Perceptual multistability refers to the phenomenon of spontaneous perceptual switching between two or more likely interpretations of an image. Although frequently explained by processes of adaptation or hysteresis, we show that perceptual switching can arise as a natural byproduct of perceptual decision making based on probabilistic (Bayesian) inference, which interprets images by combining probabilistic models of image formation with knowledge of scene regularities. Empirically, we investigated the effect of introducing scene regularities on Necker cube bistability by flanking the Necker cube with fields of unambiguous cubes that are oriented to coincide with one of the Necker cube percepts. We show that background cubes increase the time spent in percepts most similar to the background. To characterize changes in the temporal dynamics of the perceptual alternations beyond percept durations, we introduce Markov Renewal Processes (MRPs). MRPs provide a general mathematical framework for describing probabilistic switching behavior in finite state processes. Additionally, we introduce a simple theoretical model consistent with Bayesian models of vision that involves searching for good interpretations of an image by sampling a posterior distribution coupled with a decay process that favors recent to old interpretations. The model has the same quantitative characteristics as our human data and variation in model parameters can capture between-subject variation. Because the model produces the same kind of stochastic process found in human perceptual behavior, we conclude that multistability may represent an unavoidable by-product of normal perceptual (Bayesian) decision making with ambiguous images. Figure 15. Assessing stationarity using survival probability curves. (A) No-context condition: Because the dark-blue (V A survival) has the same asymptote as the light-blue curve (1j V B survival), the survival of a percept at asymptote is independent of initial state-indicating a stationary process (B) Similar behavior is observed in the case of context. The two red survival curves correspond to V A context and the two green curves to V B context. 1 SE bars are shown.

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Abstract-Composing a representative workload is a crucial step during the design process of a microprocessor. The workload should be composed in such a way that it is representative for the target domain of application and yet, the amount of redundancy in the workload should be minimized as much as possible in order not to overly increase the total simulation time. As a result, there is an important trade-off that needs to be made between workload representativeness and simulation accuracy versus simulation speed.Previous work used statistical data analysis techniques to identify representative benchmarks and corresponding inputs, also called a subset, from a large set of potential benchmarks and inputs. These methodologies measure a number of program characteristics on which Principal Components Analysis (PCA) is applied before identifying distinct program behaviors among the benchmarks using cluster analysis. In this paper we propose Independent Components Analysis (ICA) as a better alternative to PCA as it does not assume that the original data set has a Gaussian distribution, which allows ICA to better find the important axes in the workload space. Our experimental results using SPEC CPU2000 benchmarks show that ICA significantly outperforms PCA in that ICA achieves smaller benchmark subsets that are more accurate than those found by PCA.

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