Search

In this paper we discuss the necessity of insight in the cognitive processes involved in environment navigation into mathematical models for pedestrian motion. We first provide a review of psychological literature on the cognitive processes involved in walking and on the quantitative one coming from applied mathematics, physics, and engineering. Then, we present a critical analysis of the experimental setting for model testing and we show experimental results given by observation. Finally we propose a cognitive model making use of psychological insight as well as optimization models from robotics. [ABSTRACT FROM AUTHOR]

Some search tasks involve looking for a category target in clutter. This is the task faced, for example, by a baggage screener looking for weapons in a suitcase. Such tasks presumably involve the segmentation and recognition of the target object, but it is unknown whether they also involve the segmentation and recognition of the distractor objects. To examine the depth of distractor processing in this task, we had observers search through cluttered displays composed of normal and chimerical distractors. The normal distractors were photographs of recognizable objects, while the chimerical distractors were created by interchanging parts between the normal objects. The obsever's task was to identify the display quadrant that contained an animal or a vehicle target. We varied the difficulty of the search task by varying target and distractor discriminability, target uncertainty, and target occlusion. Only when the target was partially occluded did we find an effect of distractor type. In this case, observers may have found the target through a process of mentally eliminating whole distractor objects. When the target was unoccluded, we found no evidence that observers selected and rejected whole distractors during search. This second result supports our previous claim that often the items for search in clutter are not whole objects. [ABSTRACT FROM AUTHOR]

An airport security worker searching a suitcase for a weapon is engaging in an especially difficult search task: the target is not well-specified, it is not salient, and it is not predicted by its context. Under these conditions, search may proceed item-by-item. In the experiment reported here we tested whether the items for this form of search are whole familiar objects. Our displays were composed of color photographs of ordinary objects, that were either uniform in color and texture (simple), or had two or more parts with different colors or textures (compound). The observer's task was to detect the presence of a target belonging to a broad category (food). We found that when the objects were presented in a sparse array, search times to find the target were similar for displays composed of simple and compound objects. But when the same objects were presented as dense clutter, search functions were steeper for displays composed of compound objects. We attribute this difference to the difficulty of segmenting compound objects in clutter: compared with simple objects, compound objects are less likely to be organized into a single object by bottom-up grouping processes. Our results indicate that while search rates in a sparse display may be determined by the number of objects, search rates in clutter are also affected by the number of object parts. [ABSTRACT FROM AUTHOR]

When searching for a target object, observers use an internal representation of the target's appearance as a search template. This study used naturalistic stimuli to examine the specificity of this template. Observers first learned several name-image pairs; they then participated in a search experiment in which the names served as cues and the images served as targets. To test whether the observers searched for the targets using an exact image template, we included targets that were transformations of the studied image and targets that belonged to the same subordinate-level category as the studied image. The same stimuli were also used in a search experiment involving image cues. The name cue and image cue experiments produced different patterns of results. Unlike image cues, name cues produced similar benefits for transformations of the studied images as for the studied images themselves. Also unlike image cues, names cues produced no benefit for members of the same subordinate-level category as the studied image. These results suggest that when observers are trained on an image, they develop a search template that is relatively specific for the image but still tolerant to changes in scale and orientation.

We propose a measure of clutter for real images that can be used to predict search times. This measure uses an efficient segmentation algorithm (P. Felzenszwalb & D. Huttenlocher, 2004) to count the number of regions in an image. This number is not uniquely defined, however, because it varies with the scale of segmentation. The relationship between the number of regions and the scale of segmentation follows a power law, and the exponent of the power law is similar across images. We fit power law functions to the multiple scale segmentations of 160 images. The power law exponent was set to the average value for the set of images, and the constant of proportionality was used as a measure of image clutter. The same 160 images were also used as stimuli in a visual search experiment. This scale-invariant measure of clutter accounted for about 40% of the variance in the visual search times.