1. JP Data

-Note: Large chunks of code have been hidden to improve readibility. ## 1. Load Packages Used in Data Analysis -Code hidden

2. Data Read-In

-Code hidden

3. Data Pre-processing

Creating Trial Number Column

length(df$Session[df$Subject == 56001 & df$Session == 1]) #160 rows ("trials") per part.
## [1] 160
dim(df) #21,440 total rows ("trials")
## [1] 21440     8
21440/160 #160 rows repeated 134 times
## [1] 134
df$Trial <- rep(seq(1:160), 134) #Code creates a sequence of 1-160, per participant per session, and stores this in the "Trial" variable

Probe accuracy, incorrect trials, improbable reaction times

-The code below when run illustrates that there are no administrations #With <50% accuracy. Nothing excluded here.

df %>% 
  group_by(Subject, Session, Cue) %>% 
  summarise(Percent_Accurate = mean(Accuracy)) %>% 
  filter(Percent_Accurate < 0.50) 
## `summarise()` has grouped output by 'Subject', 'Session'. You can override
## using the `.groups` argument.
## # A tibble: 0 × 4
## # Groups:   Subject, Session [0]
## # … with 4 variables: Subject <dbl>, Session <fct>, Cue <fct>,
## #   Percent_Accurate <dbl>

-The code below indicates the overall # of trials and the counts/percents for 1) incorrect trials to be excluded, and 2) improbable reaction time trials (i.e., <200ms OR >1500ms) to be excluded. The code immediately following then excludes this data.

df %>% 
  group_by(Session) %>% 
  summarise(Overall_Trials = n(), #Overall trial counts
            Count_Inc_Trial = sum(ifelse(Accuracy == 0,1,0)), #Incorrect trial count
            Per_Inc_Trial = 1 - mean(Accuracy), #Percent of trials that are incorrect
            Count_Inc_RT = sum(ifelse(RT < 200 | RT > 1500, 1, 0)), 
            #Count of trials whose reaction times are < 200 or > 1500
            Per_Inc_RT = 1 - mean(ifelse(RT < 200 | RT > 1500, 0, 1)))
## # A tibble: 6 × 6
##   Session Overall_Trials Count_Inc_Trial Per_Inc_Trial Count_Inc_RT Per_Inc_RT
##   <fct>            <int>           <dbl>         <dbl>        <dbl>      <dbl>
## 1 1                 4160              84        0.0202           79     0.0190
## 2 2                 4160             161        0.0387           80     0.0192
## 3 3                 3680              60        0.0163           70     0.0190
## 4 4                 3680              82        0.0223           62     0.0168
## 5 5                 2880              71        0.0247           69     0.0240
## 6 6                 2880              74        0.0257           90     0.0312

-Filtering out improbable reaction times

df <- df %>% 
  filter(Accuracy == 1) %>%  #Exclude incorrect trials
  filter(RT > 200 & RT < 1500) #Exclude RT's <200ms or >1500ms

-Maximum & Minimum RT after exclusions

max(df$RT)
## [1] 1499
min(df$RT)
## [1] 207

-Code for Median Absolute Deviation and SD calculations for Reaction Times hidden

-MAD & SD Calculation Results

df %>% 
  group_by(Session) %>% 
  summarise(Ovr_Trl_Aftr_Exc = n(), #Overall trial counts after excluding for above-noted criteria
            Count_MAD3_Over = sum(ifelse(RT > MAD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
            Per_MAD3_Over = mean(ifelse(RT > MAD_Exclude_Score, 1, 0)),
            Count_SD3_Over = sum(ifelse(RT > SD_Exclude_Score, 1,0)), #Count of trials that had RT > 3MAD above individual median
            Per_SD3_Over = mean(ifelse(RT > SD_Exclude_Score, 1, 0)))
## # A tibble: 6 × 6
##   Session Ovr_Trl_Aftr_Exc Count_MAD3_Over Per_MAD3_Over Count_SD3_Over
##   <fct>              <int>           <dbl>         <dbl>          <dbl>
## 1 1                   4005             205        0.0512             79
## 2 2                   3931             138        0.0351             54
## 3 3                   3551             115        0.0324             47
## 4 4                   3538             119        0.0336             50
## 5 5                   2746             114        0.0415             54
## 6 6                   2725             128        0.0470             52
## # … with 1 more variable: Per_SD3_Over <dbl>

-Excluding Folks based on SD for Reaction Times

df <- df %>% 
  filter(RT < SD_Exclude_Score) #Exclude folks whose RT are >3SD above their individual means

