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The HoneyComb Paradigm for Research on Collective Human Behavior
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A Cognitive Computational Approach to Social and Collective Decision-Making.

Alan N Tump1,2, Dominik Deffner1,2, Timothy J Pleskac3

  • 1Center for Adaptive Rationality, Max Planck Institute for Human Development.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|September 6, 2023
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Summary
This summary is machine-generated.

Understanding social influence requires examining cognitive mechanisms. This study links individual cognition and group dynamics using computational models, revealing feedback loops between personal decision-making and social behavior.

Keywords:
cognitive modelingcollective dynamicsdrift-diffusion modelgroupsreinforcement learning

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Area of Science:

  • Cognitive Science
  • Social Psychology
  • Computational Neuroscience

Background:

  • Collective dynamics are crucial for decision-making, but their outcomes (adaptive vs. maladaptive) depend on underlying cognitive mechanisms.
  • Social influence can lead to accurate information spread or maladaptive social contagion.
  • Linking individual cognition to collective behavior is a key challenge in understanding social dynamics.

Purpose of the Study:

  • To mechanistically link individual cognitive processes with emergent collective dynamics.
  • To demonstrate the utility of a cognitive computational approach for studying social influence.
  • To investigate the bidirectional relationship between individual cognition and social systems.

Main Methods:

  • Utilizing cognitive modeling in conjunction with interactive group experiments.
  • Applying evidence-accumulation and reinforcement-learning models to analyze collective behavior.
  • Simultaneously examining how individual cognition influences social systems and vice versa.

Main Results:

  • Demonstrated that cognitive models can bridge individual and collective levels of analysis.
  • Showcased the power of computational approaches in understanding social decision-making.
  • Provided a framework for studying the dynamic feedback between individual and social cognition.

Conclusions:

  • Cognitive modeling offers a powerful approach to mechanistically understand collective dynamics.
  • The interplay between individual cognition and social systems is bidirectional and dynamic.
  • This framework facilitates the study of adaptive and maladaptive social behaviors.