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Table 1. 
A linear mixed effects regression (including a by-subject random intercept to account for repeated within-subjects measurements) predicting Log W from Tsimane’ education level and task (computer vs. card version).
AICBIClogLikdeviancedf.resid
401.8 423.7 −194.9 389.8 276 
 
Scaled residuals: 
Min 1Q Median 3Q Max 
−2.1824 −0.6418 −0.0226 0.4943 4.9975 
 
Random effects: 
Groups Name Variance SD  
Subject (Intercept) 0.02481 0.1575  
Residual  0.20978 0.4580  
Number of obs: 282, groups: subject, 141 
 
Fixed effects: 
 Estimate SE t value  
(Intercept) −1.252289 0.041399 −30.249  
Education −0.042551 0.008400 −5.066  
task1 −0.165655 0.037229 −4.450  
Education:task1 0.031667 0.007554 4.192  
 
Correlation of fixed effects: 
 (Intr) Eductn task1  
Education −0.681    
task1 0.000 0.000 −0.681  
AICBIClogLikdeviancedf.resid
401.8 423.7 −194.9 389.8 276 
 
Scaled residuals: 
Min 1Q Median 3Q Max 
−2.1824 −0.6418 −0.0226 0.4943 4.9975 
 
Random effects: 
Groups Name Variance SD  
Subject (Intercept) 0.02481 0.1575  
Residual  0.20978 0.4580  
Number of obs: 282, groups: subject, 141 
 
Fixed effects: 
 Estimate SE t value  
(Intercept) −1.252289 0.041399 −30.249  
Education −0.042551 0.008400 −5.066  
task1 −0.165655 0.037229 −4.450  
Education:task1 0.031667 0.007554 4.192  
 
Correlation of fixed effects: 
 (Intr) Eductn task1  
Education −0.681    
task1 0.000 0.000 −0.681  

Note: summary(lmer(W_value_lg ∼ Education * task + (1 | subject), REML=F, data=gathered_d)) Linear mixed model fit by maximum likelihood [’lmerMod’] Formula: W_value_lg ∼ Education * task + (1 | subject)

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