Search Results (164)

In today’s fierce market competition, enterprises must quickly attract consumers’ attention to products and prompt them to make purchases. Based on regulatory focus theory, this study examines the impact of the congruence between different types of goal framing in advertising (promotion vs. prevention) and product types (hedonic vs. utilitarian) on individual consumer decision-making, as well as the underlying psychological mechanisms. The findings are as follows: (1) A goal-framing effect was observed, such that individuals allocated more attention and exhibited higher purchase intentions toward products presented with promotion-framed advertising. (2) A matching effect between goal-framing type and product type was identified: promotion framing increased purchase intentions for hedonic products, whereas prevention framing increased purchase intentions for utilitarian products. (3) Processing fluency mediated the effect of goal–product matching on consumer decision-making. (4) The presence of time pressure amplified the goal-framing effect, leading to stronger preferences under promotion-framed advertisements, as reflected in both longer fixation durations and higher purchase intentions. By integrating regulatory focus theory with product type matching, this study leverages eye-tracking data to reveal the cognitive processes underlying consumer decision-making and the moderating role of time pressure on goal-framing effects. The findings enrich the motivational perspective in consumer behavior research and provide empirical guidance for designing differentiated advertising strategies and optimizing advertising copy. Full article

Numbers present persistent challenges in interpreting, yet cognitive mechanisms underlying notation-specific processing remain underexplored. While eye-tracking studies in visually-assisted simultaneous interpreting have advanced number research, they predominantly examine Arabic numerals in non-Chinese contexts—neglecting notation diversity increasingly prevalent in computer-assisted interpreting systems where Automatic Speech Recognition outputs vary across languages. Addressing these gaps, this study investigated how number notation (Arabic digits vs. Chinese character numbers) affects trainee interpreters’ cognitive effort and performance in Chinese-to-English sight translation. Employing a mixed-methods design, we measured global (task-level) and local (number-specific) eye movements alongside expert assessments, output analysis, and subjective assessments. Results show that Chinese character numbers demand significantly greater cognitive effort than Arabic digits, evidenced by more and longer fixations, more extensive saccadic movements, and a larger eye-voice span. Concurrently, sight translation quality decreased markedly with Chinese character numbers, with more processing attempts yet lower accuracy and fluency. Subjective workload ratings confirmed higher mental, physical, and temporal demands in Task 2. These findings reveal an effort-quality paradox where greater cognitive investment in processing complex notations leads to poorer outcomes, and highlight the urgent need for notation-specific training strategies and adaptive technologies in multilingual communication. Full article

Didactic digital tools can commence, enhance, and strengthen reading fluency in children undergoing long-term hospitalization due to oncology conditions. However, resources specifically designed to support rapid naming and decoding in Spanish remain scarce. This study presents the design, development, and evaluation of a game prototype aimed at addressing this gap among Spanish-speaking preschoolers in hospital settings. Developed using Unity through a design-based research methodology, the game comprises three narratively linked levels targeting rapid naming, decoding, and fluency. A sequential exploratory mixed-methods design (QUAL-quan) guided the evaluation. Qualitative data were obtained from a focus group of hospital teachers (N = 6) and interviews with experts (N = 30) in relevant fields. Quantitative validation involved 274 experts assessing the game’s contextual, pedagogical, and technical quality. The prototype was also piloted with four end-users using standardised tests for rapid naming, decoding, and fluency in Spanish. Results indicated strong expert consensus regarding the game’s educational value, contextual fit, and usability. Preliminary findings suggest potential for fostering and supplementing early literacy skills in hospitalised children. Further research with larger clinical samples is recommended to validate these outcomes. Full article

