Load data.

Descriptives

How many participants in each condition?

## Source: local data frame [4 x 2]
## 
##   training_condition   n()
##                (chr) (int)
## 1      active_active    48
## 2     active_passive    44
## 3     passive_active    50
## 4    passive_passive    45

How long did each condition take?

## Source: local data frame [4 x 3]
## 
##   training_condition m_train_time m_exp_time
##                (chr)        (dbl)      (dbl)
## 1      active_active    0.6523878   4.718435
## 2     active_passive    0.7422754   4.329580
## 3     passive_active    0.5518863   4.333936
## 4    passive_passive    0.6128059   4.380295

Visualization

Relational pretest broken down by question and condition

Sanity check on entity pretest for all 16 question_shape combinations

Sanity check on entity pretest for shape questions

How do we define chance performance on the entity test? Chance level changes for each question:

  • Rectangle: 2/4 are rectangles
  • Parallelogram: 4/4 are paralleograms
  • Square: 1/4 are squares
  • Rhombus: 2/4 are rhombi

Overall accuracy analysis for both entity and relational tests

Within subjects change scores all shapes

Accuracy on the learned shape

Overall accuracy analysis for both entity and relational tests

Within subjects change scores for learned shape tests

Models (todo)

Predict overall accuracy based on condition and test type

Predict accuracy for learned shape based on condition and test type

Predict change scores for learned shape based on condition and test type

Exploratory Analyses

Look at individual participants

Training time distribution for each condition

Median split on training time for active learning

Overall accuracy analysis collapsing across entity and relational tests shape learned

Overall accuracy for shape learned collapsing across tests

Total difference score analysis

Plot

Dotplot

Model

## 
## Call:
## lm(formula = tot_diff_score ~ training_condition, data = ss)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.252435 -0.063229 -0.002435  0.068994  0.277778 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                        0.036458   0.013803   2.641  0.00897 **
## training_conditionactive_passive  -0.034023   0.019959  -1.705  0.08995 . 
## training_conditionpassive_active  -0.017887   0.019324  -0.926  0.35586   
## training_conditionpassive_passive  0.007192   0.019843   0.362  0.71742   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.09563 on 183 degrees of freedom
## Multiple R-squared:  0.02698,    Adjusted R-squared:  0.01102 
## F-statistic: 1.691 on 3 and 183 DF,  p-value: 0.1705

Total difference score analysis for learned shape

Plot

Dotplot

Model