Artificial Intelligence for Psychological State Recognition: A Perspective based on Theravada Meditational Practices in Sri Lanka for Health Intervention

 The World Health Organization defines health as a state of complete physical, mental and social wellbeing, and not merely the absence of disease or infirmity. The emphasis of physical fitness is not the sole basis of being healthy and it includes being mentally and emotionally fit, which is also a part of the overall lifestyle of a person. Mental stress is a key contributing psychological factor to health problems, and a vast amount of research literature links stress to adverse health outcomes. Stress can directly affect health through autonomic and neuroendocrine responses and indirectly through changes in health behaviours [1].Research on scientific conceptualizations of meditational practices as an alternative mind-body therapy is a key area related to psychological health that received scientific attention recently. There are several types of meditation practices, and they are not limited to the different traditions of Buddhism, but practiced in some other Asian religions, including Hinduism [2]. Buddhist meditation traditions include Theravada, Mayahana and other related traditions, such as Vajirayana. Physiologically, meditation affects heart rate,respiratory rate, blood pressure, cortical activity, metabolism, respiration and skin conductance, while psychological effects include perceptual ability, memory and intelligence, and creativity and selfactualization [2],[3]. As revealed by studies, different meditation traditions have exclusive influences on activating the autonomic system and attentional mechanisms. Theravada and Mahayana types of meditation increase the parasympathetic activation that results in a relaxation response. On the other hand, a robust arousal response is triggered by enhanced sympathetic activation caused by Hindu Tantric practice [2].The new developments in artificial intelligence have changed how psychological states are identified, classified, and managed for human well-being. Shatte et al [4] conducted a review on machine learning in mental health with over 300 studies and identified, (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health and (iv) research and clinical administration as the main research domains. Support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering werethe main machine learning techniques used, and common diseases addressed include depression, schizophrenia, and Alzheimer’s disease. In a similar study by Su et al [5] with over 135 deep learning research, it was revealed that the analysis of genetics and genomics data for understanding psychological health status, vocal and visual expression data analysis for diagnosis, and assessment of the risk of psychological disorders using social media data is possible in addition to the above findings. These studies prove, along with evidence for improvements, the potential of artificial intelligence in enhancing the diagnosis and treatment of patients with psychological health conditions. An important area of research in recognizing human emotional states is machine learning classifiers and signals extracted from bio signals like an electroencephalogram (EEG) [6]. EEG signals provide rich information about mental states and conditions, while high resolution, low cost and convenience are additional advantages [7]. Amihai and Kozhevnikov [2] identified the unique influences of different meditative traditions through EEG patterns that could be differentiated from other altered states.Investigations of the influences of the specific cultural and philosophical contexts in which meditation practices arose on psychophysiological outcomes using artificial intelligence-based interventions is an emerging area of research by cognitive scientists and neuroscientists. The scientific examination of the meditative practices on Zen, Yoga and recent trends in mindfulness are prominent in the literature; however, the influences of Theravada meditation on the physiology and behaviour of the practitioners are still at the early stages of receiving attention from the scientific community. States of mind resulting from meditation can vary based on numerous factors. Different states of mind that can be achieved through Theravada meditation are (i) relaxed and calm state of mind arising from long-term practice of meditation, (ii) concentrative and mindfulness state (iii) perfect states of concentrative meditation or ‘dhayana’ (iv) mindfulness or ‘vidarshana’ state. These states and the different psychological impacts are some of the areas of Theravada tradition where artificial intelligence and associated technologies are required to explore the possibilities of meditative mind-body therapy. To the best of my knowledge, there is no literature on recognising the psychological states when themeditator is in varied and advanced states of mind. Amihai and Kozhevnikov [2] have pointed out that studies are needed to investigate the mechanisms underlying changes of the autonomic nervous system during meditation to unveil the precise factors that cause the arousal and relaxation responses, such as the respiration changes. Since meditation is increasingly being used as a form of therapy, a transformation that should occur through artificial intelligence-related applications is to change the state of mind to achieve a certain level of psychological states such as relaxation and relief from anxiety and tension. This raises the opportunity to change the brain signal pattern or lifestyle using an artificial way in a form of a mechanism to alert the psychological states by identifying brain waves. Identifying the psychological states of meditators in their real-life and normal behaviour rather than in the controlled environment is a challenging area of future research. Although most studies have induced stress in controlled environments, developing a protocol that sustains real scenarios such as virtual reality and an online system with stress recognition in real-time will be better than offline experiments [7]. Technological advances like social media, smartphones, wearable and neuroimaging provide opportunities to obtain an extensive range of data at a rapidly growing rate which could be used for psychological state identification. One such research area under investigation is the development of models for optimal emotion recognition based on machine learning algorithms and EEG-video fusion [8]. Research on emotion recognition, multimedia feature fusion and human perception based on artificial intelligence and associated technologies have potential for meditative mind-body therapy. The impact of meditation in different states of mind has been proven to some extent by research. Theravada traditions of meditation that underscore ‘internally steadying’ or stabilize the ‘unstable mind’ can potentially intervene in the health of the practitioner strikingly. Artificial intelligence and associated technologies have a greater potential to recognise psychological states and thereby change the brain signal pattern in an artificial way to cultivate the state of quiescence and tranquility

