Chapter 27 [fMRI] NPSses01ses03 ~ singletrial

What is the purpose of this notebook?

  • Here, I model NPS dot products as a function of cue, stimulus intensity and expectation ratings.
  • One of the findings is that low cues lead to higher NPS dotproducts in the high intensity group, and that this effect becomes non-significant across sessions.
  • 03/23/2023: For now, I’m grabbing participants that have complete data, i.e. 18 runs, all three sessions.

Contrast weight table

Table 27.1: Contrast weights
pain vicarious cognitive
task_V_gt_C 0.00 0.50 -0.50
task_P_gt_VC 0.66 -0.34 -0.34

27.1 NPS ~ paintask: 2 cue x 3 stimulus_intensity

Q. Within pain task, Does stimulus intenisty level and cue level significantly predict NPS dotproducts?

Lineplots

27.1.1 Linear model results (NPS ~ paintask: 2 cue x 3 stimulus_intensity)

model.npscuestim <- lmer(NPSpos ~ 
                          CUE_high_gt_low*STIM_linear +CUE_high_gt_low * STIM_quadratic +
                          (CUE_high_gt_low+STIM|sub), data = data_screen
                    )
## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+02
sjPlot::tab_model(model.npscuestim,
                  title = "Multilevel-modeling: \nlmer(NPSpos ~ CUE * STIM + (CUE + STIM | sub), data = pvc)",
                  CSS = list(css.table = '+font-size: 12;'))
Multilevel-modeling: lmer(NPSpos ~ CUE * STIM + (CUE + STIM | sub), data = pvc)
  NPSpos
Predictors Estimates CI p
(Intercept) 4.02 3.16 – 4.88 <0.001
CUE high gt low -0.60 -1.05 – -0.15 0.010
STIM linear 1.14 0.57 – 1.72 <0.001
STIM quadratic 0.13 -0.32 – 0.58 0.572
CUE high gt low * STIM
linear 		-0.68 -1.71 – 0.35 0.196
CUE high gt low * STIM
quadratic 		-0.80 -1.70 – 0.10 0.082
Random Effects
σ2 58.96
τ00 sub 21.50
τ11 sub.CUE_high_gt_low 0.65
τ11 sub.STIMlow 1.47
τ11 sub.STIMmed 0.45
ρ01 -1.00
-1.00
-1.00
N sub 91
Observations 5132
Marginal R2 / Conditional R2 0.006 / NA

Linear model eta-squared

## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+02
Parameter Eta2_partial CI CI_low CI_high
CUE_high_gt_low 0.0313484 0.95 0.0041210 1
STIM_linear 0.0888723 0.95 0.0302969 1
STIM_quadratic 0.0000749 0.95 0.0000000 1
CUE_high_gt_low:STIM_linear 0.0003320 0.95 0.0000000 1
CUE_high_gt_low:STIM_quadratic 0.0006019 0.95 0.0000000 1

Linear model Cohen’s d: NPS stimulus_intensity d = 1.16, cue d = 0.45

## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+02
t df d
CUE_high_gt_low -2.5895817 207.2100 -0.3597943
STIM_linear 3.8948423 155.5223 0.6246309
STIM_quadratic 0.5653502 4269.0719 0.0173054
CUE_high_gt_low:STIM_linear -1.2920309 5026.8336 -0.0364465
CUE_high_gt_low:STIM_quadratic -1.7399770 5027.2880 -0.0490802