Background/Objectives: Developmental dyslexia is characterised by neuropsychological processing deficits and marked hemispheric functional asymmetries. To uncover latent neurophysiological features linked to reading impairment, we applied dimensionality reduction and clustering techniques to high-density electroencephalographic (EEG) recordings. We further examined the functional relevance of these features to reading performance under standardised test conditions. Methods: EEG data were collected from 200 children (100 with dyslexia and 100 age- and IQ-matched typically developing controls). Principal Component Analysis (PCA) was applied to high-dimensional EEG spectral power datasets to extract latent neurophysiological components. Twelve principal components, collectively accounting for 84.2% of the variance, were retained. K-means clustering was performed on the PCA-derived components to classify participants. Group differences in spectral power were evaluated, and correlations between principal component scores and reading fluency, measured by the TILLS Reading Fluency Subtest, were computed. Results: K-means clustering trained on PCA-derived features achieved a classification accuracy of 89.5% (silhouette coefficient = 0.67). Dyslexic participants exhibited significantly higher right parietal–occipital alpha (P8) power compared to controls (mean = 3.77 ± 0.61 vs. 2.74 ± 0.56; p < 0.001). Within the dyslexic group, PC1 scores were strongly negatively correlated with reading fluency (r = −0.61, p < 0.001), underscoring the functional relevance of EEG-derived components to behavioural reading performance. Conclusions: PCA-derived EEG patterns can distinguish between dyslexic and typically developing children with high accuracy, revealing spectral power differences consistent with atypical hemispheric specialisation. These results suggest that EEG-derived neurophysiological features hold promise for early dyslexia screening. However, before EEG can be firmly established as a reliable molecular biomarker, further multimodal research integrating EEG with immunological, neurochemical, and genetic measures is warranted. Full article

This study explores opportunities for improving the learning process of design students using multimedia and microlearning, with a focus on architectural visualization. It analyzes the learning habits of students and faculty in higher education, reflects on the need for digitalization and adaptation to the cognitive characteristics of Generations Z and Alpha, and emphasizes the importance of visual perception in design thinking. The research includes a survey of 130 respondents from eight Bulgarian universities and an experiment with three groups of students using different learning methods—live demonstration, video demonstration, and a combined approach. The results indicate that the combined method leads to the highest levels of understanding, confidence, and task performance. The research is grounded in pedagogical theories related to visual learning and cognitive engagement, particularly relevant for Generations Z and Alpha. Students expressed a preference for short, practice-oriented formats, such as project-based learning and video tutorials, aligning with their digital fluency and attention patterns. The results underline the importance of incorporating multimedia elements and flexible instructional strategies to support motivation, engagement, and effective skill development in design education. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

The degradation of physical student examination archives, particularly handwritten essay booklets, presents a significant barrier to longitudinal academic research, institutional record preservation, and student performance analysis. This study introduces a novel natural language processing (NLP)-based framework for the automated reconstruction of damaged academic essay manuscripts using a span-infilling transformer architecture. A synthetic dataset comprising 5000 paired samples of damaged Text and full Text was curated from archived Data Science examination scripts collected at the Center for Applied Data Science, Sol Plaatje University, South Africa. The proposed method fine-tunes a T5-based encoder–decoder model, leveraging span corruption and task-specific prompting to restore missing or illegible segments. Comprehensive evaluation using ROUGE-L, BLEU-4, and BERTScore demonstrates substantial improvements over baseline models including BERT and GPT-2. Qualitative assessments by academic experts further validate the fluency, coherence, and contextual relevance of restored texts. Training dynamics reveal stable convergence without overfitting, while ablation studies confirm the contribution of each architectural component. Token-level error analyses and confidence-scored predictions provide additional interpretability. The proposed framework offers a scalable and effective solution for educational institutions seeking to digitize and recover lost historical student essay records, with potential extensions to other domains, such as digital humanities and archival restoration. Full article

The integration of virtual and augmented reality is transforming processes in the field of product design. This study evaluates the usability of immersive digital tools applied to industrial design through a combined market research and empirical case study, using the software ‘Gravity Sketch’ and the immersive headset ‘Meta Quest 3’. An embedded single case study was conducted based on the international standard ISO 9241-11, considering the dimensions of effectiveness, efficiency, and satisfaction, analysed through nine indicators: tasks completed, time to complete tasks, dimensional accuracy, interoperability, interactivity, fatigue, human error, learning curve, and perceived creativity. The results show a progressive improvement in user–system interaction across the seven Design Units, as users become more familiar with immersive technologies. Effectiveness improves as users gain experience, though it remains sensitive to design complexity. Efficiency shows favourable values even in early stages, reflecting operational fluency despite learning demands. Satisfaction records the greatest improvement, driven by smoother interaction and greater creative freedom. These findings highlight the potential of immersive tools to support design processes while also underlining the need for future research on sustained usability, interface ergonomics, and collaborative workflows in extended reality environments. Full article

