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This book demonstrates the importance of cognitive research, and stresses that much can be achieved in this field with even the simplest of equipment. The book offers explanations of electroencephalography and rheoencephalography.

Digital cognitive training (DCT) has been found to be more effective than traditional paper-and-pencil training in enhancing overall cognitive function. However, a significant barrier to its long-term implementation is that older adults with mild cognitive impairment (MCI) do not continue to use it or even show a dropoff in usage after the initial engagement. Such short-term engagement may limit the potential benefits of DCT, as sustained use is required to achieve more pronounced cognitive improvements. Exploring the reasons for the shift in discontinuous usage behavior is crucial for promoting successful DCT implementation and maximizing its positive effects. This study aimed to explore the intrinsic reasons for the transition from initial acceptance to discontinuous usage behavior among older adults with MCI throughout the DCT process, by employing the extended model of IT continuance (ECM-ITC). We employed a qualitative research methodology and conducted 38 semistructured interviews before and after the use of DCT (3 times per week over 1 month, with each session lasting 30 minutes) with 19 older adults with MCI (aged 60 years or older) and 4 family members between January and March 2024. Thematic analysis and deductive framework analysis were used to identify the reasons for the discontinuous usage of DCT, with mapping to the ECM-ITC. Most participants failed to complete the standard dosage of DCT. Data analysis revealed the reasons for the shift to discontinuous usage. Despite their need to improve cognitive function, participants found the cognitive training confusing and discovered that DCT did not align with their preferred method of training upon actual use. The disparity between their vague expectations and reality, combined with the contradiction between the \"delayed gratification\" of DCT and their desire for \"immediate gratification,\" made it difficult for them to discern the usefulness of DCT. Participants also viewed DCT as an additional financial burden and tended to avoid training under family pressure. They relied on motivational measures, which further weakened their intention to continue DCT, ultimately leading to the inability to develop continuous usage behavior. Continuous usage behavior differs from initial acceptance as it evolves dynamically with user experience over time. To encourage older adults with MCI to persistently engage with DCT, it is essential to not only thoroughly consider their genuine preferences and the potential disruptions DCT may bring to their lives but also bridge the gap between expectations and actual experiences. While ensuring that older adults receive appropriate external incentives and encouragement, it is equally important to foster their intrinsic motivation, thereby gradually cultivating the habit of sustained DCT usage.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions ( n  = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world. Protocol registration The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1 This Registered Report presents evidence from 87 countries and regions showing that brief emotion-regulation interventions consistently reduced negative emotions and increased positive emotions during the COVID-19 pandemic.

The first practical guide to research methods in memory studies. This book provides expert appraisals of a range of techniques and approaches in memory studies, and focuses on methods and methodology as a way to help bring unity and coherence to this new field of study.

Psychology is the scientific study of behavior and mental processes. This book presents a comprehensive study of the fundamental principles, issues, and methodologies that form the basis of the field of psychology. It explores various areas of psychology, including human behavior, growth, and development, emotions, motivation, learning, perception, thinking, memory, intelligence, personality, psychological testing, and behavior. The book aims to provide readers with a broad understanding of the field of psychology, covering a wide range of topics from basic human behavior to complex mental processes. It serves as an introductory textbook for students of psychology, as well as a reference guide for professionals and researchers in the field.

Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to raise awareness on error bars of cross-validation, which are often underestimated. Simple experiments show that sample sizes of many neuroimaging studies inherently lead to large error bars, eg±10% for 100 samples. The standard error across folds strongly underestimates them. These large error bars compromise the reliability of conclusions drawn with predictive models, such as biomarkers or methods developments where, unlike with cognitive neuroimaging MVPA approaches, more samples cannot be acquired by repeating the experiment across many subjects. Solutions to increase sample size must be investigated, tackling possible increases in heterogeneity of the data.

Cognition-oriented treatments (COTs) are a group of non-pharmacological treatments aimed at maintaining or improving cognitive functioning. Specific recommendations on the use of these interventions in people living with dementia (PLwD) are included in the Italian Guideline on the Diagnosis and Treatment of Dementia and Mild Cognitive Impairment, developed by the Italian National Institute of Health. This systematic review and meta-analysis, based on the GRADE methodology, is part of the guideline. Considered outcomes included the cognitive functions, quality of life, and functional abilities of PLwD, taking into account disease severity, modality and system of delivery, and form of the intervention. The effectiveness of these interventions on caregivers’ outcomes was also assessed. Both group and individual cognitive stimulation were reported as effective in supporting cognitive functions in PLwD at any degree of severity. Individual cognitive training and group cognitive training were reported as effective in improving global cognitive functions in people with mild dementia. Cognitive rehabilitation appeared to be effective only in improving the functional abilities of people with mild dementia. Cognitive rehabilitation appeared to be the most effective in improving caregivers’ outcomes, with results suggesting a reduction in care burden. The observed differences in the effectiveness of these interventions in people with different disease severity can be explained by the intrinsic characteristics of each intervention. Despite the large number of available studies, a high clinical, statistical, and methodological heterogeneity was observed. More methodologically rigorous studies are needed to clarify the effectiveness of each protocol and modality of intervention.

Alzheimer’s disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtle imaging differences. Furthermore, differentiating early MCI (EMCI) from late MCI (LMCI) is also important for interventions. This study proposes a deep learning-based approach using a weighted probability-based ensemble method to integrate results from three-dimensional residual networks (3D ResNet). (1) This study employs 3D ResNet-18, 3D ResNet-34, and 3D ResNet-50 architectures with the Convolutional Block Attention Module (CBAM). The attention mechanism enhances performance by helping the model focus on pertinent information. Data augmentation techniques are applied to address limited data and improve accuracy. (2) To overcome the limitation of the individual convolutional neural network (CNN), an ensemble learning method is adopted. The method assigns weights to each 3D CNN model based on prediction accuracy and integrates them to obtain the final result. Our method achieves accuracy of 94.87%, 92.31%, 95.49%, and 95.97% for MCI vs. NC, MCI vs. AD, EMCI vs. LMCI, and NC vs. EMCI vs. LMCI vs. AD, respectively. The results demonstrate the effectiveness of our method for AD diagnosis.

Exploring students’ cognitive abilities has long been an important topic in education. This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influencing students’ cognitive abilities, and it delved into the differences that arise when using various explainability AI algorithms to analyze educational data mining models. In this paper, five AI models were used to model educational data. Subsequently, four interpretable algorithms, including feature importance, Morris Sensitivity, SHAP, and LIME, were used to globally interpret the results, and PSM causal tests were performed on the factors that affect students’ cognitive abilities. The results reveal that self-perception and parental expectations have a certain impact on students’ cognitive abilities, as indicated by all algorithms. Our work also uncovers that different explainability algorithms exhibit varying preferences and inclinations when interpreting the model, as evidenced by discrepancies in the top ten features highlighted by each algorithm. Morris Sensitivity presents a more balanced perspective, while SHAP and feature importance reflect the diversity of interpretable algorithms, and LIME shows a unique perspective. This detailed observation highlights the practical contribution of interpretable AI algorithms in the field of educational data mining, paving the way for more refined applications and deeper insights in future research.