Learning from outcomes shapes reliance on moral rules versus cost–benefit reasoning

Many controversies arise from disagreements between moral rules and 'utilitarian' cost-benefit reasoning (CBR). Here we show how moral learning from consequences can produce individual differences in people's reliance on rules versus CBR. In a new paradigm, participants (total N = 2,328) faced realistic dilemmas between one choice prescribed by a moral rule and one by CBR. The participants observed the consequences of their decision before the next dilemma. Across four experiments, we found adaptive changes in decision-making over 13 choices: participants adjusted their decisions according to which decision strategy (rules or CBR) produced better consequences. Using computational modelling, we showed that many participants learned about decision strategies in general (metacognitive learning) rather than specific actions. Their learning transferred to incentive-compatible donation decisions and moral convictions beyond the experiment. We conclude that metacognitive learning from consequences shapes moral decision-making and that individual differences in morality may be surprisingly malleable to learning from experience.

Authors

Institutions

Publication Details

Journal
Nature Human Behaviour
Published
2025-08-11
DOI
https://doi.org/10.1038/s41562-025-02271-w
Citations
2
Primary Topic
Psychology of Moral and Emotional Judgment
Type
article
Field-Weighted Citation Impact
2.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Learning from outcomes shapes reliance on moral rules versus cost–benefit reasoning

Vanessa Cheung, Maximilian Maier, Falk Lieder
2 citations
Nature Human Behaviour
Psychology of Moral and Emotional Judgment
2.86
article

Learning from outcomes shapes reliance on moral rules versus cost–benefit reasoning

Vanessa Cheung, Maximilian Maier, Falk Lieder
article en
2 citations

Abstract

Many controversies arise from disagreements between moral rules and 'utilitarian' cost-benefit reasoning (CBR). Here we show how moral learning from consequences can produce individual differences in people's reliance on rules versus CBR. In a new paradigm, participants (total N = 2,328) faced realistic dilemmas between one choice prescribed by a moral rule and one by CBR. The participants observed the consequences of their decision before the next dilemma. Across four experiments, we found adaptive changes in decision-making over 13 choices: participants adjusted their decisions according to which decision strategy (rules or CBR) produced better consequences. Using computational modelling, we showed that many participants learned about decision strategies in general (metacognitive learning) rather than specific actions. Their learning transferred to incentive-compatible donation decisions and moral convictions beyond the experiment. We conclude that metacognitive learning from consequences shapes moral decision-making and that individual differences in morality may be surprisingly malleable to learning from experience.

Nature Human BehaviourVol. 10(2)
University of California, Los Angeles (US), Max Planck Institute for Intelligent Systems (DE), University College London (GB), Maxim Integrated (United Kingdom) (GB)
Princeton University
Peace, Justice and strong institutions
Openalex Percentile: Top 9%
Psychology of Moral and Emotional Judgment
2.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Learning from outcomes shapes reliance on moral rules versus cost–benefit reasoning — Vanessa Cheung, Maximilian Maier, et al. · Nature Human Behaviour (2025) | TGRS Research Map | TGRS