27.1.2 2 cue * 3 stimulus_intensity * expectation_rating

data_screen$EXPECT <- data_screen$event02_expect_angle
model.nps3factor <- lmer(NPSpos ~ 
                          CUE_high_gt_low*STIM_linear*EXPECT +
                           CUE_high_gt_low*STIM_quadratic*EXPECT +
                          (CUE_high_gt_low  |sub), data = data_screen
                    )
sjPlot::tab_model(model.nps3factor,
                  title = "Multilevel-modeling: \nlmer(NPSpos ~ CUE * STIM * EXPECTATION + (CUE + STIM + EXPECT | sub), data = pvc)",
                  CSS = list(css.table = '+font-size: 12;'))
Multilevel-modeling: lmer(NPSpos ~ CUE * STIM * EXPECTATION + (CUE + STIM + EXPECT | sub), data = pvc)
  NPSpos
Predictors Estimates CI p
(Intercept) 0.84 -0.07 – 1.75 0.071
CUE high gt low -2.06 -2.96 – -1.17 <0.001
STIM linear -0.60 -1.54 – 0.33 0.208
EXPECT 0.07 0.06 – 0.07 <0.001
STIM quadratic 0.73 -0.10 – 1.56 0.084
CUE high gt low * STIM
linear 		-1.52 -3.39 – 0.35 0.112
CUE high gt low * EXPECT -0.02 -0.03 – -0.00 0.017
STIM linear * EXPECT 0.04 0.02 – 0.05 <0.001
CUE high gt low * STIM
quadratic 		-1.14 -2.80 – 0.53 0.180
EXPECT * STIM quadratic -0.01 -0.03 – 0.00 0.101
(CUE high gt low * STIM
linear) * EXPECT 		-0.01 -0.04 – 0.02 0.530
(CUE high gt low
EXPECT) STIM quadratic 		0.01 -0.02 – 0.03 0.627
Random Effects
σ2 55.66
τ00 sub 14.44
τ11 sub.CUE_high_gt_low 2.75
ρ01 sub -0.66
ICC 0.21
N sub 91
Observations 4948
Marginal R2 / Conditional R2 0.070 / 0.268

eta squared

Parameter Eta2_partial CI CI_low CI_high
CUE_high_gt_low 0.0763280 0.95 0.0315791 1
STIM_linear 0.0003322 0.95 0.0000000 1
EXPECT 0.0627455 0.95 0.0513958 1
STIM_quadratic 0.0006244 0.95 0.0000000 1
CUE_high_gt_low:STIM_linear 0.0005275 0.95 0.0000000 1
CUE_high_gt_low:EXPECT 0.0058467 0.95 0.0005392 1
STIM_linear:EXPECT 0.0046408 0.95 0.0019729 1
CUE_high_gt_low:STIM_quadratic 0.0003756 0.95 0.0000000 1
EXPECT:STIM_quadratic 0.0005628 0.95 0.0000000 1
CUE_high_gt_low:STIM_linear:EXPECT 0.0000825 0.95 0.0000000 1
CUE_high_gt_low:EXPECT:STIM_quadratic 0.0000495 0.95 0.0000000 1

Cohen’s d

t df d
CUE_high_gt_low -4.5261619 247.9098 -0.5749276
STIM_linear -1.2603822 4780.9479 -0.0364565
EXPECT 16.6679460 4149.9121 0.5174789
STIM_quadratic 1.7281591 4779.7399 0.0499933
CUE_high_gt_low:STIM_linear -1.5898455 4789.2961 -0.0459461
CUE_high_gt_low:EXPECT -2.3777784 961.3625 -0.1533761
STIM_linear:EXPECT 4.7210572 4780.3786 0.1365646
CUE_high_gt_low:STIM_quadratic -1.3394853 4775.3494 -0.0387673
EXPECT:STIM_quadratic -1.6405772 4779.7124 -0.0474598
CUE_high_gt_low:STIM_linear:EXPECT -0.6280425 4781.4227 -0.0181652
CUE_high_gt_low:EXPECT:STIM_quadratic 0.4866098 4779.6409 0.0140771

27.2 NPS_ses01 ~ SES * CUE * STIM

Q. Is the cue effect on NPS different across sessions?

Quick answer: Yes, the cue effect in session 1 (for high intensity group) is significantly different; whereas this different becomes non significant in session 4. To unpack, a participant was informed to experience a low stimulus intensity, when in fact they were delivered a high intensity stimulus. This violation presumably leads to a higher NPS response, given that they were delivered a much painful stimulus than expected. The fact that the cue effect is almost non significant during the last session indicates that the cue effects are not just an anchoring effect.

