OSCR

Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering.

Code ↔ Paper

17 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 17 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Hybrid model ↔ HybridModelling/src/analysis/recover/recover.jl, lines 139–199 · score 0.72 · neural network, cognitive model, hybrid model, Adam, cm, nn
  2. [2] § Methods › Hybrid model ↔ HybridModelling/src/analysis/fit/hybrid-model/fit-hybrid.jl, lines 54–127 · score 0.67 · cognitive model, hybrid model, Adam, cm, nn, minimizing
  3. [3] § Results › Sampling behavior adaptively scales with task difficulty and uncertainty ↔ BehavioralModelling/src/speed-accuracy/trial-level/green-dots.jl, the whole file · a weak match · score 0.66 · Poisson, mixed, blocked, behavior, unattended, visit
  4. [4] § Results › The ANN can be transformed into an interpretable symbolic function ↔ HybridModelling/src/analysis/symbolic-regression/cross-task-generalization/compare-loss.jl, lines 64–137 · score 0.61 · Wilcoxon signed rank, Cross task generalization, symbolic model, symbolic regression, UCB model, median
  5. [5] § Methods › Markov decision process–based model ↔ OptimalPolicy/src/mdp.jl, lines 132–197 · score 0.60 · beta binomial, reward function, transitions, policy, optimal
  6. [6] § Results › The ANN-derived VoI predicts participants’ sampling decisions ↔ HybridModelling/src/analysis/compare/losses/compare-losses.jl, the whole file · a weak match · score 0.60 · cross validation, loss relative, linear model, symbolic model, UCB model, trained
  7. [7] § Methods › fMRI data preprocessing ↔ BrainModelling/first-level/script/feat/create_design.py, lines 13–144 · score 0.59 · high pass filtered, motion, template, Preprocessing, temporally, Brain
  8. [8] § Results › The ANN can be transformed into an interpretable symbolic function ↔ HybridModelling/src/analysis/symbolic-regression/cross-task-generalization/compare-loss.jl, lines 245–300 · score 0.58 · Wilcoxon signed rank, symbolic regression, median, UCB, ANN
  9. [9] § Methods › Symbolic regression ↔ HybridModelling/src/analysis/symbolic-regression/after-first-visit/estimate.jl, lines 112–135 · score 0.56 · combined score, Symbolic regression, loss, visit, switching, hybrid
  10. [10] § Methods › fMRI data analysis ↔ BrainModelling/first-level/script/feat/create_design.py, lines 13–144 · score 0.55 · motion parameters, confound, volumes, temporal, preprocessing, voxel
  11. [11] § Methods › Symbolic regression ↔ HybridModelling/src/analysis/symbolic-regression/interpret/make-figure.jl, lines 1–51 · score 0.55 · Symbolic regression, switch function, Na, Nu, jl, visit
  12. [12] § Results › Sampling behavior adaptively scales with task difficulty and uncertainty ↔ OptimalPolicy/src/solve.jl, lines 1–43 · score 0.54 · optimal policy, dynamic programming
  13. [13] § Methods › Markov decision process–based model ↔ OptimalPolicy/src/solve.jl, lines 1–43 · score 0.54 · backward induction, recursion, policy, optimal
  14. [14] § Methods › Behavioral analysis ↔ HybridModelling/src/analysis/fit/symbolic-model/symbolic2ann/make-figure.jl, lines 353–411 · score 0.53 · voiU, voiA, logistic, fitted, unattended, ANN
  15. [15] § Methods › ROI analysis ↔ BrainModelling/second-level/script/post-feat/roi/fit-data/fit-model.jl, lines 1–53 · score 0.53 · effsize, ROI, cope, voxel, mixed, formula
  16. [16] § Results › The ANN-derived VoI predicts participants’ sampling decisions ↔ HybridModelling/src/analysis/fit/cross-validation.jl, lines 1–31 · score 0.52 · cross validation, linear model, symbolic model, UCB model, loss, fit
  17. [17] § Methods › Behavioral analysis ↔ HybridModelling/src/analysis/fit/symbolic-model/symbolic2ann/fit.jl, lines 96–165 · score 0.51 · voiU, voiA, fitted, switching, unattended, visit

