Discriminative Power of Handwriting and Drawing Features in Depression.

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.

Reciprocity versus Self-Interest in a Competitive Interaction Context: An Experimental Study.

In social interactions, the reciprocity norm implies to adjust one's behavior to that of the other agents. Conversely, behaving according to self-interest involves taking into account the reciprocity principle only if it does not hinder the achievement of one's goals. However, reciprocity and self-interest may conflict with each other, as when returning a kind action involves sacrificing the possibility to achieve a personal objective. The conflict could be exacerbated by some contextual factors, such as competitive pressures. This study investigated, in a competitive interaction context, which principle prevails when the two conflict. To this end, 276 unpaid participants (M = 138) took part in a two-stage experiment entailing a simulated interaction with a fictitious opponent, which behaved selfishly, fairly or altruistically toward them during the first stage. Participants had to decide whether or not to reciprocate the opponent's previous behavior, which in the critical experimental conditions conflicted with the goal to successfully complete the experiment. So, they were faced with a moral dilemma. Competition degree was manipulated to make the conflict between reciprocity and self-interest more or less harsh. Moreover, we tested whether the putative effect of experimental manipulation was mediated by changes in context-related affective states and personal beliefs about morality. Results showed that decision-making was principally influenced by reciprocity. Regardless of the competition degree, participants preferred to engage in reciprocal behavior even when this compromised their personal interest. Affective states and beliefs changed in response to the experimental manipulation, but they did not mediate the effect of the independent variable on decision-making.

Mood State Detection in Handwritten Tasks Using PCA-mFCBF and Automated Machine Learning.

In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen's position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation-based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively.

Social Influence Maximization in Hypergraphs.

This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

'Not only faces': specialized visual representation of human hands revealed by adaptation.

Classical neurophysiological studies demonstrated that the monkey brain is equipped with neurons selectively representing the visual shape of the primate hand. Neuroimaging in humans provided data suggesting that a similar representation can be found in humans. Here, we investigated the selectivity of hand representation in humans by means of the visual adaptation technique. Results showed that participants' judgement of human-likeness of a visual probe representing a human hand was specifically reduced by a visual adaptation procedure when using a human hand adaptor but not when using an anthropoid robotic hand or a non-primate animal paw adaptor. Instead, human-likeness of the anthropoid robotic hand was affected by both human and robotic adaptors. No effect was found when using a non-primate animal paw as adaptor or probe. These results support the existence of specific neural mechanisms encoding human hand in the human's visual system.

Perfectionism and Burnout During the COVID-19 Crisis: A Two-Wave Cross-Lagged Study.

The current study aims at examining the relationship between the perfectionism two-factor model (i.e., concerns and strivings) and burnout dimensions measured by using the BAT (Burnout Assessment Tool) through a longitudinal study. A two-wave cross-lagged study was conducted using path analysis in SEM (Structural Equation Modeling) of 191 workers. Results confirmed the predictive role of perfectionistic concerns on the burnout dimensions, whereas perfectionistic strivings were not significantly related, suggesting that perfectionism should be monitored by employers and clinicians to prevent employee burnout. Limitations and future research directions are envisaged.