models with high predictive accuracy, a model with a good fit is not the same as a new scientific theory. Models don't need to be correct, they just need to be useful. But the word \"theory\" imply causal mechanisms that must be rigorously tested for correctness. \n\n- Interpretation of Statistical Significance: Some of the key comparisons, while promising, are not statistically conclusive. In the comparison against the CENTAUR foundation model (Table 1), the LLM-generated models do not show a statistically significant performance difference in two of the four domains (Decision-making, p=0.78; Planning, p=0.18). The claim that LLM-generated models \"matched or exceeded\" CENTAUR's performance across domains should be qualified to reflect this.", "was_revised": true, "final_justification": "The authors' response clarified the few issues and questions I had. Therefore, I maintain my score as Accept.", "rebuttal": "Dear reviewer MxWn,\n\nThank you very much for your comments. We appreciate that you found our paper addresses a significant and challenging problem in cognitive science, and the GeCCo framework an original and innovative approach to this problem. We especially like that you believe our pipeline is a compelling example of AI-assisted science and is likely to have a strong impact on the field. Below, we discuss the specific concerns that you raised and how we have sought to address them. We believe that by addressing them we have further improved clarity and contribution of our paper. \n\n1. Clarified the Democratization Motive\n2. Addressed the difference between models and theories\n3. Addressed the dependence of GeCCo-generated models on the template\n4. Clarified the GeCCo-CENTAUR comparison\n5. Clarified the BIC interpretation and model quality assurance \n6. Addressed data-driven model discovery vs LLM's pre-trained knowledge\n\n**1. Democratization Motive** \n\nCurrently, cognitive modeling requires extensive familiarity with literature, proficiency in programming, and a reasonably strong mathematical background, thus restricting participation to a relatively small community of experts. GeCCo creates a more democratized ecosystem by automating a technically demanding exercise like cognitive modeling, thereby broadening participation beyond traditional cognitive modeling communities. \n\nWe envision the primary beneficiaries as:\n- Experimental psychologists, including graduate students or junior researchers, who have conceptual insights but lack coding or modeling expertise, or would benefit from additional modeling ideas with a quick implementation. \n- Interdisciplinary researchers (e.g., neuroscientists, economists, behavioral ecologists) seeking cognitive models for hypothesis testing without diverting extensive resources into specialized methodological training.\n- Educators and students, for whom GeCCo can serve as a practical learning tool, facilitating hands-on experience with computational cognitive modeling early in their training.\n\nWe believe this democratization is desirable for the scientific community because it directly accelerates cognitive research by (a) increasing the speed and volume of hypothesis testing, (b) enabling a more diverse range of researchers to contribute to theory development, and (c) encouraging interdisciplinary collaboration.\n\n**2. Difference between models and theories** \n\nWe agree that predictive accuracy alone does not establish a model as a scientific theory, and the term “theory” carries stronger commitments to causal explanation, mechanistic grounding, and empirical validation. We will revise the manuscript to avoid overstating claims and use the term “model” rather than “theory” throughout, except where we are referring to future theoretical implications. Additionally, we will incorporate the reviewer’s point in the limitations section (certain parts used verbatim):  “While the work demonstrates the ability to generate novel models with high predictive accuracy, a model with a good fit is not the same as a new scientific theory. To discover theories, future work must rigorously test the causal role of the cognitive mechanisms underlying the GeCCo models through carefully designed experiments.”\n\n**3. Dependence of GeCCo-generated models on the template** \n\nWe agree that a weak effect of the template doesn’t imply a null effect. To quantify the diversity of GeCCo-generated models relative to the provided code template, we embedded each cognitive model into a high-dimensional semantic space using the all-MiniLM-L6-v2 model from SentenceTransformers and computed the cosine distance between each generated model and its corresponding template, across all iterations and runs. We then ran a one-sample t-test over (1 – cosine distance) for each cognitive domain to assess whether the distance was significantly greater than 0. Across all four domains, we observed a significant semantic divergence from the original template. For more complex domains, such as Planning and Working memory, we observed higher semantic divergence from the original template (Planning: M = 0.24 $\\pm$ 0.01, t(35) = 39.79, p < .001; Working memory: M = 0.19 $\\pm$ 0.005, t(35) = 38.56, p < .001). For simpler task domains, like Learning and Decision making, the divergence was smaller but still reliably nonzero (Decision making: M = 0.07 $\\pm$ 0.002, t(149) = 32.18, p < .001; Learning: M = 0.16 $\\pm$ 0.005, t(83) = 34.79, p < .001), possibly reflecting either task simplicity or implicit instruction-following by the model. These results suggest that GeCCo explores a diverse space of models, particularly when task complexity demands richer solutions. \n\nWe also conducted 2 additional experiments to further explore the effect of the template: \n\n1. Auto-generated code template:  We modified the GeCCo pipeline to allow the LLM (LLaMA-3.1 70B) to generate an initial code template using a more detailed natural language description of the function specification. We applied this version to two cognitive domains (learning, decision making) and found that the best model discovered in each case, in terms of BIC, closely matched the performance of the models from the original pipeline using hard-coded templates (learning: M = 77.89 $\\pm$ 5.57  vs.  79.77  $\\pm$ 6.29, t(15) = 0.90, p = 0.384; decision-making: M = 39.30  $\\pm$ 4.59 vs. 39.30  $\\pm$ 4.59, t(52) = -1.29, p = 0.202).