Cognition-Emotion Interaction during L2 Sentence Comprehension: The Correlation of ERP and GSR Responses to Sense Combinations

This study mainly examined the role of the combination of three senses (i.e., auditory, visual, and tactile) and five senses (i.e., auditory, visual, tactile, olfactory, and gustatory) in the correlation between electrophysiological and electrodermal responses underlying second language (L2) sentence comprehension. Forty subjects did two acceptability judgment tasks, encompassing congruent and semantically/pragmatically incongruent sentences. The event-related potential (ERP) and galvanic skin response (GSR) data for both the target and final words of the sentences were collected and analyzed. The results revealed that there is an interaction between cognitive and emotional responses in both semantically and pragmatically incongruent sentences, yet the timing of the interaction is longer in sentences with pragmatic incongruity due to their complexity. Based on the ERP and GSR correlation results, it was further found that the five-sense combination approach improves L2 sentence comprehension and interest in learning materials yet reduces the level of excitement or arousal. While this approach might be beneficial for some learners, it might be detrimental for those in favor of stimulating learning environments.

Intelligence, emotional intelligence, and emo-sensory intelligence: Which one is a better predictor of university students' academic success?

The primary aim of this study was to determine the role of psychometric intelligence (IQ), emotional intelligence (EQ), and emo-sensory intelligence (ESQ) in university students' academic achievement. To this end, 212 university students at different academic levels, composed of 154 females and 58 males, were asked to complete the Raven's Progressive Matrices, the Bar-On Emotional Quotient Inventory, and the Emo-Sensory Intelligence Scale. Data were then matched with students' Grade Point Averages as a measure of their academic achievement. The results revealed that students' level of IQ and EQ could positively predict their academic achievement. In the case of their ESQ level, its auditory sub-component was found to be a positive predictor of academic success. Results were discussed, and possible implications and applications for increasing students' chances for success were presented.

Vocabulary Repetition Following Multisensory Instruction Is Ineffective on L2 Sentence Comprehension: Evidence From the N400

Putting the principles of multisensory teaching into practice, this study investigated the effect of audio-visual vocabulary repetition on L2 sentence comprehension. Forty participants were randomly assigned to experimental and control groups. A sensory-based model of instruction (i.e., emotioncy) was used to teach a list of unfamiliar vocabularies to the two groups. Following the instruction, the experimental group repeated the instructed words twice, while the control group received no vocabulary repetition. Afterward, their electrophysiological neural activities were recorded through electroencephalography while doing a sentence acceptability judgment task with 216 sentences under acceptable (correct) and unacceptable (pragmatically violated) conditions. A one-way analysis of variance (ANOVA), a multivariate analysis of variance (MANOVA), and a Bayesian repeated-measures ANOVA were used to compare the behavioral and neurocognitive responses [N400 as the main language-related event-related brain potential (ERP) effect] of the two groups. The results showed no significant N400 amplitude difference in favor of any of the groups. The findings corroborated the ineffectiveness of two repetitions preceded by multisensory instruction on L2 sentence comprehension.

Multisensory Input Modulates P200 and L2 Sentence Comprehension: A One-Week Consolidation Phase

Multisensory input is an aid to language comprehension; however, it remains to be seen to what extent various combinations of senses may affect the P200 component and attention-related cognitive processing associated with L2 sentence comprehension along with the N400 as a later component. To this aim, we provided some multisensory input (enriched with data from three (i.e., exvolvement) and five senses (i.e., involvement)) for a list of unfamiliar words to 18 subjects. Subsequently, the words were embedded in an acceptability judgment task with 360 pragmatically correct and incorrect sentences. The task, along with the ERP recording, was conducted after a 1-week consolidation period to track any possible behavioral and electrophysiological distinctions in the retrieval of information with various sense combinations. According to the behavioral results, we found that the combination of five senses leads to more accurate and quicker responses. Based on the electrophysiological results, the combination of five senses induced a larger P200 amplitude compared to the three-sense combination. The implication is that as the sensory weight of the input increases, vocabulary retrieval is facilitated and more attention is directed to the overall comprehension of L2 sentences which leads to more accurate and quicker responses. This finding was not, however, reflected in the neural activity of the N400 component.

Examining the Role of Teachers' Stroking Behaviors in EFL Learners' Active/Passive Motivation and Teacher Success

Due to the important role that teachers’ professional success plays in the effectiveness of their students and the education system in which they are involved, the present study investigated whether teacher stroke can predict teacher success through the mediation of students’ active and passive motivation. For this aim, a group of 437 Iranian university English as a Foreign Language (EFL) students were targeted to respond to the teacher success, teacher stroke, and student motivation questionnaires. The main results of the study, obtained through running correlation and structural equation modeling (SEM), were first, while positive stroke showed a positive correlation with teacher success, it did not directly predict success; yet mediated by active motivation, it was a positive predictor of success; second, while teacher success had no significant relationship with total motivation, it was positively correlated with active and passive motivation, separately; third, in terms of gender differences, for the female participants, stroke, mediated by active motivation, was a better predictor of teacher success; fourth, high scores in positive, verbal, and conditional stroke were in association with high scores in active motivation, which significantly predicted teacher success. Based on the results, it can be concluded that teacher stroke, as an instance of positive teacher interpersonal communication behaviors, increases students’ active motivation for foreign language learning, which in turn results in their higher perceptions of English teachers’ professional success.

