An Application of Behavior Modeling Training to Complex Skill Acquisition

Despite a preponderance of research on Behavior Modeling Training (BMT), there is a lack of research investigating BMT in complex skill acquisition contexts. This laboratory study addresses this gap in the literature by comparing the effectiveness of two forms of BMT--either using a coping model or a mastery model--with two forms of control training--either a review of the task instructions or additional unstructured practice--on a computer task that simulates the demands of a dynamic aviation environment. The results showed that BMT had a positive effect on the learning of complex skills. However, the positive effects on skill acquisition were not substantially more than a review of the task instructions. Furthermore, the effects of BMT were stronger for transfer to a related task. BMT was also associated with self-efficacy during training, enjoyment of training, perceptions of training utility, motivation, and strategy change; however, the results did not support mediation. Contrary to what was hypothesized, there were no performance-related differences between the two behavioral modeling conditions, although the coping model condition led to higher levels of self-efficacy and motivation than the mastery model. These results are discussed in terms of the need to better understand the mechanisms underlying the effectiveness of BMT

Collaborative Training With a More Experienced Partner: Remediating Low Pretraining Self-Efficacy in Complex Skill Acquisition

Objective: This study examined the effectiveness of collaborative training for individuals with low pretraining self-efficacy versus individuals with high pretraining selfefficacy regarding the acquisition of a complex skill that involved strong cognitive and psychomotor demands. Background: Despite support for collaborative learning from the educational literature and the similarities between collaborative learning and interventions designed to remediate low self-efficacy, no research has addressed how selfefficacy and collaborative learning interact in contexts concerning complex skills and human-machine interactions. Method: One hundred fifty-five young male adults trained either individually or collaboratively with a more experienced partner on a complex computer task that simulated the demands of a dynamic aviation environment. Participants also completed a task-specific measure of self-efficacy before, during, and after training. Results: Collaborative training enhanced skill acquisition significantly more for individuals with low pretraining self-efficacy than for individuals with high pretraining self-efficacy. However, collaborative training did not bring the skill acquisition levels of those persons with low pretraining self-efficacy to the levels found for persons with high pretraining self-efficacy. Moreover, tests of mediation suggested that collaborative training may have enhanced appropriate skill development strategies without actually raising self-efficacy. Conclusion: Although collaborative training can facilitate the skill acquisition process for trainees with low self-efficacy, future research is needed that examines how the negative effects of low pretraining self-efficacy on complex skill acquisition can be more fully remediated. Application: The differential effects of collaborative training as a function of self-efficacy highlight the importance of person analysis and tailoring training to meet differing trainee needs.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

What agricultural practices are most likely to deliver ‘sustainable intensification’ in the UK?

Sustainable intensification is a process by which agricultural productivity is enhanced whilst also creating environmental and social benefits. We aimed to identify practices likely to deliver sustainable intensification, currently available for UK farms but not yet widely adopted. We compiled a list of 18 farm management practices with the greatest potential to deliver sustainable intensification in the UK, following a well-developed stepwise methodology for identifying priority solutions, using a group decision-making technique with key agricultural experts. The list of priority management practices can provide the focal point of efforts to achieve sustainable intensification of agriculture, as the UK develops post-Brexit agricultural policy, and pursues the second Sustainable Development Goal, which aims to end hunger and promote sustainable agriculture. The practices largely reflect a technological, production-focused view of sustainable intensification, including for example, precision farming and animal health diagnostics, with less emphasis on the social and environmental aspects of sustainability. However, they do reflect an integrated approach to farming, covering many different aspects, from business organization and planning, to soil and crop management, to landscape and nature conservation. For a subset of ten of the priority practices, we gathered data on the level of existing uptake in English and Welsh farms through a stratified survey in seven focal regions. We find substantial existing uptake of most of the priority practices, indicating that UK farming is an innovative sector. The data identify two specific practices for which uptake is relatively low, but which some UK farmers find appealing and would consider adopting. These practices are: prediction of pest and disease outbreaks, especially for livestock farms; staff training on environmental issues, especially on arable farms

