Confirmation Bias as a Mechanism to Focus Attention Enhances Signal Detection

Confirmation bias has been traditionally seen as a detrimental aspect of the human mind, but recently researchers have also argued that it might be advantageous under certain circumstances. To test this idea, we developed a minimally complex agent-based model in which agents detect binary signals. Compared to unbiased agents, biased agents have a higher chance to detect the signal they are biased for, and a lower chance to detect other signals. Additionally, detecting signals is associated with benefits, while missing signals is associated with costs. Given this basic assumptions, biased agents perform better than unbiased agents in a wide variety of possible scenarios. Thus, we can show that confirmation bias increases the fitness of agents in an evolutionary algorithm. We conclude that confirmation bias sensitizes agents towards a certain type of data, which allows them to detect more signals. We discuss our findings in relation to topics such as polarization of opinions, the persistence of first impressions, and the social theory of reasoning.