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Empathy motivation is preserved following amygdala damage.

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R · 713 lines · 30 KB · no license

  1. ## Empathy motivation is preserved following amygdala damage ##
  3. #### Load Required Packages ####
  4. require(base)
  5. require(float)
  6. require(quanteda)
  7. require(Matrix)
  8. require(stats)
  9. require(dplyr)
  10. require(tidyr)
  11. require(rio)
  12. require(SnowballC)
  13. require(stringi)
  14. require(stringr)
  15. require(textclean)
  16. require(tidyr)
  17. require(utils)
  18. require(bayesanova)
  19. require(lme4)
  21. #### This will bring up a dialogue box to import the SPSS file into the environment ####
  22. EmpLesion <- import(file.choose(new=FALSE), setclass = getOption("rio.import.class", "data.frame"))
  24. # Remove participants who did not complete the affective EST #
  25. EmpLesionNoEST <- subset(EmpLesion, AffectiveEST_Avg != 'NA')
  27. EmpLesionNoCogEST <- subset(EmpLesion, CognitiveEST_Avg != 'NA')
  30. #### Empathy Choice Bayes Anova Testing ####
  32. ## Affective EST ##
  34. amygAff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_Avg
  35. bdcAff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_Avg
  36. hcAff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_Avg
  38. ## Cognitive EST ##
  40. amygCog <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_Avg
  41. bdcCog <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_Avg
  42. hcCog <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_Avg
  44. ## Affective EST ##
  46. assumption.check(amygAff,bdcAff,hcAff,conf.level=0.95)
  48. set.seed(42)
  50. result = bayes.anova(first=amygAff,second=bdcAff,third=hcAff,n=10000)
  51. anovaplot(result)
  53. post.pred.check(anovafit = result, ngroups = 3, out = EmpLesionNoEST$AffectiveEST_Avg,
  54. reps = 50, eta = c(1/3,1/3,1/3))
  56. ## Cognitive EST ##
  58. assumption.check(amygCog,bdcCog,hcCog,conf.level=0.95)
  60. set.seed(43)
  62. result = bayes.anova(first=amygCog,second=bdcCog,third=hcCog,n=10000)
  63. anovaplot(result)
  65. post.pred.check(anovafit = result, ngroups = 3, out = EmpLesionNoCogEST$CognitiveEST_Avg,
  66. reps = 50, eta = c(1/3,1/3,1/3))
  69. #### Cognitive Cost Bayes Testing ####
  71. ## Data Gather for Cognitive Cost Bayes Analyses ##
  73. ## Affective EST ##
  75. # Empathy Deck #
  77. amygAff_Aeff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_EmpathyEffort
  78. bdcAff_Aeff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_EmpathyEffort
  79. hcAff_Aeff <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_EmpathyEffort
  81. amygAff_Aavr <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_EmpathyAversion
  82. bdcAff_Aavr <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_EmpathyAversion
  83. hcAff_Aavr <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_EmpathyAversion
  85. amygAff_Aeffi <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_EmpathyEfficacy
  86. bdcAff_Aeffi <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_EmpathyEfficacy
  87. hcAff_Aeffi <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_EmpathyEfficacy
  89. # Non-Empathy Deck #
  91. amygAff_AeffNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_NonEmpathyEffort
  92. bdcAff_AeffNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_NonEmpathyEffort
  93. hcAff_AeffNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_NonEmpathyEffort
  95. amygAff_AavrNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_NonEmpathyAversion
  96. bdcAff_AavrNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_NonEmpathyAversion
  97. hcAff_AavrNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_NonEmpathyAversion
  99. amygAff_AeffiNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==1,]$AffectiveEST_NonEmpathyEfficacy
  100. bdcAff_AeffiNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==3,]$AffectiveEST_NonEmpathyEfficacy
  101. hcAff_AeffiNonEmp <- EmpLesionNoEST[EmpLesionNoEST$LesionGroup==4,]$AffectiveEST_NonEmpathyEfficacy
  103. ## Cognitive EST ##
  105. # Empathy Deck #
  107. amygCog_Aeff <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_EmpathyEffort
