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providing an affordable, safe, and responsive on-demand tool for monitoring fire effects 5 at a much finer spatial resolution than is possible with current technology. Using 6 spectroscopic analysis of a variety of live as well as combusted vegetation samples to 7 identify the spectral separability of vegetation classes, an optimal set of spectra was 8 selected to be utilized by machine learning classifiers. This approach allows high 9 resolution mapping of wildland fire severity and extent. 10

Pictograms were created in which the outline of a work denoting an object was shaped to be the same as the object itself. A number of objects were presented, some drawn as pictograms, some as outline shapes, and some as normally printed words. The experiment was designed to test if recognition memory was superior for the pictograms as compared to outline pictures or words, and if this would be true whether the subjects were asked to attend to the form or only the content of the stimuli. One group of subjects was trained to respond OLD only if the test item was the same object in the same form, and NEW only to objects never before shown in any form. Recognition accuracy (a signal detection analysis) was greatest for the pictograms, and poorest for the words in both groups. Though the subjects could disregard form, they were most accurate when probed with the same form as presented. But in all comparisons subjects were most accurate when forced to recall both the form and the content. These and other results were taken to be mildly supportive of a dual coding hypothesis, and of the utility of these new stimuli.

ABSTRACT

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