Increased exposure to environmental risk factors alters neural oscillatory activity during language learning in children.

• In Event: Poster Session 14
  In Poster Session: PS 14 Topic - Language, Communication

Abstract

Many language studies demonstrate that children from low-income homes tend to have poorer language outcomes, resulting in lower levels of school readiness and academic achievement (McLaughlin & Sheridan, 2016). Children from such homes tend to experience a greater number of environmental factors causing stress (risk factors). This exposure affects neural structure and function, creating differences in children in high-risk environments compared to their peers (Kluczniok & Mudiappa, 2018). These neural effects may differentially impact processes supporting word-learning (Ralph et al., 2020), in ways not captured when examining SES effects in general. By using time-frequency analysis, differences in cognitive strategies for word learning can be quantified through neural oscillatory activity and correlated with combinations of individual risk factors (Davidson & Indefrey, 2007).

In EEG signals, theta power is thought to increase during retrieval from lexical memory and integration in context, and alpha power decreases with the increased working memory demands of linguistic processing (Prystauka & Lewis, 2019). Differences in spectral activity reflect differences in cognitive processes engaged, thus analysis of these frequencies implicated in word-learning would clarify the neurocognitive basis behind SES-related differences in language development.

Research Goal. I propose using time-frequency analysis, focused on changes in theta and alpha frequencies, to identify event-related spectral perturbations, providing a window into how risk factor exposure influences language processing in children.

Participants included 109 bilingual children between ages 8-15 (M=11.12, SD=2.25). Children were divided into low, medium, and high-risk groups based on the number of risk factors reported out of a possible nine (qualification for free/reduced lunch, home environment score, maternal education, zip code, number of moves in the last year, average hours of sleep, number of household books, average daily reading time, and whether they shared bedrooms; from Kluczniok & Mudiappa, 2018), matched for age and gender. We recorded EEG behavioral responses during a word learning task in which participants were presented with sets of three
sentences ending with the same non-word, and asked to discern this non-word’s meaning in context.

Proposed Analyses. EEG data will be epoched from final word onset to 1000 msec post stimulus. Data will be averaged across trials and subjects, and computed using log power values minus the baseline (Delorme & Makeig, 2004). In the Matlab EEGlab toolbox, we will perform random permutation statistical analysis of the EEG data, computing p-values for time and frequency points for each comparison of interest.

Hypotheses. Previous studies have illustrated a positive linear relationship between risk factor exposure and language outcomes (Chapman, 2004). Thus, we anticipate that our higher-risk group will produce fewer correct responses on the word learning task, indicating fewer words learned, and exhibit differences in theta and alpha activation compared to our lower-risk groups.

Importance of the work. This study takes important steps to clarify factors that affect word learning and what their particular neural ramifications are. These results would provide evidence for a fundamental academic advantage afforded to children exposed to fewer risk factors that could lead their higher-risk peers to fall behind.

Authors

• Analia Marzoratti, University of Texas at Dallas
  Presenting Author

• Yvonne Ralph, University of Texas at Tyler
  Non-Presenting Author

• Mandy J Maguire, University of Texas at Dallas
  Non-Presenting Author