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Copyright of Revista Médica Clínica Las Condes is the property of Editorial Sanchez y Barcelo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkish Journal of Psychiatry is the property of Turk Psikiyatri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turk Psikiyatri Dergisi is the property of Turk Psikiyatri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article is an obituary for Daniel Dennett, an influential American philosopher and cognitive scientist who passed away in 2024 at the age of 82. Dennett made significant contributions to philosophy, particularly in the areas of intentionality, consciousness, free will, and religion. He wrote numerous books and articles, formulating naturalistic views and challenging traditional philosophical approaches. Dennett's philosophy aimed to demystify phenomena that are often considered magical or beyond scientific understanding. He emphasized the importance of philosophy in scientific inquiry and criticized the dismissal of philosophical ideas by some scientists. [Extracted from the article]

Background: The constant rise in the prevalence of major depressive disorder calls for new, effective, and accessible interventions that can rapidly and effectively reach a wide range of audiences. Recent developments in the digital health domain suggest that dedicated online platforms may potentially address this gap. Focusing on targeting ruminative thought, a major symptomatic hallmark of depression, in this study we hypothesized that delivering a digital health–based intervention designed to systematically facilitate thought progression would substantially alleviate depression. Objective: The study aims to investigate the efficacy of a novel digital intervention on the reduction of depressive symptoms. This intervention was designed as an easy-to-use gamified app specifically aimed to facilitate thought progression through intense practicing of associative, semantically broad, fast, and creative thought patterns. Methods: A randomized clinical trial was conducted, comparing changes in depression symptoms between participants who used the app in the intervention group (n=74) and waitlist control group (n=27) over the course of 8 weeks. All participants filled out a battery of clinical questionnaires to assess the severity of depression at baseline and 4 and 8 weeks after starting the study. These primarily included the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Patient Health Questionnaire-9 as well as the Positive Affect Negative Affect Scale-Negative Affect Score, Ruminative Response Scale, and Symptoms of Depression Questionnaire. Additional questionnaires were implemented to assess anxiety, positive affect, anhedonia, and quality of life. Results: The results indicate that across multiple clinical measurements, participants in the intervention group who played the gamified app showed greater and faster improvement in depressive symptoms compared with their waitlist control counterparts. The difference between the groups in MADRS improvement was –7.01 points (95% CI –10.72 to –3.29; P <.001; Cohen d =0.67). Furthermore, the difference in improvement between groups persisted up to 4 weeks posttrial (MADRS differences at week 12: F 49,2=6.62; P =.003; ηp2=0.21). At the end of the trial, participants who played the app showed high interest in continuing using the app. Conclusions: The results demonstrate that a gamified app designed to facilitate thought progression is associated with improvement in depressive symptoms. Given its innovative and accessibility features, this gamified method aiming to facilitate thought progression may successfully complement traditional treatments for depression in the future, providing a safe and impactful way to enhance the lives of individuals experiencing depression and anxiety. Trial Registration: ClinicalTrials.gov NCT05685758; https://clinicaltrials.gov/study/NCT05685758 [ABSTRACT FROM AUTHOR]

One of the most important elements of advertising campaigns is their message. Many factors can influence message effectiveness, inter alia, the content, the form, the carrier, or the location of the advertisement. Together, these aspects can determine how visible the advertising message is to the audience. At the campaign-planning stage, it is usually hard to tell how the advertisement should be presented and properly exposed so that the recipients perceive and remember it. We propose performing the pre-test of advertisements with the use of a virtual reality system that provides the environment close to the natural conditions and using cognitive neuroscience tools to record the objective measures of the audience's opinions. Our proposal shows the procedure for designing and conducting an experiment that allows for an assessment of the advertising message effectiveness based on its visibility. The article also presents the preliminary results of a pilot experiment that was designed to test both the virtual reality system and the proposed procedure. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of telecommunications, the evolution of Communication Radio Access Networks (CRAN) has introduced unprecedented challenges to the security of IT networking environments. As the demand for high-speed connectivity and seamless data transmission grows, safeguarding CRAN becomes paramount. With the proliferation of cyber-attacks and the complexity of CRAN architecture, conventional security measures prove insufficient, necessitating an innovative and adaptive approach. Existing methodologies lack the adaptability required to combat emerging threats effectively. This research bridges this gap by proposing the integration of the Dehaene-Changeux Model, renowned for its applicability in cognitive neuroscience, with Moth-Flame Optimization, a nature-inspired algorithm known for its efficiency in solving complex optimization problems. This research addresses the pressing need for a robust security framework using the Dehaene-Changeux Model Driven Moth-Flame Optimization approach. It elucidates the utilization of the Dehaene-Changeux Model to mimic cognitive responses, coupled with Moth-Flame Optimization for realtime adaptability. These models form a dynamic defense mechanism against evolving security threats in the CRAN environment. Results obtained from simulation and testing validate the efficacy of the proposed security model. The adaptive nature of the Dehaene-Changeux Model, combined with the optimization capabilities of Moth-Flame Optimization, showcases a significant enhancement in CRAN security. The research contributes a pioneering solution to fortify IT networking environments in CRAN, ensuring resilience against current and future cyber threats. [ABSTRACT FROM AUTHOR]

