InFoRM: a unified inverse and forward model for sensorimotor control.

Code ↔ Paper

4 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 4 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Methods › Statistical analysis ↔ kbstatlib/effprint.m, the whole file · a weak match · score 0.62 · eta squared, thumb, Cohen, medium
[2] § Methods › Statistical analysis ↔ kbstatlib/kbstat.m, lines 2661–2802 · score 0.53 · pairwise comparisons, Post hoc, correlations
[3] § Results › Accuracy ↔ emm/emmeans.m, lines 1–106 · score 0.52 · generalised linear mixed, confidence interval, marginal, model
[4] § Methods › Statistical analysis ↔ demo/demo_08_glmm_gamma.m, lines 20–34 · score 0.52 · log link function, Gamma, skewed, GLMM, variable

Paper

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

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The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 86 lines · 3 KB · MIT · 1 match

function out = effprint(effects, effectType)
%% Interpret effect sizes of a certain types in terms of 'small', 'medium'
%% and 'large' according to Cohen's rule of thumb.
% Source: Cohen, J. 1988. Statistical Power Analysis for the Behavioral
% Sciences. Vol. 2nd. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
%
% SYNTAX
% out = effprint(effects, effectType)
%
% INPUT
% effects (Nx1 or 1xN double) Effect sizes
% effectType (char) Type of effect size. Possible values:
% 'eta2' eta squared (for one-way ANOVA) or partial
% eta squared (for multi-way ANOVA)
% 'd' Cohen's d
% 'r' Pearson's correlation coefficient r
% 'rho' Spearman's correlation coefficient rho
% 'R2' Coefficient of determination, the square of
% the Pearson correlation r.
% 'f2' Amount of bias in an F-test (ANOVA or multiple
% regression)
% 'delta' Cliff's delta, effect size for ordinal data.
% Measure of how often the values in one
% distribution are larger than the values in a
% second distribution
% 'omega' Effect size used for chi-squared test
% 'g' Hedges' g, based on a standardized difference.
switch effectType
case 'eta2'
p = [.01 .06 .14];
case 'd'
p = [.2 .5 .8];
case 'r'
p = [.1 .3 .5];
case 'rho'
p = [.1 .3 .5];
case 'R2'
p = [.1 .3 .5].^2;
case 'f2'
p = [.02 .15 .35];
case 'delta'
p = [.25 .75 1.25];
case 'omega'
p = [.1 .3 .5];
case 'g'
p = [.05 .15 .25];
end
q = [mean([0,p(1)]), mean([p(1),p(2)]), mean([p(2),p(3)]), mean([p(3),1])];
ranges = [q(1), mean([p(1),q(2)]), mean([q(2),p(2)]), mean([p(2),q(3)]), mean([q(3),p(3)]), mean([p(3),1])];
nEffects = length(effects);
out = cell(size(effects));
for iEffect = 1:nEffects
effect = abs(effects(iEffect));
if 0 <= effect && effect < ranges(1)
out{iEffect} = 'very small';
elseif ranges(1) <= effect && effect < ranges(2)
out{iEffect} = 'small';
elseif ranges(2) <= effect && effect < ranges(3)
out{iEffect} = 'small to medium';
elseif ranges(3) <= effect && effect < ranges(4)
out{iEffect} = 'medium';
elseif ranges(4) <= effect && effect < ranges(5)
out{iEffect} = 'medium to large';
elseif ranges(5) <= effect && effect < ranges(6)
out{iEffect} = 'large';
elseif ranges(6) <= effect && effect <= 1
out{iEffect} = 'very large';
else
if effect < 0
warning('Eta must be between 0 and 1, and is %f',effect);
out{iEffect} = 'too small';
else
warning('Eta must be between 0 and 1, and is %f',effect);
out{iEffect} = 'too large';
end
end
end
if nEffects == 1
out = out{1};
end
end

effprint.m at commit d483b1c, under MIT · at the source

Overview

Authors: Myriam Lauren de Graaf^1,2,3, Lena Kloock¹, André Schwarze¹, Meike Gerlach¹, Andrea Arensmann¹, Kim Joris Boström¹, Ricarda I Schubotz^2,4, Heiko Wagner^1,2,3

