Superseded models • aphantasiaEmotions

library(aphantasiaEmotions)
library(ggplot2)
library(patchwork)

pkg   <- "aphantasiaEmotions"
refit <- "never"

options("marginaleffects_safe" = FALSE)
draws <- seq(1, 4000, 1) # To limit draws that will be used for marginaleffects

The 4-group categorical model and the Bayesian GAM were this study’s original, primary models, reported in the first version of the manuscript. The model-comparison arc now prefers the floor-group and segmented models instead, as both fit the data decisively better by LOO. This page is not a retraction of the original models so much as a record of them: they are genuinely informative, we checked them thoroughly, and we think it is worth showing that check in full rather than letting them quietly disappear from the documentation once something else took the spotlight.

The code and figures below are close to unchanged from the original, single-vignette version of this package’s report. They are reused here rather than redone, since the underlying question they answer (how does each model describe the data) hasn’t changed, even though our headline answer to the overall research question has moved on to a different model.

For completeness, and because a reviewer specifically asked what the simpler, more common 2-group split (aphantasia, VVIQ $\leq$ 32, vs. typical) would have shown, we also report the contrasts of this model here, even though it was not part of our original analysis plan.

Total TAS-20 scores

4-group categorical model

lm_categorical_4g <- fit_brms_model(
  formula = tas ~ vviq_group_4,
  data    = all_data,
  prior   = brms::prior(normal(0, 20), class = "b"),
  file    = system.file("models", "lm_categorical_4g_tot.rds", package = pkg),
  file_refit = refit
)

contrasts_tot <- marginaleffects::comparisons(
  lm_categorical_4g,
  variables = list("vviq_group_4" = "pairwise"),
  draw_ids = draws
)

report_rope(contrasts_tot, contrast) |> knitr::kable(digits = 3)

contrast	Estimate	95% CI	d	PD	Below ROPE	Inside ROPE	Above ROPE
hyperphantasia - aphantasia	-8.322	[-11.641, -4.971]	0.67	1.0	1.000	0.000	0.00
hyperphantasia - hypophantasia	-14.556	[-17.975, -11.215]	1.17	1.0	1.000	0.000	0.00
hyperphantasia - typical	-6.967	[-9.8, -4.215]	0.56	1.0	1.000	0.000	0.00
hypophantasia - aphantasia	6.217	[3.397, 8.991]	0.50	1.0	0.000	0.000	1.00
typical - aphantasia	-1.326	[-3.466, 0.768]	0.11	0.9	0.531	0.459	0.01
typical - hypophantasia	-7.592	[-9.63, -5.453]	0.61	1.0	1.000	0.000	0.00


p_contr_tot <- plot_posterior_contrasts(
  contrasts_tot,
  lm_categorical_4g,
  base_size = 12,
  rope_txt = 3,
  dot_size = 1,
  x_lab = "Effect size (TAS score difference)",
  axis_relative_x = 0.7
)

p_tot <- plot_group_violins(
  tas ~ vviq_group_4,
  y_lab = "Total TAS Score",
  base_size = 12
) +
  plot_alexithymia_cutoff(txt_size = 2, txt_x = 1.4, label = "Alexithymia") +
  scale_discrete_aphantasia() +
  scale_x_aphantasia(add = c(0.4, 0.7))

p_tot + p_contr_tot

Left: violin plots of total TAS score by VVIQ group (complete aphantasia, hypophantasia, typical imagery, hyperphantasia), with the clinical alexithymia cutoff marked. Right: posterior contrasts between each pair of groups, with ROPE-based evidence annotations.

(Convergence and posterior predictive check: model diagnostics §Categorical, 4 groups.)

