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The Modified Temptation Resistance Task: A Paradigm to Elicit Children's Strategic Lie-telling

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Moral learning: Psychological and philosophical perspectives.

Fiery Cushman¹, Victor Kumar², Peter Railton³

¹Department of Psychology, Harvard University, United States.

|June 21, 2017

Summary

Recent research explores how humans learn moral judgment and behavior, integrating machine learning techniques like Bayesian inference and reinforcement learning to understand moral development.

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Area of Science:

Cognitive Science
Moral Psychology
Machine Learning

Background:

Extensive research has focused on the cognitive and affective mechanisms of moral judgment and behavior.
A critical gap existed in understanding the learning processes underlying these moral capacities.

Discussion:

This special issue highlights emerging research on moral learning, applying formal methods from machine learning.
It examines how social and moral learning may involve specialized cognitive systems.
The review situates new findings within the broader context of moral psychology.

Key Insights:

Novel models integrate Bayesian inference, reinforcement learning, and other machine learning techniques to explain moral learning.
Research investigates the acquisition of moral values, rules, and understanding of character and relationships.
The study addresses the philosophical implications of computational models of moral development.

Outlook:

Future research will likely continue to integrate computational approaches with cognitive and affective science.
Understanding moral learning is crucial for a comprehensive theory of human morality.
This work opens new avenues for studying the development and evolution of moral cognition.