In an experimental assessment of a choral responding procedure for increasing children's response to teacher commands, decreased levels of off-task behavior, as well as increased levels of correct responding, resulted from the procedures for three handicapped preschool children during large group instruction.

Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes.

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A study was conducted to train a five-year-old, severely handicapped boy to activate an adapted battery-operated and electronically controlled toy. To activate the toy the student was required to manually depress a Zygo tread switch which, while depressed, maintained activation of the toy. During the baseline condition the student was provided only with a verbal prompt (and the occurrence of switch activation was recorded). During three subsequent intervention phases verbal prompting only, and verbal and physical prompting conditions were alternated between daily morning and afternoon sessions. Training involved the use of verbal and three different types of physical prompts. Prompts were systematically withdrawn in two consecutive phases after the student's switch activation increased sharply. In the final condition a return to baseline (verbal prompts only) was instituted. The study yielded several interesting findings: (a) the physical prompting procedure was effective in rapidly establishing the response; (b) the response was later also performed when the student was given only a verbal prompt; (c) gradual withdrawal of physical prompting did not result in a concomitant decrease in the behavior, and (d) the behavior maintained during the final condition in which only verbal prompts were provided. The study indicates that systematic prompting, prompt fading, and the use of electronically mediated devices can be effective in establishing purposeful communicative and leisure behaviors in low functioning severely handicapped students. Implications are discussed for the widespread applicability of this technology.