Artificial ASMR: A Cyber-Psychological Study

The popularity of Autonomous Sensory Meridian Response (ASMR) has skyrockteted over the past decade, but scientific studies on it are still few and immature. With our attention caught by the common acoustic patterns in ASMR audios, we investigate the correlation between the time-frequency and cyclic features of audio signals and their effectiveness in triggering ASMR effects. A cyber-psychological approach that combines signal processing, artificial intelligence, and experimental psychology is taken, with which we are able to identify ASMR-related acoustic features, and therewith synthesize random artificial ASMR audios.Comment: Submitted to ICASSP 202

Искусственный интеллект в правосудии: юридико-психологические аспекты правоприменения

The subject. Artificial intelligence is considered as an interdisciplinary legal and psychological phenomenon. The special need to strengthen the psychological component in legal research of artificial intelligence and its introduction into the practice of law enforcement and justice, in particular, is substantiated.The main goal of the study is to confirm or refute hypothesis that AI may be implemented in justice and to substantiate the legal limits of such implementation.The methodology. Based on the comparison of the current legislation, the practice of its application, and other empirical data, internal and external legal and psychological factors of legal regulation and the use of artificial intelligence in jurisprudence and judicial proceed- ings are identified.The main results, scope of application. The analysis of legal and doctrinal definitions of artificial intelligence in jurisprudence has shown that their defining and integral part is relationships that are the result of psychological practices and the subject of psychological science (internal factors). Legal studies of artificial intelligence are based on a psychological conceptual apparatus, all of them legally describe artificial intelligence, first of all, as a psychological phenomenon and build an analogy between the psychology of a living intelligent subject and an inanimate object, humanizing the latter. The federal legislator is also following the path of using the psychological conceptual apparatus. Such categories like human cognitive functions and intellectual activity are applied in Russian Federal Law "On conducting an experiment to establish special regulation in order to create the necessary conditions for the development and implementation of artificial intelligence technologies in the subject of the Russian Federation - the federal city of Moscow and amending Articles 6 and 10 of the Federal Law "On Personal Data". The legal and psychological analysis of the practice of using elements of artificial intelligence in corporate governance, justice, labor relations, social insurance, electoral procedures has been subjected.The conclusion is substantiated that an indispensable condition for the introduction of arti- ficial intelligence and its elements into justice is trust on the part of the disputing parties and the court. Such trust is provided with a real possibility of verifying the actions and decisions made with artificial intelligence by psychologically acceptable and legally formalized methods (external factors). The use of artificial intelligence in law enforcement in general and justice in particular is possible in two directions: (1) solving problems related to the approximation of specialized artificial intelligence systems in legal proceedings to human capabilities and their integration to enhance intelligence; (2) creating artificial intelligence, which is the integration of already created elements of artificial intelligence into a single system capable of participating in justice, but does not have the properties of free will and does not acquire legal personality. Law enforcement using artificial intelligence should comply with the principles enshrined in the European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and their environment, the provisions of which should be implemented in domestic legislation, having previously been revised in accordance with the national legal tradition.Искусственный интеллект рассмотрен как междисциплинарное юридико-психологическое явление. Обоснована особая потребность усиления психологической составляющей в юридических исследованиях искусственного интеллекта и внедрения его в практику правоприменения, и правосудия в частности. На основе сопоставления действующего законодательства, практики его применения, иных эмпирических данных выделены внутренние и внешние юридико-психологические факторы правового регулирования и применения искусственного интеллекта в юриспруденции и судопроизводстве. Предпринятый анализ легальных и доктринальных определений искусственного интеллекта в юриспруденции показал, что определяющая и неотъемлемая их часть – терминология и отношения, являющиеся следствием психологических практик и предметом изучения психологической науки (внутренние факторы). Непременным условием внедрения в правосудие слабого искусственного интеллекта и его элементов является доверие со стороны спорящих сторон и суда, обеспечиваемое реальной возможностью верификации совершаемых им действий и принимаемых решений психологически допустимыми для человека и юридически оформленными методами (внешние факторы). Правоприменение искусственного интеллекта должно соответствовать принципам, закрепленным в Европейской этической хартии о применении искусственного интеллекта в судебных системах, положения которой следует имплементировать в отечественное законодательство, предварительно переработав в соответствии с национальной правовой традицией.

