Computer simulated stimuli can provide a flexible method for creating artificial scenes in the study of visual perception of material surface properties. Previous work based on this approach reported that the properties of surface roughness and glossiness are mutually interdependent and therefore, perception of one affects the perception of the other. In this case roughness was limited to a surface property termed bumpiness. This paper reports a study into how perceived gloss varies with two model parameters related to surface roughness in computer simulations: the mesoscale roughness parameter in a surface geometry model and the microscale roughness parameter in a surface reflectance model. We used a real-world environment map to provide complex illumination and a physically-based path tracer for rendering the stimuli. Eight observers took part in a 2AFC experiment, and the results were tested against conjoint measurement models. We found that although both of the above roughness parameters significantly affect perceived gloss, the additive model does not adequately describe their mutually interactive and nonlinear influence, which is at variance with previous findings. We investigated five image properties used to quantify specular highlights, and found that perceived gloss is well predicted using a linear model. Our findings provide computational support to the 'statistical appearance models' proposed recently for material perception.

Previous work has demonstrated that search for a target in noise is consistent with the predictions of the optimal search strategy, both in the spatial distribution of fixation locations and in the number of fixations observers require to find the target. In this study we describe a challenging visual-search task and compare the number of fixations required by human observers to find the target to predictions made by a stochastic search model. This model relies on a target-visibility map based on human performance in a separate detection task. If the model does not detect the target, then it selects the next saccade by randomly sampling from the distribution of saccades that human observers made. We find that a memoryless stochastic model matches human performance in this task. Furthermore, we find that the similarity in the distribution of fixation locations between human observers and the ideal observer does not replicate: Rather than making the signature doughnut-shaped distribution predicted by the ideal search strategy, the fixations made by observers are best described by a central bias. We conclude that, when searching for a target in noise, humans use an essentially random strategy, which achieves near optimal behavior due to biases in the distributions of saccades we have a tendency to make. The findings reconcile the existence of highly efficient human search performance with recent studies demonstrating clear failures of optimality in single and multiple saccade tasks.

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The majority of work on the perception of gloss has been performed using smooth surfaces (e.g., spheres). Previous studies that have employed more complex surfaces reported that increasing mesoscale roughness increases perceived gloss [Psychol. Sci.19, 196 (2008), J. Vis.10(9), 13 (2010), Curr. Biol.22, 1909 (2012)]. We show that the use of realistic rendering conditions is important and that, in contrast to [Psychol. Sci.19, 196 (2008), J. Vis.10(9), 13 (2010)], after a certain point increasing roughness further actually reduces glossiness. We investigate five image statistics of estimated highlights and show that for our stimuli, one in particular, which we term "percentage of highlight area," is highly correlated with perceived gloss. We investigate a simple model that explains the unimodal, nonmonotonic relationship between mesoscale roughness and percentage highlight area.

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