CogLunch: Setayesh Radkani "What people learn from punishment: Joint inference of wrongness and punisher’s motivations"
Description
Speaker: Setayesh Radkani
Affiliation: SaxeLab & JazLab
Title: What people learn from punishment: Joint inference of wrongness and punisher’s motivations
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. The expansive literature on punishment often treats and studies these inferences separately. We propose that the key to understanding these seemingly contradictory consequences of punishment is to consider them simultaneously. We further propose a computational modeling framework that unifies the communicative and reptutational aspects of punishment, by modeling observers as performing inverse planning using an intuitive mental model of how authorities make punitive decisions. Across three studies, we validate this model of what observers learn from punishment. Finally, we discuss the implications of this modeling framework for thinking about punishment in real-life contexts.
Location: 46-3310