Cognitive Stages Research Articles

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [18F]FDG PET study.

In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [18F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD. This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction. We evaluated the IDLR model's ability to distinguish between subjects with different cognitive statuses and MCI trajectories (sMCI and pMCI) and compared results with radiomic and deep learning (DL) models. A Cox model tested the IDLR signature's predictive capability for MCI to AD progression. Correlation analyses identified critical IDLR features and verified their clinical diagnostic value. The IDLR model achieved the best classification results for subjects with different cognitive statuses as well as in those with MCI with distinct trajectories, with an accuracy of 76.51% [72.88%, 79.60%], (95% confidence interval, CI) while those of radiomic and DL models were 69.13% [66.28%, 73.12%] and 73.89% [68.99%, 77.89%], respectively. According to the Cox model, the hazard ratio (HR) of the IDLR model was 1.465 (95% CI: 1.236-1.737, p < 0.001). Moreover, three crucial IDLR features were significantly different across cognitive stages and were significantly correlated with cognitive scale scores (p < 0.01). Preliminary results demonstrated that the IDLR model based on [18F]FDG PET images enhanced accuracy in diagnosing the clinical spectrum of AD. Question The study addresses the lack of interpretability in existing DL classification models for diagnosing the AD spectrum. Findings The proposed interpretable DL radiomics model, using radiomics-supervised DL features, enhances interpretability from traditional DL models and improves classification accuracy. Clinical relevance The IDLR model interprets DL features through radiomics supervision, potentially advancing the application of DL in clinical classification tasks.

Enhancing visual working memory in schizophrenia: effects of frontoparietal theta tACS in low-performing patients

Schizophrenia is a complex neuro-psychiatric disorder including positive symptoms, negative symptoms, and cognitive deficits. A key cognitive dysfunction in schizophrenia is a deficit in visual working memory (VWM). VWM involves three distinct stages: encoding, maintenance, and retrieval. The deficit in any one stage would produce the same symptom (i.e., poor VWM), although their causes are not the same. In this study, we used a retro-cue VWM task that provides helpful cues at different stages: early in maintenance (early cue), late in maintenance (late cue), or during retrieval (retrieval cue). This modification would help “tag” or identify the cognitive stage(s) most responsible for impaired VWM performance in schizophrenia. Additionally, we took advantage of this tagging feature and applied 6 Hz transcranial alternating current stimulation (tACS) over the right dorsolateral prefrontal cortex (DLPFC) and right posterior parietal cortex (PPC)—which has previously been shown to enhance VWM in low-performing healthy individuals—to examine whether tACS would improve a specific stage or all stages of VWM processing in schizophrenia. We observed that cues significantly enhanced performance in low-performing patients, who benefited equally from early and late maintenance cues, but not from retrieval cues. These low-performers also responded well to theta tACS in their overall VWM performance as opposed to a specific VWM stage. No improvement effect was observed in high-performing patients for both retro cue and tACS. Together, our data suggest that 1) low-performing patients’ VWM deficits likely stem from poor memory consolidation rather than retrieval, 2) right frontoparietal theta tACS can improve low-performing patients’ VWM performance, and 3) such facilitatory tACS effect is not selective of a specific VWM stage and thus is likely driven by an improvement in overall visual attention.

Cognitive Impact of β-Amyloid Load in the Rapid Eye Movement Sleep Behavior Disorder-Lewy Body Disease Continuum.