Sequence-to-sequence neural machine translation (NMT) has achieved great success with many language pairs. However, its performance remains constrained in low-resource settings such as Mongolian–Chinese translation due to its strong reliance on large-scale parallel corpora. To address this issue, we propose ILFDN-Transformer, a Mongolian–Chinese NMT model that integrates implicit language features and a deliberation network to improve translation quality under limited-resource conditions. Specifically, we leverage the BART pre-trained language model to capture deep semantic representations of source sentences and apply knowledge distillation to integrate the resulting implicit linguistic features into the Transformer encoder to provide enhanced semantic support. During decoding, we introduce a deliberation mechanism that guides the generation process by referencing linguistic knowledge encoded in a multilingual pre-trained model, therefore improving the fluency and coherence of target translations. Furthermore, considering the flexible word order characteristics of the Mongolian language, we propose a Mixed Positional Encoding (MPE) method that combines absolute positional encoding with LSTM-based dynamic encoding, enabling the model to better adapt to complex syntactic variations. Experimental results show that ILFDN-Transformer achieves a BLEU score improvement of 3.53 compared to the baseline Transformer model, fully demonstrating the effectiveness of our proposed method. Full article

Background/Objectives: Cognitive impairment is one of the most common and debilitating clinical features of Multiple Sclerosis (MS). Neuropsychological assessment, however, is time-consuming and requires personal resources, so, due to limited resources in daily clinical practice, information on cognitive profiles is often lacking, despite its high prognostic relevance. Time-saving and effective tools are required to bridge this gap. This study evaluates the sensitivity and specificity of a revised version of TRACK-MS (TRACK-MS-R), a recently published screening tool to identify cognitive impairment in MS in a fast and reliable way, offering a balance between efficiency and diagnostic yield for the individual patient. Methods: In this prospective cross-sectional study, 102 MS patients and 94 age-, sex-, and education-matched healthy controls (HC) completed an extensive neuropsychological assessment, including TRACK-MS-R, to test for cognitive processing speed (Symbol Digit Modalities Test, SDMT) and verbal fluency (Regensburger Word Fluency Test, RWT). Sensitivity of TRACK-MS-R was assessed by using the BICAMS-M battery as a reference, and specificity was determined by comparing MS patients to HC. Results: TRACK-MS-R demonstrated high sensitivity (97.44%) when compared to the gold standard as represented by BICAMS-M for early and accurately detecting cognitive impairment in MS patients. Additionally, as a potential cognitive marker, TRACK-MS-R showed a specificity of 82.98% in distinguishing MS patients from healthy controls. Conclusions: TRACK-MS-R proves to be a highly sensitive and time-efficient screening tool for detecting cognitive impairment in patients with MS, while demonstrating good specificity compared to HC. Whereas high sensitivity is a prerequisite for a valid screening tool, its relatively modest specificity compared to BICAMS-M (62.9%) calls for caution in interpreting standalone results but instead indicates more extensive neuropsychological testing. Its briefness and diagnostic accuracy support its implementation in routine clinical practice, particularly in time-constrained settings. Full article

The present study analyzes individual differences in the facilitative processing of grammatical gender by heritage speakers of Spanish, asking whether these differences correlate with lexical proficiency. Results from an eye-tracking study in the Visual World Paradigm replicate prior findings that, as a group, heritage speakers of Spanish show facilitative processing of gender. Importantly, in a follow-up within-group analysis, we test whether three measures of lexical proficiency—oral picture-naming, verbal fluency, and LexTALE—predict individual performance. We find that lexical proficiency, as measured by LexTALE, predicts overall word recognition; however, we observe no effects of the other measures and no evidence that lexical proficiency modulates the strength of the facilitative effect. Our results highlight the importance of carefully selecting tools for proficiency assessment in experimental studies involving heritage speakers, underscoring that the absence of evidence for an effect of proficiency based on a single measure should not be taken as evidence of absence. Full article