Human-artificial intelligence engagement exploring the perspectives of users and tourism managers

The progress and sophistication of technological systems promise to accelerate the tourism sector, influencing business management at the commercial, human resources, and planning levels. The main objective of this thesis is to analyse the evolution of the literature on artificial intelligence and how it can be integrated with the constructs of engagement, intimate knowledge, authenticity, attachment, and psychological ownership. Several analyses were carried out to achieve the intended results. First, a comprehensive literature review was done, through the analysis of scientific articles, to understand the development of scientific research on artificial intelligence and user engagement. Secondly, a qualitative study was conducted to evaluate the impact of virtual assistants in the tourism industry, both at the organization and customer levels, using the thematic analysis method in structured interviews with top managers of the tourism sector. The results show that the benefits of using artificial intelligence outweigh the negative ones and will impact firm management. Finally, two quantitative studies were performed to analyse which factors influence customer engagement. The first study analyses the constructs of authenticity and attachment as motivators of engagement between tourists and virtual assistants, proving that these factors significantly influence the interaction between both. The second study investigates the communication between the virtual assistant and the user, emphasizing the importance of intimate knowledge, authenticity, and connection as psychological motivators. The results show that all three constructs significantly impact customer engagement with the virtual assistant

Analysis of crowd behavior through pattern virtualization

The study of the concentration of individuals in public places such as squares, shopping malls, parks, gardens, etc., is an open study field in the different disciplines of science, that leads to the need of having systems that allow to forecast and to predict eventualities in uncontrolled situations, as it is the case of an earthquake. From that assumption, artificial intelligence, as a branch of computational sciences, studies the human behavior in a virtual way in order to obtain simulations based on social, psychological, neuro-scientific areas, among others, with the purpose of linking these theories to the area of artificial intelligence. This paper presents a way to generate virtual multitudes with heterogeneous behaviors, in such a way that the individuals that form the multitude present different behaviors

Modelling children’s entertainment in the playware playground

This paper introduces quantitative measurements/metrics of qualitative entertainment features within interactive playgrounds inspired by computer games and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. For this purpose the innovative Playware playground is presented and a quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced. Evolving artificial neural networks (ANNs) are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment according to player reaction time with the game. The limitations of the methodology and the extensibility of the proposed approach to other genres of digital entertainment are discussedpeer-reviewe

Design Cognition: Cognitive Science in Design

To complement theories developed in cognitive science that yielded some understanding of human intelligence, cross disciplinary research has begun to explore the intellectual aspects of creativity and the design knowledge created during the creative processes. Following this new trend, this book systematically explains well developed theories in cognitive science, their applications, and future possible directions of utilizing cognitive science in design. From explaining the fundamental concepts of cognition, elaborating advanced research methodologies, and discussing individual design thinking and creative processes through psychological experiments; phenomenon of design cognition should be understood better. Other studies, which embrace human cognition, including artificial intelligence and neuroscience are also covered briefly in this book to catch up.https://lib.dr.iastate.edu/arch_books/1008/thumbnail.jp

Preventive and curative personality profiling based on EEG, ERP, and big five personality traits: a literature review

Healthy lifestyle is a significant factor that impacts on the budget for medicine. According to psychological studies, personality traits based on the Big Five personality traits especially the neuroticism and conscientiousness, have the ability to predict healthy lifestyle profiling. Electrophysiological signals have been used to explore the nature of individual differences and personality that are related to perception. In this paper, we reviewed studies examining healthy lifestyle profile i.e., preventive and curative using electroencephalography (EEG) and event-related potential (ERP) signals. This study proposed a general experimental model by reviewing the literature to build suitable experimental design for implementing artificial intelligence techniques based on the machine learning

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Magic in the machine: a computational magician's assistant

A human magician blends science, psychology and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical effect required. The complexity is often caused by interacting and often conflicting physical and psychological constraints that need to be optimally balanced. Normally this tuning is done by trial and error, combined with human intuitions. Here we focus on applying Artificial Intelligence methods to the creation and optimisation of magic tricks exploiting mathematical principles. We use experimentally derived data about particular perceptual and cognitive features, combined with a model of the underlying mathematical process to provide a psychologically valid metric to allow optimisation of magical impact. In the paper we introduce our optimisation methodology and describe how it can be flexibly applied to a range of different types of mathematics based tricks. We also provide two case studies as exemplars of the methodology at work: a magical jigsaw, and a mind reading card trick effect. We evaluate each trick created through testing in laboratory and public performances, and further demonstrate the real world efficacy of our approach for professional performers through sales of the tricks in a reputable magic shop in London