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Whom do we prefer to learn from in observational reinforcement learning?
Gota Morishita1, Carsten Murawski1, Nitin Yadav1
1Centre for Brain, Mind and Markets, The University of Melbourne, Parkville, Victoria, Australia.
Plos Computational Biology
|December 8, 2025
Summary
People prefer learning from experts with low decision noise, not those who
Area of Science:
- Cognitive Neuroscience
- Social Psychology
- Behavioral Economics
Background:
- Observational learning is a key aspect of human intelligence.
- Understanding partner selection in social learning is crucial.
- Existing research focuses on the mechanisms, not the preference for whom to learn from.
Purpose of the Study:
- Investigate partner selection in observational learning.
- Test hypotheses regarding decision noise preference.
- Explore the link between learning style and partner choice.
Main Methods:
- A preregistered experimental design.
- Participant preference assessment for learning partners.
- Analysis of decision noise and imitation strategies.
Main Results:
- Most participants preferred learning from low-noise (high-performing) individuals.
- Preference for low-noise partners correlated with imitation behavior.
- Decision noise as a factor in social learning partner selection.
Conclusions:
- Learners favor high-performing partners over those who "free ride" on exploration.
- Imitation learning is associated with a preference for low-noise partners.
- Provides a computational framework for understanding social learning preferences.
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