Using machine-learning strategies to solve psychometric problems.

Validating scales for clinical use is a common procedure in medicine and psychology. Through the application of computational methods, we present a new strategy for estimating construct validity and criterion validity. XGBoost, Random Forest and Support-Vector machine learning algorithms were employed in order to make predictions based on the pattern of participants' responses by systematically controlling computational experiments with artificial experiments whose results are guaranteed.

Questionnaire-based computational screening of adult ADHD.

Background: ADHD is classically seen as a childhood disease, although it persists in one out of two cases in adults. The diagnosis is based on a long and multidisciplinary process, involving different health professionals, leading to an under-diagnosis of adult ADHD individuals. We therefore present a psychometric screening scale for the identification of adult ADHD which could be used both in clinical and experimental settings.

Establishment of MOS-SF36 percentile ranks in the general youth French population.

Background: The SF-36 is a generic quality of life questionnaire, massively translated and widely used to obtain physical and mental health status. However, validation work in the French language was carried out over a generation ago. The objective of this study was to obtain the norms of the SF-36 in the French young population.

Age-related decline in motion contrast sensitivity due to lower absorption rate of cones and calculation efficiency.

Motion perception is affected by healthy aging, which impairs the ability of older adults to perform some daily activities such as driving. The current study investigated the underlying causes of age-related motion contrast sensitivity losses by using an equivalent noise paradigm to decompose motion contrast sensitivity into calculation efficiency, the temporal modulation transfer function (i.e.