Pith Number

pith:6S5SADMT

pith:2026:6S5SADMTNW2RH27KM6KXB3HIJZ

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

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

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

arxiv:2602.07519 v3 · 2026-02-07 · cs.LG

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

That the code faithfully reproduces the original mathematical definitions of the Rescorla-Wagner, Pearce-Kaye-Hall, Mackintosh, and Le Pelley models and that the novel unified learning rate extension is a valid synthesis without introducing implementation artifacts.

C3one line summary

PALMS is a computational tool implementing canonical and attentional Pavlovian learning models with support for large experiments and a new unified learning rate variant that combines Mackintosh and Pearce-Hall ideas.

References

102 extracted · 102 resolved · 0 Pith anchors

[1] E. Alonso, E. Mondragón, What have computational models ever done for us?: A case study in classical conditioning, International Journal of Artificial Life Research (IJALR) 4 (1) (2014) 1–12.doi:10.40 2014 · doi:10.4018/ijalr.2014010101

[2] Neuhaus, W and Reininger-Gutmann, B and Rinner, B and Plasenzotti, R and Wilflingseder, D, et al., The rise of three Rs centres and platforms in Europe, Alternatives to Laboratory Animals 50 (2) (2022 2022 · doi:10.1177/02611929221099165

[3] R. Rescorla, Pavlovian conditioning. it’s not what you think it is, The American Psychologist 43 3 (1988) 151–160.doi:10.1037/0003-066X.43.3.151 1988 · doi:10.1037/0003-066x.43.3.151

[4] D. R. Shanks, Learning: From association to cognition, Annual Review of Psychology 61 (2010) 273–301.doi:10.1146/annurev.psych.093008.100519. 31 2010 · doi:10.1146/annurev.psych.093008.100519

[5] D. T. Benton, An associative-learning account of how infants learn about causal action in animates and inanimates: A critical reexamination of four classic studies, Journal of Experimental Psychology: 2024 · doi:10.1037/xge0001656

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:16.261077Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f4bb200d936db513ebea679570ece84e6acf611856a581a8538bbfd94944baf0

Aliases

arxiv: 2602.07519 · arxiv_version: 2602.07519v3 · doi: 10.48550/arxiv.2602.07519 · pith_short_12: 6S5SADMTNW2R · pith_short_16: 6S5SADMTNW2RH27K · pith_short_8: 6S5SADMT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6S5SADMTNW2RH27KM6KXB3HIJZ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f4bb200d936db513ebea679570ece84e6acf611856a581a8538bbfd94944baf0

Canonical record JSON

{
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    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-02-07T12:33:22Z",
    "title_canon_sha256": "b162c9bc6735bf9138a3967891a0972dad242865ff2e01fd043edc5d7bea2e02"
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    "kind": "arxiv",
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