27.2.0.1 Session wise plots

27.2.1 Here are the stats models: NPS~session * cue * stimulus_intensity

  1. Calculate difference score
  • average high and low cue within run.
  • calculate difference between high and low cue per run
  • each participant has 6 contrast scores
  • run this as a function of stimulus intensity and sessions

27.3

27.4 OUTCOME ~ NPS

Q. Do higher NPS values indicate higher outcome ratings? (Pain task only)

Yes, Higher NPS values are associated with higher outcome ratings. The linear relationship between NPS value and outcome ratings are stronger for conditions where cue level is congruent with stimulus intensity levels. In other words, NPS-outcome rating relationship is stringent in the low cue-low intensity group, as is the case for high cue-ghigh intensity group.

27.4.1 outcome_rating * cue

27.4.2 outcome_ratings * stimulus_intensity * cue

27.4.3 demeaned outcome rating * cue

## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 45 rows containing non-finite values (`stat_smooth()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 45 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 45 rows containing missing values (`geom_point()`).

27.4.4 demeaned_outcome_ratings * stimulus_intensity * cue

27.4.5 Is this statistically significant?

# organize variable names
# NPS_demean vs. NPSpos
model.npsoutcome <- lmer(OUTCOME_demean ~ CUE_high_gt_low*STIM_linear*NPSpos + CUE_high_gt_low*STIM_quadratic*NPSpos + (CUE_high_gt_low*STIM*NPSpos|sub), data = demean_dropna)
sjPlot::tab_model(model.npsoutcome,
                  title = "Multilevel-modeling: \nlmer(OUTCOME_demean ~ CUE * STIM * NPSpos + (CUE * STIM *NPSpos | sub), data = pvc)",
                  CSS = list(css.table = '+font-size: 12;'))
Multilevel-modeling: lmer(OUTCOME_demean ~ CUE * STIM * NPSpos + (CUE * STIM *NPSpos | sub), data = pvc)
  OUTCOME_demean
Predictors Estimates CI p
(Intercept) -4.09 -5.55 – -2.62 <0.001
CUE high gt low 8.58 6.51 – 10.66 <0.001
STIM linear 22.06 19.50 – 24.63 <0.001
NPSpos 0.93 0.70 – 1.17 <0.001
STIM quadratic -0.59 -2.43 – 1.26 0.533
CUE high gt low * STIM
linear 		2.76 -1.46 – 6.97 0.199
CUE high gt low * NPSpos 0.05 -0.17 – 0.26 0.676
STIM linear * NPSpos 0.24 -0.02 – 0.50 0.073
CUE high gt low * STIM
quadratic 		-4.76 -8.43 – -1.09 0.011
NPSpos * STIM quadratic 0.01 -0.21 – 0.23 0.932
(CUE high gt low * STIM
linear) * NPSpos 		-0.25 -0.77 – 0.26 0.333
(CUE high gt low
NPSpos) STIM quadratic 		-0.14 -0.57 – 0.29 0.530
Random Effects
σ2 765.22
τ00 sub 55.66
τ11 sub.CUE_high_gt_low 36.16
τ11 sub.STIMlow 46.24
τ11 sub.STIMmed 22.42
τ11 sub.NPSpos 0.89
τ11 sub.CUE_high_gt_low:STIMlow 21.05
τ11 sub.CUE_high_gt_low:STIMmed 10.13
τ11 sub.CUE_high_gt_low:NPSpos 0.19
τ11 sub.STIMlow:NPSpos 0.14
τ11 sub.STIMmed:NPSpos 0.09
τ11 sub.CUE_high_gt_low:STIMlow:NPSpos 0.56
τ11 sub.CUE_high_gt_low:STIMmed:NPSpos 0.42
ρ01 -0.75
-0.71
-0.77
-0.58
0.85
-0.02
0.09
-0.50
-0.11
0.07
0.26
N sub 84
Observations 5007
Marginal R2 / Conditional R2 0.186 / NA

27.5 NPS ~ expectation_rating

Q. What is the relationship betweeen expectation ratings & NPS? (Pain task only)

Do we see a linear effect between expectation rating and NPS dot products? Also, does this effect differ as a function of cue and stimulus intensity ratings, as is the case for behavioral ratings?

Quick answer: Yes, expectation ratings predict NPS dotproducts; Also, there tends to be a different relationship depending on cues, just by looking at the figures, although this needs to be tested statistically.

