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Empathy for pain persists across live two-way video interactions and viewing of prerecorded videos.

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
  1. [1] § Methods › Statistical analyses ↔ Analysis code/analysis_code.zip/GLMM_EDA_coupling.Rmd, lines 17–40 · score 0.65 · Linear mixed models, right skewed, glmer, log, family, gamma
  2. [2] § Methods › Statistical analyses ↔ Analysis code/analysis_code.zip/LMM_ECG_coupling.Rmd, lines 22–44 · score 0.56 · Linear mixed models, right skewed, lme4, family, gamma, IBI
  3. [3] § Results › Temporal presence does not influence affective empathy ↔ Analysis code/analysis_code.zip/LMM_behavior_affective_empathy.Rmd, lines 44–107 · score 0.54 · affective empathy, unpleasantness ratings, predict observers, video call, prerecording, interaction
  4. [4] § Results › Temporal presence does not modulate physiological coupling between observers and targets ↔ Analysis code/analysis_code.zip/GLMM_EDA_coupling.Rmd, lines 43–105 · score 0.54 · targets SCR, random slopes, predicting observers, video call, GLMM, coupling
  5. [5] § Results › Temporal presence does not modulate physiological coupling between observers and targets ↔ Analysis code/analysis_code.zip/LMM_ECG_coupling.Rmd, lines 47–111 · score 0.52 · targets IBI, random slopes, predicting observers, video call, coupling, prerecording

Paper

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

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The authors' code

R Markdown · 117 lines · 3.3 KB · no license · 2 matches

  1. ---
  2. title: "PiP23 - GLMM EDA coupling"
  3. author: "Jannik Heimann"
  4. date: "`r Sys.Date()`"
  5. output: pdf_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  11. library(lme4)
  12. library(ggplot2)
  13. library(MuMIn)
  15. ```
  17. ## Build generalized linear mixed models to predict observer EDA data with target EDA
  19. Fixed effects structure according to hypotheses!
  20. Family gamma function is used for positive, slightly right skewed data.
  23. ```{r model}
  25. DF <- read.csv('PiP23_EDA_GLMM_4000_7000ms_all.csv')
  27. # prepare simple effect coding
  28. DF$cond[DF$cond==2] <- -0.5 # OFF condition
  29. DF$cond[DF$cond==1] <- 0.5 # ON condition
  30. DF$EDA_targ = scale(DF$EDA_targ)
  33. M3 <- glmer(EDA_obs ~ 1 + cond * EDA_targ + (1 + cond * EDA_targ | obs), data = DF, family = Gamma(link = "log"), glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
  35. summary(M3)
  37. cat("\nR-squared marginal and conditional for the full model\n")
  38. print(r.squaredGLMM(M3))
  40. ```
  43. ## Plot Model 3
  45. ```{r Plot Model 3}
  47. DF1 <- DF
  48. DF1$EDA_obs <- DF1$EDA_obs + 1
  49. DF1 <- DF1[!is.na(DF1$EDA_obs), ]
  50. DF1 <- DF1[!is.na(DF1$EDA_targ), ]
  51. row.names(DF1) <- NULL
  52. DF1$EDA_obs <- log(DF1$EDA_obs)
  54. randef_EDA<- ranef(M3)
  56. ref_EDA<-randef_EDA$obs[,3]
  58. observers <- unique(DF1$obs)
  60. count<-1
  61. slopes_EDA<- NULL
  62. for (obser in observers){
  63. slope<- ref_EDA[count]
  64. data <- data.frame(DF1[DF1$obs == obser,])
  65. trials <- nrow(data)
  66. slope_all<- data.frame(rep(slope,trials))
  67. slopes_EDA<- rbind(slopes_EDA, slope_all)
  68. count <- count+1
  69. }
  71. colnames(slopes_EDA)<-c("slopes")
  73. DF1$slopes <- slopes_EDA$slopes
  75. DF1$EDA_obs_pred<- predict(M3, type ="response")
  77. DF1_off <- DF1[DF1$cond == -0.5,]
  78. DF1_on <- DF1[DF1$cond == 0.5,]
  80. DF1$cond<-as.numeric(DF1$cond)
  81. DF1$cond<-factor(DF1$cond, levels = c(0.5,-0.5), labels = c("video call","prerecording"))
  84. SCR <- ggplot(data = DF1, aes(EDA_targ, EDA_obs_pred, color = slopes, linetype = cond, group = interaction(slopes,cond,obs))) +
  85. stat_summary(fun = mean, geom ="line", aes(EDA_targ, EDA_obs_pred, color = slopes, linetype = cond), linewidth = 1.2) +
  86. geom_smooth(data = DF1_on, aes(EDA_targ, EDA_obs_pred), color = "black", inherit.aes = FALSE, method = "glm", alpha = 0, linewidth = 4) +
  87. geom_smooth(data = DF1_off, aes(EDA_targ, EDA_obs_pred), color = "black", inherit.aes = FALSE, method = "glm", alpha = 0, linewidth = 4, linetype = "dotted") +
  88. scale_color_gradient2(low = "darkorange3", high = "darkgreen", mid = "orange2", midpoint=0, name = "random slope\nof target SCR")+theme_classic() +
  89. theme_classic() +
  90. labs(x = "target SCR (z-std)", y = "predicted observers'\n SCR") +
  91. theme(
  92. axis.title.x = element_text(size = 13),
  93. axis.title.y = element_text(size = 13),
  94. legend.position = "bottom",legend.title = element_text(size = 12),
  95. legend.text = element_text(size = 12),axis.text.y = element_text(size =12),
  96. axis.text.x = element_text(size = 12)
  97. ) +
  98. guides(
  99. linetype = guide_legend(nrow = 2, byrow = TRUE)
  100. )
  102. print(SCR)
  105. ```
  107. # BIC to Bayes Factor
  109. ```{r BF}
  111. M3r <- glmer(EDA_obs ~ 1 + EDA_targ + (1 + cond * EDA_targ | obs), data = DF, family = Gamma(link = "log"), glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000)))
  113. BF_SCR <- exp((BIC(refitML(M3))-BIC(refitML(M3r)))/(-2))
  115. print(BF_SCR)
  117. ```

