Neuronal network and awareness measures of post-decision wagering behavior in detecting masked emotional faces

Awareness can be measured by investigating the patterns of associations between discrimination performance (first-order decisions) and confidence judgments (knowledge). In a typical post-decision wagering (PDW) task, participants judge their performance by wagering on each decision made in a detection task. If participants are aware, they wager advantageously by betting high whenever decisions are correct and low for incorrect decisions. Thus, PDW - like other awareness measures with confidence ratings - quantifies if the knowledge upon which they make their decisions is conscious. The present study proposes a new method of assessing the association between advantageous wagering and awareness in the PDW task with a combination of log-linear (LLM) modeling and neural network simulation to reveal the computational patterns that establish this association. We applied the post-decision wagering measure to a backward masking experiment in which participants made first-order decisions about whether or not a masked emotional face was present, and then used imaginary or real monetary stakes to judge the correctness of their initial decisions. The LLM analysis was then used to examine whether advantageous wagering was aware by testing a hypothesis of partial associations between metacognitive judgments and accuracy of first-order decisions. The LLM outcomes were submitted into a feed-forward neural network. The network served as a general approximator that was trained to learn relationships between input wagers and the output of the corresponding log-linear function. The simulation resulted in a simple network architecture that successfully accounted for wagering behavior. This was a feed-forward network unit consisting of one hidden neuron layer with four inputs and one output. In addition, the study indicated no effect of the monetary incentive cues on wagering strategies, although we observed that only low-wager input weights of the neural network considerably contributed to advantageous wagering

Computational Models of Consciousness-Emotion Interactions in Social Robotics: Conceptual Framework

There is a little information on how to design a social robot that effectively executes consciousness-emotion (C-E) interaction in a socially acceptable manner. In fact, development of such socially sophisticated interactions depends on models of human high-level cognition implemented in the robot’s design. Therefore, a fundamental research problem of social robotics in terms of effective C-E interaction processing is to define a computational architecture of the robotic system in which the cognitive-emotional integration occurs and determine cognitive mechanisms underlying consciousness along with its subjective aspect in detecting emotions. Our conceptual framework rests upon assumptions of a computational approach to consciousness, which points out that consciousness and its subjective aspect are specific functions of the human brain that can be implemented into an artificial social robot’s construction. Such research framework of developing C-E addresses a field of machine consciousness that indicates important computational correlates of consciousness in such an artificial system and the possibility to objectively describe such mechanisms with quantitative parameters based on signal-detection and threshold theories

Post-decision wagering affects metacognitive awareness of emotional stimuli : an event related potential study

The present research investigated metacognitive awareness of emotional stimuli and its psychophysiological correlates. We used a backward masking task presenting participants with fearful or neutral faces. We asked participants for face discrimination and then probed their metacognitive awareness with confidence rating (CR) and post-decision wagering (PDW) scales. We also analysed psychophysiological correlates of awareness with event-related potential (ERP) components: P1, N170, early posterior negativity (EPN), and P3. We have not observed any differences between PDW and CR conditions in the emotion identification task. However, the "aware" ratings were associated with increased accuracy performance. This effect was more pronounced in PDW, especially for fearful faces, suggesting that emotional stimuli awareness may be enhanced by monetary incentives. EEG analysis showed larger N170, EPN and P3 amplitudes in aware compared to unaware trials. It also appeared that both EPN and P3 ERP components were more pronounced in the PDW condition, especially when emotional faces were presented. Taken together, our ERP findings suggest that metacognitive awareness of emotional stimuli depends on the effectiveness of both early and late visual information processing. Our study also indicates that awareness of emotional stimuli can be enhanced by the motivation induced by wagering.angielsk

Consciousness and Social Cognition from an Interactionist Perspective: A New Approach on Understanding Normal and Abnormal Relations between Metacognition and Mindreading

Contemporary discussions on relations between metacognition and mindreading result in several theoretical accounts allowing various combinations of both mechanisms in the process of formation of beliefs, intentions, and decisions with respect to oneself or others. In fact, various prefrontal areas of the brain are activated when individuals mentalize about themselves and about other people. Interestingly, the latest accounts of the relationship between mindreading and metacognition clearly favor arguments for interactionism between functionally different mechanisms in the formation of our social knowledge. In particular, a two-level architecture enables a mutual interaction within a complex metacognitive system that is evolutionarily structured into higher and lower level metacognition with different functions and tasks. In our opinion, cognitive architecture of such systems needs to include conscious mechanisms that incorporate information accessibility as activation through the interaction. Here, we will argue that the combination of the two-level account on mindreading and metacognition along with a global broadcasting architecture embedded in the human brain is a good starting point that explains formation of accurate social knowledge and access to such knowledge. In our opinion, it becomes clear that consciousness via the interaction activates many unconscious brain regions, including interpreter systems such as metacognition and mindreading

Rationale and design of Mind-HF: randomized trial of the original Mindfulness-Based Heart Training for patients with heart failure

