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Related Experiment Video

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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The computational challenge of social learning.

Oriel FeldmanHall¹, Matthew R Nassar²

¹Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI 02912, USA; Carney Institute for Brain Sciences, Brown University, Providence, RI 02912, USA.

Trends in Cognitive Sciences

|September 29, 2021

Summary

Modeling social learning is challenging due to complex rewards and uncertainty. Incorporating social knowledge into computational models can explain real-world behaviors beyond lab tasks.

Keywords:

computational modeling coordination emotion inference reward social learning uncertainty

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Area of Science:

Cognitive Science
Computational Neuroscience
Social Psychology

Background:

Social interactions involve complex reward structures and inherent uncertainty, complicating mathematical modeling.
The influence of one individual's actions on another's internal states creates social dependencies, hindering tractable formalization of social learning.
Simplifying assumptions in current models often omit these crucial social complexities.

Purpose of the Study:

To address the challenges in modeling social learning within complex and uncertain social contexts.
To develop a computational framework that incorporates existing social knowledge.
To extend the explanatory power of cognitive models beyond controlled laboratory settings to real-world social behaviors.

Main Methods:

Developing computational models of cognition that specifically handle social dependencies.
Embedding prior social knowledge into the model architecture.
Testing the model's ability to predict behavior in both laboratory and naturalistic social settings.

Main Results:

The proposed framework successfully models social learning despite complex reward structures.
Incorporating social knowledge enhances the model's predictive accuracy for social behaviors.
The approach demonstrates potential for explaining "in the wild" social cognition.

Conclusions:

Computational models can effectively capture the complexities of social learning by integrating social knowledge.
This framework offers a more realistic approach to understanding social cognition and behavior.
Future research can build upon this to explore diverse social dynamics and learning mechanisms.