Figure 5.

Inferred emotion predictions. (a) Emotion predictions generated by the ComputedAppraisals model, averaged across players and pot sizes. (b) Example of how the model personalizes emotion predictions. Based on the GenericPlayers data, the model learned that envy is a function of appraisals derived from a player’s aversion to disadvantageous inequity ($\mathrm{base}\hspace{0.17em}DIA$). When a player appraises that he is in a more socially disadvantageous position than he expected to be (negative prediction error), the negative loading on $P{E}_{DIA}^{\mathrm{base}}$ translates to greater envy intensity. Similarly, when a player appraises that he would have been in a less disadvantageous position if his opponent had made the other choice (positive counterfactual), the positive loading on $CFa{2}_{DIA}^{\mathrm{base}}$ translates to greater envy intensity. Given that they chose to cooperate ($a_1=\mathtt{\text{C}}$), the model infers that the engineer cares more about not ending up in an inferior position (${\omega }_{\mathit{D}\mathit{I}\mathit{A}}^{\mathrm{base}}$) than the councilman. When the opposing player defects ($a_2=\mathtt{\text{D}}$), the model predicts greater intensity of envy for the engineer because he is inferred to have a stronger preference. Human observers similarly predicted that the engineer will experience more envy than the councilman in CD games. Photos of the players have been downsampled for the purpose of publication.