Created: 9/4/2021.

1 Data and scripts

The following are all statistical analyses reported in the paper. All data files are available at Github repository: https://github.com/tuomaseerola/sad-music-hormones

1.1 Data files

  • background.csv: Individual differences variable (QoL, TAS, MSI, etc.) with unique ID for each participant.

  • emotions.csv: Self-reported emotions (moved, relaxed, sad, anxious, pleasantness).

  • mood_and_emotions.csv: Mood and emotions (pos and neg moods).

  • aggregate_data.csv: Hormone variables (PRL, OXY, CRT, ACTH, CRP) and participants IDs and Conditions.

  • EDA.csv: Electrodermal variables (CDA.ISCR,CDA.nSCR,CDA.AmpSum,CDA.Tonic) and participants IDs and Conditions.

  • HRV.csv: Heart rate variability predictors (SDNN,LF,HF,LF/HF) and participants IDs and Conditions.

1.2 R Scripts

  • analysis_moods.R: Reads and preprocesses data (relative change, outliers), and runs GLMM and produces planned contrasts.
  • analysis_emotions.R: Reads and preprocesses data (relative change, outliers), and runs GLMM and produces planned contrasts.
  • analysis_EDA.R: Reads and preprocesses data (relative change, outliers), and runs GLMM and produces planned contrasts.
  • analysis_HRV.R: Reads and preprocesses data (relative change, outliers), and runs GLMM and produces planned contrasts.
  • figure1.R: Combines the plots created by the other scripts and produces a figure.

2 Analysis of moods

source('analysis_moods.R')
contrast estimate SE df t.ratio p.value
HIGH - LOW -0.0420139 0.0508369 60 -0.8264444 0.4118265
contrast effect.size SE df lower.CL upper.CL
HIGH - LOW -0.2100264 0.2548547 60 -0.7198117 0.2997589
contrast estimate SE df t.ratio p.value
HIGH - LOW 0.0577381 0.1253117 60 0.460756 0.6466395

2.1 Negative moods

Neg. moods across conditions and groups.
Empathy Condition n m sd se LCI UCI
HIGH Silence 32 -7.112 12.352 2.184 -11.392 -2.832
HIGH Music 32 4.874 32.739 5.788 -6.470 16.217
LOW Silence 30 -1.534 12.922 2.359 -6.158 3.090
LOW Music 30 2.246 25.675 4.688 -6.942 11.433
Pos. mood in Silence.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -5.577 5.773 115.299 -0.966 0.336
Pos. mood in Music
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 2.628 5.773 115.299 0.455 0.65
Pos. mood in High Empathy
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy -11.985 5.073 60 -2.363 0.021
Pos. mood in Low Empathy
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy -3.78 5.239 60 -0.721 0.473

2.2 Positive moods

Pos. moods across conditions and groups.
Empathy Condition n m sd se LCI UCI
HIGH Silence 32 -9.823 17.539 3.100 -15.900 -3.746
HIGH Music 32 3.095 26.908 4.757 -6.228 12.418
LOW Silence 30 -9.098 8.871 1.620 -12.272 -5.923
LOW Music 30 -9.728 20.512 3.745 -17.068 -2.388
Pos. mood in Silence.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -0.681 5.042 114.032 -0.135 0.893
Pos. mood in Music
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 12.823 5.004 113.739 2.563 0.012
Pos. mood in High Empathy
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy -12.873 4.403 60.055 -2.924 0.005
Pos. mood in Low Empathy
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 0.631 4.502 59.153 0.14 0.889

3 Analysis of emotions

source('analysis_emotions.R')
MANOVA across emotions (variable) and Emapthy.
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 33.149 11.113 304.586 2.983 0.003
as.numeric(variable) 12.741 3.265 246.000 3.902 0.000
as.numeric(Empathy) -6.115 7.097 304.586 -0.862 0.390
as.numeric(variable):as.numeric(Empathy) -1.410 2.085 246.000 -0.676 0.500
Comparison of Empathy groups across emotions scales.
contrast variable estimate SE df t.ratio p.value
HIGH - LOW sad 8.254 5.861 272.453 1.408 0.160
HIGH - LOW anxious 2.596 5.861 272.453 0.443 0.658
HIGH - LOW relaxed 16.836 5.861 272.453 2.872 0.004
HIGH - LOW moved 14.874 5.861 272.453 2.538 0.012
HIGH - LOW pleasantness 9.166 5.861 272.453 1.564 0.119
Self-report means across empathy groups.
variable Empathy n m sd se LCI UCI
sad HIGH 32 49.664 26.157 4.624 40.601 58.726
sad LOW 30 41.409 26.745 4.883 31.839 50.980
anxious HIGH 32 24.098 26.779 4.734 14.820 33.376
anxious LOW 30 21.502 26.012 4.749 12.194 30.810
relaxed HIGH 32 79.395 17.557 3.104 73.312 85.478
relaxed LOW 30 62.559 25.943 4.737 53.276 71.842
moved HIGH 32 67.217 20.940 3.702 59.962 74.472
moved LOW 30 52.343 25.191 4.599 43.329 61.357
pleasantness HIGH 32 84.758 14.606 2.582 79.697 89.818
pleasantness LOW 30 75.592 16.703 3.050 69.615 81.569