ORCID iDs: Myriam Lauren de Graaf, Lena Kloock

Department of Movement Science, University of Münster, Horstmarer Landweg 62b, 48149 Münster, Germany
Otto Creutzfeldt Centre for Cognitive and Behavioural Neuroscience, University of Münster, Fliednerstraße 21, 48149 Münster, Germany
Centre for Data Science and Complexity (CDSC), University of Münster, Corrensstraße 2, 48149 Münster, Germany
Institute of Psychology, University of Münster, Fliednerstraße 21, 48149 Münster, Germany

Institutions: University of Münster (Germany)

Journal: Scientific reports, volume 16, issue 1, article 8490

Dates: received 4 November 2025; accepted 9 February 2026; published online 9 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41598-026-39944-z · PMID 41803239 · PMCID PMC12972123 · OpenAlex W7134279886

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), systems (subfield)

Methods: Connectivity, Statistics, Machine learning

Keywords: Motor control, Internal models, Forward model, Inverse model, Reservoir computing, Neural networks, Engineering, Neuroscience

MeSH: Feedback, Sensory*, Models, Neurological*, Neural Networks, Computer*, Biomechanical Phenomena, Humans, Movement (* major topic)

Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Westfälische Wilhelms-Universität Münster

Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

Sensorimotor control models traditionally consist of two types of internal models: inverse models, which compute the motor commands needed to reach a desired movement goal, and forward models, which predict the resulting sensory feedback. These models are usually considered separate entities, but it is unclear whether such separation exists in the nervous system. Additionally, maintaining separate networks may be more computationally expensive. Therefore, we investigated whether these functions could be executed within a single neural circuit: an inverse-forward-recognition model (InFoRM). We implemented InFoRM using neural networks and compared their ability to reproduce cyclic reaching movements with that of control architectures based on classical, separated inverse and forward models. Desired movement trajectories were represented by recorded three-dimensional kinematics, while efferent (muscle activation) and afferent (muscle length and velocity) signals were obtained through inverse dynamics. Our findings show that InFoRM significantly outperforms control architectures across various conditions, while requiring fewer resources. The network is also able to morph to untrained movement directions, generating motor commands and predicted feedback that had not been learned. These findings demonstrate the computational advantages of integrating inverse and forward processes within a single neural network, suggesting that such unified sensorimotor models may be worthwhile to explore further.

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 4 matches between paragraphs and lines of code.

kimbostroem/kbstat

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: d483b1ca2ba214976f9248b909940b91625121b0, 29 June 2026

Languages: MATLAB (42)

Size: 59 files, 42 scripts

Software Heritage: not archived

Found in: the references

Holds: README, license file, CITATION.cff

Not found: environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (8 files), Violinplot-Matlab (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