Rapid eye movement sleep behavior disorder (RBD) is linked to the diffuse-malignant subtype and higher cognitive burden in Lewy body disease (LBD). This study explores brain β-amyloid deposition and its association with cognitive decline across the RBD-LBD continuum. Patients with isolated RBD (iRBD), Parkinson's disease with probable RBD (PDRBD), and dementia with Lewy bodies with probable RBD (DLBRBD) underwent 18F-florbetaben positron emission tomography, 3T magnetic resonance imaging scans, and comprehensive neuropsychological assessments. Subjects were categorized as cognitively normal (NC), mild cognitive impairment (MCI), or dementia. Global and regional standardized uptake value ratios (SUVR) were estimated in predefined cognitive volumes of interest (VOI) derived from voxel-wise comparison analysis among the cognitive groups, namely the prefrontal, parietal, precentral cortices, lingual gyrus, and supplementary motor area. Generalized linear models assessed the relationship between 18F-florbetaben SUVRs and neuropsychological testing, adjusting for age and sex. Subgroup analysis focused on the polysomnography-confirmed iRBD-continuum subset (n = 41) encompassing phenoconverters and nonconverters in our prospective iRBD cohort. Eighty-six subjects were classified as follows: 14 NC, 54 MCI, and 18 dementia. The proportion of positive β-amyloid scans increased with advanced cognitive stages (P = 0.038). β-Amyloid signals in cognitive VOIs were elevated in subgroups showing impairment in Trail-Making Test B (TMT-B). A linear association between TMT-B z score and global cortical β-amyloid levels was observed in the iRBD-continuum subset (P = 0.013). Cortical β-amyloid accumulates with declines in executive function within the RBD-LBD continuum. TMT-B performance may be a useful marker associating with β-amyloid load, particularly in the iRBD population. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The influence of message frame and product type on green consumer purchase decisions : an ERPs study

Green consumption is a crucial pathway towards achieving global sustainability goals. Product-oriented green advertisements can effectively stimulate consumers’ latent needs and convert them into eventual purchasing intentions and behaviors, thereby promoting green consumption. Given that neuromarketing methods facilitate the understanding of consumers’ decision-making processes, this study combines prospect theory and need fulfillment theory, employing event-related potentials (ERPs) as measures to explore changes in consumers’ cognitive resources and emotional arousal levels when confronted with green products and advertising information. This enables inference regarding consumers’ acceptance of purchasing and their psychological processes. Behavioral results indicated that message framing influences consumers’ purchases, with consumers consuming more green in response to negatively framed advertisements. EEG results indicated that matching positive framing with utilitarian green products was effective in increasing consumers’ cognitive attention in the early cognitive stage. In the late stage of cognition negative frames stimulated consumers’ mood swings more, and the influence of product type depended on the role of message frames, and the consumption motivation induced by the product, whose influence was overridden by external evaluations such as message frames. These research findings provide an explanation for the impact of frame information on consumers’ purchasing decisions at different stages, assisting marketers in devising diverse promotional strategies based on product characteristics to foster the development and practice of green consumption. This will further embed the concept of green consumption advocated by organizations such as the United Nations Environment Programme (UNEP), World Wildlife Fund (WWF), and Greenpeace into the public consciousness.

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

The relationship between cerebrovascular disease (CVD) and amyloid beta (Aβ) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers-including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)-would correlate with Aβ positivity on positron emission tomography (Aβ-PET). We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=1352). Logistic regression was used to calculate odds ratios (ORs), with Aβ-PET positivity as the standard-of-truth. Following adjustment, WMHs (OR=1.25) and superficial CMBs (OR=1.45) remained positively associated with Aβ-PET positivity (p<0.001). Deep CMBs and lacunes exhibited a varied relationship with Aβ-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ-PET (pseudo-R2=0.41). The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. gov: ADNI-2 (NCT01231971), ADNI-3 (NCT02854033). Cerebrovascular biomarkers linked to amyloid beta (Aβ) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aβ-PET positivity. Relationships with Aβ-PET vary by cognitive stage. Novel accessible model predicts Aβ-PET status. Study supports multimodal diagnostic approaches.

ЦИФРЛЫҚ ШЫНДЫҚ ТУДЫРҒАН МӘСЕЛЕЛЕР

In this article, the author raises the problem of how the "normal existence" has changed in the eyes of the modern generation. The fundamental changes caused by technological infrastructure and the shifts caused by them in the life of society have repeatedly changed in the life of one generation. The development of digital reality with invaluable speed has become a reality that humanity must accept, study, adapt. This is a new stage of cognition of the world. A new stage requires a new person who can think in a new way about himself, about the surrounding reality and about his role in this reality. In the article, the author highlighted the problems that a person faces in the conditions of a new digital reality. The new time is constantly changing. This change has become an ongoing process. A person constantly lives in a state of expectation. The virtual world not only provides opportunities, but also creates contradictions. The article raises these questions. The author says that through information culture it is possible to preserve essential moral norms and values.

