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Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and parameter recovery study.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Simulation study › Network architectures and simulation-based training ↔ training.py, the whole file · a weak match · score 0.74 · summary network, inference network, epoch, online, training, transformer
  2. [2] § Simulation study › Performance validation ↔ evaluation_clean.ipynb, lines 72–113 · score 0.73 · positive reactivity, negative reactivity, positive regulation, negative regulation, inference, anchoring
  3. [3] § Simulation study › Performance validation ↔ evaluation_noisy.ipynb, lines 75–116 · score 0.73 · positive reactivity, negative reactivity, positive regulation, negative regulation, inference, anchoring
  4. [4] § Simulation study ↔ training.py, the whole file · a weak match · score 0.59 · summary network, inference network, embed, trained, event, Simulation
  5. [5] § Simulation study › Performance validation › Correlation ↔ correlation.py, the whole file · a weak match · score 0.56 · ground truths, posterior samples, inferred, correlation

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 73 lines · 2.3 KB · no license · 2 matches

  1. import os
  2. os.environ["KERAS_BACKEND"] = "tensorflow"
  4. import bayesflow as bf
  6. import keras
  8. from miva import prior, prior_noisy, likelihood, underreport_both, design
  10. train_config = {
  11. "epochs": 500,
  12. "num_batches_per_epoch": 200,
  13. "batch_size": 32
  14. }
  16. setups = ["clean"]
  18. models = {
  19. "scm": (bf.networks.StableConsistencyModel, {})
  20. }
  22. for setup in setups:
  24. for model_name, model in models.items():
  26. if setup == "noisy":
  28. simulator = bf.make_simulator([prior_noisy, likelihood, underreport_both], meta_fn=design)
  30. adapter = (
  31. bf.Adapter()
  32. .convert_dtype("float64", "float32")
  33. .broadcast(["M", "D"], to="data", exclude=(1, 2), squeeze=1)
  34. .sqrt(["M", "D"])
  35. .concatenate(["b", "rp", "rn", "dp", "dn", "s", "affect_prob", "event_prob"], into="inference_variables")
  36. .rename("data_underreported_both", "summary_variables")
  37. .concatenate(["M", "D"], into="inference_conditions")
  38. )
  39. else:
  41. simulator = bf.make_simulator([prior, likelihood], meta_fn=design)
  43. adapter = (
  44. bf.Adapter()
  45. .convert_dtype("float64", "float32")
  46. .broadcast(["M", "D"], to="data", exclude=(1, 2), squeeze=1)
  47. .sqrt(["M", "D"])
  48. .concatenate(["b", "rp", "rn", "dp", "dn", "s"], into="inference_variables")
  49. .rename("data", "summary_variables")
  50. .concatenate(["M", "D"], into="inference_conditions")
  51. )
  53. workflow = bf.BasicWorkflow(
  54. simulator=simulator,
  55. adapter=adapter,
  56. inference_network=model[0](**model[1]),
  57. summary_network = bf.networks.FusionTransformer(
  58. summary_dim=64,
  59. embed_dims=(64,64,64),
  60. num_heads=(4,4,4),
  61. mlp_depths=(1,1,1),
  62. mlp_widths=(128,128,128),
  63. dropout=0.0,
  64. template_dim=128
  65. ),
  66. checkpoint_filepath=f"{model_name}_{setup}_fusion3",
  67. initial_learning_rate=1e-4,
  68. standardize="inference_variables"
  69. )
  71. history = workflow.fit_online(**train_config)
  73. keras.backend.clear_session()

training.py, no license · at the source

Overview

Authors: Maria Wirth1, Andreas Voss2, Stefan T Radev3, Klaus Rothermund1
ORCID iDs: Maria Wirth
  1. Department of Psychology, Friedrich Schiller University Jena, Am Steiger 3/1, 07743 Jena, Germany
  2. Department of Psychology, Heidelberg University, Heidelberg, Germany
  3. Department of Cognitive Science and Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY USA
Institutions: Friedrich Schiller University Jena (Germany); Heidelberg University (Germany); Rensselaer Polytechnic Institute (United States)
Journal: Behavior research methods, volume 58, issue 9, article 242
Dates: received 20 March 2025; accepted 24 June 2026; published online 20 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13428-026-03122-w · PMID 42477244 · PMCID PMC13384981 · OpenAlex W7169789678
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Spectral & time-frequency, Machine learning
Keywords: Affect dynamics, Simulation-based inference, Emotional reactivity, Emotion regulation, Mathematical modeling, Neuronal networks
MeSH: Affect*, Computer Simulation*, Individuality*, Models, Psychological*, Neural Networks, Computer*, Humans (* major topic)
Topic: Mental Health Research Topics (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 137 references in the paper

Abstract

No matter how angry, sad, or happy we are, eventually, we will feel different. Studying this ebb and flow of affective experience in daily life provides important insights into psychological functioning and well-being. We have developed a parsimonious formalized model of intraindividual variability in affect (MIVA), resting on the assumption that such affective changes reflect transactions between an individual and their proximal environment. We provide an outline of its theoretical background, scope, and mathematical formulation. We situate MIVA within the research field of affect dynamics and illustrate the models’ behavior under realistic conditions using a simulation study. We use simulation-based inference to train a custom neural network on MIVA simulations, which we employ to rapidly estimate the model’s parameters on a multitude of synthetic experiments with different configurations. Our simulation study demonstrates that the synthesis between a computational model and probabilistic neural networks results in an efficient and flexible tool for model-based inference of affect dynamics. Our simulation study also offers insights into the data requirements for a precise recovery of the model’s parameters and recommendations for future data collection. The potential of MIVA for providing insights into affect dynamics is discussed.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF spxf8

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Jupyter (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (4 files), Keras (3 files), SciPy (3 files), Matplotlib (2 files), pandas (2 files), seaborn (2 files), Numba (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/spxf8/

Code availability

Scripts included in this manuscript are available at https://osf.io/spxf8/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data included in this manuscript are available at https://osf.io/spxf8/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 6 MeSH terms, 134 references.

Cite

This paper

Wirth, M., Voss, A., Radev, S. T., & Rothermund, K. (2026). Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and parameter recovery study. Behavior research methods, 58(9), 242. https://doi.org/10.3758/s13428-026-03122-w

BibTeX

@article{wirth2026modeling,
author = {Wirth, Maria and Voss, Andreas and Radev, Stefan T and Rothermund, Klaus},
title = {{Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and parameter recovery study}},
journal = {Behavior research methods},
year = {2026},
month = jul,
volume = {58},
number = {9},
pages = {242},
publisher = {Springer Science+Business Media},
issn = {1554-351X},
doi = {10.3758/s13428-026-03122-w},
url = {https://doi.org/10.3758/s13428-026-03122-w},
pmid = {42477244},
pmcid = {PMC13384981}
}

RIS

TY - JOUR
AU - Wirth, Maria
AU - Voss, Andreas
AU - Radev, Stefan T
AU - Rothermund, Klaus
TI - Modeling intraindividual variability in affect (MIVA): Formalized theoretical approach, computational model, and parameter recovery study
T2 - Behavior research methods
J2 - Behav Res Methods
PY - 2026
DA - 2026/07/20
VL - 58
IS - 9
SP - 242
SN - 1554-351X
PB - Springer Science+Business Media
DO - 10.3758/s13428-026-03122-w
UR - https://doi.org/10.3758/s13428-026-03122-w
LA - en
ER -

CSL-JSON

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