OSCR

Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals.

Code ↔ Paper

25 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 25 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Perceptual evidence accumulation task ↔ Functions/StateMatrices/DemonstratorTaskSMA.m, lines 102 to 238 · score 0.89 · reward delivery, go cue, rewarded port, left port, right port, center port · Report a mismatch
  2. [2] § Methods › Perceptual evidence accumulation task ↔ labdata_plugin/pluginschema.py, lines 12 to 86 · score 0.87 · category boundary, left pokes, right pokes, stimulus train, early withdrawal, stimulus rate · Report a mismatch
  3. [3] § Methods › Movement and posture analysis ↔ chiCa/encoding_model_single_timepoints.py, lines 81 to 126 · score 0.81 · tail tip, nose tip, tail center, body part, ear, rectum · Report a mismatch
  4. [4] § Results › Neural representations of trial history cannot solely be explained by body posture or movements ↔ encoding_model_fitting.py, lines 275 to 323 · score 0.78 · Motion energy SVD, head orientation angle, body position, Video SVD, encoding models, fitted · Report a mismatch
  5. [5] § Results › Neural representations of trial history cannot solely be explained by body posture or movements ↔ reviewer_figure2.py, lines 451 to 491 · score 0.75 · Motion energy SVD, cross validation, Video SVD, explained variance, encoding models, ridge · Report a mismatch
  6. [6] § Methods › Behavioral training procedure ↔ Functions/StateMatrices/DemonstratorSelfInitiatedTaskSMA.m, the whole file · a weak match · score 0.72 · stimulus playing, initiate trials, center fixation, right ports, early withdrawal, volume · Report a mismatch
  7. [7] § Methods › Behavioral training procedure ↔ labdata_plugin/pluginschema.py, lines 12 to 86 · score 0.69 · center poke, response port, center fixation, early withdrawal, mastering, volume · Report a mismatch
  8. [8] § Results › Neural dynamics representing trial history are low-dimensional and similar between subjects ↔ figure4.py, lines 178 to 216 · score 0.68 · encoding weights projected, procrustes distance, chest point, PCA, transformations, dimensions · Report a mismatch
  9. [9] § Results › Neural representations of trial history cannot solely be explained by body posture or movements ↔ encoding_model_fitting.py, lines 374 to 456 · score 0.67 · regressors shuffled, single regressor, explained variance, encoding models, fluctuations, head · Report a mismatch
  10. [10] § Results › Neural representations of trial history cannot solely be explained by body posture or movements ↔ chiCa/encoding_model_full_version.py, lines 269 to 321 · score 0.66 · regressors shuffled, single regressor, explained variance, encoding models, fluctuations, head · Report a mismatch
  11. [11] § Results › Trial history can be decoded from ACC population activity ↔ statistical_testing.R, lines 138 to 205 · score 0.66 · ITI phases, shuffled control, decoding accuracy, trial history context, decoders, strength · Report a mismatch
  12. [12] § Results › Neural dynamics representing trial history are low-dimensional and similar between subjects ↔ figure4.py, lines 178 to 216 · score 0.65 · procrustes distance, encoding weights, chest point, tuning, transformations, dimensions · Report a mismatch
  13. [13] § Methods › Miniature microscope imaging and image processing ↔ chiCa/neuron_selection_GUI.py, lines 21 to 63 · score 0.65 · identified putative neurons, CaImAn, background, CNMF, fluorescence, motion · Report a mismatch
  14. [14] § Results › Trial history can be decoded from ACC population activity ↔ statistical_testing.R, lines 138 to 205 · score 0.65 · ITI duration, trial history decoding, early ITI, decoding accuracy, decoders, phases · Report a mismatch
  15. [15] § Methods › Perceptual evidence accumulation task ↔ Functions/StateMatrices/DemonstratorSelfInitiatedTaskSMA.m, the whole file · a weak match · score 0.65 · state machine, right ports, center port, volume, valve, LED · Report a mismatch
  16. [16] § Methods › Data reporting and statistical analyses ↔ statistical_testing.R, lines 1 to 45 · score 0.65 · lmerTest, emmeans, Sidak, marginal, coefficients, linear · Report a mismatch
  17. [17] § Methods › Miniature microscope imaging and image processing ↔ analysis/analysis_caiman.py, lines 465 to 508 · score 0.64 · identified putative neurons, CaImAn, background, CNMF, fluorescence, matrix · Report a mismatch
  18. [18] § Methods › Perceptual evidence accumulation task ↔ Functions/StateMatrices/DemonstratorTaskSMA.m, lines 102 to 238 · score 0.59 · right ports, center port, TTL, valve, camera, LED · Report a mismatch
  19. [19] § Results › Neural dynamics representing trial history are low-dimensional and similar between subjects ↔ figure4.py, lines 382 to 411 · score 0.59 · chest tuning motifs, chest position tuning, dimensions, video · Report a mismatch
  20. [20] § Results › Perceptual decisions in expert performers are modulated by trial history ↔ figure1_and_supplementary_figure1.py, lines 221 to 246 · score 0.58 · lower lapse rates, incorrect right, sensitivity, bias, sensory, Trial history · Report a mismatch
  21. [21] § Results › Trial history can be decoded from ACC population activity ↔ reviewer_figure2.py, lines 394 to 421 · score 0.57 · cross decoding accuracy, late ITI, shuffled accuracies, sem, Bars, phase · Report a mismatch
  22. [22] § Results › Trial history can be decoded from ACC population activity ↔ chiCa/decoding_utils.py, lines 1292 to 1415 · score 0.57 · confusion matrices, cross decoding, shuffled accuracies, neural activity, coefficient, decoder · Report a mismatch
  23. [23] § Results › Neural dynamics representing trial history are low-dimensional and similar between subjects ↔ statistical_testing.R, lines 47 to 91 · score 0.57 · subject procrustes distance, encoding weights, chest, dimensions, trial history, video · Report a mismatch
  24. [24] § Results › Perceptual decisions in expert performers are modulated by trial history ↔ figure1_and_supplementary_figure1.py, lines 157 to 212 · score 0.55 · Wilson score interval, stimulus rate, Psychometric, Figure 1, trial history, error · Report a mismatch
  25. [25] § Results › Neural representations of trial history cannot solely be explained by body posture or movements ↔ encoding_model_fitting.py, lines 374 to 456 · score 0.53 · unique explained variance, Video SVD, full model, fitted, shuffled, regressors · Report a mismatch