27.5.1 NPS ~ expect * cue

27.5.2 NPS ~ expect * cue * stim

27.5.3 NPS ~ demeaned_expect * cue

27.5.4 NPS ~ demeaned_expect * cue * stim

cue_high = demean_dropna[demean_dropna$cue_name == "high", ]
fig.height = 50
fig.width = 10
ggplot(aes(x=EXPECT_demean, y=NPSpos), data=cue_high) +
  geom_smooth(method='lm', se=F, size=0.75) +
  geom_point(size=0.1) + 
    # geom_abline(intercept = 0, slope = 1, color="green", 
    #              linetype="dashed", size=0.5) +
  facet_wrap(~sub) + 
  theme(legend.position='none') + 
  xlim(-50,50) + ylim(-50,50) +
  xlab("raw data from each participant: n-1 lagged outcome angle") + 
  ylab("n current expectation rating") 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 287 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 287 rows containing missing values (`geom_point()`).

cue_low = demean_dropna[demean_dropna$cue_name == "low", ]
fig.height = 50
fig.width = 10
ggplot(aes(x=EXPECT_demean, y=NPSpos), data=cue_low) +
  geom_smooth(method='lm', se=F, size=0.75) +
  geom_point(size=0.1) + 
    # geom_abline(intercept = 0, slope = 1, color="green", 
    #              linetype="dashed", size=0.5) +
  facet_wrap(~sub) + 
  theme(legend.position='none') + 
  xlim(-50,50) + ylim(-50,50) +
  xlab("raw data from each participant: n-1 lagged outcome angle") + 
  ylab("n current expectation rating") 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 154 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 154 rows containing missing values (`geom_point()`).

27.5.4.1 facetwrap

# 74, 85, 118
fig.height = 50
fig.width = 10
ggplot(aes(x=EXPECT_demean, y=NPSpos), data=demean_high) +
  geom_smooth(method='lm', se=F, size=0.75) +
  geom_point(size=0.1) + 
    # geom_abline(intercept = 0, slope = 1, color="green", 
    #              linetype="dashed", size=0.5) +
  facet_wrap(~sub) + 
  theme(legend.position='none') + 
  xlim(-50,50) + ylim(-50,50) +
  xlab("raw data from each participant: n-1 lagged outcome angle") + 
  ylab("n current expectation rating") 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 142 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 142 rows containing missing values (`geom_point()`).

# 74, 85, 118, 117
fig.height = 50
fig.width = 10
ggplot(aes(x=EXPECT_demean, y=NPSpos), data=demean_low) +
  geom_smooth(method='lm', se=F, size=0.75) +
  geom_point(size=0.1) + 
    # geom_abline(intercept = 0, slope = 1, color="green", 
    #              linetype="dashed", size=0.5) +
  facet_wrap(~sub) + 
  theme(legend.position='none') + 
  xlim(-50,50) + ylim(-50,50) +
  xlab("raw data from each participant: n-1 lagged outcome angle") + 
  ylab("n current expectation rating") 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 142 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 142 rows containing missing values (`geom_point()`).

# 74, 85, 118, 117
fig.height = 50
fig.width = 10
ggplot(aes(x=EXPECT_demean, y=NPSpos), data=demean_med) +
  geom_smooth(method='lm', se=F, size=0.75) +
  geom_point(size=0.1) + 
    # geom_abline(intercept = 0, slope = 1, color="green", 
    #              linetype="dashed", size=0.5) +
  facet_wrap(~sub) + 
  theme(legend.position='none') + 
  xlim(-50,50) + ylim(-50,50) +
  xlab("raw data from each participant: n-1 lagged outcome angle") + 
  ylab("n current expectation rating") 
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 157 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 157 rows containing missing values (`geom_point()`).

#### subjectwise plot v2

ggplot(demean_med, aes(y = NPSpos, 
                       x = EXPECT_demean, 
                       colour = cuetype, shape = sub), size = .3, color = 'gray') + 
  geom_point(size = .1) + 
  geom_smooth(method = 'lm', formula= y ~ x, se = FALSE, size = .3) +
  scale_color_manual(values = c("cuetype-high" = "#941100", "cuetype-low" = "#BBBBBB"), ) +
  theme_classic()
## Warning: The shape palette can deal with a maximum of 6 discrete values because
## more than 6 becomes difficult to discriminate; you have 86. Consider
## specifying shapes manually if you must have them.
## Warning: Removed 1512 rows containing missing values (`geom_point()`).

