Present and future temporal profiles and their relationship to health intentions and behaviors: A test on a Norwegian general population sample

We investigated the temporal profiles of a Norwegian general population sample and their relation to health behaviors and intentions. The profiles were based on variables from the present and future dimensions of the Zimbardo Time Perspective Inventory (ZTPI), the Consideration of Future Consequences Scale (CFCS), and the combination of both scales. The analysis revealed that there were only two stable clusters that corresponded to the present and the future profiles. Generally, CFCS‐based present and future profiles were more effective at predicting health behaviors and intentions than ZTPI‐based profiles. Profiles based on the CFCS variables were more predictive of eating behaviors. However, the profiles based on the combination of both CFCS and ZTPI were more predictive of smoking, exercising, and health intentions than the profiles based solely on the CFCS. The variation in walking was explained only by the profiles based on a combination of CFCS and ZTPI.     

Strengthening and Expanding Child Services in Low Resource Communities: The Role of Task‐Shifting and Just‐in‐Time Training

In the United States, the demand for child mental health services is increasing, while the supply is limited by workforce shortages. These shortages are unlikely to be corrected without significant structural changes in how mental health services are provided. One strategy for bridging this gap is task‐shifting, defined as a process by which services that are typically delivered by professionals are moved to individuals with less extensive qualifications or training. Although task‐shifting can increase the size of the workforce, there are challenges related to training new workers. In this paper, we propose Just‐In‐Time Training (JITT) as one strategy for improving task‐shifting efforts. We define JITT as on‐demand training experiences that only include what is necessary, when it is necessary, to promote competent service delivery. We offer a proof of concept from our own work shifting counseling and academic support tasks from school mental health professionals to pre‐baccalaureate mentors, citing lessons learned during our iterative process of JITT development. We conclude with a series of key considerations for scaling up the pairing of task‐shifting and JITT, including expanding the science of JITT and anticipating how task‐shifting and JITT would work within the context of dynamic mental health service systems.     

Contribution of upper trunk rotation to hand forward-backward movement and propulsion in front crawl strokes

The study aims to test three hypotheses: (a) the rotation of the upper trunk consists of roll, pitch and yaw of frequencies harmonic to the stroke frequency of the front crawl stroke, (b) the rotation of the upper trunk generates back-and-forth movements of the shoulders, which enhances the movements of the stroking arms, and (c) the angular velocities of roll, pitch and yaw are associated with hand propulsion (HP). Front crawl strokes performed by twenty male swimmers were measured with a motion capture system. The roll, pitch and yaw angles about the three orthogonal axes embedded in the upper trunk were determined as three sequential Cardan angles and their angular velocities were determined as the three respective components of the angular velocity. HP and the drag and lift components of HP (HP_D and HP_L) were estimated by the hand positions and the data from twelve pressure sensors attached on hands. The roll, pitch, and yaw angles were altered in frequencies harmonic to the stroke frequency during the front crawl stroke. Shoulders alternately moved back and forth due to the upper trunk rotation. In the pull phase the angular velocity of roll was correlated with HP_L (r = −0.62, p = 0.004). Based on the back-and-forth movements of the shoulders and roll motion relative to a hand movement, the arm-stroke technique of the front crawl swimming was discussed in terms of increasing the hand velocity and HP.     

Future Time Perspective and Gratitude in Daily Life: A Micro‐longitudinal Study

Preliminary cross‐sectional evidence suggests that future time perspective (FTP) is associated with dispositional gratitude, but research on daily perceptions of FTP and their relations to daily gratitude is lacking. In this study, we addressed this gap by examining how FTP and gratitude jointly unfold in daily life and how these relations vary within and across individuals. A micro‐longitudinal design (N = 331, adults aged 18–77) with daily assessments over two workweeks was employed to examine the relations between gratitude and two components of FTP (remaining opportunities and time). Three important results from random intercepts cross‐lagged panel models stand out. First, we found evidence for within‐person day‐to‐day carry‐over effects in FTP and gratitude. Second, FTP and gratitude were systematically related within and across individuals. Third, age and dispositional forms of FTP and gratitude predicted between‐person differences in FTP and gratitude in daily life. Finally, exploratory multilevel analyses have shown that the associations between daily FTP and gratitude vary across ages at the between‐person level but not at the within‐person level. Overall, these findings advance our understanding of perceptions of FTP in daily life and their associations with gratitude in adulthood. © 2019 European Association of Personality Psychology     

Consciousness in a multilevel architecture: What causes the lateralization of effective connectivity under resting state?