Paper

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The authors' code

R · 206 lines · 12 KB · GPL-3.0 · 4 matches

  1. # This script performs all the statistical testing for the analysis in
  2. # "Anterior cingulate neurons combine outcome monitoring of past decisions
  3. # with ongoing movement signals", by Oesch et al.
  4. #
  5. # The analyses here assume that all the results data frames are saved as .csv
  6. # files into the same file directory (base_dir, to be set by the user below).
  7. # To export the results from statistical analyses use the following:
  8. # write.csv(summary(fit)$coefficients, "path_to_my_file.csv") for lme results, and
  9. # write.csv(summary(emm, by = NULL, adjust = "sidak")$contrasts,"path_to_my_file.csv")
  10. # for the contrasts on the computed marginal means with p-values adjusted for
  11. # multiple comparison using sidak's method.
  12. #
  13. # LO, February 2026
  14. #-------------------------------------------------------------------------------
  16. #Importing
  17. library(lmerTest)
  18. library(ggplot2)
  19. library(emmeans)
  21. #Define the base directory for all the analysis files
  22. base_dir <- "/Users/loesch/Documents/Churchland_lab/Trial-history_manuscript/data_tables"
  24. # --------------Figure 1F----------------------------------------
  25. # Comparing the magnitudes of the model weights to the shuffled control
  26. data <- read.csv(file.path(base_dir, "Fig1F_logreg_weights.csv"), header=TRUE, sep=",")
  27. data$is_shuffle <- relevel(factor(data$is_shuffle), ref="shuffle")
  28. fit <- lmerTest::lmer(abs_logreg_weights ~ regressor * is_shuffle + (1 |subject), data = data)
  29. summary(fit) #
  30. emm = emmeans(fit, specs = pairwise ~ is_shuffle | regressor, pbkrtest.limit = 4272)
  31. summary(emm, by=NULL, adjust = "sidak")
  33. # -------------Figure 1H-----------------------------------------
  34. # Fitting linear relationship between the difference between history strength
  35. # and stimulus weight, and performance
  36. data <- read.csv(file.path(base_dir, "Fig1H_hist_delta_vs_performance.csv"), header=TRUE, sep=",")
  37. fit <- lmerTest::lmer(performance ~ hist_stim_coef_delta + (1 |subject), data = data, REML=FALSE)
  38. summary(fit)
  40. # -----------Figure 2F----------------------------------------------
  41. # Test for decoding accuracy difference between data and shuffle
  42. data <- read.csv(file.path(base_dir, "Fig2F_decoding_accuracy_decoder_vs_shuffle.csv"), header=TRUE, sep=",")
  43. data$condition <- relevel(factor(data$condition), ref="shuffle") #Set shuffle to be the reference class
  44. fit <- lmerTest::lmer(accuracy ~ condition * time + (1 |subject/session), data = data, REML=FALSE)
  45. summary(fit)
  47. # -----------Figure 2G---------------------------------------------
  48. # Comparing the decoding accuracy between the different trial history contexts
  49. data <- read.csv(file.path(base_dir, "Fig2G_class_wise_decoding_accuracy.csv"), header=TRUE, sep=",")
  50. fit <- lmerTest::lmer(accuracy ~ history_context * time + (1 |subject/session), data = data, REML=FALSE)
  51. summary(fit)
  52. emm = emmeans(fit, specs = pairwise ~ history_context | time, pbkrtest.limit = 12236)
  53. summary(emm, by = NULL, adjust = "sidak")
  55. # ------------Figure 2J-----------------------------------------------
  56. # Compare cross-decoding accuracy with shuffled control
