Goal-directed actions involve problem solving-how to coordinate perception and action to get the job done. Whereas previous work focused on the ages at which children succeed in problem solving, we focused on how children solve motor problems in real time. We used object fitting as a model system to understand how perception and action unfold from moment to moment. Preschoolers (N=25) and adults (N=24) inserted 3D shapes into their corresponding openings in a "shape-sorting" box. We applied a new combination of real-time methods to the problem of object fitting-head-mounted eye tracking to record looking behaviors, video micro-coding to record adjustments in object orientation between reach and insertion, and real-time analysis techniques (recurrent quantification analysis and Granger causality) to test the timing relations between visual and manual actions. Children, like adults, solved the problem successfully. However, adults outperformed children in terms of their speed in fitting, and speed depended on when adjustments of object orientation occurred. Adults adjusted object orientation during transport, whereas children adjusted object orientation after arriving at the box. Children's delays in adjustment resulted from delays in looking at the target shape and its corresponding aperture. Findings show that planning is a real-time cascade of perception and action, and highlight looking as the basis for planning actions prospectively. We suggest that developmental improvements in problem solving are driven by real-time changes in the instigation of the planning cascade and the timing of its components.
Abstract-Parallel vectors (PV), the loci where two vector fields are parallel, are commonly used to represent curvilinear features in 3D for data visualization. Methods for extracting PV usually operate on a 3D grid and start with detecting seed points on a cell face. We propose, to the best of our knowledge, the first provably correct test that determines the parity of the number of PV points on a cell face. The test only needs to sample along the face boundary and works for any choice of the two vector fields. A discretization of the test is described, validated, and compared with existing tests that are also based on boundary sampling. The test can guide PV-extraction algorithms to ensure closed curves wherever the input fields are continuous, which we exemplify in extracting ridges and valleys of scalar functions.
Background: Motor impairments contribute to performance variability in children with cerebral palsy (CP) during motor skill learning. Non-immersive virtual environments (VEs) are popular interventions to promote motor learning in children with hemiplegic CP. Greater understanding of performance variability as compared to typically developing (TD) peers during motor learning in VEs may inform clinical decisions about practice dose and challenge progression.Purpose: (1) To quantify within-child (i.e., across different timepoints) and between-child (i.e., between children at the same timepoint) variability in motor skill acquisition, retention and transfer in a non-immersive VE in children with CP as compared to TD children; and (2) To explore the relationship between the amount of within-child variability during skill acquisition and learning outcomes.Methods: Secondary data analysis of 2 studies in which 13 children with hemiplegic CP and 67 TD children aged 7–14 years undertook repeated trials of a novel standing postural control task in acquisition, retention and transfer sessions. Changes in performance across trials and sessions in children with CP as compared to TD children and between younger (7–10 years) and older (11–14 years) children were assessed using mixed effects models. Raw scores were converted to z-scores to meet model distributional assumptions. Performance variability was quantified as the standard deviation of z-scores.Results: TD children outperformed children with CP and older children outperformed younger children at each session. Older children with CP had the least between-child variability in acquisition and the most in retention, while older TD children demonstrated the opposite pattern. Younger children with CP had consistently high between-child variability, with no difference between sessions. Within-child variability was highest in younger children, regardless of group. Within-child variability was more pronounced in TD children as compared to children with CP. The relationship between the amount of within-child variability in performance and performance outcome at acquisition, retention and transfer sessions was task-specific, with a positive correlation for 1 study and a negative correlation in the other.Conclusions: Findings, though preliminary and limited by small sample size, can inform subsequent research to explore VE-specific causes of performance variability, including differing movement execution requirements and individual characteristics such as motivation, attention and visuospatial abilities.
The required actions to solve many everyday motor problems are not immediately apparent. How do children discover these hidden demands? Exploration was assessed in 24- to 56-month-olds (n = 47; 26 girls) by tracking how children touched a tablet screen to open “virtual cabinets” with different locks. Children were strategic explorers. Hypothesis-driven exploration increased with age by first focusing on the appropriate area (“hypothesis” about where to act) and then on the appropriate action (“hypothesis” about how to act). Even when children did not hypothesize about where and how to solve the problem, they showed more directed than random exploration, and directed exploration increased with age. However, children did not generalize exploration of hidden demands from one problem to another.
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