Bayesian GAM

gam_tot <- fit_brms_model(
  formula = tas ~ s(vviq),
  data    = all_data,
  prior   = brms::prior(normal(0, 20), class = "b"),
  file    = system.file("models", "gam_tot.rds", package = pkg),
  file_refit = refit
)

slopes_tot <- modelbased::estimate_slopes(
  gam_tot,
  trend = "vviq",
  by = "vviq",
  length = 75,
  rope_ci = 1
)

check_slope_evidence(slopes_tot) |> knitr::kable(digits = 3)

VVIQ	Median	CI	PD	Evidence
16	0.472	[0.106, 0.959]	0.997	Non null
17	0.471	[0.106, 0.952]	0.997	Non null
18	0.465	[0.107, 0.932]	0.998	Non null
19	0.452	[0.111, 0.888]	0.998	Non null
20	0.431	[0.112, 0.83]	0.998	Non null
21	0.400	[0.11, 0.761]	0.998	Non null
22	0.357	[0.092, 0.694]	0.997	Non null
23	0.303	[0.062, 0.624]	0.995	Non null
24	0.242	[0.012, 0.55]	0.981	Non null
25	0.178	[-0.054, 0.471]	0.935	Uncertain
26	0.114	[-0.144, 0.381]	0.833	Uncertain
27	0.051	[-0.236, 0.294]	0.667	Uncertain
28	-0.009	[-0.324, 0.214]	0.528	Uncertain
29	-0.064	[-0.422, 0.15]	0.706	Uncertain
30	-0.112	[-0.505, 0.103]	0.826	Uncertain
31	-0.151	[-0.577, 0.071]	0.895	Uncertain
32	-0.182	[-0.616, 0.048]	0.930	Uncertain
33	-0.202	[-0.633, 0.036]	0.951	Uncertain
34	-0.212	[-0.624, 0.021]	0.962	Uncertain
35	-0.215	[-0.586, 0.011]	0.968	Uncertain
36	-0.211	[-0.537, 0.006]	0.971	Non null
37	-0.206	[-0.488, 0.015]	0.967	Uncertain
38	-0.197	[-0.448, 0.041]	0.954	Uncertain
39	-0.189	[-0.426, 0.068]	0.936	Uncertain
40	-0.185	[-0.418, 0.096]	0.919	Uncertain
41	-0.186	[-0.413, 0.116]	0.912	Uncertain
42	-0.191	[-0.408, 0.115]	0.913	Uncertain
43	-0.201	[-0.412, 0.091]	0.928	Uncertain
44	-0.215	[-0.418, 0.056]	0.948	Uncertain
45	-0.232	[-0.435, 0.015]	0.968	Uncertain
46	-0.253	[-0.458, -0.017]	0.981	Non null
47	-0.272	[-0.489, -0.051]	0.990	Non null
48	-0.290	[-0.514, -0.082]	0.995	Non null
49	-0.306	[-0.536, -0.104]	0.997	Non null
50	-0.318	[-0.551, -0.125]	0.998	Non null
51	-0.326	[-0.556, -0.136]	0.999	Non null
52	-0.329	[-0.553, -0.14]	0.999	Non null
53	-0.328	[-0.548, -0.136]	0.999	Non null
54	-0.324	[-0.539, -0.127]	0.999	Non null
55	-0.318	[-0.529, -0.114]	0.998	Non null
56	-0.312	[-0.515, -0.101]	0.997	Non null
57	-0.306	[-0.5, -0.097]	0.996	Non null
58	-0.301	[-0.491, -0.094]	0.996	Non null
59	-0.298	[-0.486, -0.091]	0.997	Non null
60	-0.297	[-0.493, -0.088]	0.997	Non null
61	-0.299	[-0.5, -0.086]	0.996	Non null
62	-0.303	[-0.512, -0.09]	0.995	Non null
63	-0.309	[-0.519, -0.097]	0.997	Non null
64	-0.316	[-0.528, -0.106]	0.998	Non null
65	-0.323	[-0.532, -0.118]	0.999	Non null
66	-0.329	[-0.541, -0.126]	0.999	Non null
67	-0.334	[-0.553, -0.128]	0.998	Non null
68	-0.336	[-0.57, -0.123]	0.997	Non null
69	-0.337	[-0.583, -0.108]	0.996	Non null
70	-0.336	[-0.592, -0.097]	0.995	Non null
71	-0.334	[-0.597, -0.091]	0.994	Non null
72	-0.330	[-0.594, -0.084]	0.994	Non null
73	-0.325	[-0.6, -0.074]	0.993	Non null
74	-0.321	[-0.616, -0.056]	0.989	Non null
75	-0.318	[-0.646, -0.026]	0.982	Non null
76	-0.314	[-0.683, 0.011]	0.971	Non null
77	-0.313	[-0.714, 0.045]	0.960	Uncertain
78	-0.310	[-0.737, 0.068]	0.951	Uncertain
79	-0.308	[-0.751, 0.083]	0.945	Uncertain
80	-0.308	[-0.755, 0.087]	0.943	Uncertain


