RMTS

Ambiguous RMTS (logistic regression): choice ~ culture + (trial_num | subject)

rmts_df <- data %>% 
  filter(task_name == "RMTS") %>% 
  mutate(choice = as.factor(case_when(
    resp == "1" ~ "rel",
    resp == "0" ~ "obj"))
         ) %>%
  group_by(subject) %>% 
  mutate(trial_num = as.factor(row_number())) %>% 
  select(-resp, -task_info, -trial_info, -resp_type)
# model 0: not converging 
#rmts_model <- glmer(choice ~ culture + (trial_num | subject), family = binomial, data = rmts_df)

# model 1: 
rmts_model <- glmer(choice ~ culture + (1 | subject), family = binomial, data = rmts_df)

#rmts_model
summary(rmts_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: choice ~ culture + (1 | subject)
##    Data: rmts_df
## 
##      AIC      BIC   logLik deviance df.resid 
##    530.8    545.7   -262.4    524.8     1069 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.71048 -0.00570 -0.00500  0.08017  1.75039 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  subject (Intercept) 599.1    24.48   
## Number of obs: 1072, groups:  subject, 268
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -10.2571     0.8169 -12.556   <2e-16 ***
## cultureUS    -0.2796     1.0214  -0.274    0.784    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## cultureUS -0.483

Raven (no trial yet)

Raven (logistic regression): acc ~ culture + (1 | subject) + (1 | trial)

rv_df <- data %>% 
  filter(task_name == "RV") %>% 
  mutate(acc = as.numeric(resp)) %>% 
  group_by(subject) %>% 
  mutate(trial = as.factor(row_number())) %>% 
  select(-resp, -task_info, -trial_info, -resp_type)

rv_model <- glmer(acc ~ culture + (1 | subject) + (culture | trial), family = binomial, data = rv_df)

rv_model
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: acc ~ culture + (1 | subject) + (culture | trial)
##    Data: rv_df
##       AIC       BIC    logLik  deviance  df.resid 
##  2514.918  2551.373 -1251.459  2502.918      3210 
## Random effects:
##  Groups  Name        Std.Dev. Corr
##  subject (Intercept) 1.467        
##  trial   (Intercept) 1.260        
##          cultureUS   0.421    0.43
## Number of obs: 3216, groups:  subject, 268; trial, 12
## Fixed Effects:
## (Intercept)    cultureUS  
##       2.685       -1.483
summary(rv_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: acc ~ culture + (1 | subject) + (culture | trial)
##    Data: rv_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   2514.9   2551.4  -1251.5   2502.9     3210 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -8.5970  0.0797  0.2164  0.3887  4.7045 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev. Corr
##  subject (Intercept) 2.1520   1.467        
##  trial   (Intercept) 1.5884   1.260        
##          cultureUS   0.1773   0.421    0.43
## Number of obs: 3216, groups:  subject, 268; trial, 12
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   2.6849     0.3963   6.775 1.24e-11 ***
## cultureUS    -1.4834     0.2549  -5.819 5.92e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## cultureUS -0.045

Conformance preference (pens/stickers)

Conformity preference (logistic regression): choice ~ culture

cp_df <- data %>% 
  filter(task_name == "CP") %>% 
  mutate(choice = as.factor(case_when(
    resp == "1" ~ "uniq",
    resp == "0" ~ "non_uniq"))
         ) %>% 
  select(-resp, -task_info, -trial_info, -resp_type)

cp_model <- glm(choice ~ culture, 
                   family=binomial(link="logit"),
                  data = cp_df)

cp_model
## 
## Call:  glm(formula = choice ~ culture, family = binomial(link = "logit"), 
##     data = cp_df)
## 
## Coefficients:
## (Intercept)    cultureUS  
##      0.6190      -0.3583  
## 
## Degrees of Freedom: 267 Total (i.e. Null);  266 Residual
## Null Deviance:       357.1 
## Residual Deviance: 355.1     AIC: 359.1
summary(cp_model)
## 
## Call:
## glm(formula = choice ~ culture, family = binomial(link = "logit"), 
##     data = cp_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.4490  -1.2899   0.9282   0.9282   1.0689  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.6190     0.1657   3.735 0.000188 ***
## cultureUS    -0.3583     0.2552  -1.404 0.160354    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 357.05  on 267  degrees of freedom
## Residual deviance: 355.08  on 266  degrees of freedom
## AIC: 359.08
## 
## Number of Fisher Scoring iterations: 4

