Assessing executive functions in individuals with disorders or clinical conditions can be challenging, as they may lack the abilities needed for conventional test formats. The use of more personalized test versions, such as adaptive assessments, might be helpful in evaluating individuals with specific needs. This paper introduces PsycAssist, a web-based artificial intelligence system designed for neuropsychological adaptive assessment and training. PsycAssist is a highly flexible and scalable system based on procedural knowledge space theory and may be used potentially with many types of tests. We present the architecture and adaptive assessment engine of PsycAssist and the two currently available tests: Adap-ToL, an adaptive version of the Tower of London-like test to assess planning skills, and MatriKS, a Raven-like test to evaluate fluid intelligence. Finally, we describe the results of an investigation of the usability of Adap-ToL and MatriKS: the evaluators perceived these tools as appropriate and well-suited for their intended purposes, and the test-takers perceived the assessment as a positive experience. To sum up, PsycAssist represents an innovative and promising tool to tailor evaluation and training to the specific characteristics of the individual, useful for clinical practice.

The Italian healthcare system is public and freely available to the population. With a few exceptions, there are no distinctions between the healthcare services and practices (e.g., primary care) designed for the general population and those for individuals with intellectual and developmental disabilities (IDD). Prevalence data on adults with IDD are lacking and most disability policies and resources are designed for people with disabilities broadly defined, without specification based on disability type or severity level. Recent legislation provides specific supports for individuals with severe disabilities or autism spectrum disorder. This paper describes the legislation and health policies developed for people with disabilities, the organization of the Italian healthcare system, and the organization of disability support services and healthcare services. Strengths (e.g., availability of many financial resources, adoption of biopsychosocial approach to disability, presence of innovative projects to address the healthcare needs of people with IDD) and weaknesses (e.g., lack of distinction among disability types, territorial differences, lack of disability training of healthcare providers) of the current healthcare practices are reported and discussed.

This paper introduces MatriKS, a new computerized tool for the assessment of fluidintelligence. Based on knowledge structure theory (KST), a mathematical frameworkinitially designed for efficient assessment and personalized learning, MatriKS is the firstlarge scale application of KST to fluid intelligence assessment. The validation results forMatriKS, suitable for Italian individuals aged 4 to 11 (N = 568) are presented. A multimethod approach incorporating classical test theory (CTT), the Rasch model, and KSTwas employed. CTT confirmed the reliability and validity of the test, the Rasch modelensured precise measurement across different ability levels, and KST provided nuancedinsights into fine-grained individual cognitive profiles and developmental trajectories.The study concludes by exploring the methodological and practical benefits of usingKST in assessment test construction and multi-method analysis.

Adaptive behavior comprises conceptual, social, and practical skills that individuals learn and express in their daily lives. Understanding the contextual factors that influence the development of adaptive behavior is crucial for the well-being of children. Using path analysis, we cross-sectionally examined the relations between Vineland-II adaptive behavior of Italian girls (n = 156) and boys (n = 149) aged 3–14 and parental sociocultural dimensions (socioeconomic status, cultural capital, social capital) with attended school year as a moderator. Mothers’ sociocultural level showed numerous and generally positive relations with their children’s adaptive behavior, whereas fathers’ sociocultural level exhibited fewer and more mixed relations. Furthermore, school year attended had direct negative effects on adaptive behavior and generally moderated in a positive or mixed way the relationships between parents’ sociocultural level and daughters’ and sons’ adaptive behavior, respectively. Policymakers should be aware of the relations between parental sociocultural level and their offspring’s adaptive behavior.

This study explores the conditional dependencies among items in Raven’s Colored Progressive Matrices (CPMs), a non-verbal measure of cognitive ability and fluid intelligence, within a sample of school-aged children. To this aim, we applied Bayesian networks (BNs) to data obtained from 255 participants, aged 4 to 11 years. We evaluated a variety of models, starting from the transitive independence model and progressing to sequential, data-informed structures. The results highlighted that inter-item dependencies were particularly significant among kindergarten-aged children, suggesting that their problem-solving approaches may be influenced by their previous responses. The Sequential Data-Driven model consistently outperformed traditional theory-driven hypothesis models, indicating the presence of complex interrelationships that could challenge the assumption of local independence in psychometric assessments. Cross-validation analyses revealed developmental differences in model fit, with optimal performance in kindergarten and increased variability as children transition to primary school. These findings advocate for the development of refined scoring methodologies that incorporate item interdependencies and prompt further investigation into the cognitive processes underlying these dependencies. Our study has implications for the psychometric validation of cognitive assessments, underscoring the importance of integrating complex item relationships in test design and analysis to enhance measurement accuracy and validity.

Conventional evaluations of matrix reasoning have focused predominantly on total accuracy scores, thus overlooking qualitative error patterns that may reflect distinct cognitive mechanisms in psychiatric populations. This study examined performance on MatriKS, a digital matrix reasoning task, in a sample of 28 individuals diagnosed with Schizophrenia, 31 individuals diagnosed with Bipolar disorder, and 371 healthy controls. The present study analysed four conceptual error types: repetition (R), wrong principle (WP), distractor (D), and incomplete correlate (IC). The results showed that both clinical groups exhibited a lower level of accuracy and a higher total number of errors when compared to the control group. However, these differences were not statistically significant. The qualitative error analysis revealed informative dissociations: D errors, which reflect the selection of visually salient but relationally irrelevant options, were found to be more prevalent in both clinical groups, with a tendency observed in individuals with Schizophrenia. Multinomial logistic regression with bootstrap resampling identified selective visual attention, measured by the Attentive Matrices Test, as the only significant cognitive predictor of reasoning errors. It was also found that higher visuospatial attentional capacity significantly reduced WP errors and showed a marginal protective effect against IC errors. Furthermore, the study revealed that there were no significant interactions between cognitive measures and diagnostic group. Symptom severity, as measured by the Brief Psychiatric Rating Scale, did not demonstrated correlations with total errors. The overall findings of this study underscore the clinical value of qualitative error profiling as a complement to total score metrics, and highlight selective visual attention as a key cognitive mechanism in matrix reasoning.