Comprehension Task Research Articles

ANALYTICAL EXPOSITION TEXT-SHEET FOR COLLEGE STUDENTS IN READING COMPREHENSION

The research specifically examines the analytical exposition text sheets to improve reading comprehension among third-semester English education students. By using classroom action research, the study follows a cyclic process of planning, acting, observing, and reflecting. Participants selected from a specific class undergo assessments through observation sheets, field notes, and reading comprehension tasks. The findings indicate that using analytical exposure text sheets significantly enhances students' reading comprehension, as evidenced by a notable increase in average grades. These text sheets, characterized by a clear structure for presenting arguments, logical reasoning, and supporting evidence, prove effective in fostering critical thinking skills. The success of this method underscores its potential as a valuable tool for educators, offering a structured yet flexible approach that accommodates diverse learning styles.

Deep Learning-Based Eye-Tracking Analysis for Diagnosis of Alzheimer's Disease Using 3D Comprehensive Visual Stimuli.

Alzheimer's Disease (AD) is a neurodegenerative disorder that causes a continuous decline in cognitive functions and eventually results in death. An early AD diagnosis is important for taking active measures to slow its deterioration. Traditional diagnoses are usually based on clinical experience, which is limited by several realistic factors. In this paper, we focus on exploiting deep learning techniques to diagnose AD based on eye-tracking behaviors. Visual attention, as a typical eye-tracking behavior, is of great clinical value in detecting cognitive abnormalities in AD patients. To better analyze the differences in visual attention between AD patients and normals, we first conducted a 3D comprehensive visual task on a noninvasive eye-tracking system to collect visual attention heatmaps. Then a multilayered comparison convolutional neural network (MC-CNN) is proposed to distinguish the visual attention differences between AD patients and normals. In MC-CNN, the multilayered feature representations of heatmaps were obtained by hierarchical residual blocks to better encode eye movement behaviors, which were further integrated into a distance vector to benefit the comprehensive visual task. From evaluation, MC-CNN can distinguish AD patients from normals with 0.84 accuracy, 0.86 recall, 0.82 precision, 0.83 F1-score and 0.90 area under the curve (AUC). The above results demonstrate the effectiveness of the proposed MC-CNN in AD diagnosis based on the comprehensive 3D visual task.

Shared representations in cognate comprehension and production: An online picture naming and lexical decision study with bilingual children

AbstractThe cognate facilitation effect, a classic example of cross-language interaction in the bilingual lexicon, has mostly been studied in adults. We examined the extent to which such effects occurred in simultaneous bilingual children’s word processing, to what extent these were modulated by language dominance, and to what extent this differed between comprehension and production tasks. Simultaneous bilingual Dutch-Greek children, ranging from Dutch-dominant to Greek-dominant, performed auditory lexical decision and picture-naming tasks in an online experiment. Cognate facilitation effects emerged in both tasks but manifested themselves differently. In lexical decision, there was an interaction effect with language dominance in accuracy, while in picture naming there was a main effect in reaction times. These findings suggest that, similar to what has been found for adults, simultaneous bilingual children have an integrated lexicon, in which both languages are interactively connected. Effects may differ as a combined result of factors such as comprehension versus production and individual differences in language dominance. Importantly, despite such differences, our results show that cognate effects emerge across tasks and across a range of individual children’s language dominance, indicating that shared representations within the bilingual lexicon are accessed during both word comprehension and production.

A Literature Review of Language Characteristics in Patients with Vascular Dementia

Vascular dementia may present with language disorders due to damage to frontal subcortical circuits. This study aimed to review recent studies of language abilities in patients with vascular dementia. Sixty-nine studies were classified according to language modalities (spontaneous speech, auditory comprehension, repetition and naming). First, spontaneous speech was impaired in the ability to describe pictures, objects and in the ability to convey information in discourse. Second, the auditory comprehension was impaired in word comprehension, sentence comprehension, paragraph comprehension, and speech act comprehension tasks. Third, repetition was preserved in word-level tasks but impaired at the sentence level. Fourth, the naming performance was impaired in a generative naming task (phonemic fluency) rather than in a confrontational naming task. Therefore, when summarizing the results between patient groups, in spontaneous speech, to convey information through highly informative utterances was relatively good in vascular dementia (VaD), but the number of utterances was lower than dementia of Alzheimer’s type (DAT). In auditory comprehension, VaD’s performance was good in the task of word comprehension and speech act comprehension tasks. It is organized as in repetition, VaD’s performance was relatively good in the sentence-level task, and in naming, VaD’s performance was reported to be better than DAT in the oppositional naming and generative naming semantic category tasks. In the generative naming phoneme category, opposite result was reported. Further studies on the language abilities of patients with vascular dementia needs to consider factors such as subtype and language modality. Future studies should be evenly distributed across all language domains.

