Figure 6.

Predicting specific player’s emotions. Human observers made preference and belief attributions to the 20 SpecificPlayers, based on a photo, brief description and decision, in the ‘Split or Steal’ game. (a) Based on what a SpecificPlayer was judged to care about and to expect, the models generated predictions of that player’s emotion reaction in 24 ‘Split or Steal’ games (four outcomes and eight pots). Bar colours in (b–e) correspond to the models in (a), and grey windows give the 95% bootstrap CI of the inter-rater reliability of the emotion predictions. (b) Concordance between predictions generated by the models and human observers for every emotion (collapsing across players, outcomes and pot sizes). (c) Overall fit the emotions observers predicted for the 20 SpecificPlayers. (d) The photos and descriptions of SpecificPlayers biased human observers’ judgements of the players’ motivations, expectations and emotional reactions. This plot shows how well the models were able to predict the bias in emotion predictions based on observers’ judgements of a player’s preferences and belief. Players are ordered based on how reliably observers’ emotion predictions differed from the emotions predicted for the GenericPlayers (grey windows). The model score gives the variance-scaled Pearson correlation. (e) Correlation between the relative difference predicted by the models and the relative difference in observers’ emotion predictions. (b,c,e) Each bar reflects a model’s performance based on $n=12\hspace{0.17em}096$ emotion predictions of $n=1\hspace{0.17em}512$ observers. ($d$) Each bar reflects a model’s performance based on a minimum of $n=579$ empirical predictions of all 20 emotions.