Symbolic self-inflation (circles)

ratio

Symbolic self-inflation (linear regression): percent_inflation ~ culture

si_df <- data %>% 
  filter(task_name == "SI") %>% 
  filter(resp_type == "inflation_score_ratio") %>% 
  mutate(score = resp) %>% 
  select(-resp, -task_info, -trial_info, -resp_type)

si_model <- glm(score ~ culture, family=gaussian, data = si_df)

si_model
## 
## Call:  glm(formula = score ~ culture, family = gaussian, data = si_df)
## 
## Coefficients:
## (Intercept)    cultureUS  
##    0.945635    -0.002024  
## 
## Degrees of Freedom: 240 Total (i.e. Null);  239 Residual
## Null Deviance:       48.93 
## Residual Deviance: 48.93     AIC: 305.7
summary(si_model)
## 
## Call:
## glm(formula = score ~ culture, family = gaussian, data = si_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7608  -0.2105  -0.0447   0.1195   4.6030  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.945635   0.037067  25.511   <2e-16 ***
## cultureUS   -0.002024   0.059994  -0.034    0.973    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.2047229)
## 
##     Null deviance: 48.929  on 240  degrees of freedom
## Residual deviance: 48.929  on 239  degrees of freedom
## AIC: 305.67
## 
## Number of Fisher Scoring iterations: 2

diff

si_df <- data %>% 
  filter(task_name == "SI") %>% 
  filter(resp_type == "inflation_score_diff") %>% 
  mutate(score = resp) %>% 
  select(-resp, -task_info, -trial_info, -resp_type)

si_model <- glm(score ~ culture, family=gaussian, data = si_df)

si_model
## 
## Call:  glm(formula = score ~ culture, family = gaussian, data = si_df)
## 
## Coefficients:
## (Intercept)    cultureUS  
##     -13.707        7.389  
## 
## Degrees of Freedom: 240 Total (i.e. Null);  239 Residual
## Null Deviance:       360200 
## Residual Deviance: 357100    AIC: 2449
summary(si_model)
## 
## Call:
## glm(formula = score ~ culture, family = gaussian, data = si_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -252.62   -11.35     3.43    15.87   124.85  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -13.707      3.167  -4.328 2.21e-05 ***
## cultureUS      7.389      5.125   1.442    0.151    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1494.199)
## 
##     Null deviance: 360219  on 240  degrees of freedom
## Residual deviance: 357114  on 239  degrees of freedom
## AIC: 2449.5
## 
## Number of Fisher Scoring iterations: 2

Causal attribution

Poisson regression: attrib_num ~ attrib_type * culture + (attrib_type | subject) + (culture | trial)

ca_df <- data %>% 
  filter(task_name == "CA") %>% 
  mutate(attrib_num = as.numeric(resp),
         attrib_binary = replace(attrib_num, attrib_num > 1, 1),
         trial = trial_info,
         attrib_type = factor(resp_type)) %>% 
  select(-resp, -task_info)

#ca_model <- glmer(attrib_num ~ attrib_type * culture + (attrib_type | subject) + (culture | trial), family=poisson, data = ca_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular

#ca_model_binary <- glmer(attrib_binary ~ attrib_type * culture + (attrib_type | subject) + (culture | trial), family=binomial, data = ca_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular

#ca_model1 <- glmer(attrib_num ~ attrib_type * culture + (attrib_type | subject) + (1 | trial), family=poisson, data = ca_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular

#ca_model2 <- glmer(attrib_num ~ attrib_type * culture + (1 | subject) + (1 | trial), family=poisson, data = ca_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular

#ca_model3 <- glmer(attrib_num ~ attrib_type * culture + (1 | subject), family=poisson, data = ca_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular

ca_model4 <- glm(attrib_num ~ attrib_type * culture, family=poisson, data = ca_df)

ca_model4
## 
## Call:  glm(formula = attrib_num ~ attrib_type * culture, family = poisson, 
##     data = ca_df)
## 
## Coefficients:
##                                (Intercept)  
##                                    -0.7581  
##           attrib_typesituation_attribution  
##                                     0.3760  
##                                  cultureUS  
##                                    -0.2384  
## attrib_typesituation_attribution:cultureUS  
##                                     0.1819  
## 
## Degrees of Freedom: 1063 Total (i.e. Null);  1060 Residual
## Null Deviance:       876.3 
## Residual Deviance: 845.2     AIC: 1936
summary(ca_model4)
## 
## Call:
## glm(formula = attrib_num ~ attrib_type * culture, family = poisson, 
##     data = ca_df)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1682  -0.9680  -0.8593   0.4089   3.4352  
## 
## Coefficients:
##                                            Estimate Std. Error z value Pr(>|z|)
## (Intercept)                                -0.75811    0.08192  -9.254  < 2e-16
## attrib_typesituation_attribution            0.37595    0.10639   3.534  0.00041
## cultureUS                                  -0.23842    0.13916  -1.713  0.08667
## attrib_typesituation_attribution:cultureUS  0.18185    0.17670   1.029  0.30339
##                                               
## (Intercept)                                ***
## attrib_typesituation_attribution           ***
## cultureUS                                  .  
## attrib_typesituation_attribution:cultureUS    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 876.34  on 1063  degrees of freedom
## Residual deviance: 845.17  on 1060  degrees of freedom
## AIC: 1935.8
## 
## Number of Fisher Scoring iterations: 5