  57. data <- read.csv(file.path(base_dir, "Fig2J_phase_wise_cross_decoding.csv"), header=TRUE, sep=",")
  58. data$condition <- relevel(factor(data$condition), ref="shuffle")
  59. data$seed <- relevel(factor(data$seed), ref="Early ITI") #Seed here refers to the phase the decoder was trained in
  60. data$phase <- relevel(factor(data$phase), ref="Early ITI") #Phase is the testing phase
  61. fit <- lmerTest::lmer(accuracy ~ seed*phase*condition + (1 |subject/session), data = data, REML=FALSE)
  62. summary(fit)
  63. emm = emmeans(fit, specs = pairwise ~ condition | seed*phase)
  64. summary(emm, by = NULL, adjust = "sidak")
  66. # -----------Figure 4D------------------------------------------------
  67. #Compare the dimensionality of trial history, chest and video encoding
  68. data <- read.csv(file.path(base_dir, "Fig4D_encoding_weight_dimensionality.csv"), header=TRUE, sep=",")
  69. data$regressor <- relevel(factor(data$regressor), ref="previous_choice_outcome_combination")
  70. fit <- lmerTest::lmer(dimensionality ~ regressor + (1 |subject/session), data = data, REML = FALSE) #Here regressor refers to chest, trial history, etc
  71. summary(fit)
  72. emm = emmeans(fit, specs = pairwise ~ regressor )
  73. summary(emm, by = NULL, adjust = "sidak")
  75. # -----------Figure 4G-----------------------------------------------
  76. #Compare the within- and across subject procrustes distances for trial history, chest and video
  77. data <- read.csv(file.path(base_dir, "Fig4G_procrustes_distance.csv"), header=TRUE, sep=",")
  78. data$regressor <- relevel(factor(data$regressor), ref="previous_choice_outcome_combination")
  79. data$condition <- relevel(factor(data$condition), ref="within")
  80. fit <- lmerTest::lmer(procrustes_distance ~ regressor * condition + (1 |subject), data = data, REML = FALSE)
  81. summary(fit)
  82. emm = emmeans(fit, specs = pairwise ~ regressor *condition)
  83. summary(emm, by = NULL, adjust = "sidak")
  85. # ------------Supplementary Figure 1A----------------------------------
  86. # Perceptual bias
  87. data <- read.csv(file.path(base_dir, "Supplementary_Fig1A_perceptual_bias.csv"), header=TRUE, sep=",")
  88. fit <- lmerTest::lmer(param_estimate ~ trial_history + (1 |subject), data = data, REML=FALSE)
  89. summary(fit)
  90. emm = emmeans(fit, specs = pairwise ~ trial_history)
  91. summary(emm, by = NULL, adjust = "sidak")
  93. # ------------Supplementary Figure 1B----------------------------------
  94. # Sensitivity
  95. data <- read.csv(file.path(base_dir, "Supplementary_Fig1B_sensitivity.csv"), header=TRUE, sep=",")
  96. fit <- lmerTest::lmer(param_estimate ~ trial_history + (1 |subject), data = data, REML=FALSE)
  97. summary(fit)
  99. # ------------Supplementary Figure 1C----------------------------------
  100. # Sensitivity
  101. data <- read.csv(file.path(base_dir, "Supplementary_Fig1C_upper_lapse_rate.csv"), header=TRUE, sep=",")
  102. fit <- lmerTest::lmer(param_estimate ~ trial_history + (1 |subject), data = data, REML=FALSE)
  103. summary(fit)
  105. # ------------Supplementary Figure 1D----------------------------------
  106. # Sensitivity
  107. data <- read.csv(file.path(base_dir, "Supplementary_Fig1D_lower_lapse_rate.csv"), header=TRUE, sep=",")
  108. fit <- lmerTest::lmer(param_estimate ~ trial_history + (1 |subject), data = data, REML=FALSE)
  109. summary(fit)
