Figure 4.

Appraisal structure of the intuitive theory of emotion. The $\beta$ weights of the transformation were learned based on the joint distribution of appraisals and the joint distribution of emotion predictions for the GenericPlayers. A Laplace prior over the $\mathit{\beta }$ weights induces a sparse solution. To learn the scale of the prior, we cross-validated on subsets of the SpecificPlayers and generated emotion predictions for the leftout players. Saturation indicates that the 99% CI does not overlap with zero. (a) Mean weights of the learned transformation. (b) These log-scale plots show the expectation, 95% and 99% CI of the weights learned for two example emotions, gratitude and embarrassment.