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      Preferential Processing of Social Features and Their Interplay with Physical Saliency in Complex Naturalistic Scenes

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          Abstract

          According to so-called saliency-based attention models, attention during free viewing of visual scenes is particularly allocated to physically salient image regions. In the present study, we assumed that social features in complex naturalistic scenes would be processed preferentially irrespective of their physical saliency. Therefore, we expected worse prediction of gazing behavior by saliency-based attention models when social information is present in the visual field. To test this hypothesis, participants freely viewed color photographs of complex naturalistic social (e.g., including heads, bodies) and non-social (e.g., including landscapes, objects) scenes while their eye movements were recorded. In agreement with our hypothesis, we found that social features (especially heads) were heavily prioritized during visual exploration. Correspondingly, the presence of social information weakened the influence of low-level saliency on gazing behavior. Importantly, this pattern was most pronounced for the earliest fixations indicating automatic attentional processes. These findings were further corroborated by a linear mixed model approach showing that social features (especially heads) add substantially to the prediction of fixations beyond physical saliency. Taken together, the current study indicates gazing behavior for naturalistic scenes to be better predicted by the interplay of social and physically salient features than by low-level saliency alone. These findings strongly challenge the generalizability of saliency-based attention models and demonstrate the importance of considering social influences when investigating the driving factors of human visual attention.

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          Statistical Power Analysis for the Behavioral Sciences

          <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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            Measuring emotion: the Self-Assessment Manikin and the Semantic Differential.

            The Self-Assessment Manikin (SAM) is a non-verbal pictorial assessment technique that directly measures the pleasure, arousal, and dominance associated with a person's affective reaction to a wide variety of stimuli. In this experiment, we compare reports of affective experience obtained using SAM, which requires only three simple judgments, to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974) which requires 18 different ratings. Subjective reports were measured to a series of pictures that varied in both affective valence and intensity. Correlations across the two rating methods were high both for reports of experienced pleasure and felt arousal. Differences obtained in the dominance dimension of the two instruments suggest that SAM may better track the personal response to an affective stimulus. SAM is an inexpensive, easy method for quickly assessing reports of affective response in many contexts.
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              State-of-the-art in visual attention modeling.

              Modeling visual attention--particularly stimulus-driven, saliency-based attention--has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                30 March 2017
                2017
                : 8
                : 418
                Affiliations
                [1] 1Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf Hamburg, Germany
                [2] 2Department of Psychology, Julius Maximilians University of Würzburg Würzburg, Germany
                Author notes

                Edited by: Narayanan Srinivasan, Allahabad University, India

                Reviewed by: Bennett I. Berthenthal, Indiana University Bloomington, USA; Valerio Santangelo, University of Perugia, Italy

                *Correspondence: Albert End, a.end@ 123456uke.de

                This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2017.00418
                5371661
                28424635
                ab6502e3-e14e-400a-bfbd-a91f97b82f96
                Copyright © 2017 End and Gamer.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 25 November 2016
                : 06 March 2017
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 99, Pages: 16, Words: 0
                Funding
                Funded by: European Research Council 10.13039/501100000781
                Award ID: ERC-2013-StG #336305
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                social attention,overt attention,physical saliency,visual perception,naturalistic scenes,eye movements,gaze prediction

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