\n\n2. Template-free reasoning model: We also ran the R1 reasoning model without any code template at all, providing only formatting constraints and the task description in natural language. On the decision-making task, GeCCo produced a model with comparable performance to the baseline model from literature (M= 53.7 $\\pm$ 5.56; t(52) = 0.48, p = 0.31), but worse compared to the GeCCo-generated model with a template (t(52) = 3.85, p = 0.0001). \n\nThe results suggest that in the future iterations of GeCCo we could replace the fixed template with auto-generated one, and even loosen constraints on the model structure and formatting in prompts.\n\n**4. GeCCo-CENTAUR comparison** \n\nTo clarify, the goal of this analysis was not to show that GeCCo outperforms CENTAUR, but to estimate how much explainable variance in behavior each model captures. We treat CENTAUR's predictive performance as a reasonable proxy for the total explainable variance; see the control experiments section for details.\n\nWe acknowledge that the two-sided t-test we used only tests for a mean difference in negative log-likelihood (NLL) between GeCCo and CENTAUR, not for equivalence, so it can't determine whether GeCCo matches or exceeds CENTAUR. To address this, we computed the Bayes factor (BF) to quantify evidence for or against a performance difference:\n\n- Memory: log10(BF) = 3.52. Strong evidence for a difference; since CENTAUR NLL is higher, the GeCCo is decisively better.\n- Learning: log10(BF) = 8.96. Strong evidence for a difference; CENTAUR NLL is once again higher, so GeCCo is decisively better.\n- Decision making: log10(BF) = -0.81. Moderate evidence for no difference; GeCCo NLL is marginally lower, but the BF points to no clear difference between GeCCo and CENTAUR. \n- Planning: log10(BF) = -0.23. Weak evidence for no difference; CENTAUR NLL is lower, but the evidence is weak/inconclusive.\n\nThese results support our statement that GeCCo generates cognitive models that match or outperform CENTAUR in predictive accuracy. \n\n**5. BIC interpretation and quality assurance** \n\nTo ensure that the GeCCo pipeline does not generate theoretically invalid, overly complex, or overfitted models that merely capitalize on statistical regularities, we employed a multi-faceted evaluation strategy:\n\n- BIC Evaluation: We used BIC to penalize overly complex models. In all domains tested, GeCCo-generated models had fewer parameters (N) than the best-performing baseline models:\n  - Decision Making: GeCCo N=2, baseline N=5\n  - Learning: GeCCo N=5, baseline N=6\n  - Planning: GeCCo N=3, baseline N=7\n  - Working Memory: GeCCo N=5, baseline N=7\n- We tested model performance on held-out data to reduce the chance that results reflect dataset-specific statistical regularities.\n- Posterior Predictive Checks: We simulated model behavior in contexts specifically aligned with the experimental designs used to probe the target cognitive processes. This helps rule out overfitting to incidental data features and ensures the models generalize in cognitively meaningful ways.\n- Plausibility checks:  Experts in cognitive modeling reviewed each GeCCo-generated model to ensure theoretical soundness, including the use of meaningful cognitive parameters in processes like forgetting, value updating, and exploration.\n\nRegarding genuine novelty, most GeCCo-generated propose novel combinations of well-established cognitive mechanisms that, when integrated, yield strong generative performance and data fit and as such are scientifically valuable (see “Atypical Combinations and Scientific Impact” by Uzzi et al., 2013). \n\n**6. Data-driven discovery vs LLM pre-trained knowledge** \n\nAlthough our ablation experiments confirmed that participant data significantly influences the performance of GeCCo-generated models (β = -12.84, p < 0.05; see the ablation study in Section 7), it is challenging to precisely quantify the extent to which GeCCo-generated models reflect genuine data-driven discovery versus leveraging pre-trained conceptual knowledge. Disentangling their contributions remains an open and important future direction. One promising direction for future work—not yet explored in our current manuscript—would be to perform RL-based fine-tuning of LLMs directly on participant-level behavioral data, thereby explicitly encouraging cognitive model generation driven by empirical observations while still building on pre-trained conceptual knowledge. We will include this point in the discussion section of the camera-ready version."}, {"review_id": "XF9E96umT1", "reviewer": "Reviewer_xLM6", "summary": "Computational cognitive models are often hand-crafted by researchers, a process that can be highly time-consuming. In this work, the authors develop a new pipeline (GeCCo) for the automated generation of computational cognitive models. The authors apply GeCCo in 4 domains of cognitive science and assess the role of each component of their pipeline, as well as the base models.", "questions": "I would especially like a clearer description of the scale of BIC and error bar computation (as noted above), and other statistical tests run. \n\nThe ablations and Fig 11 indicate that feedback was very important. It'd be great if the authors could dig into this more (I believe the figure is never referenced in the text?) \n\nR1 beta spanned zero in the statistical models -- so can we really say it produced \"better\" models? I'd consider adjusting that sentence (or moving those analyses fully to the Supplement?)", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 4, "strengths_and_weaknesses": "The paper is very well-motivated. The problem the authors take on, and the general method they develop, could have broad implications for cognitive science. The paper definitely hits the mark on originality. \n\nEach step in