Under the guidance of the "High-tech Olympics" concept, literature review is used to explore the field of psychological sci-tech research from the perspective of cognitive neuroscience. It is stated that psychological sci-tech service aims to "enhance performance stability" by integrating cognitive neuroscience techniques with psychological services, with the features of quantifying neural activities during sports performance, identifying psychological risks before competitions and regulating athletes' mental abilities during competition. The implementation process of the services relies on the fundamental principles of action control to quantify, iterate, and monitor its effectiveness in real time by utilizing cognitive neuroscience techniques, big data, artificial intelligence algorithms and other tools. Its application pathway involves mobile electroencephalography and wireless transmission as vehicles, employing coding interfaces and service platforms to fit multi-dimensional and multi-modal physiological and psychological data, thus to dynamically monitor and adjust athletes' mental states. Psychological sci-tech services are transforming from experiential-driven to data-driven iterative development, paving the way for increased precision and efficiency in psychological services, ultimately supporting athletes in achieving stable performance during competitions. [ABSTRACT FROM AUTHOR]

Copyright of Paradigma is the property of Universidad Pedagogica Experimental Libertador and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electronic Journal of Research in Educational Psychology is the property of Editorial of the University of Almeria (Spain) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In an era of increased competition between companies, the retention, as well as the acquisition of new clients constitutes one of the greatest tasks for the management of any company. In order to successfully reach target clients and influence their purchasing decisions, marketing specialists utilise various forms of communication, including mass communication, of which the primary tool is advertising. The aim of this article is to present the results of a study on the effectiveness of advertising communication conducted through the use of electroencephalography (EEG). The author argues that a company utilising this method may significantly improve its existing knowledge of clients and facilitate the creation of appropriate strategies for the future, e.g. product launch strategies. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Estudos da Linguagem is the property of Revista de Estudos da Linguagem and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

Background: Many people with harmful addictive behaviors may not meet formal diagnostic thresholds for a disorder. A dimensional approach, by contrast, including clinical and community samples, is potentially key to early detection, prevention, and intervention. Importantly, while neurocognitive dysfunction underpins addictive behaviors, established assessment tools for neurocognitive assessment are lengthy and unengaging, difficult to administer at scale, and not suited to clinical or community needs. The BrainPark Assessment of Cognition (BrainPAC) Project sought to develop and validate an engaging and user-friendly digital assessment tool purpose-built to comprehensively assess the main consensus-driven constructs underpinning addictive behaviors. Objective: The purpose of this study was to psychometrically validate a gamified battery of consensus-based neurocognitive tasks against standard laboratory paradigms, ascertain test-retest reliability, and determine their sensitivity to addictive behaviors (eg, alcohol use) and other risk factors (eg, trait impulsivity). Methods: Gold standard laboratory paradigms were selected to measure key neurocognitive constructs (Balloon Analogue Risk Task [BART], Stop Signal Task [SST], Delay Discounting Task [DDT], Value-Modulated Attentional Capture [VMAC] Task, and Sequential Decision-Making Task [SDT]), as endorsed by an international panel of addiction experts; namely, response selection and inhibition, reward valuation, action selection, reward learning, expectancy and reward prediction error, habit, and compulsivity. Working with game developers, BrainPAC tasks were developed and validated in 3 successive cohorts (total N=600) and a separate test-retest cohort (N=50) via Mechanical Turk using a cross-sectional design. Results: BrainPAC tasks were significantly correlated with the original laboratory paradigms on most metrics (r =0.18-0.63, P <.05). With the exception of the DDT k function and VMAC total points, all other task metrics across the 5 tasks did not differ between the gamified and nongamified versions (P >.05). Out of 5 tasks, 4 demonstrated adequate to excellent test-retest reliability (intraclass correlation coefficient 0.72-0.91, P <.001; except SDT). Gamified metrics were significantly associated with addictive behaviors on behavioral inventories, though largely independent of trait-based scales known to predict addiction risk. Conclusions: A purpose-built battery of digitally gamified tasks is sufficiently valid for the scalable assessment of key neurocognitive processes underpinning addictive behaviors. This validation provides evidence that a novel approach, purported to enhance task engagement, in the assessment of addiction-related neurocognition is feasible and empirically defensible. These findings have significant implications for risk detection and the successful deployment of next-generation assessment tools for substance use or misuse and other mental disorders characterized by neurocognitive anomalies related to motivation and self-regulation. Future development and validation of the BrainPAC tool should consider further enhancing convergence with established measures as well as collecting population-representative data to use clinically as normative comparisons. [ABSTRACT FROM AUTHOR]

Developments in cognitive neuroscience and brain imaging technologies make it possible to interface directly with the human brain using sensors. Brain activity during the navigation of built space is influenced by user perception and cognition in interaction with visual stimuli. This paper has a fourfold aim: (a) to elucidate the concept of design evaluation for human well-being; (b) to investigate the principal available methodologies for non-invasive human neurological response monitoring; (c) to explain the visual simulator specification used for virtual environments; (d) to review systematically the existing literature that employed empirical methodology for the integration of an immersive visual virtual system with biometric data collection used in architecture design evaluation for pre- or post-occupancy. The paper concludes with considerations for implementing a comprehensive approach that uses biometric response as a method of design evaluation for future research. [ABSTRACT FROM AUTHOR]

