Search Results (929)

Background: Previous research has found an association between attractiveness and personality traits, but the neural mechanisms are largely unknown. Method: We used a Stroop-like paradigm combined with EEG recordings to investigate how personality traits affect the perception of facial and vocal attractiveness. Twenty-three female participants classified the attractiveness of male faces and male voices paired with positive or negative personality trait words. Results: The behavioral results indicate that personality trait words that are semantically congruent with attractiveness levels facilitate the perception of attractiveness, whereas incongruent trait information produces the opposite effect. Event-related potentials revealed that the influence of personality trait words on facial attractiveness was primarily related to motivated attention as indicated by the late positive component. In the voice task, personality trait words impacted vocal attractiveness processing first during semantic integration (N400 component) and then modulated motivated attention. Conclusions: These results suggest that alleged personality traits modify attractiveness processing in faces and voices in relatively late and partially modality-specific stages. Full article

Objective: To investigate whether prolonged Sanda combat experience improves cognitive–motor control via neuro-cardiac coupling. Methods: Nineteen national-level Sanda athletes and nineteen matched controls completed a color-word Stroop task while concurrent EEG and ECG were recorded. The conflict adaptation effect (CAE), which refers to the ability to adjust cognitive control in response to conflicting stimuli, was compared between groups, along with P600 and LSP amplitudes and heart rate variability (RMSSD, HF); mediation analysis examined vagal recovery as a pathway. Results: Athletes responded faster and showed a larger CAE than controls (p < 0.001). ERP analyses revealed larger CAE-related P600 and LSP amplitudes in athletes (p < 0.05), with LSP amplitude inversely correlating with behavioral CAE (p < 0.05). Post-task vagal rebound (ΔRMSSD and ΔHF) was significantly greater in athletes (p < 0.05), and ΔRMSSD positively correlated with CAE (p < 0.05). Mediation analysis confirmed that vagal recovery partially mediated the association between Sanda experience and improved cognitive–motor control (p < 0.05). Conclusions: Sanda training enhances cognitive–motor control by accelerating parasympathetic recovery and optimizing neural conflict processing, providing evidence for an integrated exercise–cognition–autonomic nervous system coupling model. Full article

Fulfilling the demand for functional food with cost safety and environmental sustainability, our novel anthocyanin-enriched functional drink containing the purple waxy corn cob-derived functional ingredient “MP1” showed cognitive enhancing effects with safety in bilaterally ovariectomized rats, a validated model of menopause. Since no clinical evidence that confirms the mentioned effect was available until now, we conducted a two-arm, randomized, double-blind, placebo-controlled, crossover study to confirm the benefits mentioned above. A total of 32 menopausal participants were divided into placebo and MP1 (400 mg) groups, and were subject to a 2-month study period. Safety parameters, working memory and brain components, especially N100 and P300, the negative and positive potentials derived from the event-related potential (ERP) which indicated attention and cognitive processing, together with oxidative stress markers acetylcholinesterase (AChE) and monoamine oxidase (MAO), were assessed at baseline and every month. No serious side effects or toxicity signs were observed. Subjects who consumed MP1 also had decreased N100 and P300 latency, improved working memory and decreased oxidative stress status. Therefore, a byproduct of purple corn can successfully serve as a novel functional ingredient for developing a cognitive enhancer drink with the qualities of safety, cost reduction, and environmental sustainability promotion. Full article

Objectives: To address the challenges of subjectivity, misdiagnosis and underdiagnosis in post-traumatic stress disorder (PTSD), this study proposes an objective auxiliary diagnostic method based on P300 signals. Existing studies largely rely on conventional P300 features, lacking the systematic integration of event-related potential (ERP) priors and facing limitations in spatiotemporal feature modeling. Methods: Using common spatiotemporal pattern (CSTP) analysis and quantitative evaluation, we revealed significant spatiotemporal differences in P300 signals between PTSD patients and healthy controls. ERP prior information was then extracted and integrated into a hybrid architecture combining transformer encoders and a convolutional neural network (CNN), enabling joint modeling of long-range temporal dependencies and local spatial patterns. Results: The proposed P300 spatiotemporal transformer-CNN (P300-STTCNet) achieved a classification accuracy of 93.37% in distinguishing PTSD from healthy controls, markedly outperforming traditional approaches. Conclusions: Significant spatiotemporal differences in P300 signals exist between PTSD and healthy control groups. The P300-STTCNet model effectively captures PTSD-related spatiotemporal features, demonstrating strong potential for electroencephalogram-based objective auxiliary diagnosis. Full article

