OBJECTIVE: There has been an increasing interest in understanding what contributes to the development and what maintains posttraumatic stress disorder (PTSD). The cognitive model emphasizes that it is a disturbance of the autobiographical memory for the trauma, cognitive beliefs and maladaptive behaviour that maintain trauma symptoms. Interventions are based on cognitive restructuring and behaviour experiments to modify these beliefs. In contrast, the metacognitive model emphasizes that it is the metacognitive beliefs that give rise to the cognitive attentional syndrome (CAS) that maintain trauma symptoms. The focus of treatment is reducing CAS and working on metacognitive beliefs. The aim of this study was to explore the contribution of cognitions and metacognitive beliefs to trauma symptoms and investigate what predicts symptom burden in traumatized patients.
METHODS: Participants (N = 290) diagnosed with PTSD were included, and hierarchical multiple regression analyses were performed to explore if cognitions and metacognitive beliefs explained additional and independent variance in trauma symptoms while controlling for age and gender.
RESULTS: Both cognitions and metacognitive beliefs contributed independently and significantly to predicting trauma symptoms.
CONCLUSIONS: The results provide further support for investigating what maintains trauma symptoms and what to target in treatment. This may have clinical implications for our theoretical and practical understanding of PTSD.

BACKGROUND: The cognitive model of negative symptoms of schizophrenia suggests that defeatist performance beliefs (DPB), or overgeneralized negative beliefs about one\'s performance, are an intermediary variable along the pathway from impaired neurocognitive performance to negative symptoms and functioning in daily life. Although reliable associations between these variables have been established in chronic schizophrenia, less is known about the nature of these relationships in recent-onset schizophrenia (ROSz). This current study tested the associations between DPB and variables in the cognitive model (neurocognitive performance, negative symptoms, functioning) as well as mediation by DPB of the association between neurocognitive performance and negative symptoms in ROSz.
METHODS: A total of 52 participants (32 adults with ROSz and 20 non-psychiatric healthy comparators; HC) completed in-lab measures of neurocognitive performance, self-reported defeatist performance beliefs, and clinician administered measures of negative symptoms and functional outcome. Bivariate relationships among these variables were tested with Pearson correlations. Bootstrapped regression analyses were conducted to test the strength of the indirect effect of neurocognitive performance on negative symptoms through DPB.
RESULTS: Defeatist performance beliefs were significantly elevated in ROSz, and were associated with neurocognitive performance, negative symptoms, and functional outcome as predicted by the cognitive model. There was a significant indirect effect of neurocognition on experiential negative symptoms through DPB, indicating DPB are a partial mediator of the relationship between neurocognitive performance and negative symptoms.
CONCLUSIONS: These findings are consistent with the cognitive model of negative symptoms and extend previous findings in both ROSz and established schizophrenia. Specifically, these data demonstrate that DPB are elevated among ROSz and the associations with neurocognition and clinical outcomes (e.g., negative symptoms and functioning) are of similar magnitude to those reported in chronic schizophrenia. DPB may therefore be a viable treatment target in the early course of illness.

Humans use pictures to model the world. The structure of a picture maps to mind space to form a concept. When an internal structure matches the corresponding external structure, an observation functions. Whether effective or not, the observation is self-consistent. In epistemology, people often differ from each other in terms of whether a concept is probabilistic or certain. Based on the effect of the presented IG and pull anti algorithm, we attempt to provide a comprehensive answer to this problem. Using the characters of hidden structures, we explain the difference between the macro and micro levels and the same difference between semantics and probability. In addition, the importance of attention is highlighted through the combination of symmetry and asymmetry included and the mechanism of chaos and collapse revealed in the presented model. Because the subject is involved in the expression of the object, representationalism is not complete. However, people undoubtedly reach a consensus based on the objectivity of the representation. Finally, we suggest that emotions could be used to regulate cognition.