3. Descriptive Statistics

df %>% select(RT, Condition, Session, Cue) %>%  group_by(Condition, Session, Cue) %>% 
  summarise(across(everything(), list(mean = mean, sd = sd, min = min, max = max)))
## `summarise()` has grouped output by 'Condition', 'Session'. You can override
## using the `.groups` argument.
## # A tibble: 36 × 7
## # Groups:   Condition, Session [18]
##    Condition Session Cue    RT_mean RT_sd RT_min RT_max
##    <chr>     <fct>   <fct>    <dbl> <dbl>  <dbl>  <dbl>
##  1 A         1       opioid    623.  210.    339   1447
##  2 A         1       pain      635.  213.    317   1440
##  3 A         2       opioid    580.  144.    207   1295
##  4 A         2       pain      576.  151.    367   1356
##  5 A         3       opioid    557.  125.    207   1167
##  6 A         3       pain      562.  133.    312   1316
##  7 A         4       opioid    551.  140.    342   1348
##  8 A         4       pain      552.  144.    328   1241
##  9 A         5       opioid    536.  141.    301   1217
## 10 A         5       pain      534.  137.    322   1219
## # … with 26 more rows

4. Attentional Bias (AB) Calculations

-Code for AB calculations hidden

-Attentional Bias Metrics Per Subject, Condition, Session, & Cue

By_Subject_Output
## # A tibble: 268 × 10
## # Groups:   Subject, Condition, Session [134]
##    Subject Condition Session Cue    Mean_Bias Mean_Toward Mean_Away Peak_Toward
##      <dbl> <chr>     <fct>   <fct>      <dbl>       <dbl>     <dbl>       <dbl>
##  1   56001 C         1       opioid     6.24         97.9     103.          442
##  2   56001 C         1       pain      33.2         139.       61.2         429
##  3   56001 C         2       opioid    12.3         114.      110.          402
##  4   56001 C         2       pain       0.776       100.      107.          380
##  5   56001 A         3       opioid    22.9         115.      113.          350
##  6   56001 A         3       pain       4.97         65.3      89.6         228
##  7   56001 A         4       opioid   -39.8          82.1     106.          240
##  8   56001 A         4       pain       1.86         75.7     101.          326
##  9   56001 B         5       opioid    -0.504        88.8      65.7         251
## 10   56001 B         5       pain       9.94        110.       52.1         305
## # … with 258 more rows, and 2 more variables: Peak_Away <dbl>,
## #   Variability <dbl>
By_Condition_Output
## # A tibble: 36 × 9
## # Groups:   Condition, Session [18]
##    Condition Session Cue   Mean_Bias Mean_Toward Mean_Away Peak_Toward Peak_Away
##    <chr>     <fct>   <fct>     <dbl>       <dbl>     <dbl>       <dbl>     <dbl>
##  1 A         1       opio…     -4.88       112.      117.         323.      317.
##  2 A         1       pain       1.90       117.      125.         400.      353.
##  3 B         1       opio…     -3.52        95.1      90.3        318.      313.
##  4 B         1       pain       6.39        84.6      90.5        292.      290.
##  5 C         1       opio…     -9.58       119.      128.         364.      367.
##  6 C         1       pain       5.10       130.      118.         396.      386.
##  7 A         2       opio…     20.0        102.       96.6        317.      329.
##  8 A         2       pain     -10.2         93.9     105.         354.      341.
##  9 B         2       opio…      3.00       106.      109.         406.      331.
## 10 B         2       pain      28.8        117.       96.1        364.      299.
## # … with 26 more rows, and 1 more variable: Variability <dbl>
By_Session_Output
## # A tibble: 12 × 8
## # Groups:   Session [6]
##    Session Cue    Mean_Bias Mean_Toward Mean_Away Peak_Toward Peak_Away
##    <fct>   <fct>      <dbl>       <dbl>     <dbl>       <dbl>     <dbl>
##  1 1       opioid    -6.27         109.      113.        337.      335.
##  2 1       pain       4.50         113.      112.        366.      347.
##  3 2       opioid     1.82         108.      110.        357.      341.
##  4 2       pain       6.25         110.      112.        359.      342.
##  5 3       opioid    -0.233        111.      108.        373.      364.
##  6 3       pain      -4.52         105.      111.        338.      353.
##  7 4       opioid     4.76         111.      110.        377.      352.
##  8 4       pain       2.01         110.      106.        343.      362.
##  9 5       opioid    -3.19         106.      105.        324.      348.
## 10 5       pain     -13.3          103.      107.        326.      342.
## 11 6       opioid    -3.74         105.      110.        335.      329.
## 12 6       pain     -12.3          106.      116.        313.      348.
## # … with 1 more variable: Variability <dbl>
By_Cue_Output
## # A tibble: 2 × 7
##   Cue    Mean_Bias Mean_Toward Mean_Away Peak_Toward Peak_Away Variability
##   <fct>      <dbl>       <dbl>     <dbl>       <dbl>     <dbl>       <dbl>
## 1 opioid     -1.01        108.      110.        352.      345.        94.2
## 2 pain       -1.76        108.      111.        343.      349.        94.7