  108. bdcCog_Aeff <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_EmpathyEffort
  109. hcCog_Aeff <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_EmpathyEffort
  111. amygCog_Aavr <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_EmpathyAversion
  112. bdcCog_Aavr <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_EmpathyAversion
  113. hcCog_Aavr <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_EmpathyAversion
  115. amygCog_Aeffi <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_EmpathyEfficacy
  116. bdcCog_Aeffi <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_EmpathyEfficacy
  117. hcCog_Aeffi <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_EmpathyEfficacy
  119. # Non-Empathy Deck #
  121. amygCog_AeffNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_NonEmpathyEffort
  122. bdcCog_AeffNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_NonEmpathyEffort
  123. hcCog_AeffNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_NonEmpathyEffort
  125. amygCog_AavrNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_NonEmpathyAversion
  126. bdcCog_AavrNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_NonEmpathyAversion
  127. hcCog_AavrNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_NonEmpathyAversion
  129. amygCog_AeffiNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==1,]$CognitiveEST_NonEmpathyEfficacy
  130. bdcCog_AeffiNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==3,]$CognitiveEST_NonEmpathyEfficacy
  131. hcCog_AeffiNonEmp <- EmpLesionNoCogEST[EmpLesionNoCogEST$LesionGroup==4,]$CognitiveEST_NonEmpathyEfficacy
  134. #### Cognitive Costs - Effort ####
  136. ## Affective EST ##
  138. # omit bdc with missing data #
  139. bdcAff_Aeffomit <- as.numeric(na.omit(bdcAff_Aeff))
  141. assumption.check(amygAff_Aeff,bdcAff_Aeffomit,hcAff_Aeff,amygAff_AeffNonEmp,bdcAff_AeffNonEmp,hcAff_AeffNonEmp,conf.level=0.95)
  143. set.seed(46)
  145. result = bayes.anova(first=amygAff_Aeff,second=bdcAff_Aeffomit,third=hcAff_Aeff,
  146. fourth=amygAff_AeffNonEmp,fifth=bdcAff_AeffNonEmp,sixth=hcAff_AeffNonEmp, n=10000)
  147. anovaplot(result)
  149. ## Cognitive EST ##
  151. set.seed(47)
  153. result = bayes.anova(first=amygCog_Aeff,second=bdcCog_Aeff,third=hcCog_Aeff,
  154. fourth=amygCog_AeffNonEmp,fifth=bdcCog_AeffNonEmp,sixth=hcCog_AeffNonEmp, n=10000)
  156. anovaplot(result)
  158. #### Cognitive Costs - Aversiveness #####
  160. ## Affective EST ##
  162. # omit bdc with missing data #
  163. bdcAff_Aavromit <- as.numeric(na.omit(bdcAff_Aavr))
  165. assumption.check(amygAff_Aavr,bdcAff_Aavromit,hcAff_Aavr,amygAff_AavrNonEmp,bdcAff_AavrNonEmp,hcAff_AavrNonEmp,conf.level=0.95)
  167. set.seed(50)
  169. result = bayes.anova(first=amygAff_Aavr,second=bdcAff_Aavromit,third=hcAff_Aavr,
  170. fourth=amygAff_AavrNonEmp,fifth=bdcAff_AavrNonEmp,sixth=hcAff_AavrNonEmp, n=10000)
  171. anovaplot(result)
  173. ## Cognitive EST ##
  175. set.seed(51)
  177. result = bayes.anova(first=amygCog_Aavr,second=bdcCog_Aavr,third=hcCog_Aavr,
  178. fourth=amygCog_AavrNonEmp,fifth=bdcCog_AavrNonEmp,sixth=hcCog_AavrNonEmp, n=10000)
  180. anovaplot(result)
  182. #### Cognitive Costs - Efficacy ####
  184. ## Affective EST ##
  186. # omit bdc with missing data #
  187. bdcAff_Aeffiomit <- as.numeric(na.omit(bdcAff_Aeffi))
  189. assumption.check(amygAff_Aeffi,bdcAff_Aeffiomit,hcAff_Aeffi,amygAff_AeffiNonEmp,bdcAff_AeffiNonEmp,hcAff_AeffiNonEmp,conf.level=0.95)
  191. set.seed(54)
  193. result = bayes.anova(first=amygAff_Aeffi,second=bdcAff_Aeffiomit,third=hcAff_Aeffi,
  194. fourth=amygAff_AeffiNonEmp,fifth=bdcAff_AeffiNonEmp,sixth=hcAff_AeffiNonEmp, n=10000)
  195. anovaplot(result)
  197. ## Cognitive EST ##
  199. set.seed(55)
  201. result = bayes.anova(first=amygCog_Aeffi,second=bdcCog_Aeffi,third=hcCog_Aeffi,
  202. fourth=amygCog_AeffiNonEmp,fifth=bdcCog_AeffiNonEmp,sixth=hcCog_AeffiNonEmp, n=10000)
  204. anovaplot(result)
  207. #### Affective EST - Text Data ####
  208. ## Create tibble for just empathy responses ##
  210. EmpLesiontibble <- tibble(id = EmpLesionNoEST$ID,
  211. lesionGroup = EmpLesionNoEST$LesionGroup,
  212. choice.1 = EmpLesionNoEST$AffectiveEST_Q1Choice,
  213. choice.2 = EmpLesionNoEST$AffectiveEST_Q2Choice,
  214. choice.3 = EmpLesionNoEST$AffectiveEST_Q3Choice,