Trilingual parallel processing: Do the dominant languages grab all the attention?

Abstract Twenty-five L1 Nepali speaking participants living in Trondheim, Norway who spoke English as L2 and Norwegian as L3 (late adult learners) participated in this study. Participants’ L2 proficiency was established as advanced in LexTALE. We administered language comprehension and production tasks in a trilingual design. In a mouse tracking trilingual parallel activation experiment, participants performed a language comprehension task in which they listened to the spoken word in their L1, L2 and L3 and clicked on the matching target picture. Mouse trajectories of their response pattern were recorded and analyzed. The language production task included a phonological and a semantic verbal fluency task (VFT), which also served as an executive control task. VFT showed their dominance in L1 and L2 compared to L3. This study contributes novel knowledge on trilingual parallel activation and suggests that in the presence of a non-dominant L3, a dominant L1 and a dominant L2 are processed faster than the non-dominant language in phonologically competing conditions.

Variable effects of speakers’ visual cues and accent on L2 listening comprehension: A mixed-methods approach

Few studies have explored the influence of a speaker’s accent and visual (facial and gestural) cues on second-language (L2) listening comprehension. The current mixed-methods between-groups design investigated: (1) the effects of accent and visual cues on Arab students’ comprehension of recorded lectures delivered by two speakers: first language (L1) American English and second language (L2) English (strong Vietnamese accent), (2) students’ assessments of the comprehensibility and English nativelikeness of the accents, and (3) students’ perceptions of and preferences for visual cues in communication. A total of 120 Arab university students were distributed across three stimulus conditions: auditory-visual (AV)-gesture-face, AV-face, and A-only within two accent conditions (L1, L2 English). Materials included: a listening comprehension task, 9-point rating scales for comprehensibility and nativelikeness of accent, and a speaker’s accent and visual cues (SAVC) questionnaire. ANCOVAs were conducted on listening comprehension scores and the ratings, with students’ listening proficiency as the covariate. Students with higher listening proficiency received higher comprehension scores and tended to recognize the L1 English accent as nativelike; however, proficiency was not significantly related to their comprehensibility ratings of either accent. The L1 English speaker’s gestures were associated with higher comprehensibility ratings; the L2 English speaker’s gestures were associated with a stronger nonnativelike accent, which students found frustrating to comprehend. Although SAVC responses revealed a general preference for facial cues and gestures in daily communication, the L2 English speaker’s gestures in the study were not considered helpful. Thematic analysis of interviews emphasized: (1) the variable effectiveness of visual cues, (2) the important role of topic and accent familiarity, (3) the relationship between listening proficiency and interlocutor/instructor preference, and (4) strategies to deal with unfamiliar accents. Pedagogical implications include increased awareness of variability in visual cues across speakers and the need to build familiarity with various accents and gesture patterns.

Inferential Comprehension Abilities in French-Speaking Preschoolers Exposed to Neglect in the Early Longitudinal Language and Neglect Study.

Using a longitudinal design, this study aimed to describe inferential comprehension abilities of neglected French-speaking preschool children from 42 to 66 months of age in comparison to non-neglected peers, to examine the association with receptive vocabulary, and to determine whether rates of change in inferential abilities over time was stable between the two group conditions. An inferential comprehension task and the French version of the Peabody Picture Vocabulary Test-Fourth Edition were administered to a group of neglected children (n = 37-40) and to a group of same-age non-neglected children (n = 71-91) at 42, 54, and 66 months old, as part of the Early Longitudinal Language and Neglect study. Results show that children exposed to neglect obtain significantly lower scores compared to their same-age peers on inferential comprehension and receptive vocabulary measures at all three time points (p < .001) with large to very large effect sizes and indicate moderate to strong correlations between the two variables. Children from the neglected group present difficulties in inferencing compared to same-age non-neglected peers, a disadvantage that remains stable over time. This study demonstrates the significant gap in inferential comprehension abilities between neglected and non-neglected preschool children. These results reiterate the importance of early detection of language comprehension difficulties in young children coming from vulnerable environments.