p_slopes_tot <- plot_gam_slopes(
  slopes_tot,
  .f_groups = dplyr::case_when(
    vviq <= 24 ~ 1,
    vviq <= 35 ~ 2,
    vviq <= 36 ~ 3,
    vviq <= 45 ~ 4,
    vviq <= 76 ~ 5,
    vviq <= 80 ~ 6
  ),
  y_lab = "TAS variation per unit change in VVIQ",
  base_size = 12
)

p_gam_tot <- plot_gam_means(
  gam_tot,
  y_lab = "Total TAS score",
  legend_relative = 0.85,
  base_size = 12
) +
  plot_coloured_subjects(x = all_data$vviq, y = all_data$tas, size = 1) +
  plot_alexithymia_cutoff(txt_x = 26, label = "Alexithymia") +
  scale_discrete_aphantasia() +
  scale_x_vviq()

p_gam_tot + p_slopes_tot

Left: fitted GAM curve of total TAS score across the VVIQ range, with individual data points coloured by VVIQ group and the clinical alexithymia cutoff marked. Right: the estimated slope (rate of change of TAS with VVIQ) across the VVIQ range, coloured by evidence strength.

(Convergence and posterior predictive check: model diagnostics §GAM.)

2-group categorical model

lm_categorical_2g <- fit_brms_model(
  formula = tas ~ vviq_group_2,
  data    = all_data,
  prior   = brms::prior(normal(0, 20), class = "b"),
  file    = system.file("models", "lm_categorical_2g_tot.rds", package = pkg),
  file_refit = refit
)

contrasts_2g_tot <- marginaleffects::comparisons(
  lm_categorical_2g,
  variables = list("vviq_group_2" = "pairwise"),
  draw_ids = draws
)

report_rope(contrasts_2g_tot, contrast) |> knitr::kable(digits = 3)

contrast	Estimate	95% CI	d	PD	Below ROPE	Inside ROPE	Above ROPE
typical - aphantasia	-4.842	[-6.364, -3.316]	0.39	1	1	0	0


p_contr_2g_tot <- plot_posterior_contrasts(
  contrasts_2g_tot,
  lm_categorical_2g,
  base_size = 12,
  rope_txt = 3,
  dot_size = 1,
  x_lab = "Effect size (TAS score difference)",
  axis_relative_x = 0.7
)

p_2g_tot <- plot_group_violins(
  tas ~ vviq_group_2,
  y_lab = "Total TAS Score",
  base_size = 12,
  violin_flip = 1,
  violin_nudge = c(-0.2, 0.2)
  ) +
  plot_alexithymia_cutoff(txt_size = 2, txt_x = 1.4, label = "Alexithymia") +
  scale_discrete_aphantasia() +
  scale_x_aphantasia(add = c(0.7, 0.7))

p_2g_tot + p_contr_2g_tot

Left: violin plots of total TAS score by 2-group VVIQ split (aphantasia, VVIQ <= 32, vs. typical), with the clinical alexithymia cutoff marked. Right: posterior contrast between the two groups, with ROPE-based evidence annotation.

(Convergence and posterior predictive check: model diagnostics §Categorical, 2 groups.)

TAS-20 sub-scales

The same two models were fit separately on each of the three TAS-20 sub-scales (DIF, DDF, EOT). Full figures and statistical results for these were part of the original manuscript; they are not reproduced here since the pattern across sub-scales did not depart dramatically from the one observed for the total TAS. We refer interested readers to the original manuscript for these details, or to the new final per-sub-scale analysis using the best model (the “floor-group” model) on its dedicated page.