the proposed pipeline was also generally well-motivated, and I appreciated that the authors conducted rigorous ablations into the role of each component. \n\nWhere the paper struggled, in some respects, is in the actual communication of the experimental findings -- particularly cross-model comparisons. The authors rely heavily on BIC as a measure; however, the actual scale of BIC is not communicated anywhere in the paper. It is clear in many cases that BIC \"goes down\" for the LLM-generated models, but it's hard to understand from the aggregate measures alone whether that is \"significant\" (beyond statistical significance). The error bars were hard to interpret as well (and seemed large in many instances). Is this SEM over seeds (the 5 runs?) or bootstrapped over participant data? \n\nMoreover, in many instances, the BIC is actually not substantially going down? The role of the base models in producing good computational cognitive models is also highly sporadic. I did not walk away with a clear picture on the role of the base model in synthesis. The motivation suggested that the authors did care about base model, and of course -- as in good research! -- the results may be surprising (or negative, into the role of base models, as they seem to be here). However, I would want to see a bit more of an analysis into the role of base model. One possible way the authors could better understand the role of base model is by trying LLaMA 3.1 8b? Or at least one other model of the same class as one of the models they considered here? The choice of models seemed a bit scattered. For instance, we may expect that the results overall are worse when using 8b. But if they are not, then that would throw a bit of a wrench into GeCCo and the results would seem too sporadic; like it just matters that the models \"happen\" upon a good synthesized model. If possible, I'd encourage the authors to try 8b if they have not already. \n\nThere was also limited detail on the way the paired t-tests was computed. Paired over per-participant judgements?", "was_revised": true, "final_justification": "The paper is a substantive modeling advance. While I had initial concerns about the clarity and quality of the results, the authors assuaged many of my concerns in their rebuttal. I am hopeful that by having them clarify the prose -- this will make for a strong paper that I believe warrants acceptance.", "rebuttal": "Dear Reviewer xLM6, \n\nThank you for your comments. We are happy that the reviewer found are paper to be ‘very well-motivated’ and that our paper hits the mark in terms of ‘originality’. In particular, we are happy that the reviewer shares our enthusiasm about GeCCo having ‘broad implications for cognitive science’.  Below, we address the concerns and questions you have raised and how we addressed them.  We believe that resolving them has further improved the clarity and contribution of our paper. \n\n1. Clarified the interpretation of BIC values\n2. Clarified the role of base models\n3. Clarified the performance metrics and statistical tests \n4. Clarified the role of feedback \n5. Clarified the claims regarding R1 performance\n\n**1. Interpretation of BIC values** \n\nWe interpreted your comment as a suggestion to clarify the range and interpretation of BIC values. For each GeCCo-generated model, we used the average left-out sample BIC of the best-performing literature model as a benchmark for best-case fit, and a random guessing model as the lower bound. Based on this, the BIC ranges for the four domains are as follows (in the order of best literature model – random guessing):\n\n- Decision Making: [52.42 – 133.08]\n- Learning: [85.24 – 221.80]\n- Planning: [437.24 – 585.01]\n- Working Memory: [275.00 – 415.27]\n\nOur primary goal was to interpret BIC scores in a relative sense, ensuring that the GeCCo models at least approximated the BIC scores of the best existing models. To further support these comparisons, we also conducted exceedance probability (EXP) analysis, which estimates the likelihood that a given model is the most frequent explanation of participants’ behavior (Stephan et al., 2009). The results are as follows:\n\n- Decision Making: GeCCo model EXP = 0.99, Literature model EXP = 0.01\n- Learning: GeCCo model EXP = 0.97, Literature model EXP = 0.03\n- Planning: GeCCo model EXP = 0.85, Literature model EXP = 0.15\n- Working Memory: GeCCo model EXP = 0.99, Literature model EXP = 0.01\n\nError bars in the main figures reflect variability across participants. We will update the figure captions to include BIC scores for the random guessing models and the explanation of how the SEMs were computed for greater clarity.\n\nRegarding the relatively small differences in BIC values, we would like to emphasize that the literature models used as baselines for comparison are among the most well-established and validated cognitive models in their respective domains, developed over many years of research. Therefore, outperforming these models is understandably challenging. Nonetheless, GeCCo offers valuable insights by generating alternative, cognitively plausible models that match, and in some cases outperform, these strong baselines. We believe this ability to propose viable alternative explanations remains an important contribution.\n\n**2. The role of base models**\n\nTo explore how different model features (size, model family, and reasoning capabilities) influence performance on the model generation task, we selected three models—Llama, Qwen, and R1 (distilled Llama). While LLama and Qwen come from two different model families and sizes (70B and 72B, respectively), R1-distilled-Llama-70B provides the means to examine whether reasoning capability contributes to the quality of the generated models. As per the reviewer's suggestion, we considered the smaller version of Llama with 8B parameters.\n\nSpecifically, we tested Llama-8B in the decision making domain, which presents the simplest model case, and found that the average BIC for the best model generated by Llama-8B was 89.58 (SEM over participants = 2.20), which is roughly twice as high as the BICs for models generated by R1-distilled-Llama-70B and Llama-70B (M = 39.30  $\\pm$ 4.59)—indicating comparatively weaker performance. This model effectively just picked an option with a higher sum over features scaled by estimated weights.  \n\nCritically, we would also like to bring to your attention that running Llama-8B requires providing additional constraints and information to the pipeline. Without this extra guidance, the model frequently failed to adhere to the required code structure or produce syntactically correct or executable code, which is a requirement for the GeCCo pipeline. In fact, when we attempted to run GeCCo with Llama-8B on more complex domains (e.g., planning or working memory), the generated code failed to compile (syntax errors) or did not follow the specified structure even with extra guidance.  \n\n**3. Performance metrics and statistical tests**\n\nWe thank the reviewer for suggesting these writing improvements. We computed BIC scores for each model on a per-participant basis, and then applied a paired-sample t-test to compare two models across participants, accounting for the within-subject design (i.e., each participant contributes one BIC score per model). We will include this information at the first mention of the t-test in the main text of the camera-ready paper. \n\n**4. The role of feedback**\n\nWe suspect that the reviewer confused the title of Figure 11 (A), which currently is “learning: full feedback condition”, as referring to the feedback provided to the LLM to improve performance. We apologize for the confusing title and would like to clarify that the results in Figure 11 demonstrate iterative improvements of GeCCo-generated models in two experiments: two-armed bandit task with full feedback (Chambon et al. 2020) and multi-attribute decision making (Hilbig et al. 2014). To avoid further confusion,  we will adjust the title of A to  “two-armed bandit task with full feedback (Chambon et al. 2020)” and also refer to it in the main text of the camera-ready version. \n\nNevertheless, below we still provide our speculation regarding the role of feedback in GeCCo: \n\nWe think feedback was the strongest contributor to GeCCo's performance for three reasons: (1)  it provides the best-performing model, selected based on BIC, as a reference. This is crucial as GeCCo can use it as a reference model and improve its predictive performance further; (2) it provides a list of parameter names used so far by GeCCo, whose size grows over iterations. GeCCo seems to be effectively making use of this information available in its context window to come up with new combinations that might successfully improve predictive performance. (3) The feedback also explicitly encourages exploration, which might prevent GeCCo from being stuck in a narrow space of models. Future work should conduct more targeted ablation studies to verify if these are indeed the reason why feedback plays such a crucial role in GeCCo.\n\n**5. R1 performance** \n\nWe would appreciate it if the reviewer could clarify their question. Our current interpretation of the question is that it concerns the BIC differences between the baseline models and the R1 model, as presented in Figure 2B and Figure 4B. We would like to clarify that our claim regarding the superiority of the R1 model pertains specifically to the decision-making domain (Figure 2B), where the BIC difference is indeed statistically significant. In the case of planning (Figure 4B), we explicitly noted that while the average BIC of the R1-generated model is lower, the BIC differences were not statistically significant (although the exceedance probability favored the R1 model). Please let us know if we have misunderstood the question."}, {"review_id": "awcL5WSiMk", "reviewer": "Reviewer_L7y4", "summary": "Cognitive models, in the sense of this work, are interpretable generative models with a small number of parameters that aim to explain human behavior in an experiment. Here, the authors prompt LLMs to write a Python program to explain the data from a human experiment. The prompting method is quite simple, works for open weights models, and yields interpretable models that have a better fit to human data.", "questions": "It is hard to tell if the differences in prompting strategy between the current paper and the Li et al model discovery paper were significant, since no attempt is made to compare the new prompting pipeline to the Li et al one. Could you provide some clarifications on the comparison?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "STRENGTHS\n\nThe paper is well-written and the framing is compelling. \n\nThe problem is important: an automated way to speed up model discovery could accelerate science in general and cognitive science in particular.\n\nIn four case studies, the paper shows that the method is effective in finding interpretable models that explain the human data well, sometimes better than the best existing hand-crafted models.\n\nThorough follow-up experiments and ablations isolate the components of the prompting pipeline and the LLMs that make them most effective at this task. \n\nThe fit to human data is measured carefully using BIC.\n\nWEAKNESSES\n\nFrom a methodological perspective, the novelty is somewhat limited. Prior work has shown that LMs can be prompted to construct programs that explain data, especially the Li et al paper on statistical model discovery (cited in this manuscript). So if I understand correctly the main novelty here is the application to cognitive science, but this isn't made fully clear in the introduction. I am happy to be corrected on this and would appreciate author engagement.\n\nFrom the cognitive science perspective, it was hard for me to appreciate what new insight emerged from the automatically constructed models. There are four case studies, each on a different cogntiive domain, and there's not enough background information on any of them to convey to a reader who's not already familiar with those tasks what the LLM-generated cognitive model discovered and how it differs from existing models. Perhaps one of the case study is a slam dunk for the method but this should be made clearer; one option could be to move one or two the case studies to the appendix and focusing on the biggest success stories.