A Psycholinguistic Perspective on Sentence Understanding for Second Language Learners

This study delves into the intricacies of second-language sentence comprehension, investigating the impact of psycholinguistic variables. An online sentence comprehension task engaged 100 proficient English speakers with intermediate Spanish proficiency, integrating eye-tracking technology. Assessing sentence complexity, word order, lexical ambiguity, and working memory, the findings highlight higher accuracy in simple declarative sentences, while non-canonical word orders led to significantly prolonged response times. Lexical ambiguity correlated with increased error rates, yet superior working memory capacities were associated with enhanced processing efficiency. The research underscores the necessity for nuanced language instruction, emphasizing metacognitive strategies to address challenges posed by sentence complexity and ambiguity. Despite limitations in sample size and controlled settings, this study lays a foundational framework for future cognitive investigations into second-language sentence comprehension.

Combining permuted language model and adversarial training for Chinese machine reading comprehension

Due to the polysemy and complexity of the Chinese language, Chinese machine reading comprehension has always been a challenging task. To improve the semantic understanding and robustness of Chinese machine reading comprehension models, we propose a model that utilizes adversarial training algorithms and Permuted Language Model (PERT). Firstly, we employ the PERT pre-training model to embed paragraphs and questions into vector space to obtain corresponding sequential representations. Secondly, we use a multi-head self-attention mechanism to extract key textual information from the sequence and employ a Bi-GRU network to semantically fuse the output feature vectors, aiming to learn deep semantic representations in the text. Finally, we introduce perturbations into the model training process. We achieve this by utilizing adversarial training algorithms such as Fast Gradient Method (FGM) and Projected Gradient Descent (PGD). These algorithms generate adversarial samples to enhance the model’s robustness and stability when facing diverse inputs. We conducted comparative experiments on the publicly available Chinese reading comprehension datasets CMRC2018 and DRCD. The experimental results show that our proposed model has achieved significant improvements in both EM and F1-Score compared to the baseline model. To validate the model’s generalization and robustness, we utilized ChatGPT to construct a scientific dataset that includes a large number of domain-specific terms, sentences with mixed Chinese and English, and complex comprehension tasks. Our model also performed remarkably well on the self-built dataset. In conclusion, the proposed model not only effectively enhances the understanding of semantic information in Chinese text but also demonstrates a certain level of generalization capability.

Impact of subjective and objective language function and psychological distress on quality of life in glioma patients awaiting surgery

ABSTRACT Background Maintaining quality of life (QoL) and identifying contributing factors is an important aspect of treatment for glioma patients. Little is known about to what extent language function and psychological distress impact QoL before surgery. Aims The aim of the present study was to investigate the impact of subjective and objective language function, as well as psychological distress on domains of QoL before surgery. Methods and procedures Twenty-seven patients (52% female) with a suspected glioma, grade 1‒3 based on symptoms and MRI imaging diagnostics were assessed pre-surgery. Subjective language concerns, psychological distress and QoL were investigated with self-reported questionnaires. A Subjective language index was calculated, based on items addressing word-finding, expression of thoughts, reading and writing. Objective language function was assessed with tasks of naming (The Boston Naming Test), verbal comprehension (Vocabulary and Similarities), verbal fluency (Semantic and Phonemic fluency) and verbal short-term and working memory (Digit span forward and backward). The strength of associations was determined using Spearman’s rho correlations. Linear regression analyses were used to examine predictors for QoL. Outcomes and Results Clinically significant reductions in QoL were found in 48% of the sample. Subjective language concerns were highly common, with 85% reporting some degree of difficulty. Group means on objective language tests were within normative range on the included measures. The Subjective language index correlated significantly with several QoL domains, whereas objective performance mainly correlated with functional well-being. Psychological distress was a strong predictor for QoL. Conclusions The findings demonstrate that reductions in QoL may occur during the diagnostic phase, even before glioma treatment starts. Subjective language concerns and psychological distress contributed to all aspects of QoL and highlight the importance of acknowledging patient-reported information. The results suggest a comprehensive multi-modal assessment of glioma patients pre-surgery, to establish a base-line and facilitate patient-centered treatment planning.