4 Analysis of hormones

source('analysis_hormones.R')
PRL Baseline
contrast estimate SE df t.ratio p.value
HIGH - LOW -0.091 0.14 60 -0.649 0.519
OXY Baseline
contrast estimate SE df t.ratio p.value
HIGH - LOW 0.131 0.115 60 1.134 0.261
CRT Baseline
contrast estimate SE df t.ratio p.value
HIGH - LOW -0.172 0.117 60 -1.471 0.147
ACTH Baseline
contrast estimate SE df t.ratio p.value
HIGH - LOW -0.005 0.109 60 -0.048 0.961
CRP Baseline
contrast estimate SE df t.ratio p.value
HIGH - LOW 0.078 0.163 60 0.479 0.634

4.1 PRL

Prolactin contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -2.393 1.733 71.852 -1.381 0.172
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -4.253 1.733 71.852 -2.454 0.017
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 2.304 0.727 59.006 3.168 0.002
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 0.445 0.751 59.007 0.592 0.556

## OXY

Oxytocin contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy 3.155 4.401 103.83 0.717 0.475
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -9.704 4.401 103.83 -2.205 0.03
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 8.026 3.387 59.092 2.37 0.021
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy -4.833 3.499 59.098 -1.381 0.172

## CRT

Cortisol contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -0.196 0.713 82.311 -0.275 0.784
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -1.098 0.713 82.311 -1.539 0.128
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 0.57 0.4 59.002 1.425 0.159
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy -0.332 0.413 59.003 -0.803 0.425

4.2 ACTH

ACTH contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -1.131 2.527 78.415 -0.448 0.656
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -0.983 2.527 78.415 -0.389 0.698
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 0.226 1.299 59.029 0.174 0.862
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 0.375 1.341 59.031 0.28 0.781

4.3 CRP

CRP contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy 0.676 1.337 73.032 0.506 0.614
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 2.272 1.337 73.032 1.7 0.093
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy -1.14 0.588 59.225 -1.939 0.057
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 0.456 0.607 59.239 0.751 0.456

4.4 Summary of hormonal variables

A full breakdown of all variables is given in Supporting Information Table S1.

library(dplyr)
m <- df_rel %>%
  dplyr::group_by(EMPATHY,Condition) %>%
  dplyr::summarise(n=n(),PRL_M=mean(PRL,na.rm = TRUE),PRL_SE=sd(PRL,na.rm = TRUE)/sqrt(n), OXY_M=mean(OXY,na.rm = TRUE),OXY_SE=sd(OXY,na.rm = TRUE)/sqrt(n), CRT_M=mean(CRT,na.rm = TRUE),CRT_SE=sd(CRT,na.rm = TRUE)/sqrt(n), ACTH_M=mean(ACTH,na.rm = TRUE),ACTH_SE=sd(ACTH,na.rm = TRUE)/sqrt(n), CRP_M=mean(CRP,na.rm = TRUE),CRP_SE=sd(CRP,na.rm = TRUE)/sqrt(n))

knitr::kable(m,digits = 3,caption = 'Table S1. Means and standard errors of all hormone markers.')
Table S1. Means and standard errors of all hormone markers.
EMPATHY Condition n PRL_M PRL_SE OXY_M OXY_SE CRT_M CRT_SE ACTH_M ACTH_SE CRP_M CRP_SE
HIGH Silence 32 -5.646 1.268 -0.062 2.642 -4.472 0.414 -9.937 1.562 -1.630 0.839
HIGH Music 32 -7.758 1.202 -7.943 3.206 -4.864 0.534 -10.019 1.879 -0.490 0.856
LOW Silence 30 -3.259 1.386 -3.222 3.389 -4.282 0.696 -8.811 1.815 -2.306 1.018
LOW Music 30 -3.499 1.166 1.766 3.244 -3.761 0.609 -9.031 1.936 -2.762 1.069

4.5 Additional factors

Two additional covariates were added to the main analysis, hormonal contraception and menstruation cycle. Hormonal contraception was used by 28 participants and it is coded as a binary variable. Information about the phase of the participant’s menses is classified from the days since menses into five categories based on prior research (Andreano, Arjomandi, and Cahill 2008). The table also shows you how many of our participants fall in each phase.