\n\nThe tasks and resulting cognitive models are quite simple. I would have appreciated more detail on the synthetic data experiments, which could provide a nice way to determine whether the method can scale to more complex datasets.", "was_revised": true, "final_justification": "I thank the reviewers again for engaging with my comments! To summarize, I found the paper good but not excellent: on the methodological front, the idea is very sensible but seem quite related to earlier work on using LLM prompting for automatic model discovery; on the scientific front, because there are so many qualitative case studies for a short paper, it was a little hard for me to understand what insight was delivered by the method. The being said, it's a good proof of concept for automatic model discovery in cognitive science, and certainly above the acceptance threshold in my view.", "rebuttal": "Dear Reviewer L7y4, \n\nThank you for your comments. We are glad that you found the paper 'well-written’, framing ‘compelling’ and experiments ‘thorough’. We are particularly happy that you found the problem of automating cognitive model generating ‘important’. Below, we address the comments, concerns, and questions you raised in detail, and how we sought to address them. We believe that resolving them has further improved clarity and contribution of our paper.   \n\n1. Highlighted scientific insights from GeCCo-generated models\n2. Addressed the inclusion of all domain results \n3. Elaborated on the comparison to Li et al. (2024) paper\n4. Tested GeCCo performance in a more complex setting using synthetic data \n\n**1. Scientific insights from GeCCo-generated models**\n\nThe value of the GeCCo approach is in its ability to generate alternative models that explain behavioral data while achieving comparable—if not better—fit and predictive accuracy compared to the best domain-specific models in the literature. Below, we summarize domain-specific insights from GeCCo-generated models. These points are briefly mentioned in the respective results sections, and we would be happy to expand on them further in the camera-ready version:\n- Decision Making: The GeCCo-generated model uses only two parameters. Notably, it introduces a single discount term that enables the model to arbitrate between different heuristics (e.g., weighted additive vs. take-the-best), without requiring these heuristics to be explicitly encoded in the model. This results in a more parsimonious explanation of decision-making strategies.\n- Learning: The GeCCo-generated model distinguishes between two sets of Q-values: one tracking values updated based on chosen outcomes, and a separate counterfactual (meta) set updated from forgone outcomes. While these are typically combined into a single value representation with asymmetric learning rates, GeCCo separates them, raising interesting questions about how counterfactual and experienced values might be weighted during decision-making. Additionally, the model incorporates forgetting for unchosen actions, introducing a temporal asymmetry in how value information is retained, and sidesteps the need for choice stickiness, which is often included primarily to improve model fit, rather than out of theoretical interest.\n- Two-Step Task (Planning): The model produced by GeCCo avoids the standard model-based vs. model-free dichotomy, yet still captures plausible strategies participants may use. For example, it accounts for participants differentiating between common and rare transitions, and updating reward expectations accordingly, but without relying on the traditional components of the reinforcement learning framework. This aligns with recent calls in cognitive science (e.g., see Collins & Cockburn, 2020) to look beyond canonical constructs and explore alternative computational explanations.\n- Working Memory: Traditional models attribute set size effects (i.e., cognitive load) to working memory limitations. In contrast, the GeCCo-generated model attributes these effects to the RL system, specifically by allowing the learning rate to vary with set size. This challenges the common assumption that RL processes are relatively insensitive to cognitive load, but is consistent with prior findings that neural reward prediction error (RPE) signals, a hallmark of reinforcement learning, are attenuated under high cognitive load (Collins et al., 2017).\n\n**2. Inclusion of all domain results**\n\nWe understand that the descriptions in the main paper may be too concise for readers without a background in cognitive science. Due to space constraints, we have included detailed descriptions of each of the four tasks in the Appendix and referenced them in the main text.\nRegarding choosing success stories to focus on - while the definition of the success case is subjective, it is our impression that all of the GeCCo-genreated models offer interesting insights as outlined in detail above; it would be somewhat arbitrary to single out only one (or two) of them. Additionally, we believe that the consistency of performance across domains is itself a valuable and nontrivial outcome, particularly for a first time demonstration of a general purpose model generation pipeline. \n\nRegarding differences between GeCCo generated models and the respective baselines - the model insights we outlined above explain what GeCCo generated models offer beyond the baselines. We will additionally create a difference table, that will more explicitly show GeCCo-baseline model differences, including model parameters and model summaries for each of the domains. We will add this table to the Appendix, and reference it in the main text.