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Accelerating scientific progress through Bayesian adversarial collaboration.

Andrew W Corcoran1, Jakob Hohwy1, Karl J Friston2

  • 1Monash Centre for Consciousness & Contemplative Studies, Monash University, Melbourne, VIC, Australia.

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Summary
This summary is machine-generated.

Adversarial collaboration, a method for resolving scientific disputes, can be expanded beyond theory falsification. A Bayesian belief updating framework offers new tools for neuroscience research and evidence accumulation.

Keywords:
Bayesian inferenceadversarial collaborationevidence accumulationfalsificationmeta-sciencemodel comparison

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Philosophy of Science

Background:

  • Adversarial collaboration is proposed as a robust method for resolving scientific disputes.
  • Its adoption in neuroscience and related fields remains limited, potentially due to a narrow focus on theory falsification.

Purpose of the Study:

  • To advocate for a broader conceptualization of adversarial collaboration in scientific research.
  • To propose a framework based on Bayesian belief updating, model comparison, and evidence accumulation.
  • To enhance the utility of adversarial collaboration for guiding experimental design and data analysis.

Main Methods:

  • The study proposes a shift from falsification-centric adversarial collaboration to a Bayesian framework.
  • This approach incorporates Bayesian belief updating, model comparison, and evidence accumulation.
  • Worked examples illustrate the application of these methods for scoring theoretical models.

Main Results:

  • The proposed Bayesian framework expands the scope of adversarial collaboration to include various informative studies.
  • It provides formal tools for experimental design and data analysis within an adversarial context.
  • The framework allows for the quantitative assessment of empirical support for competing theories over time.

Conclusions:

  • Reconceptualizing adversarial collaboration through a Bayesian lens can overcome limitations in neuroscience.
  • This approach facilitates the integration of diverse studies and provides a common metric for evidence.
  • It offers a powerful method for tracking the evidential support of competing scientific theories.