PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training.

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.

Learning Landscape in Gamification: The Need for a Methodological Protocol in Research Applications.

In education, the term "gamification" refers to of the use of game-design elements and gaming experiences in the learning processes to enhance learners' motivation and engagement. Despite researchers' efforts to evaluate the impact of gamification in educational settings, several methodological drawbacks are still present. Indeed, the number of studies with high methodological rigor is reduced and, consequently, so are the reliability of results. In this work, we identified the key concepts explaining the methodological issues in the use of gamification in learning and education, and we exploited the controverses identified in the extant literature. Our final goal was to set up a checklist protocol that will facilitate the design of more rigorous studies in the gamified-learning framework. The checklist suggests potential moderators explaining the link between gamification, learning, and education identified by recent reviews, systematic reviews, and meta-analyses: study design, theory foundations, personalization, motivation and engagement, game elements, game design, and learning outcomes.

Preliminary evidence on machine learning approaches for clusterizing students' cognitive profile.

Assessing the cognitive abilities of students in academic contexts can provide valuable insights for teachers to identify their cognitive profile and create personalized teaching strategies. While numerous studies have demonstrated promising outcomes in clustering students based on their cognitive profiles, effective comparisons between various clustering methods are lacking in the current literature. In this study, we aim to compare the effectiveness of two clustering techniques to group students based on their cognitive abilities including general intelligence, attention, visual perception, working memory, and phonological awareness. 292 students, aged 11-15 years, participated in the study. A two-level approach based on the joint use of Kohonen's Self-Organizing Map (SOMs) and k-means clustering algorithm was compared with an approach based on the k-means clustering algorithm only. The resulting profiles were then predicted via AdaBoost and ANN supervised algorithms. The results showed that the two-level approach provides the best solution for this problem while the ANN algorithm was the winner in the classification problem. These results laying the foundations for developing a useful instrument for predicting the students' cognitive profile.

Abstract and concrete concepts in conversation.

Concepts allow us to make sense of the world. Most evidence on their acquisition and representation comes from studies of single decontextualized words and focuses on the opposition between concrete and abstract concepts (e.g., "bottle" vs. "truth"). A significant step forward in research on concepts consists in investigating them in online interaction during their use. Our study examines linguistic exchanges analyzing the differences between sub-kinds of concepts. Participants were submitted to an online task in which they had to simulate a conversational exchange by responding to sentences involving sub-kinds of concrete (tools, animals, food) and abstract concepts (PS, philosophical-spiritual; EMSS, emotional-social, PSTQ, physical-spatio-temporal-quantitative). We found differences in content: foods evoked interoception; tools and animals elicited materials, spatial, auditive features, confirming their sensorimotor grounding. PS and EMSS yielded inner experiences (e.g., emotions, cognitive states, introspections) and opposed PSTQ, tied to visual properties and concrete agency. More crucially, the various concepts elicited different interactional dynamics: more abstract concepts generated higher uncertainty and more interactive exchanges than concrete ones. Investigating concepts in situated interactions opens new possibilities for studying conceptual knowledge and its pragmatic and social aspects.

Interaction effect: Are you doing the right thing?

How to correctly interpret interaction effects has been largely discussed in scientific literature. Nevertheless, misinterpretations are still frequently observed, and neuroscience is not exempt from this trend. We reviewed 645 papers published from 2019 to 2020 and found that, in the 93.2% of studies reporting a statistically significant interaction effect (N = 221), post-hoc pairwise comparisons were the designated method adopted to interpret its results. Given the widespread use of this approach, we aim to: (1) highlight its limitations and how it can lead to misinterpretations of the interaction effect; (2) discuss more effective and powerful ways to correctly interpret interaction effects, including both explorative and model selection procedures. The paper provides practical examples and freely accessible online materials to reproduce all analyses.

Articulatory suppression delays processing of abstract words: The role of inner speech.

Compared to concrete concepts, like "book," abstract concepts expressed by words like "justice" are more detached from sensorial experiences, even though they are also grounded in sensorial modalities. Abstract concepts lack a single object as referent and are characterised by higher variability both within and across participants. According to the Word as Social Tool (WAT) proposal, owing to their complexity, abstract concepts need to be processed with the help of inner language. Inner language can namely help participants to re-explain to themselves the meaning of the word, to keep information active in working memory, and to prepare themselves to ask information from more competent people. While previous studies have demonstrated that the mouth is involved during abstract concepts' processing, both the functional role and the mechanisms underlying this involvement still need to be clarified. We report an experiment in which participants were required to evaluate whether 78 words were abstract or concrete by pressing two different pedals. During the judgement task, they were submitted, in different blocks, to a baseline, an articulatory suppression, and a manipulation condition. In the last two conditions, they had to repeat a syllable continually and to manipulate a softball with their dominant hand. Results showed that articulatory suppression slowed down the processing of abstract more than that of concrete words. Overall results confirm the WAT proposal's hypothesis that abstract concepts processing involves the mouth motor system and specifically inner speech. We discuss the implications for current theories of conceptual representation.

Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients.

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.