Brain-behavior relationships in incidental learning of non-native phonetic categories

Luthra, Sahil; Fuhrmeister, Pamela; Molfese, Peter J.; Guediche, Sara; Blumstein, Sheila E.; Myers, Emily B.

doi:10.1016/j.bandl.2019.104692

Cited by 15 publications

(6 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lim et al (2019) recognized that participants were able to incidentally learn auditory categories during videogame play without direct attention to them, the striatum was engaged during this practice, and the control of striatal activity and connectivity differed in both experimental and control groups, which evidenced the sensitivity of the striatal learning system. The results of both Luthra et al (2019) and Lim et al (2019) confirmed the efficacy of incidental learning at the level of brain functions that emphasizes the viability of this method as means of vocabulary acquisition. All of these studies showed that the use of incidental learning among ELLs is an effective approach when acquiring new L2 vocabulary.…”

Section: Figure 2 Comparison Among Modes Of Vocabulary Acquisitionsupporting

confidence: 56%

“…However, inquiries involved in neuroscience attempt to shed light on the efficacy of incidental learning when this experience is not monitored. For example, a study analyzing the neuroscientific explanation of incidental learning among students showed that during this practice, frontal lobe parts of the brain are engaged after learning (Luthra et al, 2019). The study used functional MRI scans that linked the ability to retain novel phonetic category information to the function of frontal brain regions, even when students were not provided explicit category labels (Luthra et al, 2019).…”

Section: Figure 2 Comparison Among Modes Of Vocabulary Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Cartoons as the Incidental Vocabulary Acquisition Tool for English Language Learners

Singer¹

2022

AWEJ

View full text Add to dashboard Cite

Incidental learning is a well-known process of acquiring new knowledge, vocabulary, or information without intention. Traditional or intentional learning is widely practiced in classroom environments, while incidental one receives less attention from educators, schools, and scholars. English Language Learners encounter various problems when they start learning the second language, including culture shock, lack of understanding of foreign culture, different contexts, and ineffective teaching strategies implemented by teachers. Many English Language Learners cannot communicate in a second language outside the classroom, yet, they are exposed to a variety of media, including cartoons and animated films, when they are at home. By using cartoons as the means of incidental learning among English Language Learners to acquire second language vocabulary, students could learn a foreign language faster. The current paper critically analyzes the benefits of incidental learning, the evidence of its effective implementation in the classroom with English Language Learners, and the results of previous studies using cartoons as the method of incidental learning among students, including English Language Learners. The paper recommends using incidental learning as one of the strategies to acquire second language vocabulary among English Language Learners. However, teachers must pair this practice with intentional learning to retain the results. Also, it is recommended to use cartoons with subtitles for students with basic or limited knowledge of a second language for effective accommodation and scaffolding of English Language Learners in general classrooms.

show abstract

Section: Figure 2 Comparison Among Modes Of Vocabulary Acquisitionsupporting

confidence: 56%

Section: Figure 2 Comparison Among Modes Of Vocabulary Acquisitionmentioning

confidence: 99%

Cartoons as the Incidental Vocabulary Acquisition Tool for English Language Learners

Singer¹

2022

AWEJ

View full text Add to dashboard Cite

show abstract

“…While adults require some amount of feedback to learn (Chandrasekaran et al, 2015;Chandrasekaran, Yi, et al, 2014;Lim & Holt, 2011;McClelland et al, 2002;Tricomi et al, 2006;Yi et al, 2016), adults can acquire speech categories incidentally (Y. Gabay et al, 2015;Lim et al, 2019;Luthra et al, 2019) or with task-irrelevant feedback (Goudbeek et al, 2008;McClelland et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry

McHaney

Tessmer

Roark

et al. 2021

Preprint

View full text Add to dashboard Cite

Across two experiments, we examine the relationship between individual differences in working memory (WM) and the acquisition of non-native speech categories in adulthood. While WM is associated with individual differences in a variety of learning tasks, successful acquisition of speech categories is argued to be contingent on WM-independent procedural-learning mechanisms. Thus, the role of WM in speech category learning is unclear. In Experiment 1, we show that individuals with higher WM acquire non-native speech categories faster and to a greater extent than those with lower WM. In Experiment 2, we replicate these results and show that individuals with higher WM use more optimal, procedural-based learning strategies and demonstrate more distinct speech-evoked pupillary responses for correct relative to incorrect trials. We propose that higher WM may allow for greater stimulus-related attention, resulting in more robust representations and optimal learning strategies. We discuss implications for neurobiological models of speech category learning.

show abstract

“…For instance, we assume learning only occurs during overt training. Instead, implicit learning experiments suggest that categorization performance can improve through training without explicit knowledge of the categorical structure (Luthra et al, 2019). However, our results indicate that categorization responses exhibit the greatest change during training where feedback on the interval category is present (Fig.…”

Section: Discussionmentioning

confidence: 56%

Single-trial neural dynamics influence auditory category learning

Mankel

Bidelman

2020

Preprint

View full text Add to dashboard Cite

Percepts are naturally grouped into meaningful categories to process continuous stimulus variations in the environment. Theories of category acquisition have existed for decades, but how they arise in the brain due to learning is not well understood. Here, advanced computational modeling techniques borrowed from educational data mining and cognitive psychology were used to trace the development of auditory categories within a short-term training session. Nonmusicians were rapidly trained for 20 min on musical interval identification (i.e., minor and major 3rd interval dyads) while their brain activity was recorded via EEG. Categorization performance and neural responses were then assessed for the trained (3rds) and novel untrained (major/minor 6ths) continua. Computational modeling was used to predict behavioral identification responses and whether the inclusion of single-trial features of the neural data could predict successful learning performance. Model results revealed meaningful brain-behavior relationships in auditory category learning detectible on the single-trial level; smaller P2 amplitudes were associated with a greater probability of correct interval categorization after learning. These findings highlight the nuanced dynamics of brain-behavior coupling that help explain the temporal emergence of auditory categorical learning in the brain.

show abstract

Brain-behavior relationships in incidental learning of non-native phonetic categories

Cited by 15 publications

References 53 publications

Cartoons as the Incidental Vocabulary Acquisition Tool for English Language Learners

Cartoons as the Incidental Vocabulary Acquisition Tool for English Language Learners

Working memory relates to individual differences in speech category learning: Insights from computational modeling and pupillometry

Single-trial neural dynamics influence auditory category learning

Contact Info

Product

Resources

About