A large body of evidence suggests that visual attention selects objects as well as spatial locations. If attention is to be regarded as truly object based, then it should operate not only on object representations that are explicit in the image, but also on representations that are the result of earlier perceptual completion processes. Reporting the results of two experiments, we show that when attention is directed to part of a perceptual object, other parts of that object enjoy an attentional advantage as well. In particular, we show that this object-specific attentional advantage accrues to partly occluded objects and to objects defined by subjective contours. The results corroborate the claim that perceptual completion precedes object-based attentional selection.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, ARL-TR-3833 SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTModeling Soldier-in-the-loop target acquisition performance is necessary for the development of improved sensors, more effective training methods, and better war game simulations. Accurately modeling this performance requires a detailed understanding of the environment, how a sensor responds to the environment, how it displays information to an observer, and how the observer employs that information to acquire a target. The first three requirements have been met; the fourth requirement, however, has not yet been achieved. Attempts to model the observer's visual and decision-making processes have been compromised by the analysis of the scene, based on physical parameters alone rather than how the visual system interprets the scene. Models based on such scene-derived factors have had limited success.This report takes a two-pronged approach to how future models can be improved by the sensible integration of human visual processing. One prong concerns basic research from the perceptual psychology community. Over the last few decades, this research has generated a detailed theoretical understanding of visual processing and decision making, based on visual information. The other prong concerns important models, modeling frameworks, and scene metrics from the military target acquisition community. Particular attention is paid to issues of clutter, the extendibility of the Johnson criteria, classical and neoclassical search frameworks, the selection of methods and performance metrics, and existing Night Vision and Electronic Sensors Directorate models. Issues related to the validation of target acquisition models are also discussed. iii Abstract (continued)Existing target acquisition models tend to base performance on (a) one-dimensional (1-D) metrics defining the amount of information in the target (e.g., resolvable bar cycles, contrast, area, size, perimeter, speed of motion) and how that information correlates to level of performance in a target acquisition task (i.e., detection, classification, recognition, and identification), (b) search processes that are unrealistic (e.g., that assume random eye movements), and (c) 1-D metrics to define the whole scene (clutter) or regions of the scene (e.g., clutter, conspicuity, attractiveness). These tendencies fail to account for known human behavior, although models incorporating them may be insensitive t...

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When scanning across a scene, luminance can vary by up to 100,000-to-1 (high dynamic range, HDR), requiring multiple normalizing mechanisms spanning from the retina to the cortex to support visual acuity and recognition. Vision models based on standard dynamic range (SDR) luminance contrast ratios below 100-to-1 have limited ability to generalize to real-world scenes with HDR luminance. To characterize how orientation and luminance are linked in brain mechanisms for luminance normalization, we measured orientation discrimination of Gabor targets under HDR luminance dynamics. We report a novel phenomenon, that abrupt 10-to 100-fold darkening engages contextual facilitation, distorting the apparent orientation of a high-contrast