FN400 and LPC Responses to Different Degrees of Sensory Involvement: A Study of Sentence Comprehension

The current study tested the likely effect of sensory involvement on the FN400 and late positive complex (LPC) responses to semantic and pragmatic comprehension of English sentences. Fifteen English language learners took part in the event-related potential (ERP) experiment and determined the acceptability of 432 sentences under congruent, semantically incongruent, and pragmatically incongruent conditions. Prior to the ERP recording, the subjects received different sensory instructions for six vocabulary items about which they had no previous knowledge. No sensory instruction was given for three extra words, and these served as the control group. The behavioral data corroborated that integration of more senses in instruction improved learners' pragmatic comprehension. The ERP data revealed that full sensory involvement (involvement) reduced the FN400 amplitude, facilitating real world knowledge retrieval and pragmatic comprehension. The LPC responses to semantic comprehension showed that learners reanalyzed the sentences instructed through limited sensory involvement (exvolvement) more deeply.

0438 Automatic Nighttime Agitation and Sleep Disruption Detection Using a Wearable Ankle Device and Machine Learning

Introduction

Nighttime agitation behavior such as wandering and restlessness during awake and sleep in people with Alzheimer’s disease (AD) is expensive to manage and adversely affects sleep. Nighttime agitation is mostly noted by subjective caregiver reports. An automated process for this assessment would improve clinical management. Here we report on the RestEaZeTM system that uses an ankle band and machine learning to automatically classify sleep status and nighttime agitation behaviors in older adults with AD.

Methods

We collected data on 7 adults (mean: 81 years, SD: 10.6) with AD. They wore the RestEaZeTM ankle band with a 3-axis accelerometer, a 3-axis gyroscope, and three textile capacitive sensors. A trained Research Assistant (RA) continuously observed for wandering, restlessness, wake, and sleep between 5pm and 7am using the Cohen Mansfield Agitation Inventory (CMAI). We merged, and band-pass filtered the data and divided it into 10-second non-overlapping windows. CMAI labels and time-series features (scaled using StandardScaler) extracted from the RestEaZeTM data were used to train a Random Forest binary classifier. The significant features were extracted based on the impact on the p-value for the classifier. We used the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and performed 5-fold cross-validation with a 67-33 train-test split.

Results

We report the sensitivity, specificity, accuracy, and Area-under-the Curve (AUC) for the ROC curve for the classifiers: (1) Sleep/Awake: sensitivity=0.95, specificity=0.87, accuracy=0.92, AUC=0.97; (2) Wandering/Non-Wandering: sensitivity=0.85, specificity=0.99, accuracy=0.98, AUC=0.99; and (3) Restless/Non-Restless: sensitivity=0.84, specificity=0.84, accuracy=0.84, AUC=0.92. The significant features were related to the intensity of movements.

Conclusion

Our preliminary results show the feasibility of using RestEaZeTM for quantitatively measuring nighttime agitation. These can provide clinically useful objective measures of agitation that can be automatically transmitted to clinical or research records with minimal staff time requirements.

Support

The authors acknowledge the funding support from the National Institute on Aging under award R01AG051588 and Arbor Pharmaceuticals for support for Horizant and the matching placebo.

Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation

PURPOSE

The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for development of deep learning algorithms for automatic recognition of a normal pancreas.

MATERIALS AND METHODS

Dual-phase contrast-enhanced volumetric CT acquired from 2005 to 2009 from potential kidney donors were retrospectively assessed. Four trained human annotators manually and sequentially annotated 22 structures in each datasets, then expert radiologists confirmed the annotation. For efficient annotation and data management, a commercial software package that supports three-dimensional segmentation was used.

RESULTS

A total of 1150 dual-phase CT datasets from 575 subjects were annotated. There were 229 men and 346 women (mean age: 45±12years; range: 18-79years). The mean intra-observer intra-subject dual-phase CT volume difference of all annotated structures was 4.27mL (7.65%). The deep network prediction for multi-organ segmentation showed high fidelity with 89.4% and 1.29mm in terms of mean Dice similarity coefficients and mean surface distances, respectively.

CONCLUSIONS

A reliable data collection/annotation process for abdominal structures was developed. This process can be used to generate large datasets appropriate for deep learning.