Horizon: height

linear regression: horizon_height ~ culture

HZ_height_df <- data %>% 
  filter(task_name == "HZ", resp_type == "hz_height") %>% 
  mutate(
    height = resp
         ) %>% 
  select(-resp, -task_info, -trial_info)

HZ_height_model <- lm(height ~ culture, 
                      data = HZ_height_df)

summary(HZ_height_model)
## 
## Call:
## lm(formula = height ~ culture, data = HZ_height_df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52762 -0.11897 -0.01028  0.11532  0.45623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.54377    0.01441  37.731   <2e-16 ***
## cultureUS    0.02231    0.02270   0.983    0.327    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1823 on 266 degrees of freedom
## Multiple R-squared:  0.003618,   Adjusted R-squared:  -0.0001282 
## F-statistic: 0.9658 on 1 and 266 DF,  p-value: 0.3266

Horizon: area

linear regression: sticker_area ~ culture

HZ_stkr_area_df <- data %>% 
  filter(task_name == "HZ", resp_type == "stkr_area") %>% 
  mutate(
    stkr_area = resp
         ) %>% 
  select(-resp, -task_info, -trial_info)

HZ_stkr_area_model <- lm(stkr_area ~ culture, 
                      data = HZ_stkr_area_df)

summary(HZ_stkr_area_model)
## 
## Call:
## lm(formula = stkr_area ~ culture, data = HZ_stkr_area_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -796443 -262419  -65288  227899 1024302 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   890587      28519  31.228   <2e-16 ***
## cultureUS     -30616      44925  -0.681    0.496    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 360700 on 266 degrees of freedom
## Multiple R-squared:  0.001743,   Adjusted R-squared:  -0.00201 
## F-statistic: 0.4644 on 1 and 266 DF,  p-value: 0.4962

Horizon: sticker count

linear regression: sticker_number ~ culture

HZ_stkr_n_df <- data %>% 
  filter(task_name == "HZ", resp_type == "stkr_count") %>% 
  mutate(
    stkr_count = resp
         ) %>% 
  select(-resp, -task_info, -trial_info)

HZ_stkr_n_model <- lm(stkr_count ~ culture, 
                      data = HZ_stkr_n_df)

summary(HZ_stkr_n_model)
## 
## Call:
## lm(formula = stkr_count ~ culture, data = HZ_stkr_n_df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -8.315 -4.315 -1.315  2.994 19.685 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.0062     0.4449  26.986   <2e-16 ***
## cultureUS    -0.6914     0.7008  -0.987    0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.628 on 266 degrees of freedom
## Multiple R-squared:  0.003646,   Adjusted R-squared:  -9.99e-05 
## F-statistic: 0.9733 on 1 and 266 DF,  p-value: 0.3247

Ebbinghaus

Ebbinghaus (logistic regression): correct ~ culture * context * circle_size_difference + (circle_size_difference * condition | subject)

ebb_df <- data %>% 
  filter(task_name == "EBB", task_info != "HELPFUL") %>% 
  mutate(correct = as.factor(case_when(
    resp == "1" ~ "correct",
    resp == "0" ~ "incorrect")), 
         context = task_info, 
         size_diff = as.numeric(trial_info), 
         ) %>% 
  select(-resp, -task_info, -trial_info)
  
#full model
#ebb_model <- glmer(correct ~ culture * context * size_diff + (size_diff * context | subject), family = binomial, data = ebb_df, control=glmerControl(optimizer="bobyqa"))
#convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded
#boundary (singular) fit: see ?isSingular

#model 2 (if full does not converge)
#ebb_model <- glmer(correct ~ culture * context * size_diff + (context | subject), family = binomial, data = ebb_df, control=glmerControl(optimizer="bobyqa"))
#boundary (singular) fit: see ?isSingular


