The Contributions of Image Content and Behavioral Relevancy to Overt Attention
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{"title"=>"The contributions of image content and behavioral relevancy to overt attention", "type"=>"journal", "authors"=>[{"first_name"=>"Selim", "last_name"=>"Onat", "scopus_author_id"=>"23474886100"}, {"first_name"=>"Alper", "last_name"=>"Açik", "scopus_author_id"=>"26430526500"}, {"first_name"=>"Frank", "last_name"=>"Schumann", "scopus_author_id"=>"22635530200"}, {"first_name"=>"Peter", "last_name"=>"König", "scopus_author_id"=>"7102563952"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"372990937", "doi"=>"10.1371/journal.pone.0093254", "sgr"=>"84899668057", "scopus"=>"2-s2.0-84899668057", "pmid"=>"24736751"}, "id"=>"d92c4ecf-e143-3fd5-9085-23819ce05416", "abstract"=>"During free-viewing of natural scenes, eye movements are guided by bottom-up factors inherent to the stimulus, as well as top-down factors inherent to the observer. The question of how these two different sources of information interact and contribute to fixation behavior has recently received a lot of attention. Here, a battery of 15 visual stimulus features was used to quantify the contribution of stimulus properties during free-viewing of 4 different categories of images (Natural, Urban, Fractal and Pink Noise). Behaviorally relevant information was estimated in the form of topographical interestingness maps by asking an independent set of subjects to click at image regions that they subjectively found most interesting. Using a Bayesian scheme, we computed saliency functions that described the probability of a given feature to be fixated. In the case of stimulus features, the precise shape of the saliency functions was strongly dependent upon image category and overall the saliency associated with these features was generally weak. When testing multiple features jointly, a linear additive integration model of individual saliencies performed satisfactorily. We found that the saliency associated with interesting locations was much higher than any low-level image feature and any pair-wise combination thereof. Furthermore, the low-level image features were found to be maximally salient at those locations that had already high interestingness ratings. Temporal analysis showed that regions with high interestingness ratings were fixated as early as the third fixation following stimulus onset. Paralleling these findings, fixation durations were found to be dependent mainly on interestingness ratings and to a lesser extent on the low-level image features. Our results suggest that both low- and high-level sources of information play a significant role during exploration of complex scenes with behaviorally relevant information being more effective compared to stimulus features.", "link"=>"http://www.mendeley.com/research/contributions-image-content-behavioral-relevancy-overt-attention", "reader_count"=>32, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>12, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>3, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>4, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>7, "Neuroscience"=>2, "Sports and Recreations"=>1, "Psychology"=>12, "Social Sciences"=>1, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Psychology"=>{"Psychology"=>12}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>2}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"United States"=>2, "Germany"=>2}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1464425"], "description"=>"<p>Cornerness feature was used to illustrate the time-course of a low-level feature. While, absolute D<sub>KL</sub> values <i>are</i> shown in the <i>left</i> panel, the curves were normalized to their peak value (second fixation) in order to have a better comparison of the temporal evolution. (<b>B</b>) Joint distribution of fixation durations computed separately for Natural (<i>left</i> panel) and Urban (<i>right</i> panel) categories. (<b>C</b>) For different image categories, each bar represents the contribution of low- and high-level information on the variability of duration of fixations.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "high-", "low-level", "computed", "fixations", "separately", "fixation"], "article_id"=>999262, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g008", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_Time_course_of_D_KL_values_for_high_and_low_level_features_these_were_computed_for_each_fixations_separately_starting_from_the_second_fixation_on_/999262", "title"=>"(A) Time course of D<sub>KL</sub> values for high- and low-level features; these were computed for each fixations separately starting from the second fixation on.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464422"], "description"=>"<p>(<b>A</b>) The goodness-of-fit presented as a matrix for each pair of feature combination using a linear model for the data presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g005\" target=\"_blank\">Fig. 5D</a>. Color codes for the strength of the correlation between empirical saliency maps (shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g005\" target=\"_blank\">Fig. 5D</a>) and a model that linearly combines one dimensional saliency values. The ordering of the features follows D<sub>KL</sub> values of one-dimensional saliency functions. Diagonal entries are omitted in this representation. (<b>B</b>) D<sub>KL</sub> values extracted from two-dimensional saliency functions shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g005\" target=\"_blank\">Fig. 5D</a>. Diagonal entries represents D<sub>KL</sub> values of the one-dimensional saliency functions shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g005\" target=\"_blank\">Fig. 5D</a>. (<b>C</b>) D<sub>KL</sub> values of two-dimensional saliency functions shown in (B) are compared to the sum of corresponding uni-dimensional saliency functions (shown in the diagonal in (B)). 100% represents the case where the sum of the D<sub>KL</sub> values of one-dimensional saliency functions equal to the D<sub>KL</sub> value of the joint saliency function.