Paper

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

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The authors' code

Julia · 300 lines · 9.1 KB · MIT · 2 matches

  1. using YAML, CSV, DataFrames, Statistics, GLMakie, Colors, HypothesisTests, CairoMakie;
  2. config = YAML.load_file("config.yaml");
  3. include(joinpath(config["mac"]["project"], "helper.jl"));
  5. # Dataset 1 ------------------------------------------------------------
  6. loss_symbolic = [
  7. mean(CSV.read(
  8. joinpath(
  9. config["mac"]["symbolic"], "cross-task-generalization",
  10. "fit", "Symbolic-model", "loo", "outcome",
  11. "gershman2018-data1", "losses-$(string(subject)).csv"
  12. ), DataFrame
  13. ).loss)
  14. for subject in 1:45
  15. ] |> x -> deleteat!(x, 18)
  17. loss_ucb = [
  18. mean(CSV.read(
  19. joinpath(
  20. config["mac"]["symbolic"], "cross-task-generalization",
  21. "fit", "ucb-model", "loo", "outcome",
  22. "gershman2018-data1", "losses-$(string(subject)).csv"
  23. ), DataFrame
  24. ).loss)
  25. for subject in 1:45
  26. ] |> x -> deleteat!(x, 18)
  28. loss_aucb = [
  29. mean(CSV.read(
  30. joinpath(
  31. config["mac"]["symbolic"], "cross-task-generalization",
  32. "fit", "aucb-model", "loo", "outcome",
  33. "gershman2018-data1", "losses-$(string(subject)).csv"
  34. ), DataFrame
  35. ).loss)
  36. for subject in 1:45
  37. ] |> x -> deleteat!(x, 18)
  39. begin
  40. fig = Figure(size = (600, 400));
  41. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject", limits = (0, 45, 0, 1));
  42. lines!(ax, loss_symbolic, label = "Symbolic");
  43. lines!(ax, loss_ucb, label = "UCB");
  44. lines!(ax, loss_aucb, label = "AUCB");
  45. axislegend(ax; position = :lt);
  46. fig
  47. end
  49. begin
  50. fig = Figure(size = (600, 400));
  51. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject");
  52. density!(ax, loss_symbolic .- loss_ucb)
  53. fig
  54. end
  56. begin
  57. fig = Figure(size = (600, 400));
  58. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject");
  59. density!(ax, loss_symbolic .- loss_aucb)
  60. fig
  61. end
  64. # Sign-rank test Symbolic vs UCB ------------------------------------------------------------
  65. signedrank_test_data1 = SignedRankTest(loss_symbolic, loss_ucb);
  66. print_summary(signedrank_test_data1);
  67. """
  68. Wilcoxon signed-rank test
  69. W = 4.0, n = 44, median = -0.07, P = 7.96 × 10⁻¹³
  70. """
  71. # Sign-rank test Symbolic vs aUCB ------------------------------------------------------------
  72. signedrank_test_data1 = SignedRankTest(loss_symbolic, loss_aucb);
  73. print_summary(signedrank_test_data1);
  74. """
  75. Wilcoxon signed-rank test
  76. W = 163.0, n = 44, median = -0.01, P = 4.81 × 10⁻⁵
  77. """
  81. # Dataset 2 ------------------------------------------------------------
  82. loss_symbolic = [
  83. mean(CSV.read(
  84. joinpath(
  85. config["mac"]["symbolic"], "cross-task-generalization",
  86. "fit", "Symbolic-model", "loo", "outcome",
  87. "gershman2018-data2", "losses-$(string(subject)).csv"
  88. ), DataFrame
  89. ).loss)
  90. for subject in 1:44
  91. ]
  93. loss_ucb = [
  94. mean(CSV.read(
  95. joinpath(
  96. config["mac"]["symbolic"], "cross-task-generalization",
  97. "fit", "ucb-model", "loo", "outcome",
  98. "gershman2018-data2", "losses-$(string(subject)).csv"
  99. ), DataFrame
  100. ).loss)
  101. for subject in 1:44
  102. ]
  104. loss_aucb = [
  105. mean(CSV.read(
  106. joinpath(
  107. config["mac"]["symbolic"], "cross-task-generalization",
  108. "fit", "aucb-model", "loo", "outcome",
  109. "gershman2018-data2", "losses-$(string(subject)).csv"
  110. ), DataFrame
  111. ).loss)
  112. for subject in 1:44
  113. ]
  115. begin
  116. fig = Figure(size = (600, 400));
  117. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject", limits = (0, 44, 0, 1));
  118. lines!(ax, loss_symbolic, label = "Symbolic");
  119. lines!(ax, loss_aucb, label = "AUCB");
  120. lines!(ax, loss_ucb, label = "UCB");
  121. axislegend(ax; position = :lt);
  122. fig
  123. end
  125. begin
  126. fig = Figure(size = (600, 400));
  127. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject");
  128. density!(ax, loss_symbolic .- loss_ucb)
  129. fig
  130. end
  132. begin
  133. fig = Figure(size = (600, 400));
  134. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject");
  135. density!(ax, loss_symbolic .- loss_aucb)
  136. fig
  137. end
  139. # Sign-rank test Symbolic vs UCB ------------------------------------------------------------
  140. signedrank_test_data2 = SignedRankTest(loss_symbolic, loss_ucb);
  141. print_summary(signedrank_test_data2);
  142. """
  143. Wilcoxon signed-rank test
  144. W = 4.0, n = 44, median = -0.08, P = 7.96 × 10⁻¹³
  145. """
  146. # Sign-rank test Symbolic vs aUCB ------------------------------------------------------------