27.5.4.2 subjetwise plot

27.5.5 ACCURATE Is this statistically significant?

27.5.5.1 ACCURATE: NPS ~ demean + CMC

model.npsexpectdemean <- lmer(NPSpos ~ 
                          CUE_high_gt_low*STIM_linear*EXPECT_demean +
                          CUE_high_gt_low*STIM_quadratic*EXPECT_demean +
                          EXPECT_cmc +
                          (CUE_high_gt_low |sub), data = demean_dropna
                    ) 
# CUE_high_gt_low+STIM+EXPECT_demean
sjPlot::tab_model(model.npsexpectdemean,
                  title = "Multilevel-modeling: \nlmer(NPSpos ~ CUE * STIM * EXPECT_demean + (1| sub), data = pvc)",
                  CSS = list(css.table = '+font-size: 12;'))
Multilevel-modeling: lmer(NPSpos ~ CUE * STIM * EXPECT_demean + (1| sub), data = pvc)
  NPSpos
Predictors Estimates CI p
(Intercept) 3.99 3.18 – 4.80 <0.001
CUE high gt low -2.93 -3.56 – -2.31 <0.001
STIM linear 1.21 0.60 – 1.82 <0.001
EXPECT demean 0.07 0.06 – 0.08 <0.001
STIM quadratic 0.34 -0.21 – 0.89 0.223
EXPECT cmc 0.06 0.02 – 0.09 0.004
CUE high gt low * STIM
linear 		-2.34 -3.56 – -1.12 <0.001
CUE high gt low * EXPECT
demean 		-0.03 -0.04 – -0.01 0.002
STIM linear * EXPECT
demean 		0.04 0.02 – 0.06 <0.001
CUE high gt low * STIM
quadratic 		-0.68 -1.78 – 0.41 0.222
EXPECT demean * STIM
quadratic 		-0.02 -0.04 – -0.00 0.028
(CUE high gt low * STIM
linear) * EXPECT demean 		-0.01 -0.05 – 0.03 0.710
(CUE high gt low * EXPECT
demean) * STIM quadratic 		-0.01 -0.05 – 0.02 0.434
Random Effects
σ2 55.44
τ00 sub 12.93
τ11 sub.CUE_high_gt_low 2.61
ρ01 sub -0.68
ICC 0.20
N sub 86
Observations 4883
Marginal R2 / Conditional R2 0.064 / 0.248

27.5.5.2 simple slopes when STIM == ‘high’, EXPECT_demean slope difference between high vs. low cue

interactions::sim_slopes(model=model.npsexpectdemean, pred=EXPECT_demean, modx=CUE_high_gt_low, mod2 =STIM_linear, mod2.values = 0.5, centered = 'all', data = demean_dropna)
## ███████████████████ While STIM_linear (2nd moderator) = 0.50 ███████████████████ 
## 
## JOHNSON-NEYMAN INTERVAL 
## 
## When CUE_high_gt_low is OUTSIDE the interval [1.57, 22.88], the slope of
## EXPECT_demean is p < .05.
## 
## Note: The range of observed values of CUE_high_gt_low is [-0.50, 0.50]
## 
## SIMPLE SLOPES ANALYSIS 
## 
## Slope of EXPECT_demean when CUE_high_gt_low = -0.50 (-0.5): 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.11   0.01    10.64   0.00
## 
## Slope of EXPECT_demean when CUE_high_gt_low =  0.50 (0.5): 
## 
##   Est.   S.E.   t val.      p
## ------ ------ -------- ------
##   0.07   0.01     8.31   0.00