Table S2. Summary of the phase of the mense in participants.

Phase Definition N
early-follicular phase days 1–7 since menstruation 6
late-follicular days 8–17 since menstruation 21
mid-luteal days 18–24 since menstruation 16
late-luteal days 25-29 since menstruation 16
NA not applicable, not known 3

Both of these variables have been added as random factors in the analysis of prolactinand oxytocin below. As the results without these variables is retained in the comments, it is easy to see that these variables do materially change the outcome of the analyses (there are marginal changes in the p values).

4.5.1 Impact on prolactin

m1_additional <- lme4::lmer(PRL ~ Condition * EMPATHY + (1|Hormone) + (1|MenstPhase) + (1|ID) + (1|Session), data=df_rel) # relative
em2 <- emmeans::emmeans(m1_additional,specs = ~ Condition * EMPATHY) #

# This is Between Group comparison                                     estimate     SE df t.ratio p.value
knitr::kable(emmeans::contrast(em2,method=list(Silence_High - Silence_Low)))#    -2.39 1.73 71.8 -1.381  0.1716
contrast estimate SE df t.ratio p.value
c(1, 0, -1, 0) -2.323141 1.885231 67.60644 -1.232284 0.2221114 
knitr::kable(emmeans::contrast(em2,method=list(Music_High - Music_Low)))    #    -4.25 1.73 71.8 -2.454  0.0166
contrast estimate SE df t.ratio p.value
c(0, 1, 0, -1) -4.491549 1.885231 67.60644 -2.382493 0.0200144 
# Within group comparison 
knitr::kable(emmeans::contrast(em2,method=list(Silence_High - Music_High))) #       2.3 0.727 59 3.168   0.0024
contrast estimate SE df t.ratio p.value
c(1, -1, 0, 0) 2.34162 0.7454326 56.00185 3.14129 0.0026866 
knitr::kable(emmeans::contrast(em2,method=list(Silence_Low - Music_Low)))   #     0.445 0.751 59 0.592   0.5562
contrast estimate SE df t.ratio p.value
c(0, 0, 1, -1) 0.1732115 0.7851554 56.00818 0.2206079 0.8262

4.5.2 Impact on oxytocin

m1_additional <- lme4::lmer(OXY ~ Condition * EMPATHY + (1|Hormone) + (1|MenstPhase) + (1|ID) + (1|Session), data=df_rel) # relative
em2 <- emmeans::emmeans(m1_additional,specs = ~ Condition * EMPATHY) #
# This is Between Group comparison                                     estimate     SE df t.ratio p.value
knitr::kable(emmeans::contrast(em2,method=list(Silence_High - Silence_Low)))#   3.16 4.4 104 0.717   0.4750
contrast estimate SE df t.ratio p.value
c(1, 0, -1, 0) 2.973539 4.839568 98.8125 0.6144223 0.5403485 
knitr::kable(emmeans::contrast(em2,method=list(Music_High - Music_Low)))    #  -9.7 4.4 104 -2.205  0.0297
contrast estimate SE df t.ratio p.value
c(0, 1, 0, -1) -10.95792 4.839568 98.8125 -2.264234 0.025745 
# WITHIN GROUPS
knitr::kable(emmeans::contrast(em2,method=list(Silence_High - Music_High))) #  8.03 3.39 59.1 2.370   0.0211
contrast estimate SE df t.ratio p.value
c(1, -1, 0, 0) 8.208807 3.494575 56.0254 2.349014 0.0223751 
knitr::kable(emmeans::contrast(em2,method=list(Silence_Low - Music_Low)))   # -4.83 3.5 59.1 -1.381  0.1724
contrast estimate SE df t.ratio p.value
c(0, 0, 1, -1) -5.722647 3.684879 56.11001 -1.553008 0.1260439

5 Analysis of psychophysiology

5.1 Analysis of EDA

source('analysis_EDA.R')
Table S2. Means and standard errors of all EDA measures.
Empathy Event.Name n CDA.ISCR_M CDA.ISCR_SE CDA.nSCR_M CDA.nSCR_SE CDA.AmpSum_M CDA.AmpSum_SE CDA.Tonic_M CDA.Tonic_SE
HIGH Silence 29 -29.263 5.933 -38.386 7.846 -47.522 9.442 -12.380 2.895
HIGH Music 29 -32.660 6.133 -42.576 7.428 -48.070 15.409 -14.002 3.061
LOW Silence 28 -23.579 3.970 -26.933 5.025 -39.300 6.275 -9.266 2.418
LOW Music 28 -34.531 5.067 -40.406 6.649 -53.883 6.821 -12.106 3.039

