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Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework.

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13 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 13 matches
  1. [1] § Method › Procedure ↔ EMBA_PAL-ADHD/PAL-ADHD_supps-ext.Rmd, lines 101–160 · score 0.96 · Lenovo Legion Pro, Lightning eye tracker, Cambridge RS, stable viewing distance, laptop stand, LiveTrack
  2. [2] § Method › Procedure ↔ EMBA_PAL-ADHD/PAL-ADHD_supps.Rmd, lines 100–151 · score 0.96 · Lenovo Legion Pro, Lightning eye tracker, Cambridge RS, stable viewing distance, laptop stand, LiveTrack
  3. [3] § Results › Volatility parameters and learning rate updates ↔ EMBA_PAL-ASD/PAL-ASD_results.Rmd, lines 137–272 · score 0.94 · Bayesian ANOVA revealed, interpreted cautiously, interaction log, suboptimal fit, anecdotal evidence, ranked learning rates
  4. [4] § Results › Volatility parameters and learning rate updates ↔ EMBA_PAL-ASD/PAL-ASD_results.Rmd, lines 99–135 · score 0.91 · explorative Bernoulli model, preregistered cutoff, lending support, inside ROPE, comparable phasic volatility, phasic volatility predicting
  5. [5] § Results › Model evaluation ↔ EMBA_PAL-ASD/PAL-ASD_supps-rev2.Rmd, lines 117–197 · score 0.69 · behavioural patterns, simulated reaction, oHGF, best fitting, posterior predictive checks, correlated
  6. [6] § Method › Analysis with Bayesian linear (mixed) models ↔ EMBA_PAL-ASD/PAL-ASD_results.Rmd, lines 99–135 · score 0.65 · highest density interval, Bayes Factors, posterior probabilities, HDI, intercept, favour
  7. [7] § Method › Hierarchical gaussian filter ↔ EMBA_PAL-ASD/S4_brms-HGF_PAL-ASD.Rmd, lines 581–633 · score 0.65 · cue outcome uncertainty, stimulus surprise, Stimulus uncertainty, Phasic volatility, filter, HGF
  8. [8] § Method › Hierarchical gaussian filter ↔ EMBA_PAL-ASD/PAL-ASD_supps-rev2.Rmd, lines 841–879 · score 0.65 · cue outcome uncertainty, stimulus surprise, Stimulus uncertainty, Phasic volatility, filter, HGF
  9. [9] § Method › Analysis with Bayesian linear (mixed) models ↔ EMBA_PAL-ADHD/PAL-ADHD_results.Rmd, lines 8–121 · score 0.64 · Bayes Factors, posterior probabilities, anecdotal, moderate, interval, sum
  10. [10] § Method › Probabilistic associative learning task: emotion recognition ↔ experiment/taskPAL.m, lines 268–350 · score 0.57 · fixation cross, blocks, duration, window, tone, emotional
  11. [11] § Results › Model evaluation ↔ EMBA_PAL-ASD/S5_rev1_PAL_ASD.m, lines 129–145 · score 0.56 · RW model, Model identifiability, empirical priors, S5, simulated
  12. [12] § Method › Sample ↔ EMBA_PAL-ADHD/PAL-ADHD_supps-ext.Rmd, lines 101–160 · score 0.54 · technical difficulties, smaller sample, recruited, suicidality, analysed, ADHD
  13. [13] § Method › Hierarchical gaussian filter ↔ EMBA_PAL-ASD/PAL-ASD_supps-rev2.Rmd, lines 117–197 · score 0.51 · Rescorla Wagner, oHGF, empirical priors, RW, simulated, preregistered