  215. choice.4 = EmpLesionNoEST$AffectiveEST_Q4Choice,
  216. choice.5 = EmpLesionNoEST$AffectiveEST_Q5Choice,
  217. choice.6 = EmpLesionNoEST$AffectiveEST_Q6Choice,
  218. choice.7 = EmpLesionNoEST$AffectiveEST_Q7Choice,
  219. choice.8 = EmpLesionNoEST$AffectiveEST_Q8Choice,
  220. choice.9 = EmpLesionNoEST$AffectiveEST_Q9Choice,
  221. choice.10 = EmpLesionNoEST$AffectiveEST_Q10Choice,
  222. choice.11 = EmpLesionNoEST$AffectiveEST_Q11Choice,
  223. choice.12 = EmpLesionNoEST$AffectiveEST_Q12Choice,
  224. choice.13 = EmpLesionNoEST$AffectiveEST_Q13Choice,
  225. choice.14 = EmpLesionNoEST$AffectiveEST_Q14Choice,
  226. choice.15 = EmpLesionNoEST$AffectiveEST_Q15Choice,
  227. choice.16 = EmpLesionNoEST$AffectiveEST_Q16Choice,
  228. choice.17 = EmpLesionNoEST$AffectiveEST_Q17Choice,
  229. choice.18 = EmpLesionNoEST$AffectiveEST_Q18Choice,
  230. choice.19 = EmpLesionNoEST$AffectiveEST_Q19Choice,
  231. choice.20 = EmpLesionNoEST$AffectiveEST_Q20Choice,
  232. choice.21 = EmpLesionNoEST$AffectiveEST_Q21Choice,
  233. choice.22 = EmpLesionNoEST$AffectiveEST_Q22Choice,
  234. choice.23 = EmpLesionNoEST$AffectiveEST_Q23Choice,
  235. choice.24 = EmpLesionNoEST$AffectiveEST_Q24Choice,
  236. choice.25 = EmpLesionNoEST$AffectiveEST_Q25Choice,
  237. choice.26 = EmpLesionNoEST$AffectiveEST_Q26Choice,
  238. choice.27 = EmpLesionNoEST$AffectiveEST_Q27Choice,
  239. choice.28 = EmpLesionNoEST$AffectiveEST_Q28Choice,
  240. choice.29 = EmpLesionNoEST$AffectiveEST_Q29Choice,
  241. choice.30 = EmpLesionNoEST$AffectiveEST_Q30Choice,
  242. choice.31 = EmpLesionNoEST$AffectiveEST_Q31Choice,
  243. choice.32 = EmpLesionNoEST$AffectiveEST_Q32Choice,
  244. choice.33 = EmpLesionNoEST$AffectiveEST_Q33Choice,
  245. choice.34 = EmpLesionNoEST$AffectiveEST_Q34Choice,
  246. choice.35 = EmpLesionNoEST$AffectiveEST_Q35Choice,
  247. choice.36 = EmpLesionNoEST$AffectiveEST_Q36Choice,
  248. choice.37 = EmpLesionNoEST$AffectiveEST_Q37Choice,
  249. choice.38 = EmpLesionNoEST$AffectiveEST_Q38Choice,
  250. choice.39 = EmpLesionNoEST$AffectiveEST_Q39Choice,
  251. choice.40 = EmpLesionNoEST$AffectiveEST_Q40Choice,
  252. response.1 = EmpLesionNoEST$AffectiveEST_Q1Response,
  253. response.2 = EmpLesionNoEST$AffectiveEST_Q2Response,
  254. response.3 = EmpLesionNoEST$AffectiveEST_Q3Response,
  255. response.4 = EmpLesionNoEST$AffectiveEST_Q4Response,
  256. response.5 = EmpLesionNoEST$AffectiveEST_Q5Response,
  257. response.6 = EmpLesionNoEST$AffectiveEST_Q6Response,
  258. response.7 = EmpLesionNoEST$AffectiveEST_Q7Response,
  259. response.8 = EmpLesionNoEST$AffectiveEST_Q8Response,
  260. response.9 = EmpLesionNoEST$AffectiveEST_Q9Response,
  261. response.10 = EmpLesionNoEST$AffectiveEST_Q10Response,
  262. response.11 = EmpLesionNoEST$AffectiveEST_Q11Response,
  263. response.12 = EmpLesionNoEST$AffectiveEST_Q12Response,
  264. response.13 = EmpLesionNoEST$AffectiveEST_Q13Response,
  265. response.14 = EmpLesionNoEST$AffectiveEST_Q14Response,
  266. response.15 = EmpLesionNoEST$AffectiveEST_Q15Response,
  267. response.16 = EmpLesionNoEST$AffectiveEST_Q16Response,
  268. response.17 = EmpLesionNoEST$AffectiveEST_Q17Response,
  269. response.18 = EmpLesionNoEST$AffectiveEST_Q18Response,
  270. response.19 = EmpLesionNoEST$AffectiveEST_Q19Response,
  271. response.20 = EmpLesionNoEST$AffectiveEST_Q20Response,
  272. response.21 = EmpLesionNoEST$AffectiveEST_Q21Response,
  273. response.22 = EmpLesionNoEST$AffectiveEST_Q22Response,
  274. response.23 = EmpLesionNoEST$AffectiveEST_Q23Response,
  275. response.24 = EmpLesionNoEST$AffectiveEST_Q24Response,
  276. response.25 = EmpLesionNoEST$AffectiveEST_Q25Response,
  277. response.26 = EmpLesionNoEST$AffectiveEST_Q26Response,
  278. response.27 = EmpLesionNoEST$AffectiveEST_Q27Response,
  279. response.28 = EmpLesionNoEST$AffectiveEST_Q28Response,
  280. response.29 = EmpLesionNoEST$AffectiveEST_Q29Response,
  281. response.30 = EmpLesionNoEST$AffectiveEST_Q30Response,
  282. response.31 = EmpLesionNoEST$AffectiveEST_Q31Response,
  283. response.32 = EmpLesionNoEST$AffectiveEST_Q32Response,
  284. response.33 = EmpLesionNoEST$AffectiveEST_Q33Response,
  285. response.34 = EmpLesionNoEST$AffectiveEST_Q34Response,
  286. response.35 = EmpLesionNoEST$AffectiveEST_Q35Response,