The aim of this study is to examine the impact of personality traits on students’ intention to accept the SAP S/4HANA business software. Grounded in the Big Five Factor (BFF) model of personality and the Technology Acceptance Model (TAM), the research analyzed the role of individual differences in students’ learning performance using this ERP system. The study was conducted on a sample of N = 418 first-year students who underwent a quasi-experimental treatment based on realistic business scenarios. The results indicate that conscientiousness emerged as a positive predictor, while agreeableness demonstrated negative predictive value in learning SAP S/4HANA, whereas neuroticism did not exhibit a significant effect. Moderation analysis revealed that both Perceived Usefulness and Actual Usage of technology moderated the relationship between conscientiousness and SAP learning performance, enhancing its predictive strength. These findings underscore the importance of individual differences in the process of SAP S/4HANA acceptance within an educational context and suggest that instructional strategies should be tailored to students’ personality traits in order to optimize learning outcomes. Full article

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management. Full article

The use of neurophysiological data to acquire product image avoids the inherent subjectivity in the empirical design process. In this paper, we use eye tracking–electroencephalography (ET-EEG) to study mapping among user behaviour, eye movements, EEG features and image decisions in the process of product form cognition. First, we designed an ET-EEG experiment using hair dryer stimuli, with morphology rated on a Likert scale. Then, ET-EEG data were categorized according to image category: “unambiguous (matching)”, “unambiguous (mismatching)”, “ambiguous (positive)”, or “ambiguous (negative)”. Finally, the behavioural data, eye movement indicators, and event-related potentials (ERPs) were analysed to parse the cognitive features. The behavioural, ET and ERP data were highly consistent, and their values increased as the cognition resources devoted to decision-making increased. ET-EEG physiological data thus objectively and effectively reflected users’ image cognition of products, providing theoretical support for design research on the origin of cognition. Full article

This study develops a systemic framework to conceptualize digital organizational resilience in micro, small, and medium-sized enterprises (MSMEs) as an emergent property of entangled socio-technical systems. Building on theories of distributed cognition, sociomateriality, and resilience engineering, this paper argues that resilience does not reside in isolated elements—such as leadership, technologies, or procedures—but in their dynamic interplay. Four interdependent dimensions—human, technological, organizational, and institutional—are identified as constitutive of resilience capacities. The research design is conceptual and exploratory in nature. Two theory-driven conceptual statements are formulated: first, that natural language mediation in human–machine interaction enhances coordination and adaptability; and second, that distributed cognition and prototyping practices strengthen collective problem-solving and adaptive capacity. These conceptual statements are not statistically tested but serve as conceptual anchors for the model and as guiding directions for future empirical studies. Empirical illustrations from Ecuadorian MSMEs ground the framework in practice. The evidence highlights three insights: (1) structural fragility, as micro and small firms dominate the economy but face high mortality and financial vulnerability; (2) uneven digitalization, with limited adoption of BPM, ERP, and AI due to skill and resource constraints; and (3) disproportionate gains from modest interventions, such as optimization models or collaborative prototyping. This study contributes to organizational theory by positioning MSMEs as socio-technical ecosystems, providing a conceptual foundation for future empirical validation. Full article

Industrial scheduling plays a central role in Industry 4.0, where efficiency, robustness, and adaptability are essential for competitiveness. This review surveys recent advances in reinforcement learning, digital twins, and hybrid artificial intelligence (AI)–operations research (OR) approaches, which are increasingly used to address the complexity of flexible job-shop and distributed scheduling problems. We focus on how these methods compare in terms of scalability, robustness under uncertainty, and integration with industrial IT systems. To move beyond an enumerative survey, the paper introduces a structured analysis in three domains: comparative strengths and limitations of different approaches, ready-made tools and integration capabilities, and representative industrial case studies. These cases, drawn from recent literature, quantify improvements such as reductions in makespan, tardiness, and cycle time variability, or increases in throughput and schedule stability. The review also discusses critical challenges, including data scarcity, computational cost, interoperability with Enterprise Resource Planning (ERP)/Manufacturing Execution System (MES) platforms, and the need for explainable and human-in-the-loop frameworks. By synthesizing methodological advances with industrial impact, the paper highlights both the potential and the limitations of current approaches and outlines key directions for future research in resilient, data-driven production scheduling. Full article

