When studying the working memory (WM), the \'slot model\' and the \'resource model\' are two main theories used to describe how information retention occurs. The slot model shows that WM capacity consists of a certain number of predefined slots available for information storage. This theory explains that there is a binary condition during information recall in which information is either wholly maintained within a slot or forgotten. The resource model has a resolution-based approach, suggesting a continuous resource able to be distributed among a number of items in WM capacity. Recently hybrid models have been introduced, suggesting that WM may not strictly conform to only one model. Accordingly, to understand the relationship between two of the most widely used paradigms in WM evaluation, we implemented a correlational assessment in two different psychophysics tasks, an analog recall paradigm with sequential bar presentation and a delayed match-to-sample (DMS) task with checkerboard stimuli. Our study revealed significant correlations between WM performance in the DMS task and recall error, precision, and sources of errors in the sequential paradigm. Overall, the findings emphasize the importance of considering both tasks in understanding WM processes, as they shed light on the debate between the slot and resource models by revealing overlapping elements in both theories and the tasks used to evaluate WM capacity.

Objective: Covitality is a meta-construct of positive intra/interpersonal self-schemas that organize and process life experiences. Its synergy favors psychosocial adjustment and prevents mental health problems during adolescence. At these ages, suicide is one of the leading causes of death worldwide. The purpose of this study was to determine which psychosocial strengths of the covitality model could combat adolescent suicide spectrum. Method: Participants were 5,528 Spanish adolescents aged 12-18 years, 50.74% females. The assessment protocol was completed in schools, under the supervision of the research staff. Statistical analyses were conducted using hurdle models, i.e., modeling zero-inflated count data. This process provided two sets of outcomes: the association - in probabilistic terms - between psychosocial strengths and the absence of suicide indicators (i.e., non-occurrence) and the association of these assets - via regression coefficients - with increased experimentation (i.e., duration/quantity). Results: All psychosocial strengths of the covitality model were related to the non-ocurrence of suicidal thoughts and behaviors, but not all to a shorter duration/quantity of their phenotypic manifestations. Covitality obtained greater association values on suicidal tendencies than its components analyzed independently. Belief in self and engaged living were the second-order factors with the higher estimating capacity. Specifically, emotional self-awareness, enthusiasm, gratitude, family support, and behavioral self-control were key first-order assets. Conclusions: These findings suggest that training adolescents in covitality assets could be an effective strategy for universal prevention against premature suicide. Moreover, this study provide evidence on which psychosocial strengths could counteract each phenotypic manifestation of suicide in order to customize selective and indicated preventive actions.

Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article expands the application scenarios of the Technology Acceptance Model and enriches the theory of consumer behavior in Metaverse e-commerce.

In spite of the fact that Roger Barker\'s groundbreaking research was acclaimed sixty years ago by his contemporaries, it has all been but forgotten among recent generations of psychologists. However, in the wake of developments in dynamical systems and complexity theory, its value for understanding psychological processes in everyday life should be recognized anew. Barker\'s naturalistic studies of children\'s daily behaviours in their community revealed that their actions which initially seemed only marginally predictable at the level of individual interaction were, in fact, reliably context-dependent. These results led to the discovery that there are nested structures operating in human habitats as there are throughout the natural world. Barker\'s discovery of emergent eco-psychological structures, behaviour settings, that are generated from interdependent actions among individuals in the course of everyday life has yet to be fully appreciated because of the continuing dominance of linear, mechanistic models. His recognition of nested systems operating in human habitats is finally coming into its own with the current metatheoretical shift in psychology embracing dynamical models. Additionally, new understanding arises from the consideration of convergent individual developmental histories of situated action and their role in maintaining the historical dimensions of behaviour settings. This article is part of the theme issue \'People, places, things and communities: expanding behaviour settings theory in the twenty-first century\'.

BACKGROUND: The burgeoning field of research on the dual-factor model of mental health (DFM) has highlighted its significance, yet the applicability of the DFM in military personnel and its longitudinal relationships with different dimensions of meaning in life remains unclear. This study aimed to clarify the applicability of the DFM for military personnel and to investigate longitudinal relationships between the dual factors of mental health (negative factor, positive factor) and the two dimensions of meaning in life (presence of meaning, search for meaning) in military personnel.
METHODS: In this study, data were collected in two waves (April and August 2023) from 227 Chinese military personnel. We constructed a dual-factor model with depression as the negative factor and subjective well-being as the positive factor, and we compared it with a single-factor model to determine if DFM could be applied to military personnel. We also constructed a cross-lagged model to investigate longitudinal relationships between depression, subjective well-being, presence of meaning, and search for meaning.
RESULTS: According to the findings, military personnel fit better with the DFM than with the single-factor model. Cross-lagged analysis results revealed that both the presence of meaning and the search for meaning negatively predicted depression and positively predicted subjective well-being.
CONCLUSIONS: The DFM had good applicability among military personnel. Both the presence of meaning and the search for meaning could improve military mental health, suggesting that both dimensions of meaning in life may be potential targets for improving military mental health.

