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A neural network model for the evolution of reconstructive social learning.
Jacob Chisausky1,2, Inès Marguerite Daras1, Franz J Weissing3
1Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, 9747AG, The Netherlands.
Scientific Reports
|April 29, 2025
Summary
Social learning, the process of acquiring information from others, evolves differently based on environmental stability. This study models neural networks to show how social learning strategies adapt, impacting behavior and cultural evolution.
Area of Science:
- Evolutionary biology
- Cognitive science
- Computational neuroscience
Background:
- Social learning is a key adaptation, but its evolutionary dynamics and role in information transmission are poorly understood.
- Existing models often overlook that social learning involves information reconstruction based on individual cognition.
- Understanding the interplay between individual and social learning is crucial for explaining behavioral evolution.
Purpose of the Study:
- To develop a novel modeling framework for the evolution of social learning that incorporates information reconstruction.
- To investigate the interplay between individual learning and various forms of social learning.
- To explore how environmental factors influence the evolution of learning strategies.
Main Methods:
- Developed a computational model integrating neural network evolution with a biologically realistic learning mechanism.
- Conducted individual-based simulations across environments with varying stability.
- Analyzed the impact of different social learning types (guidance vs. instruction) and learning orders on evolutionary outcomes.
Main Results:
- An effective neural network structure rapidly evolved, promoting adaptive behaviors.
- Optimal learning strategies varied with environmental stability: inborn behavior for static environments, individual learning for variable ones, and combined approaches for intermediate stability.
- Evolutionary outcomes were sensitive to the type and sequence of social learning, sometimes leading to alternative evolutionary pathways.
Conclusions:
- The developed modeling framework provides insights into the reconstructive nature of social learning and its evolutionary implications.
- Environmental stability significantly shapes the evolution of learning strategies, favoring different combinations of individual and social learning.
- The complexity of social learning evolution warrants further investigation, particularly regarding its role in cultural evolution.
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