  111. # ----------Supplementary Figure 1E--------------------------------------
  112. data <- read.csv(file.path(base_dir, "Supplementary_Fig1E_weigth_variance_no_shuffle.csv"), header=TRUE, sep=",")
  113. fit <- lmerTest::lmer(weight_variance ~ regressor + (1 |subject), data = data, REML=FALSE)
  114. summary(fit)
  116. # ----------Supplementary Figure 1G------------------------------------------
  117. data <- read.csv(file.path(base_dir, "Supplementary_Fig1G_main_effects_logreg_weights.csv"), header=TRUE, sep=",")
  118. data$is_shuffle <- relevel(factor(data$is_shuffle), ref="shuffle")
  119. data$regressor <- relevel(factor(data$regressor), ref="intercept")
  120. fit <- lmerTest::lmer(abs_logreg_weights ~ regressor * is_shuffle + (1 |subject), data = data)
  121. summary(fit)
  122. emm = emmeans(fit, specs = pairwise ~ regressor | is_shuffle, pbkrtest.limit = 5632)
  123. summary(emm, by = NULL, adjust = "sidak")
  125. #------------Supplementary Figure 2B----------------------------------------------
  126. data <- read.csv(file.path(base_dir, "Supplementary_Fig2_decoding_accuracy_different_decoders.csv"), header=TRUE, sep=",")
  127. fit <- lmerTest::lmer(accuracy ~ decoded_variable * is_history + (1 |subject/session), data = data, REML = FALSE)
  128. summary(fit)
  130. #------------Supplementary Figure 2C----------------------------------------------
  131. data <- read.csv(file.path(base_dir, "Supplementary_Fig2C_stim_phase_decoding_accuracy_by_ITI_duration.csv"), header=TRUE, sep=",")
  132. data$condition <- relevel(factor(data$condition), ref = "Shuffle")
  133. fit <- lmerTest::lmer(decoding_accuracy ~ condition * ITI_bin + (1 | subject/session), data = data, REML=FALSE)
  134. summary(fit)
  135. emm = emmeans(fit, specs = pairwise ~ condition | ITI_bin)
  136. summary(emm, by = NULL, adjust = "sidak")
  138. #------------Supplementary Figure 2D----------------------------------------------
  139. data <- read.csv(file.path(base_dir, "Supplementary_Fig2D_Stim_phase_decoding_accuracy_by_ITI_duration_hist_context.csv"), header=TRUE, sep=",")
  140. fit <- lmerTest::lmer(decoding_accuracy ~ hist_context * ITI_bin + (1 | subject/session), data = data, REML=FALSE)
  141. summary(fit)
  142. emm = emmeans(fit, specs = pairwise ~ hist_context | ITI_bin)
  143. summary(emm, by = NULL, adjust = "sidak")
  145. #------------Supplementary Figure 2E----------------------------------------------
  146. data <- read.csv(file.path(base_dir, "Supplementary_Fig2E_history_decoding_t-2_overall.csv"), , header=TRUE, sep=",")
  147. data$condition <- relevel(factor(data$condition), ref = "Shuffle")
  148. fit <- lmerTest::lmer(decoding_accuracy ~ condition * time + (1 | subject/session), data = data, REML=FALSE)
  149. summary(fit)
  151. #------------Supplementary Figure 2F----------------------------------------------
  152. data <- read.csv(file.path(base_dir, "Supplementary_Fig2F_history_decoding_t-2_by_context.csv"), header=TRUE, sep=",")
  153. fit <- lmerTest::lmer(decoding_accuracy ~ hist_context * time + (1 | subject/session), data = data, REML=FALSE)
  154. summary(fit)
  155. emm = emmeans(fit, specs = pairwise ~ hist_context | time, pbkrtest.limit = 11172)
  156. summary(emm, by = NULL, adjust = "sidak")
  158. # -----------Supplementary Figure 5B and E---------------------------------------
  159. data <- read.csv(file.path(base_dir, "Supplementary_Fig5.csv"), header=TRUE, sep=",")
