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An associative account of collective learning
Matthew Gildea1, Cristina Santos1,2, Federico Sanabria1
1Department of Psychology, Arizona State University, Tempe, AZ 85287, USA.
Royal Society Open Science
|March 27, 2025
Summary
Collective learning, where animals learn from group experience, shows benefits when tasks change. Tracking group members enhances learning and performance in dynamic environments.
Area of Science:
- Animal behavior
- Cognitive science
- Computational neuroscience
Background:
- Associative learning is a conserved adaptive mechanism studied in isolation.
- Collective learning, or group knowledge acquisition, is less understood.
- Existing research focuses more on individual social learning than group learning dynamics.
Purpose of the Study:
- To model collective learning by integrating associative learning and collective intelligence.
- To explore conditions and mechanisms for the emergence of collective learning.
- To investigate group training advantages over individual training.
Main Methods:
- Developed a parsimonious model of collective learning.
- Incorporated a Rescorla-Wagner-based associative learning rule where conspecific actions are cues.
- Utilized a horse-race action selection rule for simulations.
Main Results:
- No group training advantage observed in a simple discrimination task (A+/B-).
- A significant group-training advantage emerged when the discrimination task was reversed (A-/B+).
- Model predictions indicate superior learning and performance in dynamic environments through tracking conspecifics.
Conclusions:
- Collective learning can outperform individual learning, especially in changing environments.
- Tracking the actions of conspecifics aids in adapting to reversed discrimination tasks.
- The developed model provides insights into the emergence and mechanisms of collective learning.
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