Paper

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The authors' code

R Markdown · 276 lines · 17 KB · GPL-3.0 · 3 matches

  1. ---
  2. title: "PAL ASD results"
  3. author: "I S Plank"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  10. ls.packages = c("knitr",# kable
  11. "ggplot2", # plots
  12. "brms", # Bayesian lmms
  13. "bridgesampling", # bridge_sampler
  14. "tidyverse", # tibble stuff
  15. "ggpubr", # ggarrange
  16. "ggrain", # geom_rain
  17. "bayesplot", # plots for posterior predictive checks
  18. "SBC", # plots for checking computational faithfulness
  19. "rstatix", # anova
  20. "officer", # read_docx
  21. "effectsize", # interpret_bf
  22. "BayesFactor",
  23. "bayestestR" # equivalence_test
  24. )
  26. lapply(ls.packages, library, character.only=TRUE)
  29. # confidence interval functions
  30. lower_ci = function(var) {
  31. unname(quantile(var, probs = 0.025))
  32. }
  33. upper_ci = function(var) {
  34. unname(quantile(var, probs = 0.975))
  35. }
  37. # function to interpret posterior probabilities
  38. interpret_PP = function(p) {
  39. if (p > 0.975) {
  40. "very strong"
  41. } else if (p > 0.95) {
  42. "strong"
  43. } else if (p > 0.90) {
  44. "moderate"
  45. } else if (p > 0.80) {
  46. "small"
  47. } else if (p > 0.70) {
  48. "anecdotal"
  49. } else {
  50. "no"
  51. }
  52. }
  54. brms_dir = "./_brms_models"
  56. # load the models
  57. m.vol = readRDS(file.path(brms_dir, "m_hgf_vol.rds"))
  58. m.om3 = readRDS(file.path(brms_dir, "m_hgf_om3.rds"))
  59. m.ber = readRDS(file.path(brms_dir, "m_hgf_bern.rds"))
  61. # hypotheses
  62. h3a = hypothesis(m.vol, "0 < diagnosis1")
  63. h3b = hypothesis(m.om3, "0 < diagnosis1")
  64. e = hypothesis(m.ber, "sbe4 > 0", alpha = 0.025)
  66. # equivalence
  67. equ.vol = equivalence_test(m.vol)
  68. equ.om3 = equivalence_test(m.om3)
  69. equ.ber = equivalence_test(m.ber)
  71. # get effect sizes (Hedges, 2007)
  72. df.eff.vol = as_draws_df(m.vol) %>%
  73. mutate(
  74. group = -2*b_diagnosis1 / sigma
  75. ) %>% select(group) %>%
  76. pivot_longer(cols = everything(), values_to = "estimate") %>%
  77. group_by(name) %>%
  78. summarise(
  79. ci.lo = lower_ci(estimate),
  80. mean = mean(estimate),
  81. ci.hi = upper_ci(estimate),
  82. interpret = interpret_cohens_d(mean)
  83. )
  84. df.eff.om3 = as_draws_df(m.om3) %>%
  85. mutate(
  86. group = -2*b_diagnosis1 / sigma
  87. ) %>% select(group) %>%
  88. pivot_longer(cols = everything(), values_to = "estimate") %>%
  89. group_by(name) %>%
  90. summarise(
  91. ci.lo = lower_ci(estimate),
  92. mean = mean(estimate),
  93. ci.hi = upper_ci(estimate),
  94. interpret = interpret_cohens_d(mean)
  95. )
  97. ```
  99. # Results
  101. ## Volatility parameters and learning rates
  103. There was no credible difference between the influence of phasic volatility in autistic and comparison adults (*estimate* = `r round(h3a$hypothesis$Estimate,2)` [`r round(h3a$hypothesis$CI.Lower,2)`, `r round(h3a$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h3a$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.vol$mean, 3)` [`r round(df.eff.vol$ci.lo, 3)`, `r round(df.eff.vol$ci.hi, 3)`], see [!FIGURE 4]), contrasting our hypothesis and previous findings. However, there was `r interpret_PP(h3a$hypothesis$Post.Prob)` evidence in favour of a difference with the posterior probability approaching the preregistered cutoff of 95%, falling slightly short of credible evidence but also not lending support to comparable phasic volatility with only `r round(equ.vol$ROPE_Percentage[2]*100, 2)`% of posterior estimates inside the Region of Practical Equivalence (ROPE, highest density interval (HDI) = [`r round(equ.vol$HDI_low[2], 2)`, `r round(equ.vol$HDI_high[2], 2)`]), suggested by Kruschke [!REF2018] for assessing evidence for equivalence. Similarly, `r interpret_PP(e$hypothesis$Post.Prob)` evidence pointed towards increased phasic volatility predicting ASD in the explorative Bernoulli model, with a posterior probability of `r round(e$hypothesis$Post.Prob*100,2)`% (*estimate* = `r round(e$hypothesis$Estimate,2)` [`r round(e$hypothesis$CI.Lower,2)`, `r round(e$hypothesis$CI.Upper,2)`], inside ROPE = `r round(equ.ber$ROPE_Percentage[5]*100, 2)`%, HDI = [`r round(equ.ber$HDI_low[5], 2)`, `r round(equ.ber$HDI_high[5], 2)`]). Last, there was `r interpret_PP(h3b$hypothesis$Post.Prob)` evidence towards higher environmental tonic volatility in autistic than comparison adults (*estimate* = `r round(h3b$hypothesis$Estimate,2)` [`r round(h3b$hypothesis$CI.Lower,2)`, `r round(h3b$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h3b$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.om3$mean, 3)` [`r round(df.eff.om3$ci.lo, 3)`, `r round(df.eff.om3$ci.hi, 3)`], inside ROPE = `r round(equ.om3$ROPE_Percentage[2]*100, 2)`%, HDI = [`r round(equ.om3$HDI_low[2], 2)`, `r round(equ.om3$HDI_high[2], 2)`]).
  105. ```{r dat1, include=FALSE}
  107. # load the models
  108. m.alpha = readRDS(file.path(brms_dir, "m_hgf_alpha.rds"))
  109. aov = readRDS(file.path(brms_dir, "aov_alpha.rds"))
  111. # hypotheses
  112. h4a = hypothesis(m.alpha, "0 > - diagnosis1 + diagnosis1:level1")
  113. h4b = hypothesis(m.alpha, "0 < -diagnosis1 - diagnosis1:level1")
  115. aov.bf = aov@bayesFactor %>% arrange(desc(bf))
  117. # get effect sizes (Hedges, 2007)
  118. df.eff.alpha = as_draws_df(m.alpha) %>%
  119. mutate(
  120. sumvar = sqrt(sigma^2 + sd_subID__Intercept^2 +
  121. sd_subID__level1^2 + sd_subID__change1^2),
  122. group = 2*b_diagnosis1 / sumvar,
  123. h4a = -(-2*b_diagnosis1 + 2*`b_diagnosis1:level1`) / sumvar,
  124. h4b = -(-2*b_diagnosis1 - 2*`b_diagnosis1:level1`) / sumvar
  125. ) %>% select(group, h4a, h4b) %>%
  126. pivot_longer(cols = everything(), values_to = "estimate") %>%
  127. group_by(name) %>%
  128. summarise(
  129. ci.lo = lower_ci(estimate),
  130. mean = mean(estimate),
  131. ci.hi = upper_ci(estimate),
  132. interpret = interpret_cohens_d(mean)
  133. )
  135. ```
  137. For the model regarding learning rates, posterior predictive checks revealed a suboptimal fit of estimated parameters for the change from volatile to postvolatile phase. Thus, we complemented the examination of the posterior distributions with Bayesian ANOVA of the ranked learning rates. Posterior distributions of the preregistered model showed `r interpret_PP(h4a$hypothesis$Post.Prob)` but not credible evidence for increased learning rate of environmental changes (*estimate* = `r round(h4a$hypothesis$Estimate,2)` [`r round(h4a$hypothesis$CI.Lower,2)`, `r round(h4a$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h4a$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.alpha[df.eff.alpha$name == "h4a",]$mean, 3)` [`r round(df.eff.alpha[df.eff.alpha$name == "h4a",]$ci.lo, 3)`, `r round(df.eff.alpha[df.eff.alpha$name == "h4a",]$ci.hi, 3)`]) and `r interpret_PP(1-h4b$hypothesis$Post.Prob)` evidence *against* the hypothesis of decreased learning rate for cue-outcome associations (*estimate* = `r round(h4b$hypothesis$Estimate,2)` [`r round(h4b$hypothesis$CI.Lower,2)`, `r round(h4b$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h4b$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.alpha[df.eff.alpha$name == "h4b",]$mean, 3)` [`r round(df.eff.alpha[df.eff.alpha$name == "h4b",]$ci.lo, 3)`, `r round(df.eff.alpha[df.eff.alpha$name == "h4b",]$ci.hi, 3)`], see [!Figure 5]). The Bayesian ANOVA revealed almost identical fit for two models, both containing the predictors level and change with one of them also including their interaction (without interaction log(*BF*) = `r round(aov.bf$bf[1],2)`, with interaction log(*BF*) = `r round(aov.bf$bf[2],2)`). However, there was only anecdotal evidence in favour of these two models compared to the model with the third-best fit which additionally included the predictor group (log(*BF*) = `r round(aov.bf$bf[3],2)`). Thus, all results regarding the learning rates should be interpreted cautiously.
  139. ## Reaction times, pupil sizes and accuracies
  141. ```{r dat2, include=FALSE}
  143. m.pal = readRDS(file.path(brms_dir, "m_pal.rds"))
  144. m.pup = readRDS(file.path(brms_dir, "m_pup.rds"))
  145. aov.acc = readRDS(file.path(brms_dir, "aov_acc.rds"))
  147. bf.acc = aov.acc@bayesFactor %>% arrange(desc(bf))
  149. # hypotheses
  150. h1b = hypothesis(m.pal, "0 < diagnosis1:expected1")
  151. h1d.1 = hypothesis(m.pal, "diagnosis1:phase1 - diagnosis1:phase2 < 0")
  152. h1d.2 = hypothesis(m.pal, "2*diagnosis1:phase1 + 4*diagnosis1:phase2 < 0")
  153. e2.1 = hypothesis(m.pal, "0 < -2*expected1", alpha = 0.025)
  154. e2.6 = hypothesis(m.pal, "0 < -2*difficulty1 - difficulty2", alpha = 0.025)
  156. h2a = hypothesis(m.pup, "0 < diagnosis1:expected1")
  158. # create accuracies dataframe
  159. load("../data/PAL-ASD_data.RData")
  160. df.acc = df.tsk %>% filter(expected != "neutral") %>% droplevels() %>%
  161. group_by(subID, diagnosis, phase, expected, difficulty) %>%
  162. summarise(acc = mean(acc)*100)
  164. df.diagnosis = df.acc %>%
  165. group_by(diagnosis) %>%
  166. summarise(mean_accuracy = mean(acc, na.rm = T),
  167. sd_accuracy = sd(acc, na.rm = T))
  169. df.expected = df.acc %>%
  170. group_by(expected) %>%
  171. summarise(mean_accuracy = mean(acc, na.rm = T),
  172. sd_accuracy = sd(acc, na.rm = T))
  174. df.difficulty = df.acc %>%
  175. group_by(difficulty) %>%
  176. summarise(mean_accuracy = mean(acc, na.rm = T),
  177. sd_accuracy = sd(acc, na.rm = T))
  179. ## extract predicted differences in ms instead of log data
  180. df.new = df.tsk %>% filter(expected != "neutral") %>% droplevels() %>%
  181. select(diagnosis, phase, expected, difficulty) %>%
  182. distinct() %>%
  183. mutate(
  184. condition = paste(diagnosis, phase, expected, difficulty, sep = "_")
  185. )
  186. df.ms = as.data.frame(
  187. fitted(m.pal, summary = F,
  188. newdata = df.new %>% select(diagnosis, phase, expected, difficulty),
  189. re_formula = NA))
  190. colnames(df.ms) = df.new$condition
  192. # calculate our difference columns
  193. df.ms = df.ms %>%
  194. mutate(
  195. COMP_unexp_exp = rowMeans(across(matches("COMP_.*_unexpected_.*"))) -
  196. rowMeans(across(matches("COMP_.*_expected_.*"))),
  197. ASD_unexp_exp = rowMeans(across(matches("ASD_.*_unexpected_.*"))) -
  198. rowMeans(across(matches("ASD_.*_expected_.*"))),
  199. `h1b_COMP-ASD unexpected-expected` = COMP_unexp_exp - ASD_unexp_exp,
  200. `h1d_ASD-COMP volatile-prevolatile` =
  201. (rowMeans(across(matches("ASD_volatile_.*"))) - rowMeans(across(matches("ASD_prevolatile_.*")))) - (rowMeans(across(matches("COMP_volatile_.*"))) - rowMeans(across(matches("COMP_prevolatile_.*")))),
  202. `h1d_ASD-COMP postvolatile-volatile` =
  203. (rowMeans(across(matches("ASD_postvolatile_.*"))) - rowMeans(across(matches("ASD_volatile_.*")))) - (rowMeans(across(matches("COMP_postvolatile_.*"))) - rowMeans(across(matches("COMP_volatile_.*")))),
  204. `e21_unexpected-expected` = rowMeans(across(matches(".*_unexpected_.*"))) -
  205. rowMeans(across(matches(".*_expected_.*"))),
