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arxiv: 2602.07519 · v3 · pith:6S5SADMTnew · submitted 2026-02-07 · 💻 cs.LG

PALMS: A Computational Implementation for Pavlovian Associative Learning Models' Simulation

Martin Fixman , Alessandro Abati , Juli\'an Jim\'enez Nimmo , Sean Lim , Esther Mondrag\'on This is my paper

Pith reviewed 2026-05-16 06:22 UTC · model grok-4.3

classification 💻 cs.LG
keywords associative learningPavlovian conditioningRescorla-Wagner modelcomputational simulationPython toolattentional modelsconfigural cues
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The pith

PALMS turns mathematical models of Pavlovian learning into runnable Python simulations for complex experiments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces PALMS as a software package that encodes several associative learning models as executable code rather than static equations. It covers the basic Rescorla-Wagner model plus attentional variants from Pearce and Hall, Mackintosh, and Le Pelley, along with a new extension that unifies their differing learning-rate rules. Users enter full experimental designs through a graphical interface in a format familiar to experimentalists, and the program handles simulations involving hundreds of stimuli plus configural cue compounds. Validation comes from re-running five published experiments and showing that the outputs match the models' intended behavior.

Core claim

PALMS is a Python implementation that directly translates the update rules of the Rescorla-Wagner model, the Pearce-Kaye-Hall model, the Mackintosh Extended model, Le Pelley's Hybrid model, and a novel unified variable learning rate model into code. The package accepts alphanumeric experimental designs, computes configural cues and compounds for all models, and supports large stimulus sets typical of human studies. Running the code on five published datasets confirms that the simulations reproduce the expected associative strengths and attentional shifts.

What carries the argument

The PALMS Python package and its graphical interface, which convert the mathematical definitions of the learning models into step-by-step simulation routines that accept design files and output trial-by-trial associative values.

If this is right

  • Researchers can identify which parameters most influence predictions by running controlled simulations.
  • Experimental designs can be tested and adjusted computationally before collecting real data.
  • Different models can be compared directly on the same dataset to measure relative fit.
  • New theoretical variants can be added by modifying the unified learning-rate module.
  • Configural compounds can be simulated for any of the implemented models, expanding their range beyond elemental stimuli.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same simulation structure could be reused to test whether biological learning mechanisms scale to very large stimulus sets.
  • Linking PALMS outputs to neural recording data might reveal which brain signals correspond to the model's attention and error terms.
  • The unified learning-rate rule offers a concrete starting point for deriving hybrid models that combine error-driven and attention-driven updates.

Load-bearing premise

The code faithfully reproduces the original mathematical update equations of each model without introducing bugs or unintended side effects.

What would settle it

Run PALMS on the exact designs from the five published experiments and check whether the generated associative strengths and attention values match the numerical results reported in those papers.

Figures

Figures reproduced from arXiv: 2602.07519 by Alessandro Abati, Esther Mondrag\'on, Juli\'an Jim\'enez Nimmo, Martin Fixman, Sean Lim.

Figure 1
Figure 1. Figure 1: PALMS layout displaying a MLAB model simulation of Haselgrove et al., Experiment 3 [68]. The design includes three Phases and one Group. The different sections of the layout are identified and framed by a black rounded rectangle, namely, the Design input (top of the GUI), and from left to right, the Model selection buttons, model Parameters area, Per CS Parameters area, the Plot area, the simulation Functi… view at source ↗
Figure 2
Figure 2. Figure 2: Simulation results for a within-subjects blocking design for Groups Blk Exp1 McN and Group Blk HS Target across phases. From left to right, Phase 1, Phase 2, and Phase 3. Individual stimuli are shown with different coloured lines and marker shapes. The top panel display RW simulations, whereas the bottom panel presents Mackintosh Extended simulations of the same hypothetical experiment. Random trials inclu… view at source ↗
Figure 3
Figure 3. Figure 3: Simulations of a simplified Learned Irrelevance [48]. The plots on the left show associative strength across 8 training trials during Phase 2 for the Learned Irrelevance, CS-Prexposure, and Novel groups. On the left side, the corresponding α value. From top to bottom, simulations of the Pearce, Kaye and Hall model, the Mackintosh Extended model, Le Pelleys’ Hybrid model and the MLAB model. Random trials in… view at source ↗
Figure 4
Figure 4. Figure 4: A simulation of Haselgrove et al. 2025 Experiment 2 [51]. The top panel shows the results predicted by the Pearce-Kaye-Hall model during the test in Phase 2, for Group D-novel (left panel) and Group D-repeated (right panel). The middle panel displays corresponding results as simulated by Le Pelley’s Hybrid model, using an out-of-range αH value (0.05). On the bottom panel, Le Pelley’s Hybrid model predictio… view at source ↗
Figure 5
Figure 5. Figure 5: Simulation of Byrom and Murphy 2019 Experiment 1 [91] run with 1500 random sequences. The left panel shows the results predicted by the RW model with configural cues for Group Bicond (middle lines) and Group Unicond (top and bottom lines). The right panel displays Mackintosh Extended simulated associative strength for the same groups and conditions. Consistent with the authors’ prediction, when configural … view at source ↗
Figure 6
Figure 6. Figure 6: Simulation of Hall and Pearce Experiment 2 [64]. Associative strength predictions for each group, T+, L+, and T−, during Phase 2 are displayed on the left panels. The corresponding α values on the right. Simulations from different models are shown top-to-bottom: Pearce-Kaye-Hall, Mackintosh Extended, Le Pelley’s Hybrid and MLAB. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_6.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces PALMS, a Python implementation of Pavlovian associative learning models including the canonical Rescorla-Wagner model, attentional variants (Pearce-Kaye-Hall, Mackintosh Extended, Le Pelley Hybrid), and a novel unified variable learning-rate extension that synthesizes opposing attentional accounts. The tool accepts alphanumeric experimental designs, supports hundreds of stimuli and configural cue compounds across all models, and is evaluated via re-simulation of five published experiments from the associative learning literature.