  287. response.36 = EmpLesionNoEST$AffectiveEST_Q36Response,
  288. response.37 = EmpLesionNoEST$AffectiveEST_Q37Response,
  289. response.38 = EmpLesionNoEST$AffectiveEST_Q38Response,
  290. response.39 = EmpLesionNoEST$AffectiveEST_Q39Response,
  291. response.40 = EmpLesionNoEST$AffectiveEST_Q40Response)
  293. ## Add id number (may not need with carrying over the ID) ##
  295. #EmpLesiontibble <- EmpLesiontibble %>%
  296. # mutate(EmpLesiontibble, id=row_number())
  298. ## Categorize responses based on choice ##
  300. EmpLesiontibbleChoice <- EmpLesiontibble %>%
  301. select(id:choice.40)
  303. EmpLesiontibbleChoice <- EmpLesiontibbleChoice %>%
  304. pivot_longer(cols = choice.1:choice.40,
  305. names_to="trial",
  306. values_to = "Choice")
  308. EmpLesiontibbleResponses <- EmpLesiontibble %>%
  309. select(id,lesionGroup,response.1:response.40)
  311. EmpLesiontibbleResponses <- EmpLesiontibbleResponses %>%
  312. pivot_longer(cols = response.1:response.40,
  313. names_to="trial",
  314. values_to="Text")
  316. EmpLesiontibbleLong <- cbind(EmpLesiontibbleChoice, EmpLesiontibbleResponses)
  317. EmpLesiontibbleLong <- subset(EmpLesiontibbleLong, select=-(3))
  318. EmpLesiontibbleLong <- subset(EmpLesiontibbleLong, select=-(4))
  319. EmpLesiontibbleLong <- subset(EmpLesiontibbleLong, select=-(4))
  320. EmpLesiontibbleLong <- subset(EmpLesiontibbleLong, select=-(4))
  321. trial <- gsub("choice.","", EmpLesiontibbleChoice$trial)
  322. EmpLesiontibbleLong <- cbind(EmpLesiontibbleLong,trial)
  323. rm(EmpLesiontibbleChoice)
  324. rm(EmpLesiontibbleResponses)
  325. rm(trial)
  327. ## Text Analysis ##
  329. # Import the dictionaries (Brady et al., 2017, PNAS)
  331. affectDictionary <- read.csv(file.choose(new=FALSE),header=FALSE)
  332. posAffect <- read.csv(file.choose(new=FALSE),header=FALSE)
  333. negAffect <- read.csv(file.choose(new=FALSE),header=FALSE)
  335. # Convert use of wildcard symbol in R (* -> ^) to allow for extensions of the word #
  337. affectDictionary <- lapply(affectDictionary,glob2rx)
  338. posAffect <- lapply(posAffect,glob2rx)
  339. negAffect <- lapply(negAffect,glob2rx)
  341. # Change to character array for easier comparison against the written text #
  343. affectDictionary <- array(unlist(affectDictionary))
  344. posAffect <- array(unlist(posAffect))
  345. negAffect <- array(unlist(negAffect))
  347. # Revision (10.7.2025): Combine character vectors for affect, positive, negative dictionaries and remove duplicates #
  349. combinedAffect <- c(affectDictionary,negAffect,posAffect)
  350. combinedAffect <- unique(combinedAffect)
  352. # Researcher Coding Team identified missing words from the dictionaries that were written into participant responses; some are duplicates #
  354. missedWords <- c('somber', 'curious', 'astonished', 'wondering', 'attentive','watchful','curious', 'intense', 'angry',
  355. 'cranky', 'tired', 'let down', 'wonderment', 'bewildered', 'pouty', 'wanting', 'neutral', 'miserable',
  356. 'happy', 'pain', 'spunk', 'trapped', 'hiding', 'hurt', 'safe', 'perplexed', 'leaving', 'gives up', 'beat',
  357. 'awed', 'caged', 'mischevious', 'mediocre', 'disappointed', 'defiant', 'onery', 'untrusting', 'disappointed',
  358. 'untrusting', 'contempt', 'despondent', 'mistrust', 'disgust', 'despair', 'broken', 'introverted', 'needing',
  359. 'recovering', 'disconnected', 'rambunctious', 'content', 'quiet', 'sleepy', 'sick', 'hate', 'hard', 'scared',
  360. 'bored', 'lonely', 'grief', 'lost', 'mean', 'openness', 'worn out', 'starving', 'unhealthy', 'alone', 'frown',
  361. 'flat', 'dehydrated', 'good', 'unsure', 'weary', 'tear-streaked', 'pensive', 'cautiously', 'bad', 'disheeveled',
  362. 'uninterested', 'impulsive', 'intrigued', 'assessing', 'observant', 'clinging', 'secluded', 'unwell',
  363. 'suffering', 'stoic', 'wary', 'pleading', 'trapped', 'oppression', 'concern', 'questioning', 'hungry',
  364. 'inquisitive', 'self-assured', 'cocky', 'forlorn', 'dejected', 'approval', 'puzzled', 'resilience',
  365. 'betrayed', 'cautious', 'alert', 'resolute', 'steadfast', 'sassy', 'perplexed', 'intent', 'focused',
  366. 'guarded', 'distant', 'abandoned', 'peaceful', 'care', 'shamed', 'scowling', 'disbelief', 'drive',
  367. 'revengeful', 'stern', 'hardened', 'tearful', 'indifference', 'agitated', 'inquisitive', 'dear')
