The new DRL framework groups abilities into three parts: the raw sensory data coming from the ear, the cognitive resources available to process that input, and the language knowledge that helps predict and interpret meaning. Thinking about perception this way gives researchers a map for why two people might react differently to the same noisy sentence. It also helps clinicians and designers pinpoint whether difficulty arises from reduced signal clarity, mental workload limits, or gaps in linguistic experience.
For anyone interested in human potential, inclusivity, or better communication design, the implications are wide. The framework suggests pathways to make listening environments fairer, to tailor rehabilitation, and to design devices and learning tools that match a person’s profile. Follow the link to see how these ideas tie into hearing loss, second-language listening, and everyday strategies that expand who can join a conversation.
Research on ‘cognitive listening’ has grown exponentially in recent years. Lacking, however, is a conceptual framework to organize the abundance of data from the hearing, cognitive, and linguistic sciences. We offer the data-resource-language (DRL) framework that draws from the notions of data-limited and resource-limited processes to provide a roadmap for understanding the interaction between auditory sensitivity, cognitive resources, and linguistic knowledge during speech perception, especially in adverse conditions. The DRL framework explains how these three sets of abilities predict performance and resource engagement as a function of signal quality. It also provides a platform for characterizing similarities and differences in how normal-hearing, impaired-hearing, and non-native listeners process speech in challenging conditions.