Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause ...massive stress-related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland-War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.
The coronavirus disease 2019 (COVID-19) pandemic and its immediate aftermath present a serious threat to the mental health of health care workers (HCWs), who may develop elevated rates of anxiety, ...depression, posttraumatic stress disorder, or even suicidal behaviors. Therefore, the aim of this article is to address the problem of prevention of HCWs’ mental health disorders by early prediction of individuals at a higher risk of later chronic mental health disorders due to high distress during the COVID-19 pandemic. The article proposes a methodology for prediction of mental health disorders induced by the pandemic, which includes: Phase 1) objective assessment of the intensity of HCWs’ stressor exposure, based on information retrieved from hospital archives and clinical records; Phase 2) subjective self-report assessment of stress during the COVID-19 pandemic experienced by HCWs and their relevant psychological traits; Phase 3) design and development of appropriate multimodal stimulation paradigms to optimally elicit specific neuro-physiological reactions; Phase 4) objective measurement and computation of relevant neuro-physiological predictor features based on HCWs’ reactions; and Phase 5) statistical and machine learning analysis of highly heterogeneous data sets obtained in previous phases. The proposed methodology aims to expand traditionally used subjective self-report predictors of mental health disorders with more objective metrics, which is aligned with the recent literature related to predictive modeling based on artificial intelligence. This approach is generally applicable to all those exposed to high levels of stress during the COVID-19 pandemic and might assist mental health practitioners to make diagnoses more quickly and accurately.
The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead ...to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.
Multimodal analysis of startle type responses Ćosić, Krešimir; Popović, Siniša; Kukolja, Davor ...
Computer methods and programs in biomedicine,
06/2016, Letnik:
129
Journal Article
Recenzirano
Highlights • Startle type responses were analyzed by multimodal physiological, speech and facial features. • Acoustic startle probes, airblasts, images, sounds and composite stimuli were used. • ...Dominant responses occurred in skin conductance, eye blink, head movement, speech fundamental frequency and energy. • Strongest responses were obtained for composite stimuli, what illustrates an approach to enhance the power of elicitation.
Comprehensive multimodal psychophysiological measurements and smart data analysis based on wearable and low-cost technologies could enhance traditional air traffic controller (ATC) selection process. ...Many recent studies in neuro-cognitive science and stress resilience illustrated effectiveness of these multimodal measurements and appropriate metrics in comprehensive assessment of ATCs' mental states, such as cognitive workload, cognitive decline, attention deficit, fatigue, emotional and behavioural problems, etc. Accordingly, this article is focused on innovation efforts in ATC selection protocols based on a set of comprehensive stimuli and corresponding multimodal psychophysiological measurements. The concept of enhancement of ATC selection process presented in this article includes complex physiological, oculometric and speech measurements and appropriate metrics. From these multimodal measurements during specific stimulation tasks, which include different versions of acoustic startle stimuli, airblasts, semantically relevant aversive images and sounds, different versions of Stroop tests, visual tracking test, a complex set of multimodal-multidimensional features is computed as predictors of ATC candidates' future performance, like: stress resilience, workload capacity, attention, visual performance, working memory etc. Such cost-effective, more objective, non-invasive preliminary measurements, lasting no longer than 45 minutes may have good discriminative power and might be used in ATC selection processes as enhancement of current selection procedures. Comprehensive analysis of presented multimodal features during different experimental conditions might also be very useful in selection processes of other stressful professional jobs, like first responders, pilots, astronauts etc.
The general goal of the interdisciplinary work refers to the research of complex experimental interactions and theoretical works on the subject of neural mechanisms in the perception of decision ...making; economic and perceptual decision making; high and low volatility bias of the investors perception, and the perception bias during the duration of the stimuli, according to the theory of subsequent effect. The work shows the complex interweaving of scientific achievements in the process of decision making. The given scientific and applicative research leads us towards understanding the levels of complexity of financial decision making with the principles of universality; spatial and temporal fluctuations of input in perceptual decision making (perception can be under the influence of attention and can surface subconsciously without conscious consciousness), possible extending of current results and models from two alternative choices and are they different in respect to spatial and temporal fluctuations ( our capability of deciding can result from random fluctuations in the background of electric noise in the brain) effects on the results of decision making. The focus of this research paper is the analysis of testing the perception of investors which shows us the subsequent effect of volatility, which further indicates the twisted perception after prolonged exposure to extreme levels of volatility. This established framework can give us key insight in the domain of deductive reasoning. Bias in deductions is questioned using the VIX index.