#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.6.1 (2026-06-24)
#>  os       Ubuntu 22.04.5 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language en
#>  collate  C.UTF-8
#>  ctype    C.UTF-8
#>  tz       UTC
#>  date     2026-08-07
#>  pandoc   3.8.3 @ /opt/hostedtoolcache/pandoc/3.8.3/x64/ (via rmarkdown)
#>  quarto   NA
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package            * version  date (UTC) lib source
#>    abind                1.4-8    2024-09-12 [1] RSPM
#>    aphantasiaEmotions * 1.0      2026-08-07 [1] local
#>    backports            1.5.1    2026-04-03 [1] RSPM
#>    bayesplot            1.15.0   2025-12-12 [1] RSPM
#>    bayestestR           0.18.1   2026-05-24 [1] RSPM
#>    bridgesampling       1.2-1    2025-11-19 [1] RSPM
#>    brms                 2.23.0   2025-09-09 [1] RSPM
#>    Brobdingnag          1.2-9    2022-10-19 [1] RSPM
#>  P bslib                0.12.0   2026-08-04 [?] RSPM
#>  P cachem               1.1.0    2024-05-16 [?] RSPM
#>    checkmate            2.3.4    2026-02-03 [1] RSPM
#>  P cli                  3.6.6    2026-04-09 [?] RSPM
#>    coda                 0.19-4.1 2024-01-31 [1] RSPM
#>  P codetools            0.2-20   2024-03-31 [?] CRAN (R 4.6.1)
#>    collapse             2.1.7    2026-05-19 [1] RSPM
#>  P crayon               1.5.3    2024-06-20 [?] RSPM
#>  P curl                 7.1.0    2026-04-22 [?] RSPM
#>    data.table           1.18.4   2026-05-06 [1] RSPM
#>    datawizard           1.3.1    2026-04-26 [1] RSPM
#>  P desc                 1.4.3    2023-12-10 [?] RSPM
#>  P devtools           * 2.5.2    2026-04-30 [?] RSPM
#>  P digest               0.6.39   2025-11-19 [?] RSPM
#>    distributional       0.8.1    2026-06-27 [1] RSPM
#>    dplyr                1.2.1    2026-04-03 [1] RSPM
#>  P ellipsis             0.3.3    2026-04-04 [?] RSPM
#>  P evaluate             1.0.5    2025-08-27 [?] RSPM
#>    farver               2.1.2    2024-05-13 [1] RSPM
#>  P fastmap              1.2.0    2024-05-15 [?] RSPM
#>  P fs                   2.1.0    2026-04-18 [?] RSPM
#>    generics             0.1.4    2025-05-09 [1] RSPM
#>    ggdist               3.3.3    2025-04-23 [1] RSPM
#>    ggplot2            * 4.0.3    2026-04-22 [1] RSPM
#>  P glue                 1.8.1    2026-04-17 [?] RSPM
#>    gridExtra            2.3.1    2026-06-25 [1] RSPM
#>    gtable               0.3.6    2024-10-25 [1] RSPM
#>  P htmltools            0.5.9    2025-12-04 [?] RSPM
#>  P htmlwidgets          1.6.4    2023-12-06 [?] RSPM
#>    inline               0.3.21   2025-01-09 [1] RSPM
#>    insight              1.5.2    2026-06-28 [1] RSPM
#>  P jquerylib            0.1.4    2021-04-26 [?] RSPM
#>  P jsonlite             2.0.0    2025-03-27 [?] RSPM
#>  P knitr                1.51     2025-12-20 [?] RSPM
#>    labeling             0.4.3    2023-08-29 [1] RSPM
#>  P lattice              0.22-9   2026-02-09 [?] CRAN (R 4.6.1)
#>  P lifecycle            1.0.5    2026-01-08 [?] RSPM
#>    loo                  2.10.1   2026-07-24 [1] RSPM
#>  P magrittr             2.0.5    2026-04-04 [?] RSPM
#>    marginaleffects      0.32.0   2026-02-14 [1] RSPM
#>  P Matrix               1.7-5    2026-03-21 [?] CRAN (R 4.6.1)
#>    matrixStats          1.5.0    2025-01-07 [1] RSPM
#>  P memoise              2.0.1    2021-11-26 [?] RSPM