Second-language (L2) written processing has often been linked to cognitive disfluency, resembling fluency disruptions caused by perceptual challenges, such as visual degradation. This study used Virtual Reality to investigate whether cognitive disfluency in L2 mirrors perceptual disfluency by simulating adverse weather conditions (sunny vs. rainy) and applying visual masking. Spanish–English bilinguals completed a language decision task, identifying orthotactically unmarked words as either Spanish (L1) or English (L2) while experiencing these perceptual manipulations. Results showed that visual masking significantly increased reaction times, particularly for L1 words, suggesting that masking can diminish the native language advantage. Spanish words under masking elicited slower responses than unmasked ones, whereas L2 word recognition remained comparatively stable. Additionally, rainy weather conditions consistently slowed responses across both languages, indicating a general effect of environmental disfluency. A significant interaction between language and masking emerged, highlighting distinct cognitive effects for different disfluency types. These findings suggest that cognitive disfluency in L2 does not equate to perceptual disfluency; each affects processing differently. The use of Virtual Reality enabled the controlled manipulation of realistic environmental variables, offering valuable insights into how perceptual and linguistic challenges jointly influence bilingual language processing. Full article

Large Language Models (LLMs) have been the cutting-edge technology in natural language processing (NLP) in recent years, making machine-generated text indistinguishable from human-generated text. On the other hand, “rule-based” Natural Language Generation (NLG) and Natural Language Understanding (NLU) algorithms were developed in earlier years, and they have performed well in certain areas of Natural Language Processing (NLP). Today, an arduous task that arises is how to estimate the quality of the produced text. This process depends on the aspects of text that you need to assess, varying from correct grammar and syntax to more intriguing aspects such as coherence and semantical fluency. Although the performance of LLMs is high, the challenge is whether LLMs can cooperate with rule-based NLG/NLU technology by leveraging their assets to overcome LLMs’ weak points. This paper presents the basics of these two families of technologies and the applications, strengths, and weaknesses of each approach, analyzes the different ways of evaluating a machine-generated text, and, lastly, focuses on a first-level approach of possible combinations of these two approaches to enhance performance in specific tasks. Full article

This study explores how to support Turkish–American secondary school students to co-develop covariational reasoning (CR) and representational fluency (RF) in solving contextually based quadratic function tasks in an after-school community center learning setting. We conducted a teaching experiment (n = 8) at a community center. Ongoing and retrospective analyses of classroom interaction and video transcripts revealed a culturally inclusive mathematics learning environment framework with several intertwined components: co-developing CR and RF and community-based practices. This study provides evidence that students coordinate symbolic, tabular, and graphical representations, which not only deepen their understanding of how quantities change in relation to one another but also enable them to interpret and construct representations in increasingly flexible ways. This reciprocal process of co-developing CR and RF allowed students to recognize and express quantitative relationships as meaningful functional relationships, demonstrating a dynamic interplay between reasoning about change and fluency across representations. This study situates learning within culturally inclusive learning environments and acknowledges the reflexive positionality of the teacher–researcher in relation to students. We highlight how shared community-based practices can enhance mathematics teaching and learning. Full article

In today’s market, consumers appear to be less interested in promotional strategies, particularly those that rely on text-based advertisements. Graphic storytelling can be seen as providing a more engaging visual approach to attract audiences and is increasingly being used by marketers and food packaging designers. However, the questions of whether and how graphic storytelling influences consumers’ purchase intentions remain underexplored. Based on the Transportation–Imagery Model, two experimental studies were conducted to examine the effect of graphic storytelling on narrative transportation and food purchase intention, and to explore its underlying mechanism from the perspective of cognitive fluency. The results demonstrated the positive effect of graphic storytelling on narrative transportation (Studies 1 and 2), as well as a significant impact on food purchase intention (Study 2). Furthermore, cognitive fluency was identified as a critical factor impacting narrative transportation, facilitated by graphic storytelling (Studies 1 and 2). This study extends the Transportation–Imagery Model by positioning cognitive fluency as an important antecedent of narrative transportation. Practically, the suggestion would be for restaurants and food firms to optimize their advertising by displaying cooking processes, particularly for part-prepared foods. Full article