Current input device taxonomies and other frameworks typically emphasize the mechanical structure of input devices. We suggest that selecting an appropriate input device for an interactive task requires looking beyond the physical structure of devices to the deeper perceptual structure of the task, the device, and the interrelationship between the perceptual structure of the task and the control properties of the device. We atllrm that perception is key to understanding performance of multidimensional input devices on multidimensional tasks. We have therefore extended the theory of processing of perceptual structure to graphical interactive tasks and to the control structure of input devices. This allows us to predict task and device combinations that lead to better performance and hypothesize that performance is improved when the perceptual structure of the task matches the control structure of the device. We conducted an experiment in which subjects performed two tasks with different perceptual structures, using two input devices with correspondingly different control structures, a three-dimensional tracker and a mouse. We analyzed both speed and accuracy, as well as the trajectories generated by subjects as they used the unconstrained three-dimensional tracker to perform each task. The results support our hypothesis and confirm the importance of matching the perceptual structure of the task and the control structure of the input device. Categories and Subject Descriptors: H.1.2 [Models and Principles]: User/Machine

In this article, the relation between the identification, similarity judgment, and categorization of multidimensional perceptual stimuli is studied. The theoretical analysis focused on general recognition theory (GRT), which is a multidimensional generalization of signal detection theory. In one application, 2 Ss first identified a set of confusable stimuli and then made judgments of their pairwise similarity. The second application was to Nosofsky's (1985b, 1986) identificationcategorization experiment. In both applications, a GRT model accounted for the identification data better than Luce's (1963) biased-cboice model. The identification results were then used to predict performance in the similarity judgment and categorization conditions. The GRT identification model accurately predicted the similarity judgments under the assumption that Ks allocated attention to the 2 stimulus dimensions differently in the 2 tasks. The categorization data were predicted successfully without appealing to the notion of selective attention. Instead, a simpler GRT model that emphasized the different decision rules used in identification and categorization was adequate. The perceptual processes involved when subjects identify, categorize, or judge the pairwise similarity of multidimensional perceptual stimuli are closely related (e.g., Ashby &

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The difference between integral and separable interaction of dimensions is a classic problem in cognitive psychology (Garner, 1970; Shepard, 1964) and remains an essential component of most current experimental and theoretical analyses of category learning (e.g. Ashby & Maddox, 1994; Goldstone, 1994; Kruschke, 1993; Melara, Marks & Potts, 1993; Nosofsky, 1992). So far the problem has been addressed through post-hoc analysis in which empirical evidence of integral and separable processing is used to fit human data, showing how the impact of a pair of dimensions interacting in an integral or a separable manner enters into later learning processes. In this paper, we argue that a mechanistic connectionist explanation for variations in dimensional interactions can provide a new perspective through exploration of how similarities between stimuli are transformed from physical to psychological space when learning to identify, discriminate, and categorize them. We substantiate this claim by demonstrating how even a standard backpropagation network combined with a simple image-processing Gabor filter component provides limited but clear potential to process monochromatic stimuli that are composed of integral pairs of dimensions differently from monochromatic stimuli that are composed of separable pairs of dimensions. Interestingly, the responses from Gabor filters are shown to already capture most of the dimensional interaction, which in turn can be operated upon by the neural network during a given learning task. In addition, we introduce a basic attention mechanism to backpropagation that gives it the ability to selectively attend to relevant dimensions and illustrate how this serves the model in solving a filtration vs. condensation task (Kruschke, 1993). The model may serve a...

Five experiments investigated the influence of cognitive and linguistic factors on the integration of color and letter-shape information. Subjects were briefly presented strings of colored letters that varied in pronounceability and familiarity. Detection and search tasks required subjects to identify the color of predesignated target letters. It was proposed that errors in integrating color and shape would be less likely with items from different perceptual units than from items within the same perceptual unit. If words, at some level of perceptual analysis, are processed as units whereas nonwords are processed by individual letters, then there should be more letter-shape and color feature integration errors with words than with nonwords. The first two experiments tested this prediction by comparing feature integration errors with words and nonwords. The remaining experiments manipulated letter-string pronounceability, familiarity, and the presence of vowels to isolate the factors that may influence feature integration. The results demonstrate that cognitive and linguistic factors, such as familiarity and pronounceability, can influence the combination of colors and shapes in perception.

Generalizing a property from a set of objects to a new object is a fundamental problem faced by the human cognitive system, and a long-standing topic of investigation in psychology. Classic analyses suggest that the probability with which people generalize a property from one stimulus to another depends on the distance between those stimuli in psychological space. This raises the question of how people identify an appropriate metric for determining the distance between novel stimuli. In particular, how do people determine if two dimensions should be treated as separable, with distance measured along each dimension independently (as in an L1 metric), or integral, supporting Euclidean distance (as in an L2 metric)? We build on an existing Bayesian model of generalization to show that learning a metric can be formalized as a problem of learning a hypothesis space for generalization, and that both ideal and human learners can learn appropriate hypothesis spaces for a novel domain by learning concepts expressed in that domain.

effect of time pressure and the spatial integration of the stimulus dimensions on overall similarity categorization.