#>  P mgcv                 1.9-4    2025-11-07 [?] CRAN (R 4.6.1)
#>    modelbased           0.16.0   2026-06-30 [1] RSPM
#>    mvtnorm              1.4-2    2026-07-12 [1] RSPM
#>  P nlme                 3.1-169  2026-03-27 [?] CRAN (R 4.6.1)
#>  P otel                 0.2.0    2025-08-29 [?] RSPM
#>    parameters           0.29.2   2026-06-28 [1] RSPM
#>    patchwork          * 1.3.2    2025-08-25 [1] RSPM
#>  P pillar               1.11.1   2025-09-17 [?] RSPM
#>  P pkgbuild             1.4.8    2025-05-26 [?] RSPM
#>  P pkgconfig            2.0.3    2019-09-22 [?] RSPM
#>  P pkgdown              2.2.1    2026-07-07 [?] RSPM
#>  P pkgload              1.5.3    2026-06-15 [?] RSPM
#>    posterior            1.7.0    2026-04-01 [1] RSPM
#>  P purrr                1.2.2    2026-04-10 [?] RSPM
#>    QuickJSR             1.10.0   2026-05-17 [1] RSPM
#>  P R6                   2.6.1    2025-02-15 [?] RSPM
#>  P ragg                 1.5.2    2026-03-23 [?] RSPM
#>    rbibutils            2.4.1    2026-01-21 [1] RSPM
#>    RColorBrewer         1.1-3    2022-04-03 [1] RSPM
#>  P Rcpp                 1.1.2    2026-07-05 [?] RSPM
#>    RcppParallel         6.2.0    2026-07-30 [1] RSPM
#>    Rdpack               2.6.6    2026-02-08 [1] RSPM
#>    reformulas           0.4.4    2026-02-02 [1] RSPM
#>    renv                 1.1.4    2025-03-20 [1] RSPM (R 4.6.1)
#>  P rlang                1.3.0    2026-07-05 [?] RSPM
#>  P rmarkdown            2.31     2026-03-26 [?] RSPM
#>    rstan                2.32.7   2025-03-10 [1] RSPM
#>    rstantools           2.7.0    2026-07-26 [1] RSPM
#>    S7                   0.2.2    2026-04-22 [1] RSPM
#>  P sass                 0.4.10   2025-04-11 [?] RSPM
#>    scales               1.4.0    2025-04-24 [1] RSPM
#>    see                  0.14.1   2026-06-29 [1] RSPM
#>  P sessioninfo          1.2.4    2026-06-04 [?] RSPM
#>    showtext             0.9-8    2026-03-21 [1] RSPM
#>    showtextdb           3.0      2020-06-04 [1] RSPM
#>    StanHeaders          2.32.10  2024-07-15 [1] RSPM
#>  P stringi              1.8.9    2026-08-04 [?] RSPM
#>    stringr              1.6.0    2025-11-04 [1] RSPM
#>    sysfonts             0.8.9    2024-03-02 [1] RSPM
#>  P systemfonts          1.3.2    2026-03-05 [?] RSPM
#>    tensorA              0.36.2.1 2023-12-13 [1] RSPM
#>  P textshaping          1.0.5    2026-03-06 [?] RSPM
#>  P tibble               3.3.1    2026-01-11 [?] RSPM
#>    tidyr                1.3.2    2025-12-19 [1] RSPM
#>    tidyselect           1.2.1    2024-03-11 [1] RSPM
#>  P usethis            * 3.2.1    2025-09-06 [?] RSPM
#>  P vctrs                0.7.3    2026-04-11 [?] RSPM
#>    viridis              0.6.5    2024-01-29 [1] RSPM
#>    viridisLite          0.4.3    2026-02-04 [1] RSPM
#>  P withr                3.0.3    2026-06-19 [?] RSPM
#>  P xfun                 0.60     2026-07-09 [?] RSPM
#>  P yaml                 2.3.12   2025-12-10 [?] RSPM
#> 
#>  [1] /home/runner/.cache/R/renv/library/aphantasiaEmotions-8f3b5e1f/linux-ubuntu-jammy/R-4.6/x86_64-pc-linux-gnu
#>  [2] /home/runner/.cache/R/renv/sandbox/linux-ubuntu-jammy/R-4.6/x86_64-pc-linux-gnu/e7c0fad7
#> 
#>  * ── Packages attached to the search path.
#>  P ── Loaded and on-disk path mismatch.
#> 
#> ──────────────────────────────────────────────────────────────────────────────