Modelling search for people in 900 scenes: A combined source model of eye guidance

How predictable are human eye movements during search in real world scenes? We recorded 14 observers’ eye movements as they performed a search task (person detection) in 912 outdoor scenes. Observers were highly consistent in the regions fixated during search, even when the target was absent from the scene. These eye movements were used to evaluate computational models of search guidance from three sources: Saliency, target features, and scene context. Each of these models independently outperformed a cross-image control in predicting human fixations. Models that combined sources of guidance ultimately predicted 94% of human agreement, with the scene context component providing the most explanatory power. None of the models, however, could reach the precision and fidelity of an attentional map defined by human fixations. This work puts forth a benchmark for computational models of search in real world scenes. Further improvements in modelling should capture mechanisms underlying the selectivity of observers’ fixations during search.National Eye Institute (Integrative Training Program in Vision grant T32 EY013935)Massachusetts Institute of Technology (Singleton Graduate Research Fellowship)National Science Foundation (U.S.) (Graduate Research Fellowship)National Science Foundation (U.S.) (CAREER Award (0546262))National Science Foundation (U.S.) (NSF contract (0705677))National Science Foundation (U.S.) (Career Award (0747120)

Eye movement guidance in familiar visual scenes : a role for scene specific location priors in search

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references.Ecologically relevant search typically requires making rapid and strategic eye movements in complex, cluttered environments. Attention allocation is known to be influenced by low level image features, visual scene context, and top down task constraints. Scene specific context develops when observers repeatedly search the same environment (e.g. one's workplace or home) and this often leads to faster search performance. How does prior experience influence the deployment of eye movements when searching a familiar scene? One challenge lies in distinguishing between the roles of scene specific experience and general scene knowledge. Chapter 1 investigates eye guidance in novel scenes by comparing how well several models of search guidance predict fixation locations, and establishes a benchmark for inter-observer fixation agreement. Chapters 2 and 3 explore spatial and temporal characteristics of eye guidance from scene specific location priors. Chapter 2 describes comparative map analysis, a novel technique for analyzing spatial patterns in eye movement data, and reveals that past history influences fixation selection in three search experiments. In Chapter 3, two experiments use a response-deadline approach to investigate the time course of memory-based search guidance. Altogether, these results describe how using long-term memory of scene specific representations can effectively guide the eyes to informative regions when searching a familiar scene.by Barbara Hidalgo-Sotelo.Ph.D

Person, place, and past influence eye movements during visual search

What is the role of an individual’s past experience in guiding gaze in familiar environments? Contemporary models of search guidance suggest high level scene context is a strong predictor of where observers search in realistic scenes. Specific associations also develop between particular places and object locations. Together, scene context and place-specific associations bias attention to informative spatial locations. At the level of eye fixations, it is not known whether a person’s specific search experience influences attentional selection. Eye movements are notoriously variable: people often foveate different places when searching for the same target in the same scene. Do individual differences in fixation locations influence how a scene is subsequently examined? We introduce a method, comparative map analysis, for analyzing spatial patterns in eye movement data. Using this method, we quantified the consistency of fixated locations within the same observer and between observers during search of real world scenes. Results indicated a remarkable consistency in the locations fixated by the same observer across multiple searches of a given scene. This observer-specific guidance was shown to be distinct from general scene context information or familiarity with the scene. Accordingly, this is considered evidence for a uniquely informative role of an individual’s search experience on attentional guidance in a familiar scene.National Science Foundation (U.S.) (CAREER grant 0546262)Integrative Training Program in Vision grant (T32 EY013935

Why do we miss rare targets? Exploring the boundaries of the low prevalence effect

Observers tend to miss a disproportionate number of targets in visual search tasks with rare targets. This 'prevalence effect' may have practical significance since many screening tasks (e.g., airport security, medical screening) are low prevalence searches. It may also shed light on the rules used to terminate search when a target is not found. Here, we use perceptually simple stimuli to explore the sources of this effect. Experiment 1 shows a prevalence effect in inefficient spatial configuration search. Experiment 2 demonstrates this effect occurs even in a highly efficient feature search. However, the two prevalence effects differ. In spatial configuration search, misses seem to result from ending the search prematurely, while in feature search, they seem due to response errors. In Experiment 3, a minimum delay before response eliminated the prevalence effect for feature but not spatial configuration search. In Experiment 4, a target was present on each trial in either two (2AFC) or four (4AFC) orientations. With only two response alternatives, low prevalence produced elevated errors. Providing four response alternatives eliminated this effect. Low target prevalence puts searchers under pressure that tends to increase miss errors. We conclude that the specific source of those errors depends on the nature of the search