{"paper":{"title":"PALMS: A Computational Implementation for Pavlovian Associative Learning Models' Simulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PALMS turns mathematical models of Pavlovian learning into runnable Python simulations for complex experiments.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alessandro Abati, Esther Mondrag\\'on, Juli\\'an Jim\\'enez Nimmo, Martin Fixman, Sean Lim","submitted_at":"2026-02-07T12:33:22Z","abstract_excerpt":"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-"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PALMS turns mathematical models of Pavlovian learning into runnable Python simulations for complex experiments.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"a0a33d2cac29ec2a6a1d5dad74a066c693c9e90aa844274e6a18769b3e3d59f3"},"source":{"id":"2602.07519","kind":"arxiv","version":3},"verdict":{"id":"970c795e-8e36-4067-924c-21904fae7604","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T06:22:58.666917Z","strongest_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.","one_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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_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.","pith_extraction_headline":"PALMS turns mathematical models of Pavlovian learning into runnable Python simulations for complex experiments."},"references":{"count":102,"sample":[{"doi":"10.4018/ijalr.2014010101","year":2014,"title":"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","work_id":"57568da8-8ecc-4472-8e65-b9b2c2b8c017","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1177/02611929221099165","year":2022,"title":"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","work_id":"ccc0f85c-f70e-4c6b-a28c-02a66f15fb47","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1037/0003-066x.43.3.151","year":1988,"title":"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","work_id":"e10ed4d6-430a-4e49-ab4d-5fcd5cb0dd08","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1146/annurev.psych.093008.100519","year":2010,"title":"D. R. Shanks, Learning: From association to cognition, Annual Review of Psychology 61 (2010) 273–301.doi:10.1146/annurev.psych.093008.100519. 31","work_id":"5d9ce334-dd08-4707-8107-439644901107","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1037/xge0001656","year":2024,"title":"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:","work_id":"4f52db07-e32e-4e7d-9c49-586fb42d0448","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":102,"snapshot_sha256":"f6cb7c5ea4b3d6f520d9782b635031f4c1ac28c060400fd411b75d063475bff5","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b598b5ac7e619f328443eb4613e135633a86f3e0478240c0fe3e91dd95807b69"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}