\n\n**3. Comparison to Li et al. (2024)**\n\nWe appreciate the reviewer’s observation and agree that the Li et al. paper is highly relevant to our work. We already cited it in the manuscript, but we will revise the text to more clearly acknowledge the methodological similarities in the Introduction. Both approaches share an iterative structure involving model generation, evaluation, and refinement. We also agree that the general structure of our prompt is similar to that of Li et al. However, our prompt structure was naturally derived from the requirements involved in computational cognitive modeling (having a description of the experiment, the data, and some priors regarding viable model structures). We believe that the prompt similarities between our approaches are likely a result of the shared nature of the model generation task. \n\nWe agree that perhaps the most substantive difference lies in the domain focus. Cognitive modeling introduces unique challenges—such as behaviorally grounded evaluation metrics, domain knowledge, interpretability aligned with psychological theory, and domain-specific constraints on model plausibility—that are not guaranteed to be addressed by pipelines developed for general statistical modeling.\n\nOne important implementation difference lies in the feedback mechanism: in Li et al., the feedback is LLM-generated (via a separate critic model that provides natural language critiques), whereas in GeCCo, the feedback consists of the best-performing model code and a list of previously attempted parameter names, explicitly structured to avoid duplicate models and promote exploration.\nTo clarify this connection, we will add a more detailed discussion of the similarities and differences with Li et al. in both the Introduction and Related Work sections of the paper.\n\n**4. GeCCo performance in a more complex setting using synthetic data**\n\nWe liked the reviewer’s suggestion of using synthetic experiments to verify the scalability of GeCCo to more complex setups. To this end, we conducted additional experiments using synthetic data with systematically increasing complexity. Specifically, we simulated 100 reinforcement learning (RL) agents performing multi-armed bandit tasks with a varying number of options: 2, 4, 6, and 8 arms, each with 150 trials. While 2-armed and 4-armed bandits are common in the literature, tasks with 6 or 8 arms are rarely studied due to their increased complexity.\nWe applied the GeCCo pipeline to each of these datasets and compared the resulting models to the ground-truth models used to generate the data. We found that the models discovered by GeCCo achieved comparable fit to the ground truth, and substantially outperformed a random guessing model:\n\n- 2-armed bandit task:\n  - GeCCo BIC = 135.03 (SEM = 5.73) \n  - Ground truth BIC = 131.00 (SEM = 5.63)\n  - Random guessing BIC = 207.94\n\n- 4-armed bandit task:\n  - GeCCo BIC = 350.42 (SEM = 7.39)\n  - Ground truth BIC = 337.66 (SEM = 8.86)\n  - Random guessing BIC = 415.88\n\n- 6-armed bandit task:\n  - GeCCo BIC = 483.60 (SEM = 6.51)\n  - Ground truth BIC = 467.30 (SEM = 9.84)\n  - Random guessing BIC = 537.52\n\n- 8-armed bandit task:\n  - GeCCo BIC = 551.50 (SEM = 10.50)\n  - Ground truth BIC = 552.73 (SEM = 10.54)\n  - Random guessing BIC = 623.83\n\nThese results demonstrate that GeCCo generalizes well to more complex task environments, and maintains strong performance even as the decision space increases substantially."}, {"review_id": "6gMnLGcIS0", "reviewer": "Reviewer_YeVa", "summary": "This paper is builds computational cognitive models using LLMs.  It describes the method for generating computational cognitive models called GeCCo and then applies this to 4 main experimental outcomes: \n\t1. Decision Making\n\t2. Learning\n\t3. Planning\n\t4. Working Memory\nIt then evaluates the effects of different control experiments and finishes with a discussion of the model discovery capabilities.", "questions": "How do some of the other popular models perform on these tasks (E.g. models from OpenAI / Anthropic / Google).\n\nI see you performed prompt ablations, but how did you optimize and arrive at your specific prompt form for each section?  \n\nWhat about using a reasoning models without a code template (since those don't rely as much on few shot)?", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n•\tThe proposed method has strong empirical performance across different cognitive tasks and models\n•\tThe proposed method relies only on in-context learning and is tested with open-source models, contributing to its accessibility and reproducibility\n•\tControl experiments, ablation studies, and ground truth recovery analysis add rigor to the results\n•\tGood experiment setup for the 4 classes.  Clearly written with value proposition and experimental setup.\n•\tAblation studies on prompt components was useful and Figure 9 was especially interesting.  Curious as to how variance on these components (e.g. how to formulate each of those portions of the prompts) also affects things.\n\nWeaknesses\n•\tThe paper focuses on evaluating the predictive performance of the LLM-generated models, but it does not assess their scientific value. Do the models offer new cognitive insights or reinforce existing findings? Are the underlying mechanisms theoretically plausible? The paper could benefit from additional qualitative analysis or evaluations by domain experts to help establish the scientific utility and interpretability of the generated models. \n•\tUsing BIC as the only model selection criterion may bias toward simpler models. Including alternative measures (e.g., AIC, cross-validation) would help support the paper’s claims about models’ generalizability. \n•\tThe paper fixes the number of iterations to 10 but does not analyze how model quality evolves across iterations. It would be useful to examine whether performance plateaus or continues improving, and whether fewer iterations can be used.\n•\tWould love more detail in the method section to fully understand the approach with more expanded and detailed explanation.  The current description is just so incredibly information dense, it is difficult to understand some of the exact methodology and algorithm without source code or some more structured framework describing the approach and its specifics.  