Information Selection in Noisy Environments with Large Action Spaces

Abstract

A critical aspect of human cognition is the ability to effectively query the environment for information.The 'real' world is large and noisy, and therefore designing effective queries involves prioritizing both scope -the range of hypotheses addressed by the query -and reliabilitythe likelihood of obtaining a correct answer.Here we designed a simple information-search game in which participants had to select an informative query from a large set of queries, trading off scope and reliability.We find that adults are effective information-searchers even in large, noisy environments, and that their information search is best explained by a model that balances scope and reliability by selecting queries proportionately to their expected information gain.