  206. `e26_difficult-easy` = rowMeans(across(matches(".*_difficult"))) -
  207. rowMeans(across(matches(".*_easy")))
  208. )
  210. equ.pal = equivalence_test(df.ms %>% select(starts_with("h")),
  211. range = rope_range(m.pal))
  213. df.eff.pal = as_draws_df(m.pal) %>%
  214. mutate(across(starts_with("sd")|starts_with("sigma"), ~.^2)) %>%
  215. mutate(
  216. sumvar = sqrt(rowSums(select(., starts_with("sd")|starts_with("sigma")))),
  217. h1b = 4*`b_diagnosis1:expected1` / sumvar,
  218. h1d.1 = 2*(`b_diagnosis1:phase1` - `b_diagnosis1:phase2`) / sumvar,
  219. h1d.2 = 2*(`b_diagnosis1:phase1` + 2*`b_diagnosis1:phase2`) / sumvar,
  220. e.exp = 2*b_expected1 / sumvar,
  221. e.dif = -(-2*b_difficulty1 - b_difficulty2) / sumvar
  222. ) %>% select(starts_with("e.")|starts_with("h")) %>%
  223. pivot_longer(cols = everything(), values_to = "estimate") %>%
  224. group_by(name) %>%
  225. summarise(
  226. ci.lo = lower_ci(estimate),
  227. mean = mean(estimate),
  228. ci.hi = upper_ci(estimate),
  229. interpret = interpret_cohens_d(mean)
  230. )
  232. # pup stuff
  233. df.new = m.pup$data %>%
  234. select(diagnosis, expected, rts) %>%
  235. distinct() %>%
  236. mutate(
  237. condition = paste(diagnosis, expected, rts, sep = "_")
  238. )
  239. df.ms = as.data.frame(
  240. fitted(m.pup, summary = F,
  241. newdata = df.new %>% select(diagnosis, expected, rts),
  242. re_formula = NA))
  243. colnames(df.ms) = df.new$condition
  245. # calculate our difference columns
  246. df.ms = df.ms %>%
  247. mutate(
  248. COMP_expectancy = rowMeans(across(matches("COMP_expected_.*"))) -
  249. rowMeans(across(matches("COMP_unexpected_.*"))),
  250. ASD_expectancy = rowMeans(across(matches("ASD_expected_.*"))) -
  251. rowMeans(across(matches("ASD_unexpected_.*"))),
  252. h2a = COMP_expectancy - ASD_expectancy
  253. )
  255. equ.pup = equivalence_test(df.ms %>% select(starts_with("e_") | starts_with("h")),
  256. range = rope_range(m.pup))
  258. df.eff.pup = as_draws_df(m.pup) %>%
  259. mutate(
  260. sumvar = sqrt(sigma^2 + sd_subID__Intercept^2),
  261. h2a = 4*`b_diagnosis1:expected1` / sumvar
  262. ) %>% select(starts_with("e.")|starts_with("h")) %>%
  263. pivot_longer(cols = everything(), values_to = "estimate") %>%
  264. group_by(name) %>%
  265. summarise(
  266. ci.lo = lower_ci(estimate),
  267. mean = mean(estimate),
  268. ci.hi = upper_ci(estimate),
  269. interpret = interpret_cohens_d(mean)
  270. )
  272. ```
  274. There was no credible evidence for differences between autistic and comparison adults with regards to the effect of expectancy (*estimate* = `r round(h1b$hypothesis$Estimate,2)` [`r round(h1b$hypothesis$CI.Lower,2)`, `r round(h1b$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h1b$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pal[df.eff.pal$name == "h1b",]$mean, 3)` [`r round(df.eff.pal[df.eff.pal$name == "h1b",]$ci.lo, 3)`, `r round(df.eff.pal[df.eff.pal$name == "h1b",]$ci.hi, 3)`], inside ROPE = `r round(equ.pal$ROPE_Percentage[1]*100, 2)`%, HDI = [`r round(equ.pal$HDI_low[1], 2)`, `r round(equ.pal$HDI_high[1], 2)`], see [!Figure 1]) or transition effects (prevolatile to volatile: *estimate* = `r round(h1d.1$hypothesis$Estimate,2)` [`r round(h1d.1$hypothesis$CI.Lower,2)`, `r round(h1d.1$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h1d.1$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pal[df.eff.pal$name == "h1d.1",]$mean, 3)` [`r round(df.eff.pal[df.eff.pal$name == "h1d.1",]$ci.lo, 3)`, `r round(df.eff.pal[df.eff.pal$name == "h1d.1",]$ci.hi, 3)`], inside ROPE = `r round(equ.pal$ROPE_Percentage[2]*100, 2)`%, HDI = [`r round(equ.pal$HDI_low[2], 2)`, `r round(equ.pal$HDI_high[2], 