BackgroundThere is growing debate on the use of drugs that promote cognitive enhancement. Amphetamine-like drugs have been employed as cognitive enhancers, but they show important side effects and induce addiction. In this study, we investigated the use of modafinil which appears to have less side effects compared to other amphetamine-like drugs. We analyzed effects on cognitive performances and brain resting state network activity of 26 healthy young subjects.MethodologyA single dose (100 mg) of modafinil was administered in a double-blind and placebo-controlled study. Both groups were tested for neuropsychological performances with the Raven’s Advanced Progressive Matrices II set (APM) before and three hours after administration of drug or placebo. Resting state functional magnetic resonance (rs-FMRI) was also used, before and after three hours, to investigate changes in the activity of resting state brain networks. Diffusion Tensor Imaging (DTI) was employed to evaluate differences in structural connectivity between the two groups. Protocol ID: Modrest_2011; NCT01684306; http://clinicaltrials.gov/ct2/show/NCT01684306.Principal FindingsResults indicate that a single dose of modafinil improves cognitive performance as assessed by APM. Rs-fMRI showed that the drug produces a statistically significant increased activation of Frontal Parietal Control (FPC; p<0.04) and Dorsal Attention (DAN; p<0.04) networks. No modifications in structural connectivity were observed.Conclusions and SignificanceOverall, our findings support the notion that modafinil has cognitive enhancing properties and provide functional connectivity data to support these effects.Trial RegistrationClinicalTrials.gov NCT01684306 http://clinicaltrials.gov/ct2/show/NCT01684306.

We developed a supervised machine learning classifier to identify faking good by analyzing item response patterns of a Big Five personality self-report. We used a between-subject design, dividing participants (N = 548) into two groups and manipulated their faking behavior via instructions given prior to administering the selfreport. We implemented a simple classifier based on the Lie scale's cutoff score and several machine learning models fitted either to the personality scale scores or to the items response patterns. Results shown that the best machine learning classifier-based on the XGBoost algorithm and fitted to the item responses-was better at detecting faked profiles than the Lie scale classifier. K E Y W O R D S assessment, measurement, personality, statistics, testing | 177 CALANNA et AL. S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Calanna P, Lauriola M, Saggino A, Tommasi M, Furlan S. Using a supervised machine learning algorithm for detecting faking good in a personality self-report. Int J Select Assess. 2020;28:176-185. https ://doi.

The aims of this study were to investigate the construct validity of the Other as Shamer scale (OAS) using confirmatory factor analysis (CFA) and to examine the psychometric properties of its Italian version in a sample of 687 nonclinical individuals. The CFA results indicated that the hypothesized hierarchical model (with 1 higher order factor and 3 first-order factors) was the best fitting solution. Cronbach's alpha indexes, as well as test-retest stability, provided satisfactory results. Correlations of the OAS total score and its subscales with the Beck Depression Inventory-II (rs = .30-.48) and the Teate Depression Inventory (rs = .32-.45) were both substantial and significant (p < .01). Receiver operating characteristic curves were constructed to indicate sensitivity and specificity of the OAS and its subscales when determining those nonclinical subjects who met clinical thresholds for depression symptoms. A series of cutoff scores for the OAS scale and its subscales was developed, with sensitivity values between .70 and .62, and specificity values between .71 and .62, indicating good to fair discrimination between the 2 groups (depressed vs. nondepressed). The theoretical and practical implications of these results were discussed.

Online proctoring generally refers to the practice of proctors monitoring an exam over the internet, usually through a webcam. This technology has gained relevance during the current COVID-19 pandemic, given that the social distance owing to health reasons has consequently led to the switching of all learning and assessment activities to online platforms. This paper summarises the available state-of-the-art of commercial proctoring systems by identifying the main features, describing them, and analysing the way in which different proctoring programs are grouped on the basis of the services they offer. Furthermore, the paper reports on two case studies concerning online exams taken with both automated and human proctoring approaches. The outcomes from state-of-the-art approaches and the experience gained by the two case studies are then summarised in the conclusion, where the need for an organisational effort in loading photographs that can be used to easily recognise student faces, and using an automated online proctoring program to support manual proctoring have been suggested.

Background: Telerehabilitation (TR) in chronic stroke patients has emerged as a promising modality to deliver rehabilitative treatment-at-home. The primary objective of our methodical clinical study was to determine the efficacy of a novel rehabilitative device in terms of recovery of function in daily activities and patient satisfaction and acceptance of the medical device provided. Methods: A 12-week physiotherapy program (balance exercises, upper and lower limb exercises with specific motor tasks using a biofeedback system and exergaming) was administered using the WeReha device. Twenty-five ( N = 25) chronic stroke outpatients were enrolled, and the data of 22 patients was analyzed. Clinical data and functional parameters were collected by Berg Balance scale (BBS), Barthel Index (BI), Fugl-Meyer scale (FM), Modified Rankin scale (mRS), and Technology Acceptance Model (TAM) questionnaire at baseline (T0), after treatment (T1), and at the 12-week follow-up (T2). Statistical tests were used to detect significant differences ( P < .05), and Cohen’s (Co) value was calculated. Results: BI scores improved significantly after treatment ( P = .036; Co 0.776, medium), as well as BBS scores ( P = .008; Co 1.260, high). The results in FM scale ( P = .003) and mRS scores ( P = .047) were significant post treatment. Follow-up scores remained stable across all scales, except the BI. The A and C sub-scales of the TAM correlated significantly to only a T2 to T1 difference for BI scores with P = .021 and P = .042. Conclusion: Currently, the WeReha program is not the conventional therapy for stroke patients, but it could be an integrative telerehabilitative resource for such patients as a conventional exercise program-at-home. ClinicalTrials.gov identifier: NCT03964662.