Disinformation elicits learning biases.

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Social Psychology

Background:

• Disinformation poses a threat to democratic societies.
• Understanding how disinformation impacts individual learning processes is crucial.
• Existing research lacks insight into the cognitive mechanisms through which disinformation influences learning.

Purpose of the Study:

• To investigate how learning from potential disinformation aligns with or deviates from ideal Bayesian learning principles.
• To identify specific cognitive biases that emerge when individuals learn from sources disseminating disinformation.
• To computationally model learning processes in the presence of disinformation.

Main Methods:

• Utilized a reinforcement learning framework and computational modeling.
• Conducted two experiments involving a two-armed bandit task with feedback from sources of varying credibility.
• Participants received outcome feedback, with disinformation introduced by unreliable sources misrepresenting true outcomes.

Main Results:

• Learning increased with source credibility, aligning with ideal Bayesian principles.
• Observed significant deviations from Bayesian learning, including learning from completely unreliable sources.
• Disinformation led to exaggerated learning from trustworthy sources and increased positivity bias.

Conclusions:

• Disinformation exploits cognitive learning biases, leading to suboptimal information processing.
• Identified specific mechanisms of learning bias, such as overreliance on unreliable sources and positivity bias.
• Findings have implications for developing societal strategies to mitigate the harmful effects of disinformation.