According to the cognitive model of suicide, interactions between hopelessness and attentional biases toward suicidal information create a narrowed attentional focus on suicide as a viable solution, particularly in the presence of life stress, leading to increased suicide risk. This study used a dynamic systems approach to examine the short-term temporal patterns between stress, hopelessness, suicide-specific rumination, and suicidal intent. Adults (N = 237; M = 27.12 years; 62% cisgender women; 87% White/European American) with elevated suicidal ideation completed ecological momentary assessments six times a day for 14 days. A multilevel model approach informed by dynamic systems theory was used to simultaneously assess stable and dynamic temporal processes underlying perceived stress, hopelessness, suicide-specific rumination, and suicidal intent. Each variable demonstrated temporal stability. In support of the cognitive model of suicide, we observed (1) a reciprocal relationship between stress and hopelessness such that stress and hopelessness amplified each other (early-stage processes), and (2) reinforcing loops such that hopelessness, suicide-specific rumination, and suicidal intent amplified each other (later-stage processes). A dynamic systems modeling approach underscored the negative impact of a perpetuating cycle of suicide-specific rumination, deepening hopelessness, and escalating suicidal intent on increasing suicide risk, which may be targets for intervention.

With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry. This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.

In a range of settings, human operators make decisions with the assistance of automation, the reliability of which can vary depending upon context. Currently, the processes by which humans track the level of reliability of automation are unclear. In the current study, we test cognitive models of learning that could potentially explain how humans track automation reliability. We fitted several alternative cognitive models to a series of participants\' judgements of automation reliability observed in a maritime classification task in which participants were provided with automated advice. We examined three experiments including eight between-subjects conditions and 240 participants in total. Our results favoured a two-kernel delta-rule model of learning, which specifies that humans learn by prediction error, and respond according to a learning rate that is sensitive to environmental volatility. However, we found substantial heterogeneity in learning processes across participants. These outcomes speak to the learning processes underlying how humans estimate automation reliability and thus have implications for practice.

Learning causal relationships is crucial for survival. The human brain\'s functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans.

Confirmation bias is defined as searching for and assimilating information in a way that favours existing beliefs. We show that confirmation bias emerges as a natural consequence of boundedly rational belief updating by presenting the BIASR model (Bayesian updating with an Independence Approximation and Source Reliability). In this model, an individual\'s beliefs about a hypothesis and the source reliability form a Bayesian network. Upon receiving information, an individual simultaneously updates beliefs about the hypothesis in question and the reliability of the information source. If the individual updates rationally then this introduces numerous dependencies between beliefs, the tracking of which represents an unrealistic demand on memory. We propose that human cognition overcomes this memory limitation by assuming independence between beliefs, evidence for which is provided in prior research. We show how a Bayesian belief updating model incorporating this independence approximation generates many types of confirmation bias, including biased evaluation, biased assimilation, attitude polarisation, belief perseverance and confirmation bias in the selection of sources.

BACKGROUND: This study aimed to clarify the reliability and validity of the Japanese version of the Insomnia Catastrophizing Scale (ICS) and to examine the relationship between insomnia severity and insomnia-related cognitive factors.
METHODS: A total of 786 participants were recruited via an online survey and classified into the insomnia group (n = 342) and healthy group (n = 444). The insomnia group comprised individuals who self-reported meeting the diagnostic criteria for chronic insomnia disorder in the third edition of the International Classification of Sleep Disorders.
RESULTS: The ICS is used to independently assess nighttime (ICS-N) and daytime (ICS-D) catastrophic thoughts, and item response theory revealed that each ICS-N and ICS-D item can adequately assess catastrophic thoughts during the night and day, respectively. The internal consistency and test-retest reliability of the ICS-N and ICS-D were good. Further, the ICS-N and ICS-D had a significant positive correlation with insomnia severity, hyperarousal, sleep-related safety behaviors, dysfunctional beliefs about sleep, and anxiety symptoms. Multiple regression analyses with insomnia severity as the dependent variable in the insomnia group demonstrated that catastrophic thoughts were more strongly associated with insomnia severity than sleep-related safety behaviors and dysfunctional beliefs about sleep. The interaction between nighttime catastrophic thoughts and sleep-related safety behaviors increased insomnia severity.
CONCLUSIONS: The Japanese versions of ICS-N and ICS-D were found to be superior in measuring insomnia-related catastrophic thoughts and to have high reliability and validity. Furthermore, these findings more clearly demonstrate that the catastrophic thoughts may be an important associated factor of insomnia.

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.