  369. # Remove Duplicates #
  371. missedWords <- unique(missedWords)
  373. # Convert use of wildcard symbol in R (* -> ^) to allow for extensions of the word #
  375. missedWords <- lapply(missedWords,glob2rx)
  377. # Change to character array for easier comparison against the written text #
  379. missedWords <- array(unlist(missedWords))
  381. # Combine with the earlier character vector that had the combined positive, negative, and affect dictionaries #
  383. combinedAffect <- c(combinedAffect,missedWords)
  384. combinedAffect <- unique(combinedAffect)
  386. # Delete the missedWords vector #
  388. rm(missedWords)
  390. ## Common False Positives Identified by Researcher Coding Team (in context of participant responses) ##
  392. combinedAffect <- combinedAffect[!combinedAffect %in% c('^like$','^likes$','^liked$','^plays$','^please','^pretty$')]
  393. affectDictionary <- affectDictionary[!affectDictionary %in% c('^like$','^likes$','^liked$','^plays$','^please','^pretty$')]
  394. posAffect <- posAffect[!posAffect %in% c('^like$','^likes$','^liked$','^plays$','^please','^pretty$')]
  395. negAffect <- negAffect[!negAffect %in% c('^like$','^likes$','^liked$','^plays$','^please','^pretty$')]
  397. # Word count per response #
  399. EmpLesiontibbleLong$numWords <- stri_count_words(EmpLesiontibbleLong$Text)
  401. # Text cleaning: see descriptions below #
  403. EmpLesiontibbleLong$TextCleaned <- EmpLesiontibbleLong$Text %>%
  404. str_to_lower() %>% # convert all the string to low alphabet
  405. replace_contraction() %>% # replace contraction to their multi-word forms
  406. replace_internet_slang() %>% # replace internet slang to normal words
  407. replace_emoji() %>% # replace emoji to words
  408. replace_emoticon() %>% # replace emoticon to words
  409. replace_hash(replacement = "") %>% # remove hashtag
  410. replace_word_elongation() %>% # replace informal writing with known semantic replacements
  411. replace_number(remove = T) %>% # remove number
  412. replace_date(replacement = "") %>% # remove date
  413. replace_time(replacement = "") %>% # remove time
  414. str_remove_all(pattern = "[[:punct:]]") %>% # remove punctuation
  415. str_remove_all(pattern = "[^\\s]*[0-9][^\\s]*") %>% # remove mixed string n number
  416. str_squish() %>% # reduces repeated whitespace inside a string.
  417. str_trim() # removes whitespace from start and end of string
  419. # WordStem (not used in manuscript, but provided if helpful) #
  421. EmpLesiontibbleLong$TextCleanedStem <- EmpLesiontibbleLong$TextCleaned %>%
  422. wordStem()
  424. # Build a list for the identification of affect words #
  426. EmpLesionTextList <- list(item1=EmpLesiontibbleLong$id, item2=EmpLesiontibbleLong$lesionGroup,
  427. item3=EmpLesiontibbleLong$Choice, item4=EmpLesiontibbleLong$Text,
  428. item5=EmpLesiontibbleLong$trial, item6 = EmpLesiontibbleLong$numWords,
  429. item7 = EmpLesiontibbleLong$TextCleaned, item8=EmpLesiontibbleLong$TextCleanedStem)
  431. #### Identifier Loop for Emotion Word Counts ####
  433. # These are safeties in case replication of the loop is needed (remove first # for creating a backup, remove the second # for resetting the list)
  434. #copyEmpLesiontibbleLong <- EmpLesiontibbleLong
  435. #EmpLesiontibbleLong <- copyEmpLesiontibbleLong
  437. # This for loop splits the written text into separate words, compares across each word with the three dictionaries, and stores both the
  438. # count and the word in the list for interpretation later #
  440. for (i in 1:nrow(EmpLesiontibbleLong)) {
  441. cc <- as.numeric()
  442. d <- as.numeric()
  443. e <- as.numeric()
  444. f <- as.numeric()
  445. hh <- as.character()
  446. ii <- as.character()
  447. jj <- as.character()
  448. kk <- as.character()
  449. if (i %% 100==0){
  450. print(i)}
  451. x <- unlist(strsplit(trimws(EmpLesiontibbleLong$TextCleaned[i]), "\\s+"))
  452. if(identical(x,character(0))) next
  453. for (k in 1:length(affectDictionary)) {
  454. y <- grepl(affectDictionary[k],x)
  455. cc[k] <- sum(y=="TRUE",na.rm=T)
  456. if(!(identical(grep(affectDictionary[k],x,value=TRUE),character(0)))){
  457. z <- grep(affectDictionary[k],x,value=TRUE)
  458. if(k==1){
  459. hh<-z
  460. }
  461. if(k>1){