FORMING DESIGN THINKING SKILLS DURING THE EDUCATION OF FUTURE SPECIALISTS

The article revealed the meaning of the concepts of «thinking», «design thinking» by theoretically analyzing the scientific works and identifying the psychological and pedagogical features of formation of students' design thinking skills in the educational process. The levels of development of design thinking skills of future specialists, the authors relied on the design thinking criteria, components, and terms cited by scientists in the research. Diagnosing design thinking using a three-level stage, components such as cognitive, analytical, and activity were distinguished by the improvement of the process of forming students' design thinking skills. The experimental part of the research was planned and experimental work was carried out using effective methods. Surveys were conducted to determine design thinking by methods of «critical thinking», J. Barrett's «Critical Analysis» methodology, and Michael Chaifitz's test to assess the ability to find logical errors. The results of the pedagogical experiment showed the low level of design thinking of students. It needs creation and usage for improving the educational process in HEI. A work plan was created, and during its implementation, the solution to the problem of determining the effectiveness of design thinking was demonstrated based on the analysis of the results of each of the conducted methods. Comparing the results, none of the stages of development of design thinking could exclude an insufficient level; showed the highest indicator of students' cognitive stage; the analytical component of design thinking posed the greatest challenge, with only 69% of students indicating they were ready to make logical connections, and 13% saying they had great difficulty making them. It is possible to influence students by encouraging responsibility and independence, realizing awareness, creativity, self-development, learning to monitor, generalize, compare, and increase their attention in order to form design thinking skills, and the formation of design thinking skills is not enough.

A multimodal cross-transformer-based model to predict mild cognitive impairment using speech, language and vision

Mild Cognitive Impairment (MCI) is an early stage of memory loss or other cognitive ability loss in individuals who maintain the ability to independently perform most activities of daily living. It is considered a transitional stage between normal cognitive stage and more severe cognitive declines like dementia or Alzheimer’s. Based on the reports from the National Institute of Aging (NIA), people with MCI are at a greater risk of developing dementia, thus it is of great importance to detect MCI at the earliest possible to mitigate the transformation of MCI to Alzheimer’s and dementia. Recent studies have harnessed Artificial Intelligence (AI) to develop automated methods to predict and detect MCI. The majority of the existing research is based on unimodal data (e.g., only speech or prosody), but recent studies have shown that multimodality leads to a more accurate prediction of MCI. However, effectively exploiting different modalities is still a big challenge due to the lack of efficient fusion methods. This study proposes a robust fusion architecture utilizing an embedding-level fusion via a co-attention mechanism to leverage multimodal data for MCI prediction. This approach addresses the limitations of early and late fusion methods, which often fail to preserve inter-modal relationships. Our embedding-level fusion aims to capture complementary information across modalities, enhancing predictive accuracy. We used the I-CONECT dataset, where a large number of semi-structured conversations via internet/webcam between participants aged 75+ years old and interviewers were recorded. We introduce a multimodal speech-language-vision Deep Learning-based method to differentiate MCI from Normal Cognition (NC). Our proposed architecture includes co-attention blocks to fuse three different modalities at the embedding level to find the potential interactions between speech (audio), language (transcribed speech), and vision (facial videos) within the cross-Transformer layer. Experimental results demonstrate that our fusion method achieves an average AUC of 85.3% in detecting MCI from NC, significantly outperforming unimodal (60.9%) and bimodal (76.3%) baseline models. This superior performance highlights the effectiveness of our model in capturing and utilizing the complementary information from multiple modalities, offering a more accurate and reliable approach for MCI prediction.

11-month-olds recognize the teacher–student relationship

Understanding how preverbal infants perceive and differentiate the roles of teachers and students can provide profound insights into the early stages of human learning and cognition. We thus developed a paradigm based on the task order and the interactive position to determine if infants understand teacher–student relationships. Five experiments (N = 104), reveal that, after watching the teacher agent instruct the student agent from failure to success, 11-month-old infants looked at the teacher for a longer time when they watched a new naïve agent seek information in a new ambiguous scenario. Three additional control experiments excluded alternative interpretations of infants’ selective looking, suggesting that agents’ order-related actions influenced infants’ identification. Another control experiment demonstrated the importance of the position when the teacher and the student had interactive exchanges. These findings indicate that preverbal infants can recognize the teacher–student relationship, which is crucial to accessing effective information in early life.

