PALMS: Pavlovian Associative Learning Models Simulator

2026-02-07

AI Summary

Existing tools struggle to uniformly and efficiently simulate and compare multiple classical conditioning learning models, particularly when handling multiple stimuli, configural cues, and diverse attention mechanisms. To address this limitation, this work presents a Python-based graphical simulation platform that integrates prominent models—including Rescorla-Wagner, Pearce–Kaye–Hall, Extended Mackintosh, and the Le Pelley Hybrid—and introduces a novel unified variable-learning-rate extension of the Rescorla-Wagner model. The platform supports experiment-like design inputs, enabling efficient simulation of learning processes involving hundreds of stimuli with millisecond-level runtime, real-time multi-model prediction comparison, and integrated visualization. It has successfully replicated several classic experimental findings, and its open-source, cross-platform implementation substantially enhances the scalability and accessibility of model comparison and theoretical validation in associative learning research.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Probabilistic Circuits and Graphical Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Simulations are an indispensable step in the cycle of theory development and refinement, helping researchers formulate precise definitions, generate models, and make accurate predictions. This paper introduces the Pavlovian Associative Learning Models Simulator (PALMS), a Python environment to simulate Pavlovian conditioning experiments. In addition to the canonical Rescorla-Wagner model, PALMS incorporates several attentional learning approaches, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelley's Hybrid, and a novel extension of the Rescorla-Wagner model with a unified variable learning rate that integrates Mackintosh's and Pearce and Hall's opposing conceptualisations. The simulator's graphical interface allows for the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. Moreover, it uniquely enables the simulation of experiments involving hundreds of stimuli, as well as the computation of configural cues and configural-cue compounds across all models, thereby considerably expanding their predictive capabilities. PALMS operates efficiently, providing instant visualisation of results, supporting rapid, precise comparisons of various models'predictions within a single architecture and environment. Furthermore, graphic displays can be easily saved, and simulated data can be exported to spreadsheets. To illustrate the simulator's capabilities and functionalities, we provide a detailed description of the software and examples of use, reproducing published experiments in the associative learning literature. PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The simulator source code and the latest multiplatform release build are accessible as a GitHub repository at https://github.com/cal-r/PALMS-Simulator

Problem

• Pavlovian conditioning
• associative learning models
• configural cues
• attentional learning
• model simulation

Innovation

• Pavlovian conditioning
• associative learning models
• variable learning rate
• configural cues
• computational simulation