  462. hh <- c(hh,z)}
  463. }
  464. }
  465. for(k in 1:length(negAffect)){
  466. y <- grepl(negAffect[k],x)
  467. d[k] <- sum(y=="TRUE",na.rm=T)
  468. if(!(identical(grep(negAffect[k],x,value=TRUE),character(0)))){
  469. z <- grep(negAffect[k],x,value=TRUE)
  470. if(k==1){
  471. ii<-z
  472. }
  473. if(k>1){
  474. ii <- c(ii,z)
  475. }
  476. }
  477. }
  478. for(k in 1:length(posAffect)){
  479. y <- grepl(posAffect[k],x)
  480. e[k] <- sum(y=="TRUE",na.rm=T)
  481. if(!(identical(grep(posAffect[k],x,value=TRUE),character(0)))){
  482. z <- grep(posAffect[k],x,value=TRUE)
  483. if(k==1){
  484. jj<-z
  485. }
  486. if(k>1){
  487. jj <- c(jj,z)
  488. }
  489. }
  490. }
  491. for(k in 1:length(combinedAffect)){
  492. y <- grepl(combinedAffect[k],x)
  493. f[k] <- sum(y=="TRUE",na.rm=T)
  494. if(!(identical(grep(combinedAffect[k],x,value=TRUE),character(0)))){
  495. z <- grep(combinedAffect[k],x,value=TRUE)
  496. if(k==1){
  497. kk<-z
  498. }
  499. if(k>1){
  500. kk <- c(kk,z)
  501. }
  502. }
  503. }
  504. EmpLesiontibbleLong$affectWordsCount[i] <- sum(cc)
  505. EmpLesiontibbleLong$negaffectWordsCount[i] <- sum(d)
  506. EmpLesiontibbleLong$posaffectWordsCount[i] <- sum(e)
  507. EmpLesiontibbleLong$combinedaffectWordsCount[i] <- sum(f)
  508. if(identical(hh,character(0))){
  509. EmpLesionTextList[['item9']][[i]] <- NA
  510. }
  511. else if (!(identical(hh,character(0)))) {
  512. EmpLesionTextList[['item9']][[i]] <- hh
  513. }
  514. if(identical(ii,character(0))){
  515. EmpLesionTextList[['item10']][[i]] <- NA
  516. }
  517. else if (!(identical(ii,character(0)))){
  518. EmpLesionTextList[['item10']][[i]] <- ii
  519. }
  520. if(identical(jj,character(0))){
  521. EmpLesionTextList[['item11']][[i]] <- NA
  522. }
  523. else if (!(identical(jj,character(0)))) {
  524. EmpLesionTextList[['item11']][[i]] <- jj
  525. }
  526. if(identical(kk,character(0))){
  527. EmpLesionTextList[['item12']][[i]] <- NA
  528. }
  529. else if (!(identical(kk,character(0)))) {
  530. EmpLesionTextList[['item12']][[i]] <- kk
  531. }
  532. cc <- as.numeric()
  533. d <- as.numeric()
  534. e <- as.numeric()
  535. f <- as.numeric()
  536. hh <- as.character()
  537. ii <- as.character()
  538. jj <- as.character()
  539. kk <- as.character()
  540. rm(x)
  541. rm(y)
  542. rm(z)
  543. }
  545. # Clean the environment #
  547. rm(cc)
  548. rm(d)
  549. rm(e)
  550. rm(hh)
  551. rm(i)
  552. rm(ii)
  553. rm(jj)
  554. rm(f)
  555. rm(kk)
  556. rm(k)
  558. # store the counts in the tibble as well #
  560. EmpLesiontibbleLong$affectUsed[EmpLesiontibbleLong$affectWordsCount>=1] <- 1
  561. EmpLesiontibbleLong$affectUsed[EmpLesiontibbleLong$affectWordsCount<=0] <- 0
  562. EmpLesiontibbleLong$affectUsed <- as.numeric(EmpLesiontibbleLong$affectUsed)
  564. EmpLesiontibbleLong$negaffectUsed[EmpLesiontibbleLong$negaffectWordsCount>=1] <- 1
  565. EmpLesiontibbleLong$negaffectUsed[EmpLesiontibbleLong$negaffectWordsCount<=0] <- 0
  566. EmpLesiontibbleLong$negaffectUsed <- as.numeric(EmpLesiontibbleLong$negaffectUsed)
  568. EmpLesiontibbleLong$posaffectUsed[EmpLesiontibbleLong$posaffectWordsCount>=1] <- 1
  569. EmpLesiontibbleLong$posaffectUsed[EmpLesiontibbleLong$posaffectWordsCount<=0] <- 0
  570. EmpLesiontibbleLong$posaffectUsed <- as.numeric(EmpLesiontibbleLong$posaffectUsed)
  572. EmpLesiontibbleLong$combinedaffectUsed[EmpLesiontibbleLong$combinedaffectWordsCount>=1] <- 1
  573. EmpLesiontibbleLong$combinedaffectUsed[EmpLesiontibbleLong$combinedaffectWordsCount<=0] <- 0
  574. EmpLesiontibbleLong$combinedaffectUsed <- as.numeric(EmpLesiontibbleLong$combinedaffectUsed)
  576. #### Emotion Word Count Analysis ####
  578. ## Word Count Analysis (not reported in paper)
  580. model1_fit <- glmer(formula = affectWordsCount ~ 1 + Choice*lesionGroup + (1|id),
  581. family=poisson,
  582. data = EmpLesiontibbleLong,
  583. na.action=na.exclude)
  584. summary(model1_fit)
  586. # Word Used Analysis (not reported in paper)
  588. model2_fit <- glmer(formula = affectUsed ~ 1 + Choice*lesionGroup + (1|id),
  589. family=binomial,
  590. data = EmpLesiontibbleLong,
  591. na.action=na.exclude)
  592. summary(model2_fit)
  594. ## Positive Affect Word Count Analysis (not reported in paper)
