Information Selection in Noisy Environments with Large Action Spaces

Pedro TsividisMassachusetts Institute of Technology, Cambridge, Massachusetts, USA
Samuel GershmanMassachusetts Institute of Technology, Cambridge, Massachusetts, USA
Josh TenenbaumMassachusetts Institute of Technology, Cambridge, Massachusetts, USA
Laura SchulzMassachusetts Institute of Technology, Cambridge, Massachusetts, USA

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 reliability - the 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.

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