Pride occurs in every known culture, appears early in development, is reliably triggered by achievements and formidability, and causes a characteristic display that is recognized everywhere. Here, we evaluate the theory that pride evolved to guide decisions relevant to pursuing actions that enhance valuation and respect for a person in the minds of others. By hypothesis, pride is a neurocomputational program tailored by selection to orchestrate cognition and behavior in the service of: (i) motivating the costeffective pursuit of courses of action that would increase others' valuations and respect of the individual, (ii) motivating the advertisement of acts or characteristics whose recognition by others would lead them to enhance their evaluations of the individual, and (iii) mobilizing the individual to take advantage of the resulting enhanced social landscape. To modulate how much to invest in actions that might lead to enhanced evaluations by others, the pride system must forecast the magnitude of the evaluations the action would evoke in the audience and calibrate its activation proportionally. We tested this prediction in 16 countries across 4 continents (n = 2,085), for 25 acts and traits. As predicted, the pride intensity for a given act or trait closely tracks the valuations of audiences, local (mean r = +0.82) and foreign (mean r = +0.75). This relationship is specific to pride and does not generalize to other positive emotions that coactivate with pride but lack its audience-recalibrating function.pride | valuation | decision-making | emotion | culture

When explaining an event, people tend to select a single cause out of the multiple factors that contributed -- for instance, they will say that a forest fire was caused by a lit match, without mentioning the oxygen in the air which helped fuel the fire. Recently scholars have suggested that causal selection is designed to provide explanations that are likely to generalize across a variety of background circumstances. Here, we develop a computational model of causal selection which formalizes this idea. Under minimal assumptions, the model is surprisingly simple: a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal selection is influenced by the normality of candidate causes, and outperforms other known computational models when tested against a fine-grained dataset of human graded causal judgments.

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