  596. model3_fit <- glmer(formula = posaffectWordsCount ~ 1 + Choice*lesionGroup + (1|id),
  597. family=poisson,
  598. data = EmpLesiontibbleLong,
  599. na.action=na.exclude)
  600. summary(model3_fit)
  602. # Positive Affect Word Used Analysis (reported in paper)
  604. model4_fit <- glmer(formula = posaffectUsed ~ 1 + Choice*lesionGroup + (1|id),
  605. family=binomial,
  606. data = EmpLesiontibbleLong,
  607. na.action=na.exclude)
  608. summary(model4_fit)
  610. ## Negative Affect Word Count Analysis (not reported in paper)
  612. model5_fit <- glmer(formula = negaffectWordsCount ~ 1 + Choice*lesionGroup + (1|id),
  613. family=poisson,
  614. data = EmpLesiontibbleLong,
  615. na.action=na.exclude)
  616. summary(model5_fit)
  618. # Negative Affect Word Used Analysis (reported in paper)
  620. model6_fit <- glmer(formula = negaffectUsed ~ 1 + Choice*lesionGroup + (1|id),
  621. family=binomial,
  622. data = EmpLesiontibbleLong,
  623. na.action=na.exclude)
  624. summary(model6_fit)
  626. ## Combined Affect Word Count Analysis (not reported in paper)
  628. model7_fit <- glmer(formula = combinedaffectWordsCount ~ 1 + Choice*lesionGroup + (1|id),
  629. family=poisson,
  630. data = EmpLesiontibbleLong,
  631. na.action=na.exclude)
  632. summary(model7_fit)
  634. # Combined Affect Word Used Analysis (reported in paper)
  636. model8_fit <- glmer(formula = combinedaffectUsed ~ 1 + Choice*lesionGroup + (1|id),
  637. family=binomial,
  638. data = EmpLesiontibbleLong,
  639. na.action=na.exclude)
  640. summary(model8_fit)
  643. #### Save Environment ####
  645. save.image('REnvironment_EmpathyChoiceAmygdalaLesions_02.02.2026.RData')
  647. #### Count Words ####
  649. # Count number of words written by participant and choice #
  651. EmpLesiontibbleWide <- EmpLesiontibbleLong %>%
  652. dplyr::group_by(id, Choice,lesionGroup) %>%
  653. summarise(numWordsM = mean(numWords), numWordsSD = sd(numWords),
  654. affectWordsM = mean(affectWordsCount), affectWordsSD = sd(affectWordsCount),
  655. affectUsedM = mean(affectUsed), affectUsedSD = sd(affectUsed),
  656. posaffectWordsCountM = mean(posaffectWordsCount), posaffectWordsCountSD = sd(posaffectWordsCount),
  657. negaffectWordsCountM = mean(negaffectWordsCount), negaffectWordsCountSD = sd(negaffectWordsCount),
  658. affectUsedSUM = sum(affectUsed))
  660. # Collapse number of words written by
  662. EmpLesiontibbleWideR <- EmpLesiontibbleLong %>%
  663. dplyr::group_by(id, Choice,lesionGroup) %>%
  664. summarise(nAffectWord=sum(affectWordsCount>0),nposAffect=sum(posaffectWordsCount>0),
  665. nnegAffect=sum(negaffectWordsCount>0))
  667. EmpLesiontibbleWideR$propAffect <- EmpLesiontibbleWideR$nAffectWord/40
  669. EmpLesiontibbleWideR$propposAffect <- EmpLesiontibbleWideR$nposAffect/40
  671. EmpLesiontibbleWideR$propnegAffect <- EmpLesiontibbleWideR$nnegAffect/40
  673. EmpLesiontibbleWideR2 <- EmpLesiontibbleWideR %>%
  674. dplyr::group_by(Choice,lesionGroup) %>%
  675. summarise(meanAffect = mean(propAffect), sdAffect = sd(propAffect),
  676. meanposAffect = mean(propposAffect), sdposAffect = sd(propposAffect),
  677. meannegAffect = mean(propnegAffect), sdnegAffect = sd(propnegAffect))
  679. #### Supplemental Number of Words Analyses ####
  681. require(lme4)
  682. require(nlme)
  683. require(lmerTest)
  685. EmpLesiontibbleLongAmyg <- EmpLesiontibbleLong %>%
  686. filter(lesionGroup == 1)
  688. EmpLesiontibbleLongBDC <- EmpLesiontibbleLong %>%
  689. filter(lesionGroup == 3)
  691. EmpLesiontibbleLongHC <- EmpLesiontibbleLong %>%
  692. filter(lesionGroup == 4)
  694. lm1 <- lmer(numWords ~ Choice * lesionGroup + (1|id + trial), data=EmpLesiontibbleLong)
  695. summary(lm1)
  697. lm1a <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongAmyg)
  698. summary(lm1a)
  700. lm1b <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongBDC)
  701. summary(lm1b)
  703. lm1c <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongHC)
  704. summary(lm1c)
  706. lm2a <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongAmyg)
  707. summary(lm2a)
  709. lm2b <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongBDC)
  710. summary(lm2b)
  712. lm2c <- lmer(numWords ~ Choice + (1|id + trial), data=EmpLesiontibbleLongHC)
  713. summary(lm2c)