  147. signedrank_test_data2 = SignedRankTest(loss_symbolic, loss_aucb);
  148. print_summary(signedrank_test_data2);
  149. """
  150. Wilcoxon signed-rank test
  151. W = 22.0, n = 44, median = -0.05, P = 6.09 × 10⁻¹¹
  152. """
  155. # Combine the two datasets ------------------------------------------------------------
  156. loss_symbolic = vcat(
  157. # Data 1
  158. [
  159. mean(CSV.read(
  160. joinpath(
  161. config["mac"]["symbolic"], "cross-task-generalization",
  162. "fit", "Symbolic-model", "loo", "outcome",
  163. "gershman2018-data1", "losses-$(string(subject)).csv"
  164. ), DataFrame
  165. ).loss)
  166. for subject in 1:45
  167. ],
  168. # Data 2
  169. [
  170. mean(CSV.read(
  171. joinpath(
  172. config["mac"]["symbolic"], "cross-task-generalization",
  173. "fit", "Symbolic-model", "loo", "outcome",
  174. "gershman2018-data2", "losses-$(string(subject)).csv"
  175. ), DataFrame
  176. ).loss)
  177. for subject in 1:44
  178. ]
  179. );
  181. loss_ucb = vcat(
  182. [
  183. mean(CSV.read(
  184. joinpath(
  185. config["mac"]["symbolic"], "cross-task-generalization",
  186. "fit", "ucb-model", "loo", "outcome",
  187. "gershman2018-data1", "losses-$(string(subject)).csv"
  188. ), DataFrame
  189. ).loss)
  190. for subject in 1:45
  191. ],
  192. [
  193. mean(CSV.read(
  194. joinpath(
  195. config["mac"]["symbolic"], "cross-task-generalization",
  196. "fit", "ucb-model", "loo", "outcome",
  197. "gershman2018-data2", "losses-$(string(subject)).csv"
  198. ), DataFrame
  199. ).loss)
  200. for subject in 1:44
  201. ]
  202. );
  204. loss_aucb = vcat(
  205. [
  206. mean(CSV.read(
  207. joinpath(
  208. config["mac"]["symbolic"], "cross-task-generalization",
  209. "fit", "aucb-model", "loo", "outcome",
  210. "gershman2018-data1", "losses-$(string(subject)).csv"
  211. ), DataFrame
  212. ).loss)
  213. for subject in 1:45
  214. ],
  215. [
  216. mean(CSV.read(
  217. joinpath(
  218. config["mac"]["symbolic"], "cross-task-generalization",
  219. "fit", "aucb-model", "loo", "outcome",
  220. "gershman2018-data2", "losses-$(string(subject)).csv"
  221. ), DataFrame
  222. ).loss)
  223. for subject in 1:44
  224. ]
  225. );
  227. begin
  228. fig = Figure(size = (600, 400));
  229. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject", limits = (0, 89, 0, 1));
  230. lines!(ax, loss_symbolic, label = "Symbolic");
  231. lines!(ax, loss_aucb, label = "UCB");
  232. axislegend(ax; position = :lt);
  233. fig
  234. end
  236. begin
  237. fig = Figure(size = (600, 400));
  238. ax = GLMakie.Axis(fig[1, 1], ylabel = "Loss", xlabel = "Subject");
  239. density!(ax, loss_symbolic .- loss_aucb)
  240. fig
  241. end
  245. # Sign-rank test Symbolic vs UCB ------------------------------------------------------------
  246. signedrank_test_data = SignedRankTest(loss_symbolic, loss_ucb);
  247. print_summary(signedrank_test_data);
  248. """
  249. Wilcoxon signed-rank test
  250. W = 15.0, n = 89, median = -0.07, P = 4.31 × 10⁻¹⁶
  251. """
  253. # Sign-rank test Symbolic vs aUCB ------------------------------------------------------------
  254. signedrank_test_data = SignedRankTest(loss_symbolic, loss_aucb);
  255. print_summary(signedrank_test_data);
  256. """
  257. Wilcoxon signed-rank test
  258. W = 286.0, n = 89, median = -0.03, P = 2.21 × 10⁻¹²
  259. """
  262. CairoMakie.activate!()
  263. begin
  264. fig = Figure(size = (450, 500), fontsize = 30);
  265. ax = GLMakie.Axis(
  266. fig[1, 1], ylabel = "Loss on Test Set", xlabel = "",
  267. limits = (-0.4, 1.4, 0.1, 0.9),
  268. xgridvisible = false, ygridvisible = false,
  269. xticks = (0:1, ["Symbolic", "intercept-UCB"]),
  270. yticks = 0.2:0.1:0.7,
  271. topspinevisible = false, rightspinevisible = false,
  272. xtrimspine = true, ytrimspine = true
  273. );
  274. x1 = 0 .+ (rand(89)*0.1.-0.05);
  275. x2 = 1 .+ (rand(89)*0.1.-0.05);
  276. [lines!(ax, [x1[i], x2[i]], [loss_symbolic[i], loss_aucb[i]], color=RGBA(0.8, 0.8, 0.8, 0.5)) for i in 1:89]
  277. scatter!(ax, x1, loss_symbolic, color=RGB(config["colors"]["ann"]...))
  278. scatter!(ax, x2, loss_aucb, color=RGB(config["colors"]["ucb"]...))
  281. lines!(ax, [0.2, 0.8], [mean(loss_symbolic), mean(loss_ucb)], color=RGBA(0, 0, 0, 1), linewidth = 2)
  282. scatter!(ax, [0.2], [mean(loss_symbolic)], color=RGB(config["colors"]["ann"]...), markersize = 20)
  283. scatter!(ax, [0.8], [mean(loss_ucb)], color=RGB(config["colors"]["ucb"]...), markersize = 20)
  284. display(fig)
  285. end
  287. CairoMakie.save(
  288. joinpath(
  289. config["mac"]["symbolic"], "cross-task-generalization",
  290. "figures", "compare-loss-aucb.png"
  291. ), fig, px_per_unit = 2
  292. )
  294. # Save figure
  295. [CairoMakie.save(
  296. joinpath(
  297. config["mac"]["figures"], "supplementary",
  298. "supplementary8.$ext"
  299. ), fig, px_per_unit = 2
  300. ) for ext in ["pdf", "png"]];