Integration and analysis of diverse healthcare data sources: A novel solution

Contemporary healthcare analytics requires informed decision-making through seamless integration, correlation, and curation of diverse data from sources like clinical trials, research publications, ubiquitous devices, and standard terminologies. Modern healthcare systems need to monitor temporal changes, manage key features, and deliver robust search capabilities, extending beyond electronic health records. However, existing systems lack readiness for comprehensive healthcare analytics tasks, necessitating a sophisticated solution. Our work introduces a groundbreaking comprehensive framework for managing, integrating, and processing continuously evolving healthcare data, with a focus on establishing an efficient architecture for data processing and ensuring interoperability and consistency. We incorporate a time dimension to capture critical changes for efficient data analysis and decision-making, extending from clinical trials to mapping clinical trial data to clinical research. Moreover, we curate disparate datasets, including trials, academic publications, standard medical terms, concepts, and ubiquitous device data. Employing highly efficient algorithms and methods, we optimize time and space complexity, validating the feasibility of our proposed solution. Our results demonstrate maximum linear change detection and update processing latency, showcasing efficiency compared to state-of-the-art methods. Additionally, our methods for profiling crucial entities in clinical trial data achieve consistent average accuracy, notably with the VSM model. This innovative approach significantly advances meeting dynamic requirements in contemporary healthcare analytics, particularly in clinical trials.

Product information recognition in the retail domain as an MRC problem

This paper presents the task of recognizing product information (PI) (i.e., product names, prices, materials, etc.) mentioned in customer statements. This is one of the key components in developing artificial intelligence products to enable businesses to listen to their customers, adapt to market dynamics, continuously improve their products and services, and improve customer engagement by enhancing effectiveness of a chatbot. To this end, natural language processing (NLP) tools are commonly used to formulate the task as a traditional sequence labeling problem. However, in this paper, we bring the power of machine reading comprehension (MRC) tasks to propose another, alternative approach. In this setting, determining product information types is the same as asking “Which PI types are referenced in the statement?” For example, extracting product names (which corresponds to the label PRO_NAME) is cast as retrieving answer spans to the question “Which instances of product names are mentioned here?” We perform extensive experiments on a Vietnamese public dataset. The experimental results show the robustness of the proposed alternative method. It boosts the performance of the recognition model over the two robust baselines, giving a significant improvement. We achieved 92.87% in the F1 score on recognizing product descriptions at Level 1. At Level 2, the model yielded 93.34% in the F1 score on recognizing each product information type.

Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach.

Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context. This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately. Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model's performance. We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases. The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.

PMRC: Prompt-Based Machine Reading Comprehension for Few-Shot Named Entity Recognition

The prompt-based method has been proven effective in improving the performance of pre-trained language models (PLMs) on sentence-level few-shot tasks. However, when applying prompting to token-level tasks such as Named Entity Recognition (NER), specific templates need to be designed, and all possible segments of the input text need to be enumerated. These methods have high computational complexity in both training and inference processes, making them difficult to apply in real-world scenarios. To address these issues, we redefine the NER task as a Machine Reading Comprehension (MRC) task and incorporate prompting into the MRC framework. Specifically, we sequentially insert boundary markers for various entity types into the templates and use these markers as anchors during the inference process to differentiate entity types. In contrast to the traditional multi-turn question-answering extraction in the MRC framework, our method can extract all spans of entity types in one round. Furthermore, we propose word-based template and example-based template that enhance the MRC framework's perception of entity start and end positions while significantly reducing the manual effort required for template design. It is worth noting that in cross-domain scenarios, PMRC does not require redesigning the model architecture and can continue training by simply replacing the templates to recognize entity types in the target domain. Experimental results demonstrate that our approach outperforms state-of-the-art models in low-resource settings, achieving an average performance improvement of +5.2% in settings where access to source domain data is limited. Particularly, on the ATIS dataset with a large number of entity types and 10-shot setting, PMRC achieves a performance improvement of +15.7%. Moreover, our method achieves a decoding speed 40.56 times faster than the template-based cloze-style approach.