  160. fit <- lmerTest::lmer(decoding_accuracy ~ lens_AP * lens_DV + sex + (1 | subject), data = data, REML=FALSE)
  161. summary(fit)
  163. # ---------Supplementary Figure 5C and F----------------------------------------
  164. data <- read.csv(file.path(base_dir, "Supplementary_Fig5.csv"), header=TRUE, sep=",")
  165. fit <- lmerTest::lmer(cvR ~ lens_AP * lens_DV + sex + (1 |subject), data = data, REML=FALSE)
  166. summary(fit)
  168. # --------Supplementary Figure 5D and G-----------------------------------------
  169. data <- read.csv(file.path(base_dir, "Supplementary_Fig5.csv"), header=TRUE, sep=",")
  170. fit <- lmerTest::lmer(dR ~ lens_AP * lens_DV + sex + (1 |subject), data = data, REML=FALSE)
  171. summary(fit)
  173. #---------Reviewer Figure 2A----------------------------------------------------
  174. # Test for decoding accuracy difference between data and shuffle for all sessions with no overlap between ITI phases
  175. data <- read.csv(file.path(base_dir, "Reviewer_Figure2a_decoding_accuracy_decoder_vs_shuffle_no_overlap.csv"), header=TRUE, sep=",")
  176. data$condition <- relevel(factor(data$condition), ref="shuffle") #Set shuffle to be the reference class
  177. fit <- lmerTest::lmer(accuracy ~ condition * time + (1 |subject/session), data = data, REML=FALSE)
  178. summary(fit)
  180. # -----------Reviewer Figure 2B---------------------------------------------
  181. # Comparing the decoding accuracy between the different trial history contexts for sessions with no overlap between ITI phases
  182. data <- read.csv(file.path(base_dir, "Reviewer_figure2b_class_wise_decoding_accuracy_no_overlap.csv"), header=TRUE, sep=",")
  183. fit <- lmerTest::lmer(accuracy ~ history_context * time + (1 |subject/session), data = data, REML=FALSE)
  184. summary(fit)
  185. emm = emmeans(fit, specs = pairwise ~ history_context | time, pbkrtest.limit = 12236)
  186. summary(emm, by = NULL, adjust = "sidak")
  188. # ------------Reviewer Figure 2E-----------------------------------------------
  189. # Compare cross-decoding accuracy with shuffled control for sessions with no overlap for the two ITI phases
  190. data <- read.csv(file.path(base_dir, "Reviewer_Figure2e_phase_wise_cross_decoding_no_overlap.csv"), header=TRUE, sep=",")
  191. data$condition <- relevel(factor(data$condition), ref="shuffle")
  192. data$seed <- relevel(factor(data$seed), ref="Early ITI") #Seed here refers to the phase the decoder was trained in
  193. data$phase <- relevel(factor(data$phase), ref="Early ITI") #Phase is the testing phase
  194. fit <- lmerTest::lmer(accuracy ~ seed*phase*condition + (1 |subject/session), data = data, REML=FALSE)
  195. summary(fit)
  196. emm = emmeans(fit, specs = pairwise ~ condition | seed*phase)
  197. summary(emm, by = NULL, adjust = "sidak")
  199. # -------------Reviewer Figure 3A-----------------------------------------
  200. # Fitting linear relationship between the difference between history strength
  201. # and stimulus weight, and performance excluding one animal (LY008) with sessions
  202. # showing a large difference between history strength and stimulus weight.
  203. data <- read.csv(file.path(base_dir, "Reviewer_figure3a_hist_delta_vs_performance.csv"), header=TRUE, sep=",")
  204. fit <- lmerTest::lmer(performance ~ hist_stim_coef_delta + (1 |subject), data = data, REML=FALSE)
  205. summary(fit)