compare-loss.jl at commit 3d343be, under MIT · at the source

Overview

Authors: Simone D’Ambrogio1, Jan Grohn1, Nima Khalighinejad1, Marcelo G. Mattar2, Laurence Hunt1, Matthew F. S. Rushworth1,3
ORCID iDs: Simone D’Ambrogio, Jan Grohn, Nima Khalighinejad, Marcelo G. Mattar, Matthew F. S. Rushworth
  1. Department of Experimental Psychology, University of Oxford,Oxford, UK
  2. Department of Psychology, New York University,New York, NY USA
  3. Nuffield Department of Clinical Neurosciences, University of Oxford,Oxford, UK
Institutions: University of Oxford (United Kingdom); New York University (United States)
Journal: Nature neuroscience, volume 29, issue 8, pages 2036-2047
Dates: received 25 June 2025; accepted 14 May 2026; published online 26 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02342-9 · PMID 42362882 · PMCID PMC13433322 · OpenAlex W4411641310
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.24.661282
Status: code verified
Categories: human (organism)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: Decision, Network models
MeSH: Brain*, Choice Behavior*, Decision Making*, Neural Networks, Computer*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Soft Computing, Ventral Tegmental Area, Young Adult (* major topic)
Journal subjects: Technical Report
Topic: Neural Networks and Applications (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (BB/W003392/1); Wellcome Trust (Wellcome) (221794/Z/20/Z)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Humans and other animals are driven to acquire information about opportunities in their environments, yet how they evaluate what is worth learning remains unclear. Here we combine artificial neural networks with symbolic regression to extract an expressive yet interpretable model that specifies how human participants evaluate decision-relevant information during choice. The recovered function depends primarily on the relative evidence accumulated across options rather than absolute uncertainty about each, revealing that participants seek information symmetry across alternatives rather than minimizing uncertainty option by option. This account outperforms standard models of uncertainty-based exploration and generalizes to an independent dataset. Using ultrahigh-field (7T) functional magnetic resonance imaging optimized for midbrain and brainstem, we simultaneously measured activity across five neuromodulatory nuclei and two cortical regions. Ventral tegmental area activity showed opposed coding of information and selection values, a pattern suited to arbitrating between sampling and choosing, and anterior cingulate cortex and anterior insula tracked value-of-information computations.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