EmpathyChoiceAmygdalaLesionsRScript_02.02.2026.R, no license · at the source

Overview

Authors: Julian A Scheffer1,2, Justin Reber2, C Daryl Cameron3, Justin S Feinstein2, Daniel Tranel2
  1. Department of Psychology, University of Western Ontario, London, ON N6A 5C2, Canada
  2. Department of Neurology, University of Iowa, Iowa City, IA 52242, USA
  3. Department of Psychology and Rock Ethics Institute, The Pennsylvania State University, University Park, PA 16802, USA
Institutions: Western University (Canada); University of Iowa (United States); Pennsylvania State University (United States)
Journal: Brain : a journal of neurology, volume 149, issue 5, pages 1623-1634
Dates: received 15 May 2025; accepted 18 January 2026; published online 13 March 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/brain/awag074 · PMID 41822985 · PMCID PMC13140638 · OpenAlex W7135232430
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Keywords: experience sharing, perspective taking, decision-making, free choice, mental effort, amygdala lesions
MeSH: Amygdala*, Brain Injuries*, Empathy*, Motivation*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Psychopathy, Forensic Psychiatry, Sexual Offending (Clinical Psychology, Psychology), according to OpenAlex
Funding: NIH (T32 GM108540); J.A.S.; University of Iowa Graduate College; NIMH (1 P50 MH094258-04A1); NIMH NIH HHS (1 P50 MH094258-04A1); NIH HHS (T32 GM108540)
Citations: not cited yet (Europe PMC); 85 references in the paper
Notices: A comment on this paper has been published (41885173, from Europe PMC)

Abstract

Damage to the amygdala has been linked to impairments in empathy, typically documented as deficits in accurately identifying others’ emotional experiences, especially fear. This has led some to theorize that amygdala dysfunction is a core feature of psychopathy. There is growing evidence, however, that motivation to empathize is distinct from empathic accuracy. Moreover, anecdotal observations in patients with amygdala lesions have noted their tendencies to approach, rather than avoid, empathic encounters with strangers, even when the patients have impairments in empathic accuracy.

We conducted a novel investigation specifically examining empathy motivation in patients with amygdala damage. We used a free-choice paradigm to assess motivation to empathize.

We found that damage to the amygdala was not associated with avoidance of affective or cognitive forms of empathy motivation. Patients with amygdala lesions (n = 21) exhibited similar levels of empathy motivation compared with patients with damage outside the amygdala (n = 22) and healthy individuals with no brain damage (n = 24).

These findings suggest that amygdala damage does not necessarily disrupt the motivation to empathize. A potential implication of the findings is that amygdala damage or dysfunction may not be associated with traits such as callousness, apathy or lack of caring that are often linked to psychopathy.

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

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OSF d4epm

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State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (1), SPSS (1)
Size: 8 files, 2 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lme4 (1 file), lmerTest (1 file), nlme (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/d4epm

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 10 MeSH terms, 6 funders, 67 references, 1 integrity notice.

Cite

This paper

Scheffer, J. A., Reber, J., Cameron, C. D., Feinstein, J. S., & Tranel, D. (2026). Empathy motivation is preserved following amygdala damage. Brain : a journal of neurology, 149(5), 1623-1634. https://doi.org/10.1093/brain/awag074

BibTeX

@article{scheffer2026empathy,
author = {Scheffer, Julian A and Reber, Justin and Cameron, C Daryl and Feinstein, Justin S and Tranel, Daniel},
title = {{Empathy motivation is preserved following amygdala damage}},
journal = {Brain : a journal of neurology},
year = {2026},
month = may,
volume = {149},
number = {5},
pages = {1623--1634},
publisher = {Oxford University Press},
issn = {0006-8950},
doi = {10.1093/brain/awag074},
url = {https://doi.org/10.1093/brain/awag074},
pmid = {41822985},
pmcid = {PMC13140638}
}

RIS

TY - JOUR
AU - Scheffer, Julian A
AU - Reber, Justin
AU - Cameron, C Daryl
AU - Feinstein, Justin S
AU - Tranel, Daniel
TI - Empathy motivation is preserved following amygdala damage
T2 - Brain : a journal of neurology
J2 - Brain
PY - 2026
DA - 2026/05/01
VL - 149
IS - 5
SP - 1623
EP - 1634
SN - 0006-8950
PB - Oxford University Press
DO - 10.1093/brain/awag074
UR - https://doi.org/10.1093/brain/awag074
LA - en
ER -

CSL-JSON

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"title": "Empathy motivation is preserved following amygdala damage",
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"author": [
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"family": "Scheffer",
"given": "Julian A"
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{
"family": "Reber",
"given": "Justin"
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{
"family": "Cameron",
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"issue": "5",
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"DOI": "10.1093/brain/awag074",
"PMID": "41822985",
"PMCID": "PMC13140638",
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"publisher": "Oxford University Press",
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2026,
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