People learn other people's preferences through inverse decision-making

Area of Science:

• Cognitive Science
• Psychology
• Decision Science

Background:

• Individuals infer others' preferences from observed choices.
• This inference process is crucial for social cognition and interaction.

Purpose of the Study:

• To investigate the cognitive mechanisms underlying preference learning from observed choices.
• To propose and test inverse decision-making as a model for inferring preferences.

Main Methods:

• Experiment 1: Participants ranked choices based on inferred preference strength; predictions from an inverse decision-making model were compared.
• Experiment 2 & 3: Replicated and extended previous studies using choice pairs to assess evidence for preferences, with model predictions analyzed.

Main Results:

• An inverse decision-making model successfully predicted participants' inferences about others' preferences across experiments.
• The model explained results that previous theories could not, including judgments on preference evidence.
• Participants did not assume perfect utility-maximization in observed decision-makers.

Conclusions:

• Inverse decision-making provides a robust computational framework for understanding how people learn preferences from observed behavior.
• This approach offers novel insights into social cognition and the interpretation of others' actions.
• The findings challenge assumptions of perfect rationality in models of social inference.