Introduction: There is evidence that mindfulness-based interventions can be effective in the treatment of patients with depression, anxiety, posttraumatic distress syndrome and many other disorders. Psychological disorders are common among heart failure patients. In the management of heart failure interventions based on mindfulness are used sporadically, and there is also a need for more evidence-based data to prove the efficacy of these methods.Material, methods and results: Mind-HF is a pilot, single-centre, open-label study in which 30 adults hospitalized for heart failure (HF) are randomized to start Mindfulness-Based Heart Training (MBHT) or psychoeducational intervention. The efficacy and safety of MBHT training in comparison to psychoeducational intervention will be assessed over 3 months. Both interventions will be delivered online, therefore, additional feasibility of educational interventions through online tools will also be tested.Conclusions: Mind-HF will provide evidence of the efficacy and safety of the original, HF-tailored mindfulness program delivered online. The results will thus be relevant for introducing MBHT to clinical practice for the management of HF. It will also check the feasibility of online tools in delivering education and psychological support for this group of patients

Depressive and anxiety disorders in the cardiological conditions: psychological interventions

Cardiovascular disease (CVD) remains a leading cause of mortality worldwide, despite diagnostic and pharmacological advances. These adverse effects lead to the search for new etiological factors and effective ways to prevent, treat and rehabilitate CVD. The impact of psychological factors on the development of CVD has gained scientific recognition. Mental and mood disorders, such as depressive disorders, anxiety disorders, and post-traumatic stress disorder (PTSD), can contribute to CVD and significantly affect the quality of life and prognosis of a CVD patient, often contributing to premature death. Therefore, it is essential to study the socio-demographic and psychosocial factors that are most likely to support pro-health behaviour and identify effective psychotherapeutic interventions for CVD patients. These findings result in new interventions, such as meta-cognitive therapy and mindfulness-based training, which promote healthy behaviours and improve clinical conditions and prognosis in CVD patients

Digital health and modern technologies applied in patients with heart failure: Can we support patients' psychosocial well-being?

Despite advances in the treatment of heart failure (HF), the physical symptoms and stress of the disease continue to negatively impact patients' health outcomes. Technology now offers promising ways to integrate personalized support from health care professionals via a variety of platforms. Digital health technology solutions using mobile devices or those that allow remote patient monitoring are potentially more cost effective and may replace in-person interaction. Notably, digital health methods may not only improve clinical outcomes but may also improve the psycho-social status of HF patients. Using digital health to address biopsychosocial variables, including elements of the person and their context is valuable when considering chronic illness and HF in particular, given the multiple, cross-level factors affecting chronic illness clinical management needed for HF self-care

Application of machine learning in predicting frailty syndrome in patients with heart failure

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables

Conscious access to fear-relevant information is mediated by threshold

The present report proposed a model of access consciousness to fear-relevant information according to which there is a threshold for emotional perception beyond that the subject makes hits with no false alarm. The model was examined by having the participants performed a confidence-ratings masking task with fearful faces. Measures of the thresholds for conscious access were taken by looking at the receiver operating characteristics (ROC) curves generated from a three-state low- and high-threshold (3-LHT) model by Krantz. Indeed, the analysis of the masking data revealed that the ROCs had threshold-like-nature (a two-limb shape) rather continuous (a curvilinear shape) challenging in this fashion the classical signal-detection view on perceptual processing. Moreover, the threshold ROC curve exhibited the specific y-intercepts relevant to conscious access performance. The study suggests that the threshold can be an intrinsic property of conscious access, mediating emotional contents between perceptual states and consciousness

Metapoznawczy model świadomości emocji i empiryczne dowody na jego wykonalność

W pracy zaprezentowano metapoznawczy model świadomości w kontekście przetwarzania bodźców afektywnych. Model wyjaśnia problem regulacji świadomych zachowań w odniesieniu do przetwarzania bodźców afektywnych oraz aktywności neuronalnej ludzkiego mózgu. Zgodnie z podstawowym założeniem metapoznawczych modeli świadomości wymaga się występowania reprezentacji pierwszego rzędu oraz metareprezentacji, dzięki którym podmiot zdaje sobie sprawę, że jest świadomy tych reprezentacji. W myśl modelu metapoznawczego interakcja świadomości i emocji może zachodzić przy założeniu przepływu informacji między reprezentacją sensomotoryczną emocji a metapoziomem. Dlatego metapoznawanie można odnieść do wiedzy lub funkcji wykonawczych, które monitorują lub kontrolują emocje. Przedstawiono dowody na wykonalność modelu w oparciu o badania wpływu metawiedzy na reprezentację sensomotoryczną w serii eksperymentów behawioralnych z wykorzystaniem subiektywnych skal świadomości i metod neuroobrazowania mózgu. Zaproponowany metapoznawczy model świadomości może znaleźć swoje zastosowanie w badaniach neuronaukowych mózgu do wydzielenia obszarów ś wiadomej percepcji w postrzeganiu bodź ców afektywnych, jak również w obszarze psychologii klinicznej w rozumieniu poznawczych przyczyn powstawania zaburzeń psychicznych