Lucky or clever? From expectations to responsibility judgments.

Area of Science:

• Cognitive Science
• Social Psychology
• Computational Modeling

Background:

• Understanding how individuals attribute responsibility for actions and their consequences is fundamental to social cognition.
• Existing models often struggle to integrate the agent's inferred characteristics with the causal structure of events.

Purpose of the Study:

• To propose and validate a computational model for attributing responsibility.
• To explain how observed actions inform judgments about a person's character and causal contribution to outcomes.

Main Methods:

• Developed a computational model integrating character inference and causal role assessment.
• Utilized a counterfactual approach to quantify the pivotal nature of an action.
• Conducted three experiments to test the model's predictions against human responsibility judgments.

Main Results:

• The model accurately predicts responsibility judgments across diverse scenarios.
• Responsibility is a function of the inferred change in expectation about a person's behavior (prior vs. posterior).
• The model resolves discrepancies in the literature regarding action expectations and responsibility.

Conclusions:

• Observed actions shape responsibility attributions by revealing aspects of an individual's disposition.
• The degree of an action's causal impact, particularly its counterfactual status, influences blame and credit.
• The model provides a unified framework for understanding responsibility judgments, accounting for both agent characteristics and event causality.