Significance. If the implementations prove faithful, PALMS would supply a practical, extensible simulation platform that lowers the barrier to quantitative model comparison, experimental design refinement, and exploration of configural and large-scale designs that are otherwise cumbersome to code by hand. The novel unified learning-rate synthesis is a substantive conceptual contribution whose utility hinges on demonstrated numerical fidelity to the source equations.

major comments (2)
  1. The evaluation section reports successful simulation of five published experiments yet supplies no quantitative fit statistics, RMSE values, or side-by-side numerical tables comparing PALMS outputs to the original published model predictions or to independent re-implementations; without these, the claim that the code faithfully reproduces the cited models (especially the novel unified extension for configural compounds) cannot be assessed.
  2. No unit tests, formal verification protocol, or explicit parameter-by-parameter comparison against the source equations for the unified variable learning-rate model is described; this is load-bearing for the central claim that PALMS constitutes a reliable synthesis of Mackintosh and Pearce-Hall accounts.
minor comments (2)
  1. The abstract states that 'a comprehensive description of the models' implementation is provided'; the corresponding section should include explicit pseudocode or equation-level mappings for the configural-cue handling routine used by all five models.
  2. Clarify whether the graphical interface accepts only the alphanumeric format described or also supports direct parameter entry for the free learning-rate parameters listed in the model definitions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. The points raised highlight important aspects of validation that we will strengthen in the revision. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: The evaluation section reports successful simulation of five published experiments yet supplies no quantitative fit statistics, RMSE values, or side-by-side numerical tables comparing PALMS outputs to the original published model predictions or to independent re-implementations; without these, the claim that the code faithfully reproduces the cited models (especially the novel unified extension for configural compounds) cannot be assessed.

    Authors: We agree that quantitative comparisons would allow readers to assess fidelity more rigorously. In the revised manuscript we will add side-by-side tables for all five experiments, reporting PALMS outputs alongside the original published predictions (where available) together with RMSE values. For the novel unified variable learning-rate extension, which has no prior published numerical predictions, we will supply explicit numerical examples for both elemental and configural compounds, including the exact learning-rate values generated at each trial. revision: yes

  2. Referee: No unit tests, formal verification protocol, or explicit parameter-by-parameter comparison against the source equations for the unified variable learning-rate model is described; this is load-bearing for the central claim that PALMS constitutes a reliable synthesis of Mackintosh and Pearce-Hall accounts.

    Authors: We acknowledge that a formal verification section is necessary to substantiate the synthesis claim. The revised manuscript will include a new subsection detailing unit tests for each model component, a verification protocol that walks through the unified learning-rate formula step-by-step, and direct parameter-by-parameter comparisons (e.g., attention weights, associability terms) against the combined Mackintosh and Pearce-Hall equations for representative trial sequences. revision: yes

Circularity Check

0 steps flagged

No circularity: implementation and re-simulation of existing models

full rationale

The paper introduces PALMS as a Python tool implementing canonical models (Rescorla-Wagner, Pearce-Kaye-Hall, Mackintosh, Le Pelley) plus a novel unified variable learning rate presented as a conceptual synthesis. Evaluation proceeds by re-simulating five published experiments from the literature. No load-bearing step reduces by construction to fitted parameters, self-citations, or renamed inputs; the contribution is the software artifact and its application to existing designs, which remains independent of any internal loop. The synthesis is defined explicitly rather than derived from the simulations themselves.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that standard associative learning models from the literature are correctly translated into executable code and that the novel extension constitutes a meaningful synthesis of existing attention theories.

free parameters (1)
  • learning rate parameters
    Each model variant uses learning rates that are either fixed or adjusted per simulation; these are treated as inputs rather than derived.
axioms (1)
  • domain assumption The mathematical formulations of Rescorla-Wagner, Pearce-Kaye-Hall, Mackintosh Extended, and Le Pelley Hybrid models are standard and correctly implemented.
    Implementation relies on canonical definitions from the associative learning literature without re-derivation.

pith-pipeline@v0.9.0 · 5597 in / 1244 out tokens · 77280 ms · 2026-05-16T06:22:58.666917+00:00 · methodology

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