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Agricultural land use and Skylark Alauda arvensis: a case study linking a habitat association model to spatially explicit change scenarios

The development of forward scenarios is a useful method of envisaging the environmental implications of potential changes in land use, as a tool for policy development. In this paper, a spatially explicit case study is used to provide insight into the environmental impacts of Common Agricultural Policy reform on Skylark Alauda arvensis, a species which is widespread on arable farmland, breeds in crops and has declined in recent decades. A generalized linear mixed model was used to estimate Skylark breeding population densities in different crops, using survey data collected from farms in the east of England, supplemented by the literature. Model outputs were then used to predict Skylark densities in an East Anglian Joint Character Area dominated by arable cropping. Predicted densities were mapped at field level using GIS, based on actual cropping derived from Integrated Administration and Control System data collected for the administration of subsidy payments. Three future scenarios were then created, based on expert opinion of potential changes in cropping over the next 5 years, and potential changes in Skylark density mapped on the basis of the predicted changes in cropping patterns. Overall, Skylark densities were predicted to decrease on average by 11–14% under ‘market-led’ (increasing wheat and oilseed rape, reduced set-aside) and ‘energy crop’ (5% area under short rotation coppice) scenarios, but remained virtually unchanged under an ‘environment-led’ (diverse cropping) scenario. The ‘market-led’ scenario is closest to short-term agricultural trajectories, but wider cultivation of biomass energy crops as modelled under the ‘energy crop’ scenario could occur in the medium term if energy policies are favourable. Appropriate mitigation strategies therefore need to be implemented if a continued decline in the Skylark population on lowland arable farmland is to be averted. The results provide a readily accessible visualization of the potential impacts of land-use change for policy-makers; similar techniques could be applied to visualize effects of changes arising through other drivers, including climate change

Behavioral Deficits at 18-22 Months of Age Are Associated with Early Cerebellar Injury and Cognitive and Language Performance in Children Born Extremely Preterm

To investigate associations in toddlers born extremely preterm (<28 weeks) between neonatal neuroimaging and 18- to 22-month developmental and behavioral outcomes. Cohort analysis from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network Surfactant Positive Airway Pressure and Pulse Oximetry Trial Neuroimaging and Neurodevelopmental Outcomes Study of infants born extremely preterm. Subjects underwent cranial ultrasonography and near-term magnetic resonance imaging (MRI). At 18-22 months of corrected age, the assessment included the Brief Infant Toddler Social Emotional Assessment (BITSEA) Problem and Competence Scale scores and the Bayley Scales of Infant Development, Third Edition (Bayley-III). The BITSEA Problem Scale assesses dysregulation; the Competence Scale assesses social-emotional competence. We examined associations of Problem and Competence scores and positive screen rates with cranial ultrasonography and near-term MRI. Mean BITSEA and Bayley-III scores were compared using ANOVA and positive screen rates with the χ2 test. We computed correlations between BITSEA and Bayley-III scores. Of the 397 children, positive BITSEA screens were found in 34% for the Problem score and 26% for the Competence score. Presence of lesions on near-term MRI that included cerebellar lesions were significantly associated with lower BITSEA Competence but not with Problem scores; Competence scores were inversely related to the presence/significance of lesions. Positive screens on Competence scores and on both Competence and Problem scores were significantly associated with Bayley-III cognitive and language scores <85 (P < .001). Social–emotional competence contributes to deficits in cognitive and language development. Presence of injury on near-term MRI that includes cerebellar lesions is associated with later social–emotional competence and may be a useful predictor to guide early assessment and intervention. ClinicalTrials.gov: NCT00063063 and NCT00233324

The Association Between Lower Educational Attainment and Depression Owing to Shared Genetic Effects? Results in ~25000 Subjects

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14,949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15,138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884,105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ~120,000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status