An appendix of some exemplar models would be helpful for understanding the underlying methods a bit better.", "was_revised": true, "final_justification": "This is a well written paper and I maintain my original score recommendation after reading the rebuttals.  It was a technically solid paper and contained some good contributions that are worth sharing with the field.", "rebuttal": "Dear reviewer YeVa, \n\nThank you for the comments and questions. We are glad you found that the ‘proposed method has strong empirical performance’ and that by relying on in-context learning and open-source models, it contributes to accessibility and reproducibility. We are also happy that you found the experimental setups for the four cognitive domains ‘clearly written’ along with their ‘value proposition’.  Below we discuss the specific concerns that you raised and how we have sought to address them. \n\n1. Addressed scientific value, plausibility, and insights from GeCCo models\n2. Ran an experiment with GeCCo using an alternative fitness metric\n3. Tested early stopping instead of a fixed iteration count\n4. Expanded the explanation of our approach\n5. Responded to the question on closed-source models\n6. Explained how we derived the prompt structure\n7. Ran an experiment with a reasoning model without a code template\n\n**1. Scientific value, plausibility, and insights from GeCCo models**\n\nEach GeCCo-generated model was carefully reviewed by domain experts and deemed plausible. The cognitive parameters used (e.g., forgetting rate, lapse, loss aversion, stickiness) were reasonable, and the modeled processes aligned with domain expectations. While scientific value is more subjective, we summarize our key takeaways from each model below.\n\n- Decision making: The GeCCo-generated model offers a parsimonious explanation of possible decision-making strategies. It features an elegant implementation that enables arbitration between different heuristics through a single parameter, highlighting the model’s interpretability and compactness.\n\n- Learning: The GeCCo-generated model separates two sets of Q-values: one updated from chosen outcomes and another counterfactual set from forgone outcomes. While these are often combined with asymmetric learning rates, GeCCo model keeps them distinct—raising questions about how experienced and counterfactual values are weighted. It also includes forgetting for unchosen actions, introducing temporal asymmetry in value retention, and avoids using choice stickiness, which is often added primarily to improve model fit in this task.\n\n- Planning: The two-step task is typically modeled with a hybrid model-based/model-free (MB/MF) framework. The GeCCo-generated model captures key behavioral patterns using general principles of transition learning and value updating, without explicitly separating MB and MF systems. This offers an alternative perspective, aligning with recent encouragement from cognitive scientists to move beyond strict adherence to the MB/MF dichotomy (Collins & Cockburn, 2020).\n\n- Working memory: The GeCCo-generated model attributed cognitive load effects to the reinforcement learning process rather than to working memory mechanisms. While unconventional, this aligns with findings that reward prediction errors (RPEs)—linked to striatal RL signals—are reduced under low cognitive load (Collins et al., 2017), suggesting RL processes can be, at least indirectly, shaped by information load.\n\n**2. Alternative fitness metric**\n\nWe agree that different model selection criteria (e.g., AIC) can be used to construct feedback for GeCCo. Following your suggestion, we ran the GeCCo pipeline using AIC-based feedback in two domains: decision making and planning.\n\nIn the decision-making domain, the best AIC-favored model included three parameters—temperature, discount factor, and an option difference penalty—differing from the BIC-selected model by the inclusion of one additional parameter. The AIC model outperformed the literature baseline (t(52) = 41.40, p < .001), but underperformed relative to the BIC-based GeCCo model (t(52) = 29.73, p < .001).\n\nIn the planning domain, the best AIC model included four parameters: learning rate, softmax temperature, forgetting rate, and policy weight. As in the decision-making domain, it differed from the BIC-selected model by one parameter. The AIC model's fit did not differ significantly from the literature baseline (t(15) = 1.36, p = .09) or the BIC-based GeCCo model (t(15) = 0.95, p = .82).\n\nThese results suggest that using AIC instead of BIC may lead to slightly more complex models, but the differences in model structure and overall fit are modest. The takeaway is that the GeCCo pipeline is compatible with either fitness metric for feedback generation, leaving it to the researcher to explore and choose the criterion that best suits their modeling goals.\nRegarding cross-validation, our current implementation already evaluates each model on held-out data—that is, on participants whose data are not included in either the prompt or the feedback optimization process.\n\n**3. Fixed iterations and early stopping of GeCCo**\n\nWe direct the reviewer to Figure 11 in the Appendix, which shows how GeCCo’s performance improves across iterations in two domains. BIC scores tend to decrease, then plateau, and doesn't necessarily improve after that. We will reference this figure in the main text to ensure it’s not overlooked.\n\nTo improve efficiency, we tested an early stopping version of GeCCo that halts when a generated model matches or outperforms the baseline model's BIC. Applied to the planning domain, this approach produced a model that fit the data comparably to both the literature model (t(15) = 1.36, p = 0.09) and the fixed-iteration GeCCo model (t(15) = 2.08, p = 0.07). This model included stage-dependent learning rates, a decision temperature, and a transition learning parameter.\n\nThese results suggest GeCCo does not require a fixed number of iterations, though using more iterations may benefit researchers interested in model diversity. On average, early stopping required just 2 iterations across 5 runs.