simonedambrogio/HybridModellingProject

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3d343beb6dc7f1b7c6c7521ebec61f7dcef35b12, 25 June 2025
Languages: Julia (78), Python (31), Shell (26), R (4)
Size: 67,088 files, 139 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (BehavioralModelling/Project.toml, Data/Project.toml), documentation
Not found: README, license file, CITATION.cff, tests, continuous integration
Tools: DataFrames.jl (46 files), Makie (36 files), FSL (15 files), Distributions.jl (13 files), Flux.jl (10 files), NumPy (7 files), pandas (6 files), NiBabel (5 files), data.table (2 files), tidyverse (2 files), Matplotlib (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
141 files
At the source: github.com/simonedambrogio/HybridModellingProject

Zenodo 19685085

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

Code availability

All analysis and modeling code is available via GitHub at https://github.com/simonedambrogio/HybridModellingProject and via Zenodo at 10.5281/zenodo.19685085 (ref. 75).

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 139 scripts, each with its path and the digest of its content;
  • 17 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

All behavioral and neuroimaging data supporting the findings of this study are available via GitHub at https://github.com/simonedambrogio/HybridModellingProject/tree/main/Data. Source data are provided with this paper.

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 → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 13 MeSH terms, 2 funders, 68 references.

Cite

This paper

D’Ambrogio, S., Grohn, J., Khalighinejad, N., Mattar, M. G., Hunt, L., & Rushworth, M. F. S. (2026). Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering. Nature neuroscience, 29(8), 2036-2047. https://doi.org/10.1038/s41593-026-02342-9

BibTeX

@article{dambrogio2026interpretable,
author = {D’Ambrogio, Simone and Grohn, Jan and Khalighinejad, Nima and Mattar, Marcelo G. and Hunt, Laurence and Rushworth, Matthew F. S.},
title = {{Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering}},
journal = {Nature neuroscience},
year = {2026},
month = jun,
volume = {29},
number = {8},
pages = {2036--2047},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02342-9},
url = {https://doi.org/10.1038/s41593-026-02342-9},
pmid = {42362882},
pmcid = {PMC13433322}
}

RIS

TY - JOUR
AU - D’Ambrogio, Simone
AU - Grohn, Jan
AU - Khalighinejad, Nima
AU - Mattar, Marcelo G.
AU - Hunt, Laurence
AU - Rushworth, Matthew F. S.
TI - Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/06/26
VL - 29
IS - 8
SP - 2036
EP - 2047
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02342-9
UR - https://doi.org/10.1038/s41593-026-02342-9
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02342-9",
"type": "article-journal",
"title": "Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering",
"container-title": "Nature neuroscience",
"author": [
{
"family": "D’Ambrogio",
"given": "Simone"
},
{
"family": "Grohn",
"given": "Jan"
},
{
"family": "Khalighinejad",
"given": "Nima"
},
{
"family": "Mattar",
"given": "Marcelo G."
},
{
"family": "Hunt",
"given": "Laurence"
},
{
"family": "Rushworth",
"given": "Matthew F. S."
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "8",
"page": "2036-2047",
"DOI": "10.1038/s41593-026-02342-9",
"PMID": "42362882",
"PMCID": "PMC13433322",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02342-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
26
]
]
}
}

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