2)`]; volatile to postvolatile: *estimate* = `r round(h1d.2$hypothesis$Estimate,2)` [`r round(h1d.2$hypothesis$CI.Lower,2)`, `r round(h1d.2$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h1d.2$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pal[df.eff.pal$name == "h1d.2",]$mean, 3)` [`r round(df.eff.pal[df.eff.pal$name == "h1d.2",]$ci.lo, 3)`, `r round(df.eff.pal[df.eff.pal$name == "h1d.2",]$ci.hi, 3)`], inside ROPE = `r round(equ.pal$ROPE_Percentage[3]*100, 2)`%, HDI = [`r round(equ.pal$HDI_low[3], 2)`, `r round(equ.pal$HDI_high[3], 2)`]) based on reaction times. Similarly, there was `r interpret_PP(h2a$hypothesis$Post.Prob)` credible difference in the effect of expectancy on pupil sizes in autistic compared to non-autistic adults (*estimate* = `r round(h2a$hypothesis$Estimate,2)` [`r round(h2a$hypothesis$CI.Lower,2)`, `r round(h2a$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(h2a$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pup[df.eff.pup$name == "h2a",]$mean, 3)` [`r round(df.eff.pup[df.eff.pup$name == "h2a",]$ci.lo, 3)`, `r round(df.eff.pup[df.eff.pup$name == "h2a",]$ci.hi, 3)`]), in contrast to our hypothesis. Across groups, there was `r interpret_PP(e2.1$hypothesis$Post.Prob)` evidence that expected trials led to faster responses than unexpected trials (*estimate* = `r round(e2.1$hypothesis$Estimate,2)` [`r round(e2.1$hypothesis$CI.Lower,2)`, `r round(e2.1$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(e2.1$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pal[df.eff.pal$name == "e.exp",]$mean, 3)` [`r round(df.eff.pal[df.eff.pal$name == "e.exp",]$ci.lo, 3)`, `r round(df.eff.pal[df.eff.pal$name == "e.exp",]$ci.hi, 3)`]) and easy expressions led to faster responses than difficult expressions (*estimate* = `r round(e2.6$hypothesis$Estimate,2)` [`r round(e2.6$hypothesis$CI.Lower,2)`, `r round(e2.6$hypothesis$CI.Upper,2)`], *posterior probability* = `r round(e2.6$hypothesis$Post.Prob*100,2)`%, *δ* = `r round(df.eff.pal[df.eff.pal$name == "e.dif",]$mean, 3)` [`r round(df.eff.pal[df.eff.pal$name == "e.dif",]$ci.lo, 3)`, `r round(df.eff.pal[df.eff.pal$name == "e.dif",]$ci.hi, 3)`]), suggesting that our task manipulations were successful.
  276. Accuracies were generally high, with a grand average of `r round(mean(df.acc$acc),1)`% accurate responses across diagnostic groups. An explorative Bayesian ANOVA of the ranked accuracies revealed influences of group, expectancy and difficulty but no interaction effects (log(*BF*) = `r round(bf.acc$bf[1],2)`). Accuracies were higher in the comparison than the autistic group (ASD: `r round(df.diagnosis[df.diagnosis$diagnosis == "ASD",]$mean_accuracy,1)` ± `r round(df.diagnosis[df.diagnosis$diagnosis == "ASD",]$sd_accuracy,1)`%; COMP: `r round(df.diagnosis[df.diagnosis$diagnosis == "COMP",]$mean_accuracy,1)` ± `r round(df.diagnosis[df.diagnosis$diagnosis == "COMP",]$sd_accuracy,1)`%) and in the expected than the unexpected trials (expected: `r round(df.expected[df.expected$expected == "expected",]$mean_accuracy,1)` ± `r round(df.expected[df.expected$expected == "expected",]$sd_accuracy,1)`%; unexpected: `r round(df.expected[df.expected$expected == "unexpected",]$mean_accuracy,1)` ± `r round(df.expected[df.expected$expected == "unexpected",]$sd_accuracy,1)`%). Furthermore, accuracies were lower in the difficult condition (easy: `r round(df.difficulty[df.difficulty$difficulty == "easy",]$mean_accuracy,1)` ± `r round(df.difficulty[df.difficulty$difficulty == "easy",]$sd_accuracy,1)`%; hard: `r round(df.difficulty[df.difficulty$difficulty == "difficult",]$mean_accuracy,1)` ± `r round(df.difficulty[df.difficulty$difficulty == "difficult",]$sd_accuracy,1)`%).