27.5.5.3 emtrneds

emt.t <- emtrends(model.npsexpectdemean, ~ STIM_linear | CUE_high_gt_low, 
         var = "EXPECT_demean", 
         # nuisance = c("EXPECT_cmc"),
         at = list(STIM_linear=c(0.5, 0, -0.5)),
         lmer.df = "asymp")
pairs(emt.t, simple = "each")
## $`simple contrasts for STIM_linear`
## CUE_high_gt_low = -0.5:
##  contrast                           estimate      SE  df z.ratio p.value
##  STIM_linear0.5 - STIM_linear0        0.0237 0.00746 Inf   3.181  0.0042
##  STIM_linear0.5 - (STIM_linear-0.5)   0.0474 0.01491 Inf   3.181  0.0042
##  STIM_linear0 - (STIM_linear-0.5)     0.0237 0.00746 Inf   3.181  0.0042
## 
## CUE_high_gt_low =  0.5:
##  contrast                           estimate      SE  df z.ratio p.value
##  STIM_linear0.5 - STIM_linear0        0.0199 0.00692 Inf   2.881  0.0110
##  STIM_linear0.5 - (STIM_linear-0.5)   0.0399 0.01384 Inf   2.881  0.0110
##  STIM_linear0 - (STIM_linear-0.5)     0.0199 0.00692 Inf   2.881  0.0110
## 
## Results are averaged over the levels of: STIM_quadratic 
## Degrees-of-freedom method: asymptotic 
## P value adjustment: tukey method for comparing a family of 3 estimates 
## 
## $`simple contrasts for CUE_high_gt_low`
## STIM_linear = -0.5:
##  contrast                                   estimate      SE  df z.ratio
##  (CUE_high_gt_low-0.5) - CUE_high_gt_low0.5   0.0251 0.01369 Inf   1.837
##  p.value
##   0.0662
## 
## STIM_linear =  0.0:
##  contrast                                   estimate      SE  df z.ratio
##  (CUE_high_gt_low-0.5) - CUE_high_gt_low0.5   0.0289 0.00927 Inf   3.121
##  p.value
##   0.0018
## 
## STIM_linear =  0.5:
##  contrast                                   estimate      SE  df z.ratio
##  (CUE_high_gt_low-0.5) - CUE_high_gt_low0.5   0.0327 0.01383 Inf   2.364
##  p.value
##   0.0181
## 
## Results are averaged over the levels of: STIM_quadratic 
## Degrees-of-freedom method: asymptotic
# contrast(emt.t, "revpairwise")

27.5.5.4 resourcees on simple slopes in lmer

# https://stats.stackexchange.com/questions/365466/significance-of-slope-different-than-zero-in-triple-interaction-with-factors
# https://stats.stackexchange.com/questions/586748/calculating-trends-with-emtrends-for-three-way-interaction-model-results-in-sa
# emtrends(model.npsexpectdemean, var = 'EXPECT_demean', lmer.df = "asymptotic")

27.5.5.5 ACCURATE: NPS ~ demean + CMC

model.npsexpectdemean <- lmer(NPSpos ~ 
                          CUE_high_gt_low*EXPECT_demean  +  EXPECT_cmc + factor(ses) +
                          (1|sub), data = demean_dropna
                    )
# CUE_high_gt_low+STIM+EXPECT_demean
sjPlot::tab_model(model.npsexpectdemean,
                  title = "Multilevel-modeling: \nlmer(NPSpos ~ CUE * STIM * EXPECT_demean + (1| sub), data = pvc)",
                  CSS = list(css.table = '+font-size: 12;'))
Multilevel-modeling: lmer(NPSpos ~ CUE * STIM * EXPECT_demean + (1| sub), data = pvc)
  NPSpos
Predictors Estimates CI p
(Intercept) 6.24 5.38 – 7.10 <0.001
CUE high gt low -2.18 -2.69 – -1.67 <0.001
EXPECT demean 0.05 0.04 – 0.06 <0.001
EXPECT cmc 0.05 0.01 – 0.09 0.017
factor(ses)ses-03 -3.33 -3.89 – -2.76 <0.001
CUE high gt low * EXPECT
demean 		-0.03 -0.04 – -0.01 0.003
Random Effects
σ2 55.11
τ00 sub 11.16
ICC 0.17
N sub 86
Observations 4883
Marginal R2 / Conditional R2 0.079 / 0.234