Search Results (16)

Background: Neurodivergent students, including those with Autism Spectrum Disorder (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD), frequently encounter challenges in several areas of foreign language (FL) learning, including vocabulary acquisition. This exploratory study aimed to investigate real-time English as a Foreign Language (EFL) word recognition using eye tracking within the Visual World Paradigm (VWP). Specifically, it examined whether gaze patterns could serve as indicators of successful word recognition, how these patterns varied across three distractor types (semantic, phonological, unrelated), and whether age and vocabulary knowledge influenced visual attention during word processing. Methods: Eye-tracking data were collected from 17 children aged 6–10 years with ADHD or ASD while they completed EFL word recognition tasks. Analyses focused on gaze metrics across target and distractor images to identify percentile-based thresholds as potential data-driven markers of recognition. Group differences (ADHD vs. ASD) and the roles of age and vocabulary knowledge were also examined. Results: Children with ADHD exhibited increased fixations on phonological distractors, indicating higher susceptibility to interference, whereas children with ASD demonstrated more distributed attention, often attracted by semantic cues. Older participants and those with higher vocabulary scores showed more efficient gaze behavior, characterized by increased fixations on target images, greater attention to relevant stimuli, and reduced attention to distractors. Conclusions: Percentile-based thresholds in gaze metrics may provide useful markers of word recognition in neurodivergent learners. Findings underscore the importance of differentiated instructional strategies in EFL education for children with ADHD and ASD. The study further supports the integration of eye tracking with behavioral assessments to advance understanding of language processing in atypical developmental contexts. Full article

Statistical learning (SL), the ability to extract patterns from the environment, has been assumed to play a central role in whole cognition, particularly in language acquisition. Evidence has been gathered, however, from behavioral experiments relying on simplified artificial languages, raising doubts on the generalizability of these results to natural contexts. Here, we tested if SL is affected by the composition of the speech streams by expositing participants to auditory streams containing either four nonsense words presenting a transitional probability (TP) of 1 (unmixed high-TP condition), four nonsense words presenting TPs of 0.33 (unmixed low-TP condition) or two nonsense words presenting a TP of 1, and two of a TP of 0.33 (mixed condition); first under incidental (implicit), and, subsequently, under intentional (explicit) conditions to further ascertain how prior knowledge modulates the results. Electrophysiological and behavioral data were collected from the familiarization and test phases of each of the SL tasks. Behavior results revealed reliable signs of SL for all the streams, even though differences across stream conditions failed to reach significance. The neural results revealed, however, facilitative processing of the mixed over the unmixed low-TP and the unmixed high-TP conditions in the N400 and P200 components, suggesting that moderate levels of entropy boost SL. Full article

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost. Full article

Making judgments of learning (JOLs) can reactively alter memory itself, a phenomenon termed the reactivity effect. The current study recorded electroencephalography (EEG) signals during the encoding phase of a word list learning task to explore the neurocognitive features associated with JOL reactivity. The behavioral results show that making JOLs reactively enhances recognition performance. The EEG results reveal that, compared with not making JOLs, making JOLs increases P200 and LPC amplitudes and decreases alpha and beta power. Additionally, the signals of event-related potentials (ERPs) and event-related desynchronizations (ERDs) partially mediate the reactivity effect. These findings support the enhanced learning engagement theory and the elaborative processing explanation to account for the JOL reactivity effect. Full article

Drawing upon the division of labor between orthographic and phonological information, this study investigated whether and how L2 reading proficiency moderates learners’ reliance on phonological and orthographic information in retrieving word meanings. A total of 136 Chinese collegiate students who learned English as a foreign language (EFL) completed English reading proficiency tests and were divided into higher and lower reading proficiency groups using an extreme-group approach. Behavioral tasks were used to measure the participants’ sensitivity to and processing skills of orthographic and phonological information. The analysis showed that the reliance on phonological and orthographic information differed significantly across L2 reading proficiency groups: The higher reading proficiency group was sensitive to both phonological and orthographic information within words, while the lower reading proficiency group was only sensitive to orthographic information; only orthographic processing skills significantly contributed to the word meaning retrieval of individuals in the higher reading proficiency group, while phonological processing skills were the only predictor for the lower reading proficiency group. These results suggest that the use of phonological and orthographic information vary as a function of L2 learners’ English reading proficiency. Implications regarding the changing patterns of L1 influences and the language-universal and language-specific aspects of word recognition were discussed. Full article