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

Pilot implementation of the revised criteria for staging of Alzheimer's disease by the Alzheimer's Association Workgroup in a tertiary memory clinic.

We aimed to evaluate the feasibility of the 2024 Alzheimer's Association Workgroup's integrated clinical-biological staging scheme in outpatient settings within a tertiary memory clinic. The 2018 syndromal cognitive staging system, coupled with a binary biomarker classification, was implemented for 236 outpatients with cognitive concerns. The 2024 numeric clinical staging framework, incorporating biomarker staging, was specifically applied to 154 individuals within the Alzheimer's disease (AD) continuum. The 2024 staging scheme accurately classified 95.5% AD. Among these, 56.5% exhibited concordant clinical and biological stages (canonical), 34.7% demonstrated more advanced clinical stages than biologically expected (susceptible), and 8.8% displayed the inverse pattern (resilient). The susceptible group was characterized by a higher burden of neurodegeneration and inflammation than anticipated from tau, whereas the resilient group showed the opposite. The 2024 staging scheme is generally feasible. A discrepancy between clinical and biological stages is relatively frequent among symptomatic patients with AD. The 2024 AA staging scheme is generally feasible in a tertiary memory clinic. A discrepancy between clinical and biological stages is relatively frequent in AD. The mismatch may be influenced by a non-specific pathological process involved in AD. Individual profiles like aging and lifestyles may contribute to such a mismatch. Matched and mismatched cases converge toward similar clinical outcomes.

Research on Assessment Method for Cognitive Influence Factors of Nuclear Power Plant Operators Based on Fuzzy Analytic Hierarchy Process and Deep Learning of Electroencephalogram Signals

This study explores the factors influencing the cognitive processes of operators in digital nuclear power plants, with a focus on the correlation between these factors and electroencephalogram (EEG) features. Initially, based on expert consultations, seven factors were considered: stress, time, fatigue, procedural complexity, user interface experience, procedural clarity, and efficiency. From these, four were identified as the most crucial for each stage of the cognitive process, highlighting their significant roles in influencing cognitive performance and potentially correlating with distinct EEG characteristics. These were assessed using the fuzzy analytic hierarchy process (FAHP) to determine the weightings of influences across the cognitive stages of monitoring, decision making, and execution. Employing a simulated scenario of a steam generator tube rupture, subjective questionnaires were utilized to gauge participant perceptions of influencer impacts at each stage, calculating human factors fuzzy synthetic values. Concurrently, EEG signals were segmented by operational steps, extracting around 114 features across the time, frequency, and time-frequency domains, which were then dimensionally reduced to 17 principal components via adaptive principal components analysis (APCA). A correlation analysis was performed between the human factors fuzzy synthetic values and the APCA-reduced EEG features of participants. Subsequently, the EEG feature columns of the eight selected participants were used as inputs to construct a transformer-based self-attention network model to evaluate the participants’ human factors fuzzy comprehensive values. The findings confirm the transformer model’s efficacy in assessing these values, evidencing a significant correlation between the EEG features and human factors fuzzy synthetic values. Integrating FAHP with machine learning methodologies, this model proficiently estimated operators’ cognitive states during various cognitive processes, significantly enhancing human-machine interface design and the operational safety and efficiency at nuclear power plants.

Cognitive evaluation based on regression and eye-tracking for layout on human–computer multi-interface

ABSTRACT The human–computer cooperation process guided by natural interaction, intelligent interaction, and human–computer integration is gradually becoming a new trend in human–computer interaction. Cooperative scenarios of human–computer interaction systems often contain multi-interface and multi-device results in edges often interrupt the cognitive ergonomics of interface layout. This research takes typical areas as an example to establish a stepwise regression model to predict reaction time at an arbitrary position on the left interface. It uses a foveal region to position the starting point of attention and a parafoveal region to calculate the radius of each objective area, and design 10 similar tasks to analyze eye-tracking indexes through physiological assessment. Unlike fixed thinking such as spatial proximity on multi-interfaces, this research summarises cognitive features of layout based on the positive and negative effects of edge impact through eye-tracking analysis. It analyzes cognition including input, process, and output in human–computer cooperation from human intelligence and artificial intelligence respectively, and visualises the mapping relationship between these indexes and specific stages of cognition. Besides, the quantitative evaluation of the regression equation and qualitative analysis of the eye-tracking indexes provide a reference for other interfaces around the front interface.

