Doctoral Dissertation Research: Disentangling neural indices of implicit vs. explicit second language processing

University Of Illinois At Chicago, Chicago IL

Investigators

Abstract

Given the increasing relevance of second language (L2) proficiency in a globalizing world as well as the benefits that multilingualism might confer for individuals' socioeconomic, cognitive, and academic outcomes, improving L2 learning is an area of growing importance. A critical part of this endeavor involves advancing our understanding of the relative contributions of learning that is explicit (i.e., involves conscious awareness) vs. implicit (i.e., does not involve conscious awareness). To investigate the relative contributions of and relationship between explicit and implicit L2 processing, this project combines an artificial language learning experiment with recently-developed machine learning analysis techniques for electroencephalography (EEG), a neurolinguistic method that can detect brain activity related to language processing in a non-invasive way. This will inform theoretical debates between models of language learning that posit that implicit/explicit processing are separated by a strong interface, a weak interface, or no interface. Such an improved understanding of implicit/explicit grammar processing might inform second language pedagogy by suggesting how implicit methods (e.g., naturalistic exposure) and explicit methods (e.g., grammar-oriented drills) should be balanced to maximize learning. Experiment participants will perform a language learning task involving four new words with a hidden grammatical rule (two words usually co-occur with living things and two with non-living things). Afterwards, participants will answer debriefing questions to determine if/when they became aware of the rule. Prior research indicates that even rule-unaware participants show signs of learning, in that rule-violating trials feature distinct neural activity and slower responses when compared to rule-following trials. This project extends such research with machine learning-based analysis techniques which can assess the degree to which different neurocognitive events are similar in the timing and location of brain activity. The first analysis tests whether neural markers of implicit (rule-unaware) grammar processing occur during periods of explicit (rule-aware) processing. The second analysis tests whether implicit and explicit processing differ in the occurrence of linguistic prediction (defined here as the occurrence of neural patterns related to the processing of living words as opposed to non-living words, when participants read the living/non-living-encoding artificial language word in isolation). The third analysis tests whether language task reaction times show a consistent relationship with the timing of neural markers of implicit and explicit processing, to determine whether participants’ external responses are driven by conscious vs. unconscious processing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.