Declarative and probabilistic feedback-based learning was evaluated in 8–12-year-old school-age children with developmental language disorder (DLD; n = 14) and age-matched children with typical development (TD; n = 15). Children performed a visual two-choice word-learning task and a visual probabilistic classification task while their electroencephalogram (EEG) was recorded non-invasively from the scalp. Behavioral measures of accuracy and response to feedback, and electrophysiological responses to feedback were collected and compared between the two groups. While behavioral data indicated poorer performance by children with DLD in both learning paradigms, and similar response patterns to positive and negative feedback, electrophysiological data highlighted processing patterns in the DLD group that differed by task. More specifically, in this group, feedback processing in the context of declarative learning, which is known to be dominated by the medial temporal lobe (MTL), was associated with enhanced N170, an event-related brain potential (ERP) associated with MTL activation. The N170 amplitude was found to be correlated with declarative task performance in the DLD group. During probabilistic learning, known to be governed by the striatal-based learning system, the feedback-related negativity (FRN) ERP, which is the product of the cortico-striatal circuit dominated feedback processing. Within the context of probabilistic learning, enhanced N170 was associated with poor learning in the TD group, suggesting that MTL activation during probabilistic learning disrupts learning. These results are interpreted within the context of a proposed feedback parity hypothesis suggesting that in children with DLD, the system that dominates learning (i.e., MTL during declarative learning and the striatum during probabilistic learning) dominates and supports feedback processing. Full article

Previous studies have confirmed that different degrees of expectation, including the bipolarity of the expected and unexpected, as well as an intermediate level (no expectation), can affect memory. However, only a few investigations have manipulated expectation through experimentally established schema, with no consideration of how expectation impacts both item and source memory. Furthermore, stimulus emotionality may also impact memory. Therefore, we conducted a study to investigate the effects of three levels of expectation on item and source memory while considering the impact of stimulus emotionality. The experiment began with a phase dedicated to learning the rules. In the subsequent study phase, negative and neutral words were manipulated as expected, no expectation, and unexpected, based on these rules. This was followed by tasks focused on item and source memory. The study found that there was a “U-shape” relationship between expectation and item memory. Additionally, the study revealed the distinct impacts of expectation on item and source memory. When it came to item memory, both expected and unexpected words were better remembered than those with no expectations. In source memory, expected words showed memory inferiority for expectation-irrelevant source information, but an advantage for expectation-relevant source information. Stimulus emotionality modulated the effect of expectation on both item and source memory. Our findings provide behavioral evidence for the schema-linked interactions between medial prefrontal and medial temporal regions (SLIMM) theory, which proposes that congruent and incongruent events enhance memory through different brain regions. The different patterns between item and source memory also support dual-process models. Moreover, we speculate that processing events with varying levels of emotionality may undermine the impact of expectation, as implied by other neural investigations. Full article

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids. Full article

Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user’s behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN–BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models’ performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN–BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter’s 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN–BiLSTM. Full article

Value-directed strategic processing involves attending to higher-value information while inhibiting lower-value information. This preferential processing is relatively preserved in cognitively normal older adults but is impaired in individuals with dementia. No studies have investigated whether value-directed strategic processing is disrupted in earlier stages of cognitive decline, namely, mild cognitive impairment (MCI). The current study examined behavioral and EEG differences in value-directed strategic processing between 18 individuals with MCI and 18 cognitively normal older controls using a value-directed list learning task. Behaviorally, individuals with MCI recalled fewer total and high-value words compared to controls, but no group differences were observed in low-value word recall. Neurally, individuals with MCI had reduced theta synchronization relative to controls between 100 and 200 ms post-stimulus. Greater alpha desynchronization was observed for high- versus low-value words between 300 and 400 ms in controls but not in the MCI group. The groups showed some processing similarities, with greater theta synchronization for low-value words between 700 and 800 ms and greater alpha desynchronization for high-value words between 500 and 1100 ms. Overall, value-directed strategic processing was compromised in individuals with MCI on both behavioral and neural measures relative to controls. These findings add to the growing body of literature on differences between typical cognitive aging and MCI. Full article