Comprehension of Ditransitive Constructions in Mandarin-Speaking Children with Developmental Language Disorder and Children with Autism Spectrum Disorder Plus Language Impairment.

This study examined and compared the comprehension of Mandarin ditransitive constructions in children with developmental language disorder (DLD) and children with autism spectrum disorder plus language impairment (ALI). Eighteen children with DLD, 17 children with ALI, and 27 age-matched typically developing (TDA) children, participated in a sentence-picture matching task on four patterns of Mandarin ditransitive constructions. Both children with DLD and children with ALI received significantly lower accuracy than TDA children in general and their most common errors were thematic role reversals. However, while children with ALI evinced a generalized deficit in all four patterns, only the comprehension of S1 (Subj. + Vgei + IO + DO) and S3 (Subj. + gei + IO + V + DO) was affected in children with DLD, with that of S2 (Subj. + V + DO + gei + IO) and S4 (Subj. + V + IO + DO) preserved in this population. Additionally, thematic role reversal errors were more dominant in children with DLD than in children with ALI who also committed a relatively higher proportion of Wrong Theme and No Recipient errors. It is concluded thatthe primary deficit of children with DLD lies in representing dependent relationships between the arguments and the verb as involved in thematic role assignment, but this is less critical in children with ALI, with their performance on the comprehension task possibly also related to other factors associated with the condition. To enhance the development of ditransitive constructions, intervention efforts for children with DLD and children with ALI could focus on strengthening the connection between each argument and its thematic role.

MRC-VulLoc: Software source code vulnerability localization based on multi-choice reading comprehension

Recently, automatic vulnerability detection approaches based on machine learning (ML) have outperformed traditional rule-based approaches in terms of detection performance. Existing ML-based approaches typically concentrate on function or line granularity, which fail to realize accurate vulnerability localization and are insufficient to support effective root cause analysis of vulnerability. To address this issue, we propose a new approach that maps the multi-choice reading comprehension (MRC) task to the vulnerability localization task at the granularity of vulnerability triggering path named MRC-VulLoc. Initially, we design six large datasets (including C/C++ and Java languages) in the form of MRC. Subsequently, we introduce a novel pre-trained vulnerability localization model, combining the effective code semantic comprehension ability of pre-trained model with the advantages of Bidirectional Short-Term Memory Network (Bi-LSTM) and Convolutional Neural Network (CNN) models. Lastly, we conduct experiments to evaluate the vulnerability localization with several state-of-the-art MRC approaches and vulnerability detectors. Experimental results demonstrate the effectiveness of the proposed datasets in evaluating MRC approaches for vulnerability localization. Furthermore, MRC-VulLoc achieves higher precision on vulnerability localization compared to comparative vulnerability detectors.

Interactive simulations as a tool for logistics and maintenance of IFMIF-DONES

Pre-configured virtual reality (VR) simulations of the logistics and maintenance processes have proven to be useful for identifying potential design issues as well as planning operations during an early design phase of facility. But VR simulations can also be used to deeply explore the feasibility of these procedures in a more interactive manner, so that we can identify potential risks and difficulty levels from early stages and study different maintenance strategies to assist the maintenance worker during these procedures. This article presents a framework to design and validate logistics and maintenance procedures in complex facilities, such as the International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source (IFMIF-DONES). Our framework begins with a preparatory phase where essential information about the procedures and Computer-Aided Design (CAD) models is compiled into a comprehensive Virtualization Task Document (VTD). Differently from previous work, this VTD allows representation of parallel tasks. We implement the interactive version of the virtual environment, where the different maintenance and logistics equipment, as well as a virtual maintenance worker is controlled by the user (the person executing the interactive simulation). We have validated this interactive framework with two simulations for the installation process of the Superconducting Radio Frequency Linear accelerator (SRF Linac) modules in IFMIF-DONES. In one simulation (the automatic one), the procedures are reproduced as they are planned, while in the second simulation (the interactive one) the user freely controls the movements of the moving parts of the crane, grab and release plant equipment, move platforms, etc. Based on our simulations, the interactive version allows easier detection of potential points of collisions as well as more precise assessment of the difficulty of the tasks to be performed.