PAL-ASD_results.Rmd at commit d399a35, under GPL-3.0 · at the source

Overview

Authors: Irene Sophia Plank1, Alexandra Pior1,2,3, Anna Yurova1, Julia Nowak1, Zhuanghua Shi4,5, Christine M. Falter-Wagner1
ORCID iDs: Irene Sophia Plank, Alexandra Pior, Anna Yurova, Julia Nowak, Zhuanghua Shi, Christine M. Falter-Wagner
  1. Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Medizin, Ludwig-Maximilians-Universität München,Nußbaumstraße 7, Munich, 80336 Germany
  2. Department of Sociology, Faculty of Behavioral and Social Sciences, University of Groningen,Grote Rozenstraat 1/2, Groningen, 9712 TS Netherlands
  3. Department of Psychology, Faculty of Psychology and Neuroscience, Maastricht University,Universiteitssingel 40, Maastricht, 6229 ER Netherlands
  4. Department of Psychology, LMU Munich,Leopoldstr. 13, 80802 Munich, Germany
  5. NeuroImaging Core Unit Munich (NICUM), LMU Munich,Nußbaumstraße 7, Munich, 80336 Germany
Institutions: Ludwig-Maximilians-Universität München (Germany); University of Groningen (Netherlands); Maastricht University (Netherlands)
Journal: Molecular autism, volume 17, issue 1, article 32
Dates: received 6 November 2025; accepted 13 July 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13229-026-00730-3 · PMID 42482104 · PMCID PMC13435570 · OpenAlex W7169849249
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism), autism (population), cognitive (subfield)
Methods: Machine learning, Statistics, Physiology & signal measures
Keywords: Probabilistic associative learning, Hierarchical gaussian filter, Autism spectrum disorder, Volatility, Predictive coding, Bayesian brain framework
MeSH: Autism Spectrum Disorder*, Autistic Disorder*, Brain*, Emotions*, Adult, Bayes Theorem, Facial Expression, Female, Humans, Male, Reaction Time, Young Adult (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Klinikum der Universität München (6933)
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Background: Autism spectrum disorder (ASD) affects social interaction, communication and behavioural flexibility. Recent theories grounded in the Bayesian Brain Framework propose that autistic individuals process environmental uncertainty differently, overweighting volatility when updating predictions about the world. However, the robustness of these predictive processing differences and their relevance to core symptoms remains unclear.