5. ANOVA’s

-Changing session variable to represent just pre-post

df$Session[df$Session == 3] <- 1
df$Session[df$Session == 5] <- 1
df$Session[df$Session == 4] <- 2
df$Session[df$Session == 6] <- 2

df$Session <- droplevels(factor(df$Session, levels = c(1:6), labels = c("Pre", "Post", "3", "4", "5", "6")))

table(df$Subject, df$Session, df$Condition) 
## , ,  = A
## 
##        
##         Pre Post
##   56001 143  146
##   56002 159  158
##   56003   0    0
##   56004 155  151
##   56005 159  157
##   56006 156  157
##   56007 159  144
##   56008 155  157
##   56009 158  154
##   56010 153  158
##   56011   0    0
##   56012 157  158
##   56013   0    0
##   56014   0    0
##   56015   0  150
##   56016 133  152
##   56017 152  152
##   56018   0    0
##   56019 148  151
##   56020 155  154
##   56021 154  154
##   56022   0    0
##   56023 147  141
##   56025 159  153
##   56026 159  155
##   56027 154  148
##   56028   0    0
## 
## , ,  = B
## 
##        
##         Pre Post
##   56001 146  149
##   56002   0  155
##   56003 158    0
##   56004 155  153
##   56005 140  156
##   56006 159  141
##   56007 137  141
##   56008 156  157
##   56009 158  158
##   56010 155  158
##   56011 147  148
##   56012 155  151
##   56013   0    0
##   56014 132  146
##   56015 150  145
##   56016 156  144
##   56017   0    0
##   56018 138   96
##   56019 137  152
##   56020 133  118
##   56021 142  153
##   56022 144  148
##   56023 157  156
##   56025 159  153
##   56026 144  158
##   56027 155  148
##   56028 157  153
## 
## , ,  = C
## 
##        
##         Pre Post
##   56001 152  147
##   56002 147  159
##   56003 148  139
##   56004 157  153
##   56005 157  158
##   56006 157  157
##   56007 156  150
##   56008 153  154
##   56009 154  146
##   56010 152  153
##   56011 149  155
##   56012 159  156
##   56013 149  123
##   56014 130    0
##   56015 150  159
##   56016 154  156
##   56017   0    0
##   56018   0    0
##   56019 154  156
##   56020 155  155
##   56021 152  155
##   56022 153  155
##   56023   0    0
##   56025 153  158
##   56026 151  126
##   56027 139  128
##   56028 156  153

-Something to note: There are several subjects who appear to have no data for at least Condition/1 time-point:

Folks Missing 1 pre/post data point: #Condition A: 15 #Condition B: 02 #Condition C: 14

Folks Missing both pre/post data points #Condition A: 03, 11, 13, 14, 18, 22, 28 #Condition B: 03, 13, 17 #Condition C: 17,18,23

R seems to be dropping these folks from ANOVA analysis below. I’m trying to specify a way for them to be kept and not succeeding at the moment, but even if I can, that still is going to result with at least 13/28 getting dropped. A REALLY small sample of N=15 at best or N=12 at worst.

-Changing Data into Long Format

df_long <- df %>% select(Subject, Condition, Session, Cue, mean_bias:variability) %>% 
  pivot_longer(cols = c(mean_bias:variability), names_to = "Bias_Type", values_to = "RT")

6. Opioid Cue ANOVAs

6a. Opioid Mean Bias

#DV = Mean Bias
df_long_opioid_mean_bias <- df_long %>% filter(Cue == "opioid" & Bias_Type == "mean_bias")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_mean_bias,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F  ges p.value
## 1           Session       1, 15 1730.29 0.70 .017    .417
## 2         Condition 1.87, 28.00  674.69 0.72 .013    .487
## 3 Session:Condition 1.71, 25.58  599.62 1.92 .027    .171
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

6b. Opioid Mean Away

#DV = Mean Away
df_long_opioid_mean_away <- df_long %>% filter(Cue == "opioid" & Bias_Type == "mean_away")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_mean_away,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F  ges p.value
## 1           Session       1, 15 1084.77 2.34 .010    .147
## 2         Condition 1.48, 22.25 1281.37 0.86 .007    .406
## 3 Session:Condition 1.98, 29.77  373.76 0.42 .001    .660
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

6c. Opioid Mean Toward

#DV = Mean Toward
df_long_opioid_mean_toward <- df_long %>% filter(Cue == "opioid" & Bias_Type == "mean_toward")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_mean_toward,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F  ges p.value
## 1           Session       1, 15  331.77 1.33 .002    .266
## 2         Condition 1.83, 27.44 1097.91 0.65 .007    .515
## 3 Session:Condition 1.20, 18.06 1727.81 0.21 .002    .696
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