#model 3
ebb_model <- glmer(correct ~ culture * context * size_diff + ( 1 | subject), family = binomial, data = ebb_df, control=glmerControl(optimizer="bobyqa"))

ebb_model
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: correct ~ culture * context * size_diff + (1 | subject)
##    Data: ebb_df
##       AIC       BIC    logLik  deviance  df.resid 
##  7576.153  7639.082 -3779.076  7558.153      8031 
## Random effects:
##  Groups  Name        Std.Dev.
##  subject (Intercept) 0.8399  
## Number of obs: 8040, groups:  subject, 268
## Fixed Effects:
##                   (Intercept)                      cultureUS  
##                     -0.379169                      -0.007047  
##                     contextNC                      size_diff  
##                     -3.351065                       0.172673  
##           cultureUS:contextNC            cultureUS:size_diff  
##                     -0.566036                      -0.010180  
##           contextNC:size_diff  cultureUS:contextNC:size_diff  
##                     -0.055415                       0.030202
summary(ebb_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: correct ~ culture * context * size_diff + (1 | subject)
##    Data: ebb_df
## Control: glmerControl(optimizer = "bobyqa")
## 
##      AIC      BIC   logLik deviance df.resid 
##   7576.2   7639.1  -3779.1   7558.2     8031 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0822 -0.4296 -0.1375  0.5905 11.2538 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  subject (Intercept) 0.7054   0.8399  
## Number of obs: 8040, groups:  subject, 268
## 
## Fixed effects:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   -0.379169   0.114871  -3.301 0.000964 ***
## cultureUS                     -0.007047   0.180078  -0.039 0.968785    
## contextNC                     -3.351065   0.284546 -11.777  < 2e-16 ***
## size_diff                      0.172673   0.014526  11.887  < 2e-16 ***
## cultureUS:contextNC           -0.566036   0.506029  -1.119 0.263317    
## cultureUS:size_diff           -0.010180   0.022553  -0.451 0.651717    
## contextNC:size_diff           -0.055415   0.037898  -1.462 0.143680    
## cultureUS:contextNC:size_diff  0.030202   0.066139   0.457 0.647930    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cltrUS cntxNC sz_dff clUS:NC clUS:_ cnNC:_
## cultureUS   -0.638                                           
## contextNC   -0.267  0.171                                    
## size_diff   -0.733  0.467  0.282                             
## cltrUS:cnNC  0.151 -0.236 -0.560 -0.161                      
## cltrUS:sz_d  0.471 -0.733 -0.185 -0.640  0.254               
## cntxtNC:sz_  0.281 -0.179 -0.908 -0.381  0.511   0.245       
## cltrUS:NC:_ -0.161  0.250  0.521  0.218 -0.915  -0.340 -0.573

Free Description, first mention

logistic regression: first_mention ~ culture + (1 | subject),

mention_df <- data %>% 
  filter(task_name == "FD", resp_type == "first_mention_focal") %>% 
    mutate(first_mention = as.factor(case_when(
    resp == "1" ~ "focal",
    resp == "0" ~ "background")), 
    scene = trial_info) %>% 
  select(-resp, -task_info, -resp_type, -trial_info)

#mention_model <- glmer(first_mention ~ culture + (1 | subject)+(culture | scene), family = binomial, data = mention_df)
#Error: number of observations (=1146) < number of random effects (=1148) for term (scene | subject); the random-effects parameters are probably unidentifiable

mention_model <- glmer(first_mention ~ culture + (1 | subject) , 
                   family = binomial, data = mention_df)

mention_model
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: first_mention ~ culture + (1 | subject)
##    Data: mention_df
##       AIC       BIC    logLik  deviance  df.resid 
##  2870.492  2888.334 -1432.246  2864.492      2825 
## Random effects:
##  Groups  Name        Std.Dev.
##  subject (Intercept) 1.771   
## Number of obs: 2828, groups:  subject, 244
## Fixed Effects:
## (Intercept)    cultureUS  
##      0.3629       3.0427
summary(mention_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: first_mention ~ culture + (1 | subject)
##    Data: mention_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   2870.5   2888.3  -1432.2   2864.5     2825 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1413 -0.4763  0.1452  0.4924  2.0996 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  subject (Intercept) 3.137    1.771   
## Number of obs: 2828, groups:  subject, 244
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.3629     0.1525   2.380   0.0173 *  
## cultureUS     3.0427     0.3391   8.971   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## cultureUS -0.434
summary(mention_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: first_mention ~ culture + (1 | subject)
##    Data: mention_df
## 
##      AIC      BIC   logLik deviance df.resid 
##   2870.5   2888.3  -1432.2   2864.5     2825 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1413 -0.4763  0.1452  0.4924  2.0996 
## 
## Random effects:
##  Groups  Name        Variance Std.Dev.
##  subject (Intercept) 3.137    1.771   
## Number of obs: 2828, groups:  subject, 244
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.3629     0.1525   2.380   0.0173 *  
## cultureUS     3.0427     0.3391   8.971   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## cultureUS -0.434

Free Description, focal vs background description (currently not coded)

poisson: description_num ~ description_type * culture + (description_type | subject) + (culture | scene)