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful. Full article

Objective: This study aimed to explore the impact of sanda sports experience on cognitive control using electroencephalography (EEG). Methods: The study involved 38 male participants, including 19 sanda athletes with over 5 years of training and 19 ordinary college students. A 2 × 4 mixed experimental design was used, with group (sanda athletes vs. ordinary college students) as the between-subjects variable and four experimental conditions (consistent in the previous and current trials, consistent in the previous but inconsistent in the current trials, inconsistent in the previous but consistent in the current trials, and inconsistent in both previous and current trials) as the within-subjects variable. The classic color-word Stroop task was employed to measure cognitive control function through reaction time, accuracy, and event-related potential (ERP) amplitude. Results: Sanda athletes exhibited significantly shorter reaction times than ordinary college students across all conditions (p < 0.05). There was no significant difference in accuracy between the two groups (p > 0.05). ERP results showed that sanda athletes had significantly larger amplitudes for the N200 and P300 components in incongruent trials compared to congruent trials (p < 0.05), and significantly larger N400 amplitudes in incongruent trials than ordinary college students (p < 0.05). Conclusions: Sanda athletes demonstrated faster response speed and enhanced cognitive control abilities, as indicated by ERP components, without sacrificing task accuracy. Full article

Background/Objectives: The Adaptive Control Hypothesis posits varying control demands across language contexts in production, but its role in comprehension is underexplored. We investigated if trilinguals, who manage three dual-language contexts (L1–L2, L2–L3, L1–L3), exhibit differential proactive and reactive control demands during comprehension across these contexts. Methods: Thirty-six Uyghur–Chinese–English trilinguals completed an auditory word-picture matching task across three dual-language contexts during EEG recording. We employed behavioral analysis, drift-diffusion modeling, event-related potential (ERP) analysis, and multivariate pattern analysis (MVPA) to examine comprehension efficiency, evidence accumulation, and neural mechanisms. The design crossed context (L1–L2, L2–L3, L1–L3) with trial type (switch vs. repetition) and switching direction (to dominant vs. non-dominant language). Results: Despite comparable behavioral performance, drift-diffusion modeling revealed distinct processing profiles across contexts, with the L1–L2 context showing the lowest comprehension efficiency due to slower evidence accumulation. In the L1–L3 context, comprehension-specific proactive control was indexed by a larger P300 and smaller N400 for L1-to-L3 switches. Notably, no reactive control (switch costs) was observed across any dual-language context. MVPA successfully classified contexts and switching directions, revealing distinct spatiotemporal neural patterns. Conclusions: Trilingual comprehension switching mechanisms differ from production. Reactive control is not essential, while proactive control is context-dependent, emerging only in the high-conflict L1–L3 context. This proactive strategy involves allocating more bottom-up attention to the weaker L3, which, unlike in production, enhances rather than hinders overall efficiency. Full article

Background/Objectives: Approach-avoidance behavior is critical for adaptive behavior. The neural basis of these behaviors has been investigated extensively, but the effect of odor valence is unclear. This study tested how positive, negative, and neutral odors affect behavior and event-related potentials (ERPs) in the approach-avoidance task (AAT). Methods: Thirty-two healthy participants performed an AAT. We measured reaction time, accuracy, and ERP components (P1, N1, N2, P3) to understand the process of motivational processing over time. Results: Participants responded faster and more accurately when the direction and target type were congruent under all odor conditions. Odors did not change this core consistent pattern. In contrast, ERP results revealed stage-specific modulations. P1 and N1 components reflected odor-related changes in early sensory processing. The N2 effect present under the air condition was largely absent under positive and negative odors. This suggests reduced conflict monitoring. P3 amplitudes were consistently larger for avoidance than for approach responses, regardless of odor valence. Conclusions: The results demonstrate that odor valence reorganized the neural dynamics of the AAT without changing behavioral performance. This finding shows that olfactory valence modulates attention and control mechanisms and plays a unique role in regulating human motivation. Full article

Previous research has not fully addressed the distinction between different sources of exclusion, focusing predominantly on how being excluded by strangers affects inhibitory control. To address this gap, this study employs a Go/Nogo task to examine how exclusion by individuals with varying degrees of social proximity (close vs. distant) affects inhibitory control. The results revealed that exclusion by both friends (p = 0.018) and strangers (p = 0.001) elicited significantly greater N270 amplitudes compared to inclusion by others. Conversely, the amplitude of the LPC was larger in the inclusion by others category than in both the friend (p = 0.011) and stranger (p < 0.001) exclusion categories. These results suggest that social exclusion triggers a state of heightened alertness and impairs inhibitory control, regardless of the source of the relationship. This is evidenced by the lack of significant differences in N270 and LPC amplitudes between friend and stranger exclusion. These results suggest that while the cognitive control failure theory provides a reasonable explanation for certain aspects of the data, it may not fully account for the observed phenomena. By contrast, the relevant theory of social exclusion—which emphasizes both resources and motivation—provides a better explanation for these phenomena. This study contributes to understanding the inhibitory control mechanisms underlying behavioral responses after social exclusion, and the findings further support the value of theories that emphasize both resources and motivation when interpreting such responses. Full article