Are the P600 and P3 ERP components linked to the task-evoked pupillary response as a correlate of norepinephrine activity?

During language comprehension, anomalies and ambiguities in the input typically elicit the P600 event-related potential component. Although traditionally interpreted as a specific signal of combinatorial operations in sentence processing, the component has alternatively been proposed to be a variant of the oddball-sensitive, domain-general P3 component. In particular, both components might reflect phasic norepinephrine release from the locus coeruleus (LC/NE) to motivationally significant stimuli. In this preregistered study, we tested this hypothesis by relating both components to the task-evoked pupillary response, a putative biomarker of LC/NE activity. 36 participants completed a sentence comprehension task (containing 25% morphosyntactic violations) and a non-linguistic oddball task (containing 20% oddballs), while the EEG and pupil size were co-registered. Our results showed that the task-evoked pupillary response and the ERP amplitudes of both components were similarly affected by both experimental tasks. In the oddball task, there was also a temporally specific relationship between the P3 and the pupillary response beyond the shared oddball effect, thereby further linking the P3 to NE. Because this link was less reliable in the linguistic context, we did not find conclusive evidence for or against a relationship between the P600 and the pupillary response. Still, our findings further stimulate the debate on whether language-related ERPs are indeed specific to linguistic processes or shared across cognitive domains. However, further research is required to verify a potential link between the two ERP positivities and the LC/NE system as the common neural generator.

The Effect of Background Noise, Bilingualism, Socioeconomic Status, and Cognitive Functioning on Primary School Children's Narrative Listening Comprehension.

This study focuses on 7- to 9-year-old children attending primary school in Swedish areas of low socioeconomic status, where most children's school language is their second language. The aim was to better understand what factors influence these children's narrative listening comprehension both in an ideal listening condition (in quiet) and for the primary school classroom, a typical listening condition (with multitalker babble noise). A total of 86 typically developing 7- to 9-year-olds performed a narrative listening comprehension test (Lyssna, Förstå och Minnas [LFM]; English translation: Listen, Comprehend, and Remember) in two listening conditions: quiet and multitalker babble noise. They also performed the crosslinguistic nonword repetition test and a digit span backwards (DSB) test. A predictive statistical model including these factors, the children's degree of school language exposure, parental education level, and age was derived. Listening condition had the strongest predictive value for LFM performance, followed by school language exposure and nonword repetition accuracy. Parental education level was also a significant predictor. There was a significant three-way interaction effect between listening condition, age, and DSB performance. Multitalker babble noise has a negative effect on children's narrative listening comprehension. The effect of multitalker babble noise could be explained by age differences in the ability to allocate working memory capacity during the narrative listening comprehension task, suggesting that younger children may be more vulnerable for missing information when listening in background noise than their older peers. https://doi.org/10.23641/asha.25209248.

Effects of working memory and visual search skill on Chinese reading comprehension: examining the simple view of reading

AbstractTo investigate the longitudinal effects of two domain-general cognitive abilities, namely verbal working memory and visual search skill, on Chinese reading comprehension. To evaluate whether decoding and linguistic comprehension mediate such effects. A total of 202 first-grade Chinese-speaking children from mainland China (Mage = 86 months, SD = 4.9 months; 126 boys) completed tasks that measured verbal working memory, visual search, expressive vocabulary, morphological awareness, Chinese character recognition, and word reading fluency. One year later, they completed a passage comprehension task and a nonverbal intelligence test. Structural equation modeling was conducted with decoding and linguistic comprehension being included as latent variables and mediating factors explaining the effects of working memory and visual search. The children with better verbal working memory and visual search skill exhibited better reading comprehension performance. The effect of working memory on reading comprehension was fully mediated by linguistic comprehension (captured by vocabulary and morphological awareness) but not decoding (measured by word reading accuracy and fluency). However, decoding fully mediated the impact of visual search skill. Our findings suggest that the reading comprehension ability of beginning Chinese readers is associated with verbal working memory and visual search ability. These general cognitive abilities influence reading comprehension through distinct pathways. In line with the simple view of reading theory, decoding and linguistic comprehension skills explain such impacts. This research underlines the value of simultaneously examining general cognitive abilities and exploring their interconnectedness with linguistic skills in the context of reading comprehension.