5.1.1 CDA.ISCR

CDA.ISCR contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -5.258 7.517 73.972 -0.699 0.486
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 1.444 7.517 73.972 0.192 0.848
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 3.125 4.095 54.007 0.763 0.449
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 9.827 4.194 54.116 2.343 0.023

5.1.2 CDA.AmpSum

CDA.AmpSum contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -7.723 14.421 78.371 -0.536 0.594
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 5.314 14.421 78.371 0.368 0.714
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy -0.45 8.974 53.041 -0.05 0.96
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 12.587 9.024 53.157 1.395 0.169

5.1.3 CDA.nSCR

CDA.nSCR contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -11.054 9.638 71.406 -1.147 0.255
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -2.567 9.638 71.406 -0.266 0.791
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 3.937 4.916 54.011 0.801 0.427
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 12.424 5.038 54.168 2.466 0.017

5.1.4 CDA.Tonic

CDA.Tonic contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -2.899 4.02 71.066 -0.721 0.473
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -2.111 4.02 71.066 -0.525 0.601
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 1.485 2.031 54.007 0.731 0.468
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 2.272 2.08 54.113 1.093 0.279

5.2 Analysis of HRV

source('analysis_HRV.R')
Table S2. Means and standard errors of all HRV measures.
Empathy Event.Name n VLF_M VLF_SE HF_M HF_SE LF_M LF_SE LF.HF_M LF.HF_SE SDNN_M SDNN_SE
HIGH Silence 29 -8.879 1.544 4.882 1.848 -4.477 1.403 -28.768 4.070 -4.183 0.950
HIGH Music 29 -11.980 1.711 4.923 1.647 -4.991 1.518 -30.204 4.559 -4.558 1.140
LOW Silence 28 -11.513 1.600 6.118 3.175 -4.944 2.894 -33.089 5.189 -3.863 1.803
LOW Music 28 -13.657 1.498 6.921 2.768 -8.028 2.341 -43.716 3.960 -6.001 1.655

5.2.1 SDNN

SDNN contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -0.269 2.003 67.567 -0.134 0.894
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 1.392 2.003 67.567 0.695 0.49
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 0.343 0.904 54.017 0.379 0.706
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 2.004 0.928 54.263 2.159 0.035

5.2.2 LF

LF contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy 0.01 0.016 77.396 0.617 0.539
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 0.031 0.016 77.396 1.974 0.052
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 0.004 0.009 54.03 0.453 0.652
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 0.026 0.01 54.434 2.686 0.01

5.2.3 HF

HF contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy -2.747 2.522 75.07 -1.089 0.28
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy -6.429 2.522 75.07 -2.55 0.013
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy -0.916 1.41 54.029 -0.649 0.519
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy -4.598 1.451 54.427 -3.169 0.003

5.2.4 LF/HF

LF/HF contrasts.
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Silence - Low Empathy 4.368 6.319 79.259 0.691 0.491
contrast estimate SE df t.ratio p.value
Music - High Empathy vs Music - Low Empathy 13.464 6.319 79.259 2.131 0.036
contrast estimate SE df t.ratio p.value
Silence - High Empathy vs Music - High Empathy 1.406 3.85 54.048 0.365 0.716
contrast estimate SE df t.ratio p.value
Silence - Low Empathy vs Music - Low Empathy 10.502 3.978 54.642 2.64 0.011

6 Create Figure 1

source('Figure1.R')

7 References

Andreano, Joseph M, Hamidreza Arjomandi, and Larry Cahill. 2008. “Menstrual Cycle Modulation of the Relationship Between Cortisol and Long-Term Memory.” Psychoneuroendocrinology 33 (6): 874–82.

8 Appendix

sessionInfo()
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## [1] en_US.UTF-8/en_GB.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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##  [4] reshape2_1.4.4         RColorBrewer_1.1-2     forcats_0.5.1         
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##  [79] tidyselect_1.1.0        plyr_1.8.6              magrittr_2.0.1         
##  [82] R6_2.5.0                generics_0.1.0          multcomp_1.4-16        
##  [85] DBI_1.1.1               mgcv_1.8-34             pillar_1.5.1           
##  [88] haven_2.3.1             withr_2.4.1             survival_3.2-7         
##  [91] abind_1.4-5             modelr_0.1.8            crayon_1.4.1           
##  [94] utf8_1.1.4              plotly_4.9.3            tmvnsim_1.0-2          
##  [97] rmarkdown_2.7           grid_4.0.3              readxl_1.3.1           
## [100] data.table_1.14.0       reprex_1.0.0            digest_0.6.27          
## [103] webshot_0.5.2           xtable_1.8-4            httpuv_1.5.5           
## [106] numDeriv_2016.8-1.1     depth_2.1-1.1           munsell_0.5.0          
## [109] viridisLite_0.3.0       bslib_0.2.4