Abstract:In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the cycle of theory development and refinement, assisting researchers in formulating the precise definitions that models require, and making accurate predictions. This manuscript introduces a computational implementation of Pavlovian learning models in a Python environment, termed Pavlovian Associative Learning Models' Simulation (PALMS). In addition to the canonical Rescorla-Wagner model, attentional approaches are implemented, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelley's Hybrid, and a novel extension of the Rescorla-Wagner model featuring a unified variable learning rate that synthesises Mackintosh's and Pearce and Hall's opposing conceptualisations. To our knowledge, only the first attentional model has been previously specified computationally in a general design tool. PALMS integrates a graphical interface that permits the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. It uniquely enables the simulation of experiments involving hundreds of stimuli, such as those used with human participants, and the computation of configural cues and configural-cue compounds across all models, thereby substantially broadening their predictive capabilities. A comprehensive description of the models' implementation is provided in the paper. We evaluate PALMS by simulating five published experiments in the associative learning literature that assessed the predictive scope of existing models, and we show that this implementation provides neuroscientists with a useful tool for identifying critical variables, refining experimental designs, making precise predictions, comparing model fitness, and formulating new theoretical approaches.
| Comments: | PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The environment source code and the latest multiplatform release build are accessible as a GitHub repository at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.07519 [cs.LG] |
| (or arXiv:2602.07519v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.07519 arXiv-issued DOI via DataCite |
Submission history
From: Esther Mondragón [view email]
[v1]
Sat, 7 Feb 2026 12:33:22 UTC (12,962 KB)
[v2]
Tue, 10 Feb 2026 08:39:58 UTC (12,962 KB)
[v3]
Wed, 13 May 2026 21:41:46 UTC (4,220 KB)