Here we present our answers to a critical commentary by Elkhonon Goldberg on our recent publication (Velichkovsky et al., 2018). To avoid discussions about novelty effects in the human brain activity and memory processes, we narrowed down this response to a reanalysis of our data along the lines proposed in the commentary, namely to comparing the effective links between symmetrical brain structures during the first and the last parts of a prolonged resting-state fMRI experiment. We also tested for sex differences in our results and checked for a stability of top-down interactions during the course of experiment because learning is often expressed in the weakening of upper level control over low-level mechanisms. Our attempts to test the predictions based on the novelty hypothesis has led to mixed results suggesting that the discovered right-to-left dominance of causal connections at rest may have a deeper origin than supposed in the Goldberg’s commentary.     

Infants’ statistical word segmentation in an artificial language is linked to both parental speech input and reported production abilities

Individual variability in infant's language processing is partly explained by environmental factors, like the quantity of parental speech input, as well as by infant‐specific factors, like speech production. Here, we explore how these factors affect infant word segmentation. We used an artificial language to ensure that only statistical regularities (like transitional probabilities between syllables) could cue word boundaries, and then asked how the quantity of parental speech input and infants’ babbling repertoire predict infants’ abilities to use these statistical cues. We replicated prior reports showing that 8‐month‐old infants use statistical cues to segment words, with a preference for part‐words over words (a novelty effect). Crucially, 8‐month‐olds with larger novelty effects had received more speech input at 4 months and had greater production abilities at 8 months. These findings establish for the first time that the ability to extract statistical information from speech correlates with individual factors in infancy, like early speech experience and language production. Implications of these findings for understanding individual variability in early language acquisition are discussed.     

Combining predictors for multi-layer architecture of adaptive fuzzy inference system

To solve problems with a Sugeno adaptive fuzzy neural network using training data, it is necessary to select the appropriate combination of input characteristics of the sub-adaptive neuro-fuzzy inference system (ANFIS) and to determine the appropriate topology. The multi-layer architecture of a sub-ANFIS (MLA-ANFIS) is a good model for prediction problems and solves them modularity. Since, the combination of several predictors is the current focus in the construction of hybrid intelligent systems; we created many solutions to combine machine learning methods, namely ANFIS, support vector machine (SVM), deep neural network (DNN), naive Bayes (NB), linear regression (LR), extreme learning machine (ELM), and decision tree (DT) mixed predictors, and ensemble bootstrap aggregation based on MLA-ANFIS in order to discover the optimal model of combined predictors based on the MLA-ANFIS with a combination of input features entered in the MLA-ANFIS. We implemented our approaches on 365-day concrete compressive strength, thoracic surgery, fertility diagnosis, breast, energy, and glass identification datasets from UCI. The experimental results prove that the combining predictors for the MLA-ANFIS show performance improvements compared to the pure MLA-ANFIS method.     

Mimic automata: A novel formal model for mimic computing

In this paper, we aim to establish a mathematical model for mimic computing. To this end, we present a novel automaton called a mimic automaton. First, a one-dimensional cellular automaton is used for expressing some dynamic changes in the structure of a computing unit, a sequential automaton is employed to describe some state transitions, a hierarchical automaton is employed to express the different granularities of some computing units, and a probabilistic automaton is used to depict some random changes of a computing unit. Second, a mimic automaton is obtained by combining the various types of automata mentioned above in a certain logical relationship. Compared with the existing models of mimic computing, the new model portrays the operational semantics for a mimic computing system, and it can directly describe the behaviors of a system. Furthermore, the new model possesses a stronger generality.     

Predictors of improvement in an open-trial multisite evaluation of emotion regulation group therapy

Emotion regulation group therapy (ERGT) is a novel treatment specifically targeting deliberate non-suicidal self-harm (DSH) in individuals with borderline personality disorder (BPD). Identifying robust predictors of positive response to ERGT could aid clinicians in treatment selection; however, to date, only one such study has been conducted. Thus, we aimed to replicate previously identified predictors of treatment response to ERGT by investigating demographic, clinical, and diagnostic predictors in 95 women with BPD or subclinical BPD who had participated in an open-trial evaluation of ERGT. Outcomes evaluated were frequency of DSH and emotion dysregulation. Assessments were conducted at pretreatment, post-treatment, and 6-month follow-up. Multilevel mixed linear models and multilevel negative binomial generalized estimated equations were used to identify significant interactions between the predictors and outcomes.

We found that greater pretreatment DSH frequency was associated with greater improvements in DSH during treatment (b = 0.998, SE = 0.00, p = 0.03) and follow-up (b = 0.997, SE = 0.00, p < 0.01) and that greater BPD severity was associated with greater improvements in DSH during treatment (b = 0.84, SE = 0.06, p = 0.02) and in emotion dysregulation at follow-up (b = ?3.05, SE = 1.47, p = 0.04). Co-occurring disorders were associated with poorer treatment response during follow-up. Results were generally consistent with a previous study of the predictors of response to ERGT. The findings provide further support for the utility of this treatment across a range of BPD patients, including patients with severe DSH and BPD.