6d. Opioid Peak Away

#DV = Peak Away
df_long_opioid_peak_away <- df_long %>% filter(Cue == "opioid" & Bias_Type == "peak_away")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_peak_away,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df      MSE    F   ges p.value
## 1           Session       1, 15  5111.51 1.17  .002    .296
## 2         Condition 1.64, 24.59 16820.23 0.80  .007    .438
## 3 Session:Condition 1.72, 25.80  7437.94 0.00 <.001    .993
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

6e. Opioid Peak Toward

#DV = Peak Toward
df_long_opioid_peak_toward <- df_long %>% filter(Cue == "opioid" & Bias_Type == "peak_toward")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_peak_toward,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df      MSE      F   ges p.value
## 1           Session       1, 15  7247.98 3.83 +  .012    .069
## 2         Condition 1.62, 24.32 11543.84   0.13 <.001    .840
## 3 Session:Condition 1.20, 17.99 19441.12   0.25  .002    .668
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

6f. Opioid Variability

#DV = Variability
df_long_opioid_variability <- df_long %>% filter(Cue == "opioid" & Bias_Type == "variability")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_opioid_variability,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df    MSE    F  ges p.value
## 1           Session       1, 15 305.22 1.86 .004    .192
## 2         Condition 1.86, 27.85 623.94 0.82 .006    .441
## 3 Session:Condition 1.89, 28.32 283.68 0.62 .002    .534
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7. Pain Cue ANOVAs

7a. Pain Mean Bias

#DV = Mean Bias
df_long_pain_mean_bias <- df_long %>% filter(Cue == "pain" & Bias_Type == "mean_bias")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_mean_bias,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F  ges p.value
## 1           Session       1, 15  899.92 0.37 .005    .552
## 2         Condition 1.27, 19.02 1477.83 0.28 .009    .657
## 3 Session:Condition 1.80, 26.94  418.87 0.17 .002    .825
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7b. Pain Mean Away

#DV = Mean Away
df_long_pain_mean_away <- df_long %>% filter(Cue == "pain" & Bias_Type == "mean_away")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_mean_away,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F   ges p.value
## 1           Session       1, 15  697.86 1.10  .003    .310
## 2         Condition 1.66, 24.90 1342.32 0.07 <.001    .899
## 3 Session:Condition 1.51, 22.72  809.59 0.07 <.001    .881
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7c. Pain Mean Toward

#DV = Mean Toward
df_long_pain_mean_toward <- df_long %>% filter(Cue == "pain" & Bias_Type == "mean_toward")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_mean_toward,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df     MSE    F  ges p.value
## 1           Session       1, 15 1623.54 0.22 .002    .643
## 2         Condition 1.91, 28.68  845.07 1.05 .008    .359
## 3 Session:Condition 1.69, 25.36  433.06 0.60 .002    .527
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7d. Pain Peak Away

#DV = Peak Away
df_long_pain_peak_away <- df_long %>% filter(Cue == "pain" & Bias_Type == "peak_away")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_peak_away,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df      MSE    F  ges p.value
## 1           Session       1, 15 10744.89 1.46 .006    .246
## 2         Condition 1.98, 29.73  6103.29 0.37 .002    .693
## 3 Session:Condition 1.55, 23.30  9044.77 0.71 .004    .468
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7e. Pain Peak Toward

#DV = Peak Toward
df_long_pain_peak_toward <- df_long %>% filter(Cue == "pain" & Bias_Type == "peak_toward")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_peak_toward,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df      MSE    F   ges p.value
## 1           Session       1, 15 12315.63 0.04 <.001    .853
## 2         Condition 1.32, 19.84 14603.39 0.45  .004    .562
## 3 Session:Condition 1.35, 20.18  5994.07 0.31  .001    .649
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG

7f. Pain Variability

#DV = Variability
df_long_pain_variability <- df_long %>% filter(Cue == "pain" & Bias_Type == "variability")

afex::aov_car(formula = RT ~ Session*Condition + Error(Subject/Session*Condition), data = df_long_pain_variability,
              type = 3, na.rm = T)
## Warning: More than one observation per design cell, aggregating data using `fun_aggregate = mean`.
## To turn off this warning, pass `fun_aggregate = mean` explicitly.
## Warning: Missing values for following ID(s):
## 56002, 56003, 56011, 56013, 56014, 56015, 56017, 56018, 56022, 56023, 56028
## Removing those cases from the analysis.
## Anova Table (Type 3 tests)
## 
## Response: RT
##              Effect          df    MSE    F   ges p.value
## 1           Session       1, 15 646.84 0.46  .002    .510
## 2         Condition 1.96, 29.36 390.98 0.80  .004    .457
## 3 Session:Condition 1.85, 27.72 370.63 0.04 <.001    .950
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## 
## Sphericity correction method: GG