BACKGROUND: Suicidal ideations (SI), also known as suicidal thoughts, refer to impulses, desires, and obsessions related to death. Prevalence of suicidal ideation was 14 percent. The current study assumed that identifying the true predictors of SI would allow for a greater understanding of suicide risk.
METHODS: In this cross-sectional population-based study, 17,644 residents aged from 15 to 90 years were selected through a multi-stage sampling method from 22 districts of Tehran. Using hypothesized causal models, the pathways through which various variables influenced the components of SI were identified. Also, the applicability of the compensatory and risk-protective models of resiliency for the prediction of SI was tested by using the interaction multiple regression analyses.
RESULTS: SI was experienced by 13.44% of the study population. SI was more prevalent with individuals between the ages of 15 to 24. There are no differences between men and women when covariate analysis is used. The significant predictions by anxiety and physical activity (moderate) indicate support for the compensatory model for male and female, while the significant prediction by those indicates support for the risk-protective model for females.
CONCLUSIONS: PA would both lessen the detrimental impact of anxiety on suicidal thoughts and potentially reduce the probability of SI. It\'s important to develop and evaluate PA-enhancing treatments, especially for woman who are experiencing anxiety.

Psychological well-being influences all the facets of adolescent success. This study examined the psychometric properties of PERMA, a subjective well-being measure consisting of five domains: positive emotions, engagement, relationships, meaning, and accomplishment. The sample size consisted of 502 high school students from public schools in Qatar. Contrary to expectations, the five-factor structure for PERMA did not fit our data well; however, after refining the model by the errors of the covariances between items, the one-factor model was found to be applicable. Multivariate analysis revealed that socioeconomic status (SES) and level of academic performance were independently and positively associated with overall well-being scores, whereas internalizing and externalizing behaviors were negatively associated. Notably, no significant effects of gender or age on well-being were observed. These findings suggest that interventions specific to socioeconomic status, academic excellence, and internalizing and externalizing behaviors in the school setting can produce more effective outcomes concerning student well-being.

Mathematical models explaining production effects assume that production leads to the encoding of additional features, such as phonological ones. This improves memory with a combination of encoding strength and feature distinctiveness, implementing aspects of propositional theories. However, it is not clear why production differs from other manipulations such as study time and spaced repetition, which are also thought to influence strength. Here we extend attentional subsetting theory and propose an explanation based on the dimensionality of feature spaces. Specifically, we suggest phonological features are drawn from a compact feature space. Deeper features are sparsely subselected from a larger subspace. Algebraic and numerical solutions shed light on several findings, including the dependency of production effects on how other list items are encoded (differing from other strength factors) and the production advantage even for homophones. This places production within a continuum of strength-like manipulations that differ in terms of the feature subspaces they operate upon and leads to novel predictions based on direct manipulations of feature-space properties.

Human decision making is accompanied by a sense of confidence. According to Bayesian decision theory, confidence reflects the learned probability of making a correct response, given available data (e.g., accumulated stimulus evidence and response time). Although optimal, independently learning these probabilities for all possible data combinations is computationally intractable. Here, we describe a novel model of confidence implementing a low-dimensional approximation of this optimal yet intractable solution. This model allows efficient estimation of confidence, while at the same time accounting for idiosyncrasies, different kinds of biases and deviation from the optimal probability correct. Our model dissociates confidence biases resulting from the estimate of the reliability of evidence by individuals (captured by parameter α), from confidence biases resulting from general stimulus independent under and overconfidence (captured by parameter β). We provide empirical evidence that this model accurately fits both choice data (accuracy, response time) and trial-by-trial confidence ratings simultaneously. Finally, we test and empirically validate two novel predictions of the model, namely that 1) changes in confidence can be independent of performance and 2) selectively manipulating each parameter of our model leads to distinct patterns of confidence judgments. As a tractable and flexible account of the computation of confidence, our model offers a clear framework to interpret and further resolve different forms of confidence biases.

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a computational account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms.