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      Image statistics of the environment surrounding freely behaving hoverflies

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          Abstract

          Natural scenes are not as random as they might appear, but are constrained in both space and time. The 2-dimensional spatial constraints can be described by quantifying the image statistics of photographs. Human observers perceive images with naturalistic image statistics as more pleasant to view, and both fly and vertebrate peripheral and higher order visual neurons are tuned to naturalistic image statistics. However, for a given animal, what is natural differs depending on the behavior, and even if we have a broad understanding of image statistics, we know less about the scenes relevant for particular behaviors. To mitigate this, we here investigate the image statistics surrounding Episyrphus balteatus hoverflies, where the males hover in sun shafts created by surrounding trees, producing a rich and dense background texture and also intricate shadow patterns on the ground. We quantified the image statistics of photographs of the ground and the surrounding panorama, as the ventral and lateral visual field is particularly important for visual flight control, and found differences in spatial statistics in photos where the hoverflies were hovering compared to where they were flying. Our results can, in the future, be used to create more naturalistic stimuli for experimenter-controlled experiments in the laboratory.

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          Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation

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            Bayesian surprise attracts human attention.

            We propose a formal Bayesian definition of surprise to capture subjective aspects of sensory information. Surprise measures how data affects an observer, in terms of differences between posterior and prior beliefs about the world. Only data observations which substantially affect the observer's beliefs yield surprise, irrespectively of how rare or informative in Shannon's sense these observations are. We test the framework by quantifying the extent to which humans may orient attention and gaze towards surprising events or items while watching television. To this end, we implement a simple computational model where a low-level, sensory form of surprise is computed by simple simulated early visual neurons. Bayesian surprise is a strong attractor of human attention, with 72% of all gaze shifts directed towards locations more surprising than the average, a figure rising to 84% when focusing the analysis onto regions simultaneously selected by all observers. The proposed theory of surprise is applicable across different spatio-temporal scales, modalities, and levels of abstraction.
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              Statistics of natural image categories.

              In this paper we study the statistical properties of natural images belonging to different categories and their relevance for scene and object categorization tasks. We discuss how second-order statistics are correlated with image categories, scene scale and objects. We propose how scene categorization could be computed in a feedforward manner in order to provide top-down and contextual information very early in the visual processing chain. Results show how visual categorization based directly on low-level features, without grouping or segmentation stages, can benefit object localization and identification. We show how simple image statistics can be used to predict the presence and absence of objects in the scene before exploring the image.
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                Author and article information

                Contributors
                karin.nordstrom@flinders.edu.au
                Journal
                J Comp Physiol A Neuroethol Sens Neural Behav Physiol
                J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol
                Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-7594
                1432-1351
                1 April 2019
                1 April 2019
                2019
                : 205
                : 3
                : 373-385
                Affiliations
                [1 ]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Neuroscience, , Uppsala University, ; Uppsala, Sweden
                [2 ]ISNI 0000 0001 0944 9128, GRID grid.7491.b, Neurobiology and CITEC, , Bielefeld University, ; Bielefeld, Germany
                [3 ]ISNI 0000 0004 0367 2697, GRID grid.1014.4, Centre for Neuroscience, , Flinders University, ; Adelaide, Australia
                Author information
                https://orcid.org/0000-0003-3659-013X
                https://orcid.org/0000-0002-9202-6253
                https://orcid.org/0000-0002-9336-4270
                http://orcid.org/0000-0002-6020-6348
                Article
                1329
                10.1007/s00359-019-01329-1
                6579776
                30937518
                31745da2-1fa2-43bb-8f37-837af348bda0
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 22 October 2018
                : 12 February 2019
                : 14 March 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: 2012-4740
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000181, Air Force Office of Scientific Research;
                Award ID: FA9550-15-1-0188
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006602, Air Force Research Laboratory;
                Award ID: FA9550-11-1-0349
                Award Recipient :
                Funded by: australian research council (AU)
                Award ID: DP170100008
                Award Recipient :
                Funded by: Australian Research Council (AU)
                Award ID: DP180100144
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004200, Stiftelsen Olle Engkvist Byggmästare;
                Award ID: 2016/348
                Award Recipient :
                Funded by: Cluster of Excellence ‘Cognitive Interaction Technology’ (CITEC, DE)
                Award ID: 111
                Award Recipient :
                Categories
                Original Paper
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Neurology
                image statistics,free flight behavior,hoverfly,vision,modelling
                Neurology
                image statistics, free flight behavior, hoverfly, vision, modelling

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