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "saliency"], "article_id"=>999259, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integration_of_saliency_functions_/999259", "title"=>"Integration of saliency functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464421"], "description"=>"<p>(<b>A</b>) Joint distribution of two example features F1 (luminance contrast, LC) and F2 (saturation contrast, SATC) is presented. This data doesn’t include Pink Noise category. Most of the points are located along the diagonal with a considerable accumulation of density at the lowest feature values. This joint distribution represents the co-occurrence of feature values at both actual and control fixation locations (including those that were not done on the shown image), consequently this joint distribution takes into account the central bias. (<b>B</b>) Distribution of the same feature pairs only at actually fixated locations. (<b>C</b>) The posterior probability distribution corresponding to the saliency function, p(fixation|F1, F2), is computed according to the Bayesian equality (see Materials and Methods). (<b>D</b>) Two-dimensional saliency functions are presented for a selected set of feature. The diagonal entries correspond to one-dimensional saliency functions presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g003\" target=\"_blank\">Fig. 3</a> but computed using only 8 bins. Features are ordered from left to right according to their D<sub>KL</sub> values. To compute the saliency functions the data from Natural, Fractal and Urban categories were pooled. The data obtained during presentation of Pink Noise images was discarded. Abbreviations as presented in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g001\" target=\"_blank\">Fig. 1</a>.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "two-dimensional", "saliency"], "article_id"=>999258, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computation_of_two_dimensional_saliency_maps_/999258", "title"=>"Computation of two-dimensional saliency maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464423"], "description"=>"<p>(<b>A</b>) 3 different stimuli are shown together with their actual fixation and interestingness maps (<i>second</i> and <i>third</i> rows). The first row depicts three stimuli belonging to Natural and Urban categories as they were shown during the experiment. <i>Second</i> and <i>third</i> rows depict the empirical saliency and interestingness maps overlaid on the gray scale version of the stimulus. Empirical saliency maps are probability maps that show the probability of a given location to be fixated. Similarly interestingness maps represent for a given location the probability of receiving an interestingness rating. These were obtained with the help of a pointer device by an independent set of human subjects (n = 35). These maps were treated the same way as low-level feature maps in order to compute D<sub>KL</sub> values. (<b>B</b>) Joint distribution of interestingness values and a low-level feature (Cornerness) that was most strongly correlated with fixation locations (<i>Left</i> panel). This distribution shows the co-occurrence of a low-level image based feature with interestingness ratings of human subjects at all fixated and non-fixated locations. <i>Middle</i> panel represents the distribution of same variables at exclusively fixated locations. The posterior distribution represents the two-dimensional saliency function (<i>right</i> panel). Notice that the saliency increases nearly completely as a function of interestingness ratings (contour lines) and only marginally as a function of low-level feature values. The saliency is therefore mainly modulated by the interestingness value of a location.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "interestingness"], "article_id"=>999260, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Computation_of_Interestingness_maps_/999260", "title"=>"Computation of Interestingness maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464420"], "description"=>"<p>(<b>A</b>) Each bar represents the strength of correlation between fixation and low-level feature values quantified with D<sub>KL</sub> metric and averaged across all categories. For each feature channel, four symbols show additionally the stimulus-category specific D<sub>KL</sub> values (<i>green circle</i>: Naturals; <i>red star</i>: Fractal; <i>black square</i>: Urban; <i>pink triangle</i>: Pink Noise). Horizontal bar in the abscissa depicts different clusters of features according to their sensitivity, intensity selective features, contrast selective, intrinsic dimensionality features and symmetry sensitive features. (<b>B</b>) For each stimulus category (N, F, U and P), highest D<sub>KL</sub> values for one-dimensional (leftmost bar) and two-dimensional (second bar from left) saliency functions are shown. The D<sub>KL</sub> values obtained from the saliency functions of the interestingness maps are shown in the third place. Last bars represent highest possible D<sub>KL</sub> value; these are obtained treating actual fixation maps as feature maps.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "low-level", "fixation"], "article_id"=>999257, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_strength_of_correlation_between_low_level_features_and_fixation_points_/999257", "title"=>"The strength of correlation between low-level features and fixation points.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464418"], "description"=>"<p>Entropy values for category (<i>dashed</i> line) and image (<i>solid</i> line) specific fixation maps for each stimulus category (here and in the following Figures, <b>N</b>: Natural; <b>F</b>: Fractal; <b>U</b>: Urban; <b>P</b>: Pink Noise). In the case of image specific fixation maps, the average entropy across all images belonging to a given category is shown. Error bars represents 99% bootstrap confidence intervals. Inset maps depict category specific fixation maps that represent the distribution of all fixation points across all images and subjects.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry"], "article_id"=>999255, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Exploration_strategies_/999255", "title"=>"Exploration strategies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464419"], "description"=>"<p>(<b>A–D</b>) Saliency functions, p(fixation|feature), are shown for Natural (<i>green</i>), Fractal (<i>red</i>), Urban (<i>black</i>) and Pink Noise (<i>magenta</i>) category of images. Different panels group feature maps according to their selectivity. Features selective for channel intensity (<b>LM</b>, <b>RGM</b>, <b>YBM</b>, <b>SATM</b>) and for contrast (<b>LC</b>, <b>RGC</b>, <b>YBC</b>, <b>SATC</b>, <b>TC</b>) are shown in <b>A</b> and <b>B</b>, respectively. Only those saliency functions that deviated significantly from the control distribution are shown, notice that saliency functions corresponding to Urban and Fractal categories are omitted in panel A. Last two rows (<b>C–D</b>) depict the saliency functions for features of intrinsic dimensionality (<b>S, C and E</b>) and for symmetry related features (<b>BiSymm, RaSymm-H/L</b>). Shaded areas represent 99.99% bootstrap confidence intervals. Same abbreviations as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093254#pone-0093254-g001\" target=\"_blank\">Fig. 1</a>. Saliency functions averaged across features (see text for details) are shown in (<b>E</b>) for each category of stimuli. In all panels, the horizontal line represents the histogram equalized distribution of feature values, p(feature) after correcting for the central bias of category specific fixations maps.