The Activate Test of Embodied Cognition (ATEC): Reliability, concurrent validity and discriminant validity in a community sample of children using cognitively demanding physical tasks related to executive functioning.

Embodied cognition assessment may be more closely related to how children function than standard measures of executive functioning (EF) that require little body movement. Activate Test of Embodied Cognition (ATEC) measures cognitive functioning based on cognitively demanding physical tasks assessed using an automated administration with motion capture technology. This study evaluated the psychometrics of ATEC.Children ages 5-11 years were recruited from the community (N = 55). ATEC was performed twice for a subsample, approximately 2 weeks apart. Motion capture data were collected and converted into ATEC Total Score. Concurrent measures included scores from NIH Toolbox for EF (Flanker, Working Memory, Go/No-Go task, Balloon Analogue Risk Task (BART)), and parent reports (Child Behavior Checklist (CBCL), Behavioral Rating Inventory of Executive Function (BRIEF-2) and Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) for ADHD).ATEC Total Score was significantly correlated with concurrent measures of EF and showed significant discriminant validity between At-Risk children and Normal Range children on CBCL Competency, CBCL ADHD Combined score, BRIEF-2 Global Executive Composite, BRIEF-2 Cognitive Regulation Index and SNAP-IV ADHD Combined Score. Regression analyses showed that ATEC Total score was a better predictor of CBCL Competency than any of the standard EF assessments. ATEC Total Score had excellent test-retest reliability, (ICC = .945, df = 27, p < .001) with a small practice effect (Cohen's d = 0.33). ATEC Total Score correlated with age (r = .42, p < .003) suggesting improvement with normal development. ATEC produces reliable scores that may identify children at risk for EF impairments.

Development of an ecologic marine classification in the new zealand region.

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.