Benchmarking analysis at establishing a culture of wellness

This article aims to establish trends in motor development through basketball activities as a prerequisite for building a wellness culture, revealing the impact of basketball exercises on motor activity in junior high school. The interests of the research are aimed at following the dynamics in the motor potential and establishing and evaluating the presence of wellness within the framework of school physical education. More specifically, the creation of a culture of wellness in the junior high school stage of the educational degree. A reliable and objective toolkit for diagnosing the level of motor potential in 12–14-year-old students was selected and applied, specifying the empirical data from the conducted research. The purpose of the research is to follow the dynamics of the motor potential in the two stages of research and to establish a wellness culture in the junior high school stage of the educational degree in the lesson of physical education and sports, based on a comparative analysis of the dynamics of motor indicators in the two cognitive stages of the study. Through legislative changes in school education, the direction “school health”, in particular the expected competences, is regulated. Key competence #9 requires knowledge and skills for sustainable development, implementation of a healthy lifestyle, and sports in the Bulgarian school. To achieve the goal, the following research tasks are solved: experimentation of a basketball training program aimed at the development of motor qualities, as a result of which the dynamics in the development of motor qualities of students studying basketball in physical education and sports classes. Based on the benchmarking analysis, it can be concluded that the results achieved at baseline show an improvement in the motor condition of the studied contingent. Motor indicators show intense dynamics in 12–14-year-old students, modeling their motor potential and confirming the presence of a wellness culture in the Bulgarian school. It can be summarized that a benchmarking analysis was carried out to establish dynamics in the indicators and the manifestation of motor skills between the students of the 4th and 5th grade, allowing the individualization of the differences in the motor potential between the empirical data in the cognitive stages of the study.

18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer’s disease clinical spectrum

Abstract Background With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC). Materials and Methods A 18F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (n = 129), and Harvard Aging Brain Study (HABS) NC (n = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and 18F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, 18F-FDG-PET SUVR, and amyloid PET Centiloids. Results DL output remained independently prognostic among other factors in all three datasets (P &amp;lt; .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [−0.005 to 0.044], and HABS-NC sets (0.059 [−0.003 to 0.126]). DL output showed independent (P = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [&amp;lt;0.001-0.036]) over clinical information, 18F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants. Conclusion The 18F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from 18F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.

A mediation approach in resting-state connectivity between the medial prefrontal cortex and anterior cingulate in mild cognitive impairment

Mild cognitive impairment (MCI) is recognized as the prodromal phase of dementia, a condition that can be either maintained or reversed through timely medical interventions to prevent cognitive decline. Considerable studies using functional magnetic resonance imaging (fMRI) have indicated that altered activity in the medial prefrontal cortex (mPFC) serves as an indicator of various cognitive stages of aging. However, the impacts of intrinsic functional connectivity in the mPFC as a mediator on cognitive performance in individuals with and without MCI have not been fully understood. In this study, we recruited 42 MCI patients and 57 healthy controls, assessing their cognitive abilities and functional brain connectivity patterns through neuropsychological evaluations and resting-state fMRI, respectively. The MCI patients exhibited poorer performance on multiple neuropsychological tests compared to the healthy controls. At the neural level, functional connectivity between the mPFC and the anterior cingulate cortex (ACC) was significantly weaker in the MCI group and correlated with multiple neuropsychological test scores. The result of the mediation analysis further demonstrated that functional connectivity between the mPFC and ACC notably mediated the relationship between the MCI and semantic fluency performance. These findings suggest that altered mPFC-ACC connectivity may have a plausible causal influence on cognitive decline and provide implications for early identifications of neurodegenerative diseases and precise monitoring of disease progression.