GLMM_EDA_coupling.Rmd, no license · at the source

Overview

Authors: Jannik Heimann1, Pauline Petereit2, Anat Perry3, Ulrike M. Krämer1
ORCID iDs: Ulrike M. Krämer
  1. Institute of Medical Psychology, Center of Brain, Behavior and Metabolism, University of Lübeck,Ratzeburger Allee 160, 23562 Lübeck, Germany
  2. Department of Neurology, University of Lübeck,Ratzeburger Allee 160, 23562 Lübeck, Germany
  3. Psychology Department, Hebrew University of Jerusalem,Mount Scopus, 91905 Jerusalem, Israel
Institutions: University of Lübeck (Germany); Hebrew University of Jerusalem (Israel)
Journal: Cognitive, affective & behavioral neuroscience, volume 26, issue 4, pages 1795-1816
Dates: received 7 August 2025; accepted 16 March 2026; published online 15 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13415-026-01442-0 · PMID 41986865 · PMCID PMC13385010 · OpenAlex W7154447834
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), pain (population), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Physiology & signal measures
Keywords: Electroencephalography, Empathic accuracy, Neural oscillations, Physiological coupling, Social cognition
MeSH: Brain Waves*, Empathy*, Interpersonal Relations*, Pain*, Social Interaction*, Adult, Electroencephalography, Female, Humans, Male, Video Recording, Young Adult (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (KR 3691/12-1); Universität zu Lübeck (3165)
Citations: not cited yet (Europe PMC); 143 references in the paper

Abstract

The impact of digitally mediated social interaction on understanding others and sharing their emotions has not been thoroughly investigated. We examined how live, video-mediated interaction, as opposed to watching a prerecorded video, affects behavioral, neural, and physiological aspects of empathy for pain. Thirty-five observers watched targets undergoing painful electric stimulation in an electroencephalogram study. We hypothesized that reduced temporal presence, the immediacy or delay with which information is transferred during social interactions, would result in diminished behavioral and electrophysiological empathic responses. However, observer’s behavioral empathic responses were not diminished with reduced temporal presence. On a neural level, midfrontal theta was sensitive to the other’s pain intensity, and we observed significant physiological coupling between participants. Conversely, Mu suppression was not modulated by pain intensity. Importantly, neural and physiological indices of empathy were independent of temporal presence. However, exploratory analyses indicated a latency effect of temporal presence on pain-related theta activity with an earlier theta increase in interactions with high temporal presence. The results suggest that the temporal presence of individuals may not be necessary for empathy towards another’s pain. Future studies may investigate more naturalistic social interactions and include motivational aspects of empathy. We discuss implications of these findings for debates on social presence and on second-person neuroscience.

Supplementary Information: The online version contains supplementary material available at 10.3758/s13415-026-01442-0.

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 3jdf4

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 51 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (5 files), lmerTest (5 files), lme4 (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
8 files
At the source: osf.io/3jdf4/

Code availability

The analysis code can be found at https://osf.io/3jdf4/ (10.17605/OSF.IO/3JDF4).

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 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);
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Data

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Availability of data and materials

The raw data and the final preprocessed data, which is prepared for statistical analysis, can be found at https://osf.io/3jdf4/ (10.17605/OSF.IO/3JDF4).

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 12 MeSH terms, 2 funders, 137 references.

Cite

This paper

Heimann, J., Petereit, P., Perry, A., & Krämer, U. M. (2026). Empathy for pain persists across live two-way video interactions and viewing of prerecorded videos. Cognitive, affective & behavioral neuroscience, 26(4), 1795-1816. https://doi.org/10.3758/s13415-026-01442-0

BibTeX

@article{heimann2026empathy,
author = {Heimann, Jannik and Petereit, Pauline and Perry, Anat and Krämer, Ulrike M.},
title = {{Empathy for pain persists across live two-way video interactions and viewing of prerecorded videos}},
journal = {Cognitive, affective \& behavioral neuroscience},
year = {2026},
month = apr,
volume = {26},
number = {4},
pages = {1795--1816},
publisher = {Springer Science+Business Media},
issn = {1530-7026},
doi = {10.3758/s13415-026-01442-0},
url = {https://doi.org/10.3758/s13415-026-01442-0},
pmid = {41986865},
pmcid = {PMC13385010}
}

RIS

TY - JOUR
AU - Heimann, Jannik
AU - Petereit, Pauline
AU - Perry, Anat
AU - Krämer, Ulrike M.
TI - Empathy for pain persists across live two-way video interactions and viewing of prerecorded videos
T2 - Cognitive, affective & behavioral neuroscience
J2 - Cogn Affect Behav Neurosci
PY - 2026
DA - 2026/04/15
VL - 26
IS - 4
SP - 1795
EP - 1816
SN - 1530-7026
PB - Springer Science+Business Media
DO - 10.3758/s13415-026-01442-0
UR - https://doi.org/10.3758/s13415-026-01442-0
LA - en
ER -

CSL-JSON

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