Eye guidance during real-world scene search:The role color plays in central and peripheral vision

The visual system utilizes environmental features to direct gaze efficiently when locating objects. While previous research has isolated various features' contributions to gaze guidance, these studies generally used sparse displays and did not investigate how features facilitated search as a function of their location on the visual field. The current study investigated how features across the visual field-particularly color-facilitate gaze guidance during real-world search. A gaze-contingent window followed participants' eye movements, restricting color information to specified regions. Scene images were presented in full color, with color in the periphery and gray in central vision or gray in the periphery and color in central vision, or in grayscale. Color conditions were crossed with a search cue manipulation, with the target cued either with a word label or an exact picture. Search times increased as color information in the scene decreased. A gaze-data based decomposition of search time revealed color-mediated effects on specific subprocesses of search. Color in peripheral vision facilitated target localization, whereas color in central vision facilitated target verification. Picture cues facilitated search, with the effects of cue specificity and scene color combining additively. When available, the visual system utilizes the environment's color information to facilitate different real-world visual search behaviors based on the location within the visual field

Zum Zusammenhang von Familiensoziologie und Familiendemografie seit 1945 in der Bundesrepublik Deutschland

-Germany, Germany/FRG

Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults

Whether fixation selection in real-world scenes is guided by image salience or by objects has been a matter of scientific debate. To contrast the two views, we compared effects of location-based and object-based visual salience in young and older (65 + years) adults. Generalized linear mixed models were used to assess the unique contribution of salience to fixation selection in scenes. When analysing fixation guidance without recurrence to objects, visual salience predicted whether image patches were fixated or not. This effect was reduced for the elderly, replicating an earlier finding. When using objects as the unit of analysis, we found that highly salient objects were more frequently selected for fixation than objects with low visual salience. Interestingly, this effect was larger for older adults. We also analysed where viewers fixate within objects, once they are selected. A preferred viewing location close to the centre of the object was found for both age groups. The results support the view that objects are important units of saccadic selection. Reconciling the salience view with the object view, we suggest that visual salience contributes to prioritization among objects. Moreover, the data point towards an increasing relevance of object-bound information with increasing age

How well can saliency models predict fixation selection in scenes beyond central bias?:A new approach to model evaluation using generalized linear mixed models

Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available

CRISP: a computational model of fixation durations in scene viewing

Eye-movement control during scene viewing can be represented as a series of individual decisions about where and when to move the eyes. While substantial behavioral and computational research has been devoted to investigating the placement of fixations in scenes, relatively little is known about the mechanisms that control fixation durations. Here, we propose a computational model (CRISP) that accounts for saccade timing and programming and thus for variations in fixation durations in scene viewing. First, timing signals are modeled as continuous-time random walks. Second, difficulties at the level of visual and cognitive processing can inhibit and thus modulate saccade timing. Inhibition generates moment-by-moment changes in the random walk’s transition rate and processing-related saccade cancellation. Third, saccade programming is completed in 2 stages: an initial, labile stage that is subject to cancellation and a subsequent, nonlabile stage. Several simulation studies tested the model’s adequacy and generality. An initial simulation study explored the role of cognitive factors in scene viewing by examining how fixation durations differed under different viewing task instructions. Additional simulations investigated the degree to which fixation durations were under direct moment-to-moment control of the current visual scene. The present work further supports the conclusion that fixation durations, to a certain degree, reflect perceptual and cognitive activity in scene viewing. Computational model simulations contribute to an understanding of the underlying processes of gaze control