statistical_testing.R at commit 989045b, under GPL-3.0 · at the source

Overview

Authors: Lukas T. Oesch1, Makenna C. Thomas1, Davis Sandberg1, João Couto1, Anne K. Churchland1
ORCID iDs: Lukas T. Oesch, Davis Sandberg, João Couto, Anne K. Churchland
  1. Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles,Los Angeles, CA USA
Institutions: University of California, Los Angeles (United States)
Journal: Nature communications, volume 17, issue 1, article 4354
Dates: received 30 June 2025; accepted 2 March 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70639-1 · PMID 41872182 · PMCID PMC13171896 · OpenAlex W7140081684
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: Decision, Sensory processing
MeSH: Decision Making*, Gyrus Cinguli*, Neurons*, Animals, Behavior, Animal, Male, Mice, Mice, Inbred C57BL, Movement (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Eye Institute (R01EY022979); National Science Foundation (221994); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF) (P2BEP3_200212)
Citations: cited by 2 papers (Europe PMC); 79 references in the paper

Abstract

In dynamic environments, animals must closely monitor the effects of their actions to inform switches in behavioral strategy. Anterior cingulate cortex (ACC) neurons track decision outcomes in these environments. Yet, it remains unclear whether ACC neurons similarly monitor behavioral history in static environments and, if so, whether these signals are distinct from movement representations. We recorded large-scale ACC activity in freely moving mice making visual evidence-accumulation decisions. Many ACC neurons exhibited nonlinear mixed selectivity for previous choices and outcomes (trial history) and were modulated by movements. Trial history could be stably decoded from population activity and accounted for a separable component of neural activity than posture and movements. Trial history encoding was conserved across different subjects and was unaffected by fluctuating behavioral biases. These findings demonstrate that trial history monitoring in ACC is implemented in a conserved population code that is independent of the volatility of subjects’ task environment.

