Speaker: Setayesh Radkani

Title: What people learn from punishment

Abstract: When a parent or a judge chooses to punish, they often intend to show that, and how much, the punished act was wrong. However, in light of every act of punishment, targets and observers evaluate not only the action that elicited the punishment, but also the motives and legitimacy of the authority who punished. Both in real life and laboratory settings, the same punishment can lead to contrasting and even contradictory consequences in terms of changing others’ beliefs about undesirability of the act, as well as the motivations and legitimacy of the authorities. We propose that in order to explain these seemingly discrepant findings, these two inferences should not be treated independently. We developed an experimental paradigm to control and study these inferences simultaneously, and showed that these two inferences indeed depend, with exquisite sensitivity, on one another. Further, we proposed and validated a computational framework to explain such contrasting inferences parsimoniously, modeling observers as making rational joint inferences of wrongness and punisher’s motivation by inverting a Bayesian causal model of how authorities make punitive decisions. I will end by outlining the exciting future directions that arise from this work.

Zoom link: https://mit.zoom.us/j/8796050369