Steady-state visually evoked potentials (SSVEPs) have become popular in brain-computer interface (BCI) applications in addition to many other applications on clinical neuroscience (neurodegenerative disorders, schizophrenia, epilepsy, etc.), cognitive (visual attention, working memory, brain rhythms, etc.), and use of engineering researches. Among available methods to measure brain activities, SSVEPs have advantages like higher information transfer rate, simplicity in structure, and short training time. SSVEP-based BCIs use flickering stimuli at different frequencies to discriminate distinct commands in real life. Some features are extracted from the SSVEP signals before these commands are classified. The wavelet transform (WT) has attracted researchers among feature extraction methods since it utilizes the non-stationary signals well. In the WT, a sample function (named mother wavelet) represents the SSVEP signal in both time and frequency domains. Unfortunately, there is no universal mother wavelet function that fits all the signals. Therefore, choosing an appropriate mother wavelet function may be a challenge in WT-related studies. Although there are such studies in three- and seven-command SSVEP-based studies, there is no study for two-command systems in our knowledge. In this study, two user commands flickered at the combinations of seven different frequencies were tested to determine which frequency pairs give the highest performance. For this purpose, three well-known wavelet features (energy, entropy, and variance) were calculated for each of derived EEG frequency bands from the discrete WT coefficients of SSVEP signals. The WT was repeated for six different mother wavelet functions (Haar, Db4, Sym4, Coif1, Bior3.5, and Rbior2.8). Then, four feature sets (every three features, and all together) were applied to seven commonly-used machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbors, and Ensemble Classifiers). We achieved 100% accuracies among these 3,528 runs (7 classifiers x 4 feature sets x 6 mother wavelets x 21 flickering frequency pairs) using the mother wavelet function of Haar and the Ensemble Learner classifier. The highest classifier performances are 100% when two commands have the flickering frequency pairs of (6.0 and 10 Hz), (6.5 and 8.2 Hz), or (6.5 and 10.0 Hz). We obtained three main outcomes from this study. First, the most representative mother wavelet function was Haar, while the worst one was Symlet 4. Second, the Ensemble Learner classifier gave the maximum classifier performance in a two-command SSVEP-based BCI system. Besides, two user commands from SSVEP should be one of the frequency pairs of (6.0 and 10.0 Hz), (6.5 and 8.2 Hz), and (6.5 and 10.0 Hz) to achieve the maximum accuracy. • The highest classifier accuracy is 100% on discriminating two user commands from SSVEP signals. • Two commands can flash at a rate of (6 and 10 Hz), (6.5 and 8.2 Hz), or (6.5 and 10 Hz) to achieve the maximum accuracy. • The most representative mother wavelet is the Haar function. • The Ensemble Learner Classifier gives the highest classification performances. [ABSTRACT FROM AUTHOR]

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). Thirty-four participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of the STN along the dorsolateral-ventromedial axis, identified using manually delineated high resolution anatomical images and based on a previously published atlas. Using these paradigms, all segments responded equally to the experimental manipulations, and the tasks did not provide evidence for the tripartite model. [ABSTRACT FROM AUTHOR]

Recognizing a face as familiar is essential in our everyday life. However, 'familiarity' covers a wide range - from people we see every day to those we barely know. Although face recognition is studied extensively, little is known about how the degree of familiarity affects neural face processing, despite the critical social importance of this dimension. Here we report the results of a multivariate cross-classification EEG experiment, where we study the temporal representational dynamics of the degree of familiarity. Participants viewed highly variable face images of 20 identities. Importantly, we measured face familiarity using subjective familiarity ratings in addition to testing explicit knowledge and reaction times in a face matching task. A machine learning algorithm, trained to discriminate familiar and unfamiliar faces from a separate study, was used to predict the degree of face familiarity from the pattern of the EEG data. We found that the neural representations of the degree of familiarity emerge between 400 - 600 msec post-stimulus onset for famous persons. The correlation between decoding performance and behavioral familiarity was more reliable, occurred earlier and lasted longer when personally familiar and viewers' own faces were included in the analysis. Our findings provide new insights into how the brain represents faces with various degrees of familiarity and show that the degree of familiarity can be decoded reliably from the EEG at a relatively late time window. These results support the idea that representations of familiar faces form part of a general neural signature of the familiarity component of recognition memory processes. [ABSTRACT FROM AUTHOR]

Comparative research on language, music, and action in cognitive neuroscience keeps finding evidence for both shared and non-shared components of cognitive systems. The discussions, then, tend to quickly fall into the sterile dichotomy between domain-specific versus domain-general options. In this position paper, we take issue with this dichotomy and argue for an alternative account based on neural reuse theories to understand findings on the relationship between language, music, and action. We argue that the differences between those cognitive systems can be explained in terms of the specialization of the same brain mechanism(s) for each domain, which emerges in the course of development and/or evolution. [ABSTRACT FROM AUTHOR]