Methods: Lawson and colleagues [1] reported a tendency to overestimate environmental volatility in autistic adults based on belief states extracted using a Hierarchical Gaussian Filter. We extended their paradigm to a domain central to the autistic experience: rather than distinguishing houses from faces, participants detected valence in emotional facial expressions. Preregistered hypotheses were evaluated using Bayesian linear mixed models based on data of 22 autistic and 22 non-autistic participants.

Results: Despite using an identical computational model to extract belief states, we did not find robust differences between autistic and non-autistic adults, though autistic participants showed a non-credible trend towards increased processing of phasic volatility. Largely comparable probabilistic associative learning was also reflected in response times and pupil sizes.

Limitations: While participants reacted faster to expected than unexpected trials in the beginning, this effect decreased over the course of the experiment; thus, possibly indicating that the individual blocks of stable cue-outcome associations were too short.

Conclusions: We were unable to find credible differences in environmental volatility and learning rate updates using a task designed to probe a key autistic difficulty, facial emotion recognition. The current results call into question the generalisability and clinical relevance of prior findings.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13229-026-00730-3.

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

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IreneSophia/EMBA_PAL

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d399a3524c0e563706de2ff0bfc0969d4b122c07, 24 July 2026
Languages: MATLAB (88), R (27), Python (6)
Size: 8,902 files, 121 scripts
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Found in: the text, “Method”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (25 files), brms (20 files), ggplot2 (18 files), BayesFactor (16 files), easystats (15 files), ggpubr (15 files), rstatix (15 files), Stan (9 files), Parallel Computing Toolbox (5 files), pandas (5 files), statsmodels (4 files), Matplotlib (3 files), NumPy (3 files), Statistics and Machine Learning Toolbox (1 file), Psychtoolbox (1 file), reshape2 (1 file), SciPy (1 file)
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At the source: github.com/IreneSophia/EMBA_PAL

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Cite

This paper

Plank, I. S., Pior, A., Yurova, A., Nowak, J., Shi, Z., & Falter-Wagner, C. M. (2026). Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework. Molecular autism, 17(1), 32. https://doi.org/10.1186/s13229-026-00730-3

BibTeX

@article{plank2026predicting,
author = {Plank, Irene Sophia and Pior, Alexandra and Yurova, Anna and Nowak, Julia and Shi, Zhuanghua and Falter-Wagner, Christine M.},
title = {{Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework}},
journal = {Molecular autism},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {32},
publisher = {BMC},
issn = {2040-2392},
doi = {10.1186/s13229-026-00730-3},
url = {https://doi.org/10.1186/s13229-026-00730-3},
pmid = {42482104},
pmcid = {PMC13435570}
}

RIS

TY - JOUR
AU - Plank, Irene Sophia
AU - Pior, Alexandra
AU - Yurova, Anna
AU - Nowak, Julia
AU - Shi, Zhuanghua
AU - Falter-Wagner, Christine M.
TI - Predicting emotional valence in autism: a preregistered study in the Bayesian Brain framework
T2 - Molecular autism
J2 - Mol Autism
PY - 2026
DA - 2026/07/21
VL - 17
IS - 1
SP - 32
SN - 2040-2392
PB - BMC
DO - 10.1186/s13229-026-00730-3
UR - https://doi.org/10.1186/s13229-026-00730-3
LA - en
ER -

CSL-JSON

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