Guiding Low Spatial Ability Individuals through Visual Cueing: The Dual Importance of Where and When to Look

Research suggests that spatial ability may predict success in complex disciplines including anatomy, where mastery requires a firm understanding of the intricate relationships occurring along the course of veins, arteries, and nerves, as they traverse through and around bones, muscles, and organs. Debate exists on the malleability of spatial ability, and some suggest that spatial ability can be enhanced through training. It is hypothesized that spatial ability can be trained in low-performing individuals through visual guidance. To address this, training was completed through a visual guidance protocol. This protocol was based on eye-movement patterns of high-performing individuals, collected via eye-tracking as they completed an Electronic Mental Rotations Test (EMRT). The effects of guidance were evaluated using 33 individuals with low mental rotation ability, in a counterbalanced crossover design. Individuals were placed in one of two treatment groups (late or early guidance) and completed both a guided, and an unguided EMRT. A third group (no guidance/control) completed two unguided EMRTs. All groups demonstrated an increase in EMRT scores on their second test (P < 0.001); however, an interaction was observed between treatment and test iteration (P = 0.024). The effect of guidance on scores was contingent on when the guidance was applied. When guidance was applied early, scores were significantly greater than expected (P = 0.028). These findings suggest that by guiding individuals with low mental rotation ability “where” to look early in training, better search approaches may be adopted, yielding improvements in spatial reasoning scores. It is proposed that visual guidance may be applied in spatial fields, such as STEMM (science, technology, engineering, mathematics and medicine), surgery, and anatomy to improve student's interpretation of visual content. Anat Sci Educ. © 2018 American Association of Anatomists.     

Automatic path planning for an unmanned drone with constrained flight dynamics

In the article we solve path planning task for an agent being multirotor unmanned aerial vehicle (multicopter). We propose an approach of estimating path geometry constraints based on UAV flight dynamics model and control constraints. Than we introduce a new path finding method which takes into consideration those geometry constraints and study this method both theoretically and empirically.