BACKGROUND: The bio-psycho-social model is mostly used to understand the etiology and pathogenesis of psychiatric disorders. Even though in our era, the biological factors became more dominant in the field, the stress-diathesis model is quite acceptable to explain and understand the evolution of psychotic as well as affective disorders.
BACKGROUND: In this case report we present a patient, in her late 40\'s, admitted to our department with a manic-psychotic episode for the first time in her life, after the massive terror attack of October 7, and in which we suggest that the signs and symptoms might be explained using the psycho-dynamic theory.
CONCLUSIONS: We conclude suggesting that the equilibrium of the bio-psycho-social model, should be adjusted in the context of time and space, especially during a situation of catastrophic scale in the patient environment.
CONCLUSIONS: Although stress is a risk factor for the development of affective disorders and especially manic-psychotic episodes, there is scarce literature to support it. On the other hand, psycho-dynamic theories sometimes consider stressful life events as a causative factor for the development of depressive as well as manic episodes.

Tourette syndrome (TS) has been associated with a rich set of symptoms that are said to be uncomfortable, unwilled, and effortful to manage. Furthermore, tics, the canonical characteristic of TS, are multifaceted, and their onset and maintenance is complex. A formal account that integrates these features of TS symptomatology within a plausible theoretical framework is currently absent from the field. In this paper, we assess the explanatory power of hierarchical generative modelling in accounting for TS symptomatology from the perspective of active inference. We propose a fourfold analysis of sensory, motor, and cognitive phenomena associated with TS. In Section 1, we characterise tics as a form of action aimed at sensory attenuation. In Section 2, we introduce the notion of epistemic ticcing and describe such behaviour as the search for evidence that there is an agent (i.e., self) at the heart of the generative hierarchy. In Section 3, we characterise both epistemic (sensation-free) and nonepistemic (sensational) tics as habitual behaviour. Finally, in Section 4, we propose that ticcing behaviour involves an inevitable conflict between distinguishable aspects of selfhood; namely, between the minimal phenomenal sense of self-which is putatively underwritten by interoceptive inference-and the explicit preferences that constitute the individual\'s conceptual sense of self. In sum, we aim to provide an empirically informed analysis of TS symptomatology under active inference, revealing a continuity between covert and overt features of the condition.

Resilient system performance in high-stakes settings, which includes the ability to monitor, respond, anticipate, and learn, can be enhanced for trainees through simulation of realistic scenarios enhanced by augmented reality. Active learning strategies can enhance simulation-based training, particularly the mental model articulation principle where students are prompted to anticipate what will happen next and the reflection principle where students self-assess their performance compared to a gold standard expert model. In this paper, we compared simulation-based training for trauma care with and without active learning strategies during pauses in the simulated action for progressively deteriorating patients. The training was conducted online and real-time without a facilitator, with 42 medical students viewing training materials and then immediately taking an online quiz for three types of trauma cases: hemorrhage, airway obstruction, and tension pneumothorax. Participants were randomly assigned to either the experimental or control condition in a between-subjects design. We compared performance in the control and experimental conditions based on: A) the proportion of cues correctly recognized, B) the proportion of accurate diagnoses, C) the proportion of appropriate treatment interventions, and D) verbal briefing quality on a 1-5 scale. We found that the training intervention increased recognition of subtle cues critical for accurate diagnosis and appropriate treatment interventions; the training did not improve the accuracy of diagnoses or the quality of the verbal briefing. We conclude that incorporating active learning strategies in simulation-based training improved foundational capabilities in detecting subtle cues and intervening to rescue deteriorating patients that can increase the readiness for trainees to contribute to resilient system performance in the high-stakes setting of emergency care in hospitals.