There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models. Full article

The present study deals with the perception (identification and discrimination) of an English phonemic contrast (/t∫/–/∫/, as in cheat and sheet) by speakers of two Mexican varieties of Spanish who are learning English as a foreign language. Unlike English, Spanish does not contrast /t∫/ and /∫/ phonemically. Most Spanish varieties have [t∫], but not [∫]. In northwestern Mexico, [∫] and [t∫] find themselves in a situation of “free” variation—perhaps conditioned, to some extent, by social factors, but not in complementary distribution. In this variety, [∫] and [t∫] are variants of the same phoneme. The present study compares the perceptual behavior of English learners from northwestern Mexico, with that of learners from central Mexico, whose native dialect includes only [t∫]. The results of a word-categorization task show that both groups of learners find cheat and sheet difficult to identify in the context of each other, but that, relative to the other learner group, the group of learners in northwestern Mexico find this task to be particularly challenging. The results of a categorical discrimination task show that both learner groups find the members of the /t∫/–/∫/ contrast difficult to discriminate. On average, accuracy is lower for the group of learners in northwestern Mexico than it is for the central Mexicans. The findings suggest that the phonetic variants found in one’s native dialect modulate the perception of nonnative sounds and, consequently, that people who speak different regional varieties of the same language may face different obstacles when learning the sounds of their second language. Full article

Learning a second language (L2) presents a significant challenge to many people in adulthood. Platforms for effective L2 instruction have been developed in both academia and the industry. While real-life (RL) immersion is often lauded as a particularly effective L2 learning platform, little is known about the features of immersive contexts that contribute to the L2 learning process. Immersive virtual reality (iVR) offers a flexible platform to simulate an RL immersive learning situation, while allowing the researcher to have tight experimental control for stimulus delivery and learner interaction with the environment. Using a mixed counterbalanced design, the current study examines individual differences in L2 performance during learning of 60 Mandarin Chinese words across two learning sessions, with each participant learning 30 words in iVR and 30 words via word–word (WW) paired association. Behavioral performance was collected immediately after L2 learning via an alternative forced-choice recognition task. Our results indicate a main effect of L2 learning context, such that accuracy on trials learned via iVR was significantly higher as compared to trials learned in the WW condition. These effects are reflected especially in the differential effects of learning contexts, in that less successful learners show a significant benefit of iVR instruction as compared to WW, whereas successful learners do not show a significant benefit of either learning condition. Our findings have broad implications for L2 education, particularly for those who struggle in learning an L2. Full article

Studies of novel noun learning show bilingual children rely less on the Mutual Exclusivity Constraint (MEC) for word learning than monolinguals. Shifting the focus to learning novel property terms (adjectives), the present study compared 3.5- and five-year-old bilingual and monolingual preschoolers’ adherence to the MEC. We found no bilingual-monolingual differences on a behavioral forced-choice task for the 3.5-year-olds, but five-year-old monolinguals adhered more to the MEC than bilinguals did. Older bilinguals adhered less to the MEC than younger ones, while there was no difference in MEC adherence between the younger and older monolinguals. In the 5-year-olds, we additionally acquired neurophysiological data using functional near-infrared spectroscopy (fNIRS) to allow for a first explorative look at potential neuronal underpinnings. The data show that, compared to bilinguals, monolinguals reveal higher activation over three brain regions (right frontal, left temporo-parietal, and left prefrontal) that may be involved in exploiting the MEC, building on conflict detection, inhibition, solution of a disjunction, and working memory processes. Taken together, our behavioral and neurophysiological findings reveal different paths towards novel property term learning depending on children’s language acquisition context. Full article

Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share different interests, post statuses, review and comment on various topics. Aspect extraction from this data can provide a thorough understanding of citizens’ behaviors and choices. Also, categorization of these aspects can better summarize societal concerns regarding political, economic, religious and social issues. Aspect category detection (ACD) from people reviews is one of the major tasks of aspect-based sentiment analysis (ABSA). The success of ABSA is mainly defined by the inexpensive and accurate machine-processable representation of the raw input sentences. Previous approaches rely on cumbersome feature extraction procedures from sentences, which adds its own complexity and inaccuracy in performing ACD tasks. In this paper, we propose an inexpensive and simple method to obtain the most suitable representation of a sentence-vector through different algebraic combinations of a sentence’s word vectors, which will act as an input to any machine learning classifier. We have tested our technique on the restaurant review data provided in SemEval-2015 and SemEval-2016. SemEval is a series of global challenges to evaluate the effectiveness of disambiguation of word sense. Our results showed the highest F1-scores of 76.40% in SemEval-2016 Task 5, and 94.99% in SemEval-2015 Task 12. Full article