Pervasiveness of extreme negative emotions, especially anger, hatred and humiliation, as well as negative appraisal style, has a significant impact on the process of societal radicalization. ...Dominance of such emotions, and the corresponding appraisal style, very often threaten societal security. Emotionally Based Strategic Communications (EBSC), proposed in this article, can be used as a communications strategy for mitigating negative and promoting positive emotions within societal groups exposed to radicalization processes. Essentially, EBSC as mechanisms of positive emotional regulation strategy are based on reshaping the (re)appraisal style of radicalized groups. Grounded in the appraisal theories of emotions, EBSC are entirely non-coercive, and applicable to a wide variety of groups. Such communications strategies are also extendible to Internet-based social media networks, opening new possibilities in deradicalization processes using sentiment analysis, cognitive computing, botnets and other ICT-based methods and techniques.
CAUSAL PEER EFFECTS IN FINANCIAL DECISION MAKING Njegovanović, Ana; Ćosić, Krešimir Petar
Review of Innovation and Competitiveness,
01/2016, Letnik:
2, Številka:
1
Journal Article, Book Review
Recenzirano
Odprti dostop
The research paper connects three key elements from the study (conducted using neural database of experimental asset market that have tested the fundamental mechanisms that generate peer effect, the ...neural database was measured using functional magnetic resonance imaging (fMRI); Cary Frydman, 2015- University of Southern California-Marshall School of Business) relating to: experimental control in the laboratory of random peer assignment,; neural activity in testing new prediction explaining peer effect and neural activity in the conduct of trade. The methodology used in the research of peer effect relies on the theory of predicting error, the signal which measures changes in anticipation of the net present value which generates new information. Cognitive neuroscience shows that the prediction error is measured in a certain part of the brain known as the ventral striatum. Measuring the potential value gives insights to economists on which factors affecting the subjective utility. Testing is constructed with 48 patients who were given $ 100 of experimental money and they were given the opportunity to invest in two separate assets in over two hundred experiments. The experiment showed that subjects converted their final portfolio from experimental currency to real dollars using the exchange rate of 5: 1. In addition to profits from the experiment, subjects were paid a fixed "show-up" fee of $ 20. There are two difficulties in identifying causal peer effect in economic behavior (Minsk, 1993). Correlated behavior between two representatives may potentially be the engine by common shocks of the peer group or endogenous election in the peer group. In addition to the prediction that deals with causal peer effect, there have been further developed predictions that generate different mechanisms of peer effects using neural database. Focus on neural prediction is the neural activity that generates the moment when peers allocation investment is published, respectively the display of "peer decisions". This display is exclusively linked to the processing of information as opposed to considering solutions. This is significant because neuroscience is characterized by neural activity that generates new information in decision making. Thus, neural prediction is determined by the ventral striatum, which predicts the occurrence of peer decisions. The large part of the literature in social psychology suggests that people have a direct need to follow others, especially manifested in situations where there is no objectively correct action, so the cause of intermediaries used peer action as a social anchor on which it bases its behavior. Certain dialectical relationship between neuroscience and neurofinance determines a deeper understanding of financial decision making which leads to different results and different cognitive operations. Our thoughts, although abstract in form, determine procedures of certain neural circuits within our brain. The goal of neuroscience is uncovering these circuits and the possibility of deconstructing complex thought processes in individual components and determine how they integrate into our thought process. The results lead towards the understanding of decision making which shape our future and fate. The market implications, from the aspect of neurofinances, is vital in uncovering deeper knowledge about the effect of emotions and states such as attitudes towards risk, excessive trust, heuristic bias and gender, which finally results in financial decision making on an individual and institutional basis. The implications of fear and corruption in the financial industry can be explained through neurofinance and even give us more choices in the decision process. Social decisions demand an evaluation of costs and benefits for oneself and others. Connected with emotions and caution, the amygdala is involved in decision making and social interactions. The harm caused by the amygdala deteriorates social interaction, while the social neuropeptide oxytocin affects social decisions by changing the function of the amygdala in one aspect. Empirical research, conducted on a sample of randomly selected subjects who were given identical information, shows that on the basis of the neural database gathered from experimental asset market for testing underlying mechanisms which generate peer effect (Cary Frydman, 2015). Experimental evidence of peer effect in individual behavior of trade and neural data were used for testing of experimental mechanisms generating peer effect. Although the mechanisms which create peer effect in laboratory experiments don't suit the quantitative norm they can ensure settings for probing mechanisms using neural database. The methodology of neurofinance replicates the behavior of trade in laboratory conditions which are robustly found in the field.
