Three experiments investigated feature quantity and semantic quality accounts for level of processing effects on face recognition. Experiment 1 established that as the "level of processing" of a face judgment increased, the number of eye movements and inspection time increased, and subsequent recognition performance (recognition and recognition response time) improved. Experiment 2 found that, with the number of eye movements and inspection time held constant, as the "level of processing" of a face judgment increased subjects' cognitive level of processing (assessed by task-evoked peak pupillary dilation) increased, but recognition performance did not improve. Experiment 3 indicated that, with the number of eye movements held constant, recognition performance did not improve as a function of inspection time. These findings are consistent with a feature quantity hypothesis.Experiment 1 is based on an independent study completed at Gettysburg College by Lance C. Bloom and was presented at the 61st Annual Meeting of the Eastern Psychological Association in Philadelphia, April 1, 1990.We thank Dr. Robert Bornstein for his advice, encouragement, and helpful comments on earlier drafts of this article; Robert Crowder, Geoffrey Loftus, Keith Rayner, and Don Read for helpful comments on earlier drafts of this article; Marion Willetts-Bloom for comments on earlier drafts of this article and assistance in procedure development in Experiment 1; Amy Kale and Elizabeth Peabody for assistance in scoring data; Wayne Wolfe for overseeing the construction of slide materials; and Sharon Emmons for figure consulting and engineering.

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ATwo central issues concerning M. J. Kirton's adaption-innovation (A-I) theory, the style vs. level issue and the issue of factor number, are reviewed in terms of existing empirical research. Direct tests of those two issues as they apply to A-I theory indicate that Kirton's position holds. Although the directly applicable evidence is not extensive, that which is available supports Kirton's distinction between style and level of creativity and a simple three-factor structure underlying the hypothesized A1 continuum. These two findings support the conceptualization of creativity as a non-homogeneous, differentiated set of psychological processes.Since its publication in 1976 (Kirton, 1976), M. J. Kirton's measure of cognitive style, the Kirton Adaption-Innovation Inventory (KAI), has gained considerable international acceptance as a training and consultancy tool. Its applications, by users certified in courses offered by Kirton himself, extend over many areas including employee selection, staff development and team building. The notion that cognitive style differences impact organizational processes is readily demonstrated in training situations oriented around group problem solving, decision making and creativity exercises. Generally, what Kirton refers to as 'A-I theory' has to do with such organizational behaviour (Mudd, 1994).At the same time, relatively little theoretical attention has been directed towards the nature of the psychological processes constituting the A1 continuum assumed to underlie observed differences among individual scores on the KAI measure.' The following review of empirical work relates specifically to the nature of A1 and its constituent psychological processes, as opposed to the organizational implications of A-I theory. It is suggested here that Kirton's work constitutes two important steps towards the differentiation of creativity in terms of style vs. level in the first instance and in the second, the factor-analytic work, the identification of subprocesses underlying the creative style dimension. Basic nature of the A1 continuumThe central construct of the Kirton theory is that of a cognitive style continuum, the adaption-innovation continuum (AI) (Kirton, 1976, p. 622), all levels of which * Requests for reprints.A1 refers to the underlying psychological dimension, the adaption-innovation continuum.The hyphenated form A-I is used in this text to designate Kirton's so-called A-I theory. The unhyphenated form

Estimating total pollen number and viability is labor intensive and time consuming. Nevertheless the information is crucial for a range of plant biologists from plant breeders to evolutionary biologists. Viability is determined by dye staining and counting colored (viable) and transparent (inviable) grains under a compound microscope. Existing protocols have been standardized to speed up total pollen counts but their success in determining viability is rather limited because they do not incorporate staining techniques. Some of the published protocols that determine viability do so based on statistical methods but focus on just one parameter such as pollen diameter requiring manual standardization to generate two distinct size distributions for viable and inviable pollen. We demonstrate a digital image processing protocol that can count viable and inviable pollen by distinguishing colored and transparent objects in the image space, saving valuable labor and time. Using pollen grains from two plants, Collinsia heterophylla and Brassica napus, we show that differences between viable and inviable pollen are best described by a complex measure, the grain shape. By measuring several parameters such as area, length, width, circularity and elongation, while retaining all the advantages of traditional staining process, our procedure increases the accuracy of viability estimates. The only drawback of our protocol is that it uses the NIS elements a software specific to Nikon instruments.

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