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What information is available from a brief glance at a novel scene? Although previous efforts to answer this question have focused on scene categorization or object detection, real-world scenes contain a wealth of information whose perceptual availability has yet to be explored. We compared image exposure thresholds in several tasks involving basic-level categorization or global-property classification. All thresholds were remarkably short: Observers achieved 75%-correct performance with presentations ranging from 19 to 67 ms, reaching maximum performance at about 100 ms. Global-property categorization was performed with significantly less presentation time than basic-level categorization, which suggests that there exists a time during early visual processing when a scene may be classified as, for example, a large space or navigable, but not yet as a mountain or lake. Comparing the relative availability of visual information reveals bottlenecks in the accumulation of meaning. Understanding these bottlenecks provides critical insight into the computations underlying rapid visual understanding., National Science Foundation (U.S.) (CAREER Award (0546262)), National Science Foundation (U.S.) (Grant 0705677), National Science Foundation (U.S.) (Graduate Research Fellowship)

Human observers are able to rapidly and accurately categorize natural scenes, but the representation mediating this feat is still unknown. Here we propose a framework of rapid scene categorization that does not segment a scene into objects and instead uses a vocabulary of global, ecological properties that describe spatial and functional aspects of scene space (such as navigability or mean depth). In Experiment 1, we obtained ground truth rankings on global properties for use in Experiments 2–4. To what extent do human observers use global property information when rapidly categorizing natural scenes? In Experiment 2, we found that global property resemblance was a strong predictor of both false alarm rates and reaction times in a rapid scene categorization experiment. To what extent is global property information alone a sufficient predictor of rapid natural scene categorization? In Experiment 3, we found that the performance of a classifier representing only these properties is indistinguishable from human performance in a rapid scene categorization task in terms of both accuracy and false alarms. To what extent is this high predictability unique to a global property representation? In Experiment 4, we compared two models that represent scene object information to human categorization performance and found that these models had lower fidelity at representing the patterns of performance than the global property model. These results provide support for the hypothesis that rapid categorization of natural scenes may not be mediated primarily though objects and parts, but also through global properties of structure and affordance., National Science Foundation (U.S.) (Graduate Research Fellowship), National Science Foundation (U.S.) (Grant 0705677), National Science Foundation (U.S.) (Career Award 0546262), NEC Corporation Fund for Research in Computers and Communications

Behavioral and computational studies suggest that visual scene analysis rapidly produces a rich description of both the objects and the spatial layout of surfaces in a scene. However, there is still a large gap in our understanding of how the human brain accomplishes these diverse functions of scene understanding. Here we probe the nature of real-world scene representations using multivoxel functional magnetic resonance imaging pattern analysis. We show that natural scenes are analyzed in a distributed and complementary manner by the parahippocampal place area (PPA) and the lateral occipital complex (LOC) in particular, as well as other regions in the ventral stream. Specifically, we study the classification performance of different scene-selective regions using images that vary in spatial boundary and naturalness content. We discover that, whereas both the PPA and LOC can accurately classify scenes, they make different errors: the PPA more often confuses scenes that have the same spatial boundaries, whereas the LOC more often confuses scenes that have the same content. By demonstrating that visual scene analysis recruits distinct and complementary high-level representations, our results testify to distinct neural pathways for representing the spatial boundaries and content of a visual scene., National Science Foundation (U.S.) (NSF Graduate Research Fellowship), National Science Foundation (U.S.) (CAREER Award IIS 0546262)

Behavioral and computational studies suggest that visual scene analysis rapidly produces a rich description of both the objects and the spatial layout of surfaces in a scene. However, there is still a large gap in our understanding of how the human brain accomplishes these diverse functions of scene understanding. Here we probe the nature of real-world scene representations using multivoxel functional magnetic resonance imaging pattern analysis. We show that natural scenes are analyzed in a distributed and complementary manner by the parahippocampal place area (PPA) and the lateral occipital complex (LOC) in particular, as well as other regions in the ventral stream. Specifically, we study the classification performance of different scene-selective regions using images that vary in spatial boundary and naturalness content. We discover that, whereas both the PPA and LOC can accurately classify scenes, they make different errors: the PPA more often confuses scenes that have the same spatial boundaries, whereas the LOC more often confuses scenes that have the same content. By demonstrating that visual scene analysis recruits distinct and complementary high-level representations, our results testify to distinct neural pathways for representing the spatial boundaries and content of a visual scene., National Science Foundation (U.S.) (Graduate Research Fellowship), National Science Foundation (U.S.) (CAREER Award IIS 0546262)

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009., Cataloged from PDF version of thesis., Includes bibliographical references., Human observers are able to rapidly and accurately categorize natural scenes, but the representation mediating this feat is still unknown. Here we propose a framework of rapid scene categorization that does not segment a scene into objects and instead uses a vocabulary of global, ecological properties that describe spatial and functional aspects of scene space (such as navigability or mean depth). In Chapter 1, four experiments explore the human sensitivity to global properties for rapid scene categorization, as well as the computational sufficiency of these properties for predicting scene categories. Chapter 2 explores the time course of scene understanding, finding that global properties can be perceived with less image exposure than the computation of a scene's basic-level category. Finally, in Chapter 3, I explore aftereffects to adaptation to global properties, showing that repeated exposure to many global properties produces robust high-level aftereffects, thus providing evidence for the neural coding of these properties. Altogether, these results provide support for the hypothesis that rapid categorization of natural scenes may not be mediated primarily though objects and parts, but also through global properties of structure and affordance., by Michelle R. Greene., Ph.D.