44 files

demo/demo_01_unpaired.m, MATLAB, 24 lines
demo/demo_02_paired.m, MATLAB, 29 lines
demo/demo_03_twoway.m, MATLAB, 28 lines
demo/demo_04_lmm.m, MATLAB, 26 lines
demo/demo_06_lmm_slopes.m, MATLAB, 28 lines
demo/demo_07_lmm_transform.m, MATLAB, 30 lines
demo/demo_08_glmm_gamma.m, MATLAB, 34 lines, 1 match
demo/demo_09_lmm_partial_interaction.m, MATLAB, 29 lines
demo/demo_10_outliers.m, MATLAB, 47 lines
demo/demo_11_glmm_binomial.m, MATLAB, 40 lines
demo/demo_12_multi_y.m, MATLAB, 30 lines
demo/run_demos.m, MATLAB, 34 lines
emm/contrasts_wald.m, MATLAB, 63 lines
emm/demo.m, MATLAB, 40 lines
emm/emmeans.m, MATLAB, 385 lines, 1 match
emm/emmip.m, MATLAB, 257 lines
emm/relevel_table.m, MATLAB, 67 lines
etc/bonferroni_holm.m, MATLAB, 80 lines
etc/swtest.m, MATLAB, 266 lines
kbstatlib/benjaminiHochberg.m, MATLAB, 51 lines
kbstatlib/capitalize.m, MATLAB, 18 lines
kbstatlib/checkDuplicatesTol.m, MATLAB, 32 lines
kbstatlib/effprint.m, MATLAB, 86 lines, 1 match
kbstatlib/excel2csv.m, MATLAB, 60 lines
kbstatlib/f2cohenf.m, MATLAB, 6 lines
kbstatlib/f2etaSqp.m, MATLAB, 6 lines
kbstatlib/f2smd.m, MATLAB, 6 lines
kbstatlib/kbboxchart.m, MATLAB, 185 lines
kbstatlib/kbboxchart_test.m, MATLAB, 45 lines
kbstatlib/kbcontrasts_wald.m, MATLAB, 68 lines
kbstatlib/kbsaveFigure.m, MATLAB, 33 lines
kbstatlib/kbstat.m, MATLAB, 2,877 lines, 1 match
kbstatlib/plotGroups.m, MATLAB, 308 lines
kbstatlib/saveTable.m, MATLAB, 36 lines
kbstatlib/sidak_corr.m, MATLAB, 5 lines
kbstatlib/sigprint.m, MATLAB, 19 lines
kbstatlib/sigstar.m, MATLAB, 307 lines
kbstatlib/tableLong2wide.m, MATLAB, 121 lines
kbstatlib/tableWide2long.m, MATLAB, 68 lines
violinplot/Violin.m, MATLAB, 713 lines
violinplot/kbboxchart.m, MATLAB, 175 lines
violinplot/violinplot.m, MATLAB, 193 lines
LICENSE.txt, License, 21 lines
README.md, Text, 43 lines

At the source: github.com/kimbostroem/kbstat

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;
42 scripts, each with its path and the digest of its content;
4 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

The data underlying this study are available from the corresponding author upon request.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 8 keywords, 6 MeSH terms, 1 funder, 45 references.

Cite

This paper

de Graaf, M. L., Kloock, L., Schwarze, A., Gerlach, M., Arensmann, A., Boström, K. J., Schubotz, R. I., & Wagner, H. (2026). InFoRM: a unified inverse and forward model for sensorimotor control. Scientific reports, 16(1), 8490. https://doi.org/10.1038/s41598-026-39944-z

BibTeX

@article{degraaf2026inform, author = {de Graaf, Myriam Lauren and Kloock, Lena and Schwarze, André and Gerlach, Meike and Arensmann, Andrea and Boström, Kim Joris and Schubotz, Ricarda I and Wagner, Heiko}, title = {{InFoRM: a unified inverse and forward model for sensorimotor control}}, journal = {Scientific reports}, year = {2026}, month = mar, volume = {16}, number = {1}, pages = {8490}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-39944-z}, url = {https://doi.org/10.1038/s41598-026-39944-z}, pmid = {41803239}, pmcid = {PMC12972123} }

RIS

TY - JOUR AU - de Graaf, Myriam Lauren AU - Kloock, Lena AU - Schwarze, André AU - Gerlach, Meike AU - Arensmann, Andrea AU - Boström, Kim Joris AU - Schubotz, Ricarda I AU - Wagner, Heiko TI - InFoRM: a unified inverse and forward model for sensorimotor control T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/03/09 VL - 16 IS - 1 SP - 8490 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-39944-z UR - https://doi.org/10.1038/s41598-026-39944-z LA - en ER -

CSL-JSON

{ "id": "10.1038/s41598-026-39944-z", "type": "article-journal", "title": "InFoRM: a unified inverse and forward model for sensorimotor control", "container-title": "Scientific reports", "author": [ { "family": "de Graaf", "given": "Myriam Lauren" }, { "family": "Kloock", "given": "Lena" }, { "family": "Schwarze", "given": "André" }, { "family": "Gerlach", "given": "Meike" }, { "family": "Arensmann", "given": "Andrea" }, { "family": "Boström", "given": "Kim Joris" }, { "family": "Schubotz", "given": "Ricarda I" }, { "family": "Wagner", "given": "Heiko" } ], "container-title-short": "Sci Rep", "volume": "16", "issue": "1", "page": "8490", "DOI": "10.1038/s41598-026-39944-z", "PMID": "41803239", "PMCID": "PMC12972123", "ISSN": "2045-2322", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41598-026-39944-z", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 9 ] ] } }

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