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Learned Insignificance of Credibility Signs.

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Summary

The dual-learning problem, where people assess information and source credibility simultaneously, can lead to delusion-like states. Even rational agents can lose the ability to detect deception in toxic information environments.

Keywords:
Agent‐based modelBayesian reasoningCognitionCredibilityEmergent cognitive biasesMisinformationSocial information

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Area of Science:

  • Cognitive Science
  • Social Psychology
  • Computational Modeling

Background:

  • Humans rely on social information to understand their world.
  • Modern information ecosystems present challenges in identifying deception and misinformation.
  • The dual-learning problem involves simultaneously updating beliefs about the world and source credibility.

Purpose of the Study:

  • Investigate the interaction of social influence and cognitive processing in assessing information credibility.
  • Understand how individuals lose the ability to reliably assess information, even with rational updating and objective deception cues.
  • Explore the emergence and rehabilitation of delusion-like states in response to misinformation.

Main Methods:

  • Utilized an agent-based model, the Reputation Game Simulation.
  • Simulated agents engaging in belief updating and assessing source credibility.
  • Introduced agents with consistent deceptive behavior to model epistemically toxic environments.

Main Results:

  • The dual-learning problem can be overcome with Bayesian reasoning, even with deceit.
  • Consistent deceptive behavior by some agents can cause others to ignore objective credibility cues, leading to delusion-like states.
  • These delusion-like states, where false beliefs resist correction, can be reversed in healthier information environments.

Conclusions:

  • Correcting misinformation is not the sole solution for epistemically toxic environments.
  • Socially induced cognitive biases leading to delusion-like states can be repaired.
  • Rehabilitation requires environments where credibility cues can be relearned without nonepistemic motives.