In order to improve intelligent Human-Computer Interaction it is important to create a personalized adaptive emotion estimator that is able to learn over time emotional response idiosyncrasies of ...individual person and thus enhance estimation accuracy. This paper, with the aim of identifying preferable methods for such a concept, presents an experiment-based comparative study of seven feature reduction and seven machine learning methods commonly used for emotion estimation based on physiological signals. The analysis was performed on data obtained in an emotion elicitation experiment involving 14 participants. Specific discrete emotions were targeted with stimuli from the International Affective Picture System database. The experiment was necessary to achieve the uniformity in the various aspects of emotion elicitation, data processing, feature calculation, self-reporting procedures and estimation evaluation, in order to avoid inconsistency problems that arise when results from studies that use different emotion-related databases are mutually compared. The results of the performed experiment indicate that the combination of a multilayer perceptron (MLP) with sequential floating forward selection (SFFS) exhibited the highest accuracy in discrete emotion classification based on physiological features calculated from ECG, respiration, skin conductance and skin temperature. Using leave-one-session-out crossvalidation method, 60.3% accuracy in classification of 5 discrete emotions (sadness, disgust, fear, happiness and neutral) was obtained. In order to identify which methods may be the most suitable for real-time estimator adaptation, execution and learning times of emotion estimators were also comparatively analyzed. Based on this analysis, preferred feature reduction method for real-time estimator adaptation was minimum redundancy – maximum relevance (mRMR), which was the fastest approach in terms of combined execution and learning time, as well as the second best in accuracy, after SFFS. In combination with mRMR, highest accuracies were achieved by k-nearest neighbor (kNN) and MLP with negligible difference (50.33% versus 50.54%); however, mRMR+kNN is preferable option for real-time estimator adaptation due to considerably lower combined execution and learning time of kNN versus MLP.
•We compared accuracy and learning/execution times of emotion estimation methods.•Feature selection methods had more impact on accuracy than machine learning methods.•Combination of SFFS and MLP methods exhibited the highest emotion estimation accuracy.•mRMR+kNN combination is preferable for real-time adaptation of emotion estimation.•Skin conductance features contributed the most to the emotion estimation accuracy.
Affective multimedia documents such as images, sounds or videos elicit emotional responses in exposed human subjects. These stimuli are stored in affective multimedia databases and successfully used ...for a wide variety of research in psychology and neuroscience in areas related to attention and emotion processing. Although important all affective multimedia databases have numerous deficiencies which impair their applicability. These problems, which are brought forward in the paper, result in low recall and precision of multimedia stimuli retrieval which makes creating emotion elicitation procedures difficult and labor-intensive. To address these issues a new core ontology STIMONT is introduced. The STIMONT is written in OWL-DL formalism and extends W3C EmotionML format with an expressive and formal representation of affective concepts, high-level semantics, stimuli document metadata and the elicited physiology. The advantages of ontology in description of affective multimedia stimuli are demonstrated in a document retrieval experiment and compared against contemporary keyword-based querying methods. Also, a software tool Intelligent Stimulus Generator for retrieval of affective multimedia and construction of stimuli sequences is presented.
PDF