This study applies the mental model and cognitive mapping method to involve stakeholders in delineating the mutual relations between sources of water, energy, and food (WEF) production in the Varamin Plain (VP). Through involving farmers and managerial experts, the approach facilitates the deployment of community communication patterns to recognize and comprehend problems and move from single-loop learning to double-loop learning. The dynamic model was driven from the final mental model of the participants to reflect changes in the systems over time. The system dynamic (SD) model incorporates three scenarios for enhancing irrigation efficiency, managing groundwater extraction, and satisfying environmental needs. The results uncovered that the surface and underground water resources of the VP will gradually decrease within the next two decades in the range of 158 and 2700 million cubic meters (MCM) per year. Also, the plain suffers from water insecurity and a 162 MCM shortage. Consequently, focusing on understanding the nexus and nexus governance can enhance resource management and achieve sustainable development goals. Essentially, promoting collaborative governance, such as creating cooperative organizations and implementing double-loop learning, and instituting a water market, regulatory governance, and monitoring laws can improve the state of Varamin Plain\'s resources. These results carry important policy implications for using mental models to consider dynamics for discussions on participatory management of the WEF system nexus and environmental management.

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Case formulation is a central tool for psychotherapists, which helps them tailor psychotherapy to the individual patient, particularly for treatments for complex and multilayered clinical problems, such as personality disorders (Kramer, 2019). Case formulation methodologies are still underutilized in psychotherapy research in the prediction of therapy processes. The present study included N = 60 patients with borderline personality disorder undergoing a brief treatment using an individualized treatment component (n = 31), as compared with a standard brief treatment (n = 29; Kramer et al., 2014). For each patient (in both groups as post hoc analysis based on videos), we performed a Plan analysis case formulation (Caspar, 2019): the idiographic information from the formulation was translated into quantitative scores (on a Likert-type scale) assessing patient\'s interactional agreeableness (vs. antagonism; Zufferey et al., 2019). We modeled the session-by-session predictions of the progression of the therapeutic alliance-rated by the patient and the therapist-over the course of treatment, as a function of interactional agreeableness, the individualization of treatment, as well as their interaction with the session number. Patients with high levels of agreeableness have a significant increase in their alliance assessment over time. Treatment based on the case formulation predicted session-by-session increase of the therapeutic alliance as rated by the therapists. This study was the first to explore intra- and interindividual dynamics of the therapeutic alliance in relationship with idiographic information extracted from case formulations. The results may help understand relationship struggles at the beginning of therapy for complex clinical problems, such as borderline personality disorder. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The perceived safety of tap water is an important condition for consumers to drink it. Therefore, addressing consumers\' concerns should be included in the roadmap towards the UN SDG 6 on safe drinking water for all. This paper studies consumers\' information needs regarding emerging contaminants in drinking water using a mental model approach for the development of targeted risk communication. As most consumers expect safe drinking water, free of contamination, communication on emerging contaminants may increase concerns. Here, we showed that communication strategies better tailored to consumers\' information needs result in smaller increases in risk perception compared with existing strategies.

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Observations abound about the power of visual imagery in human intelligence, from how Nobel prize-winning physicists make their discoveries to how children understand bedtime stories. These observations raise an important question for cognitive science, which is, what are the computations taking place in someone\'s mind when they use visual imagery? Answering this question is not easy and will require much continued research across the multiple disciplines of cognitive science. Here, we focus on a related and more circumscribed question from the perspective of artificial intelligence (AI): If you have an intelligent agent that uses visual imagery-based knowledge representations and reasoning operations, then what kinds of problem solving might be possible, and how would such problem solving work? We highlight recent progress in AI toward answering these questions in the domain of visuospatial reasoning, looking at a case study of how imagery-based artificial agents can solve visuospatial intelligence tests. In particular, we first examine several variations of imagery-based knowledge representations and problem-solving strategies that are sufficient for solving problems from the Raven\'s Progressive Matrices intelligence test. We then look at how artificial agents, instead of being designed manually by AI researchers, might learn portions of their own knowledge and reasoning procedures from experience, including learning visuospatial domain knowledge, learning and generalizing problem-solving strategies, and learning the actual definition of the task in the first place.