**Information on the experimental data (data folder)** The folder structure follows current recommendations (https://bids-specification.readthedocs.io/en/stable/04-modality-specific-files/07-behavioral-experiments.html). Subfolder *raw*: In every participant-specific folder, there are two data files, namely, - _event_log_learn.csv, which contains the raw behavioral data of the VISFORTH learning phase, and - _patches_first_phase.json, which contains information concerning the arrangement of the circles of each visual search field presented during the learning phase of the experiment. Subfolder *processed*: In every participant-specific folder, there are two data files, namely, - _task-rl_learn_events.csv, and - _task-rl_learn_events.tsv, which both contain the processed behavioral data of the learning phase (BIDS-conform format). **Simulated and generated data** The folder *Simulations* contains the artificial data generated for model validation. The folder *Evaluation* contains the model evaluation data, exploring the potential of the behavioral models in light of the experimental data. The folder *Posthoc* contains the post-hoc model validation data, generated based on the model evaluation data. **Information on the code files** - *abm_agent.py*: implementation of the agent class - *abm_bmc.py*: implementation of the Bayesian model comparison procedure (BIC and PEPs) - *abm_definition.py*: agent model-specific definitions for simulation, generation, estimation, and post-hoc model validation - *abm_estimation.py*: implementation of the maximum likelihood estimation procedure - *abm_evaluation_run.py*: execution of the experimental data evaluation - *abm_evaluation.py*: implementation of the experimental data evaluation - *abm_face_validity.py*: validation of the trial-by-trial expected reward value estimate updates and decisions as reported in Appendix F of the manuscript - *abm_figure_[number].py*: generation of the respective figure, included in the manuscript - *abm_figures.py*: implementation of the figure class (customization of the figure layout) - *abm_imshow.py*: implementation of the visualization of the imshow class used for visualizing data on a 2D raster - *abm_log_likelihood.py*: implementation of the log likelihood evaluation procedure - *abm_posthoc_run.py*: execution of the post-hoc model validation - *abm_posthoc.py*: implementation of the post-hoc model validation - *abm_prepare.py*: preparation of BIDS-conform experimental data for agent-based behavioral modeling - *abm_simulation.py*: implementation of the data simulation procedure - *abm_statistics.py*: implementation of the descriptive statistics evaluation - *abm_task.py*: implementation of the task model - *abm_validation_run.py*: execution of the model validation procedure - *abm_validation.py*: implementation of the model validation - *data_preprocessing_learn.py* and *data_preprocessing_functions.py*: transformation of the raw experimental data into a BIDS-conform data format