The audiovisual competition effect induced by temporal asynchronous encoding weakened the visual dominance in working memory retrieval

ABSTRACT Converging evidence suggests a facilitation effect of multisensory interactions on memory performance, reflected in higher accuracy or faster response time under a bimodal encoding condition than a unimodal condition. However, relatively little attention has been given to the effect of multisensory competition on memory. The present study adopted an adaptive staircase test to measure the point of subjective simultaneity (PSS), combined with a delayed matched-to-sample (DMS) task to probe the effect of audiovisual competition during the encoding stage on subsequent unisensory retrieval. The results showed that there was a robust visual dominance effect and multisensory interference effect in WM retrieval, regardless of the subjective synchronous or subjective asynchronous audiovisual presentation. However, a weakened visual dominance effect was observed when the auditory stimulus was presented before the visual stimulus in the encoding period, particularly in the semantically incongruent case. These findings revealed that the prior-entry of sensory information in the early perceptual stage could affect the processing in the late cognitive stage to some extent, and supported the evidence that there is a persistent advantage for visuospatial sketchpad in multisensory WM.

EXPLORING PEER COUNSELORS’ MULTICULTURAL UNDERSTANDING: ASSESSING THE URGENCY OF MULTICULTURAL COUNSELING IN ISLAMIC UNIVERSITIES

Effective peer counseling needs to have good cultural competence to ensure effective communication with the counselee. Cultural competence is increasingly important in higher education institutions as campuses are places where students from diverse backgrounds gather. Unfortunately, research exploring the perspectives of peer counselors in Islamic universities still needs further exploration. This study aims to bridge this gap by exploring the perspectives of prospective peer counselors regarding their multicultural understanding. Using a cross-sectional survey design, this research explores the perspectives of thirty-four prospective peer counselors in an Islamic university in Banjarmasin, South Kalimantan. The instrument used is a cultural awareness questionnaire developed based on multicultural awareness theory and cognitive stage development theory. The results indicate that the level of multicultural awareness among Islamic university students still needs improvement, as participants expressed agreement with 82% of items related to duality, indicating that they have a limited view of other cultures and their perspectives on other cultures are still influenced by the perspectives of others. This study then offers several implications to enhance their multicultural awareness to prepare them to become competent peer counselors.

Psychological mechanism of language cognition to "awaken" artificial intelligence.

The study of artificial intelligence needs tracing back to the working mechanism of the human brain, and based on simulating brain cognition activity, information technology is the most widely influential technology. Language cognition is the core and foundation of human cognition. Biolinguistics' core problem about brain mechanisms (a psychological mechanism) is related to the development of human language cognition in that the cognitive process of humans is from the construction of mediation and language is the very cognitive mediation. The logical law of human language cognition algorithm is about the process of human cognitive psychology that is from the neurological stage, the psychological perception stage, and to the cognitive stage of symbols, which makes psychological certainty account for the connection from mental content to language meaning. Language cognition is where the "awakening" of artificial intelligence begins. First of all, humans produce mental images based on sensory perception and categories. The referential functions of mental images extend the human cognitive scope. Then, categorization is realized through the mediation of mental images. At the time the category is formed, the symbol is required. Language is involved in the thinking process in the cognitive stage of symbols to realize the construction of the mediation to represent the category abstracted from the concrete things. Above all, human language metacognition is realized from the subject-predicate structure that is the grammatical structure on the surface but is throughout the whole process of human rational knowledge and configures the content of rational knowledge, which is the developmental goal of "awakening" artificial intelligence. The demonstration of this proposition is conducive to providing a theoretical basis for the research of the working mechanism of the human brain stimulated by artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unveiling consumer loyalty dynamics: insights for golf club loyalty programs

PurposeWith the competitive nature of golf club operations, understanding the loyalty formation process is crucial for maintaining a competitive edge. This study investigated the sequential developmental stages of consumer loyalty, progressing through cognitive, affective, and conative stages.Design/methodology/approachThe study conducted a survey targeting consumers who have experienced golf club services in the United States. This study operationalized reliability and trust as key indicators of the cognitive stage, hedonic motivation and social engagement as indicators of the affective stage, and loyalty as the indicator of the conative stage.FindingsUsing structural equation modeling (SEM), this study found that reliability has a significantly positive influence on trust. Trust has a significantly positive influence on hedonic motivation and loyalty. Furthermore, hedonic motivation has a significantly positive influence on social engagement and loyalty.Originality/valueBy integrating these findings within the Cognitive-Affective-Conative (CAC) framework, this study contributes to both the theoretical literature on consumer loyalty and provides practical insights for golf club management.