\n\n\n**4. More detailed description of the approach**\n\nWe attempt to further clarify our approach below as a step-by-step guideline. In the camera-ready version, we will:\n\n- Include the final best model for each domain in the Appendix  \n- Make our code publicly available  \n- Use the additional page to offer further methodological details as needed  \n\nGeCCo Pipeline Overview:\n\n- **Step 1: Prompt construction** (see Figure 6 in the Appendix)  \n  We construct a prompt that includes:\n  \n    - (a) a description of the experimental task  \n    - (b) data from a subset of participants  \n    - (c) instructions for the code-generation task—specifying the function name, expected input arguments, return values, etc.  \n    - (d) an example model (provided as a function template) to guide output structure and format  \n\n- **Step 2: Model generation**  \n  Using the prompt from Step 1, we prompt the LLM to generate three distinct cognitive models with non-overlapping parameter sets. These models are parsed from the LLM response, converted into executable Python functions (e.g., using `exec(function_string)`), and parameter names are extracted.\n\n- **Step 3: Model fitting and evaluation**  \n  Each model is fit to a second, held-out subset of participant data (i.e., not used in the prompt). We evaluate model fit using the Bayesian Information Criterion (BIC), and optimize using `scipy.optimize.minimize` with the L-BFGS-B algorithm, initialized from 20 random starting points.\n\n- **Step 4: Feedback construction**  \n  We identify the best-performing model based on BIC:\n    - On the first iteration, this is the best of the three newly generated models  \n    - On later iterations, it is the best model across all iterations so far  \n    - This model and a list of previously used parameter names are included in the next prompt as feedback to guide generation and avoid duplication  \n\n- **Step 5: Iterations**  \n  Steps 1–4 are repeated for 10 iterations. At each iteration > 1, model generation is guided by feedback, and the best-so-far model is updated accordingly.\n\n- **Step 6: Replication across 5 runs**  \n  The full pipeline (Steps 1–5) is run five times to assess stability.\n\n**Notes:**\n- If model generation fails (e.g., due to syntax or runtime errors), the iteration is automatically restarted.  \n- The best model from each full run is tested on a third subset of the data. These are the results reported in the main paper.  \n\n\n**5. Using closed-source models**\n\nWe were unable to consider closed-source models like GPT, Claude, or Gemini in this work due to cost constraints and our commitment to making GeCCo accessible to the broader scientific community. While it’s possible that these models might yield better performance, we believe it is our responsibility to optimize the pipeline using open-source models that are freely accessible and reproducible.\n\n**6. Deriving the prompt structure**\n\nWe identified key informational components based on the needs of computational modeling: a description of the task, participant data, priors over possible cognitive models. Additional components—such as function specifications and guardrails for the LLM—were designed based on what worked best with our text parsers. For example, we structured these elements to enable reliable function extraction from the LLM's responses. Once finalized, we used a fixed prompt structure across all domains (see Figure 6), varying only the task descriptions and corresponding data.\n\n**7. Reasoning model without a code template**\n\nAs suggested, we ran the GeCCo pipeline using a reasoning model (R1 distilled Llama, for consistency) without a predefined template in the decision-making domain. The best model included a single parameter—decision temperature—and achieved a BIC comparable to the literature model (mean = 53.7, SEM = 5.56; t(52) = 0.48, p = 0.31), but performed notably worse than the best model generated with a template. This suggests that while reasoning LLMs can produce meaningful models without templates, doing so does not necessarily yield better results."}]

Author rebuttal:

Meta-review: This paper dives into the automatic discovery of computational cognitive models, which are previously hand-crafted by researchers. LLMs with iterative feedback is developed to achieve this. The experiments are conducted on four scenarios: 1. Decision Making, 2. Learning, 3. Planning, 4. Working Memory.  The authors find the discovered models have good performance.

**Strengths**

1. The LLM-based framework is novel in the domain of computational cognitive model. The including of task instructions, participant data, and a code template is well-motivated. The iterative feedback loop is sound for getting better models.

2. The studies problem of automating computational cognitive models is important, having implications for cognitive science.

3. The tests are conducted on four scenarios, showing its good performance compared with human-handcrafted models and its findings in interpretable models.

**Weakness**

1. The technical novelty is somewhat limited from the methodological perspective, as there are already some methods proposed in scientific discovery which share similar spirits. Although the authors summarize the difference with the Li et al. paper, it is better to have a discussion with the LLM-based methods used in scientific discovery from a broader perspective.

2. This paper does not provide distinction between models and theories. The terms should be used correctly.

3. Only the metric BIC is used to fit to human data in the original paper. The authors add other metrics in the rebuttal.

4. The testing scenarios are somewhat simple and the performance in a few cases are not very significant. 


All the four reviewers give acceptance recommendation to this paper. In summary, although this paper still has some limitations, this is a good paper, particularly for the computational cognitive science domain.

Most of the reviewers think the author feedback is satisfactory and maintain their positive ratings.
Decision: Accept (poster)