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

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churchlandlab/chipmunk

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Languages: MATLAB (40), Python (4), C++ (1), C/C++ (1)
Size: 99 files, 46 scripts
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Found in: the text, “Perceptual evidence accumulation task”
Holds: README
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jcouto/fit_psychometric

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churchlandlab/chiCa

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jcouto/labdata-tools

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jcouto/wfield

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Commit: 0befe16679f2503a5126decabcd2a66e2d55d710, 26 August 2025
Languages: Python (21), Jupyter (17)
Size: 54 files, 38 scripts
Software Heritage: archived
Found in: the text, “Movement and posture analysis”
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LukasOesch/Oesch_et_al_Trial_History_ACC

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 989045b63ade4208434724e79ca766b18c21a3bf, 26 February 2026
Languages: Python (9), R (1)
Size: 13 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), NumPy (8 files), pandas (8 files), SciPy (8 files), scikit-learn (5 files), emmeans (1 file), ggplot2 (1 file), lmerTest (1 file), UMAP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
12 files
At the source: github.com/LukasOesch/Oesch_et_al_Trial_History_ACC.git

Zenodo 18793084

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), NumPy (8 files), pandas (8 files), SciPy (8 files), scikit-learn (5 files), emmeans (1 file), ggplot2 (1 file), lmerTest (1 file), UMAP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
12 files
At the source:

Code availability

All code is publicly available. The code to run the analyses and statistical models, and to generate the figure panels presented in this study, can be found in the following repository79: https://github.com/LukasOesch/Oesch_et_al_Trial_History_ACC.git. The code provided in this repository depends on the following other toolboxes: https://github.com/churchlandlab/chiCa and https://github.com/jcouto/fit_psychometric.git (https://colab.research.google.com/github/jcouto/fit_psychometric). Wherever specific packages were used to preprocess data, the packages are cited in the corresponding “Methods” section.

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:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 205 scripts, each with its path and the digest of its content;
  • 25 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

Datasets cited

Data availability

All the data used in this study have been deposited on figshare78 and are publicly available under the following accession code: 10.6084/m9.figshare.30670382. Raw data files (calcium imaging movies, behavioral videos) can be made available upon request. 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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 9 MeSH terms, 3 funders, 78 references.

Cite

This paper

Oesch, L. T., Thomas, M. C., Sandberg, D., Couto, J., & Churchland, A. K. (2026). Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals. Nature communications, 17(1), 4354. https://doi.org/10.1038/s41467-026-70639-1

BibTeX

@article{oesch2026anterior,
author = {Oesch, Lukas T. and Thomas, Makenna C. and Sandberg, Davis and Couto, João and Churchland, Anne K.},
title = {{Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {4354},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70639-1},
url = {https://doi.org/10.1038/s41467-026-70639-1},
pmid = {41872182},
pmcid = {PMC13171896}
}

RIS

TY - JOUR
AU - Oesch, Lukas T.
AU - Thomas, Makenna C.
AU - Sandberg, Davis
AU - Couto, João
AU - Churchland, Anne K.
TI - Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/24
VL - 17
IS - 1
SP - 4354
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70639-1
UR - https://doi.org/10.1038/s41467-026-70639-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70639-1",
"type": "article-journal",
"title": "Anterior cingulate neurons combine outcome monitoring of past decisions with ongoing movement signals",
"container-title": "Nature communications",
"author": [
{
"family": "Oesch",
"given": "Lukas T."
},
{
"family": "Thomas",
"given": "Makenna C."
},
{
"family": "Sandberg",
"given": "Davis"
},
{
"family": "Couto",
"given": "João"
},
{
"family": "Churchland",
"given": "Anne K."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4354",
"DOI": "10.1038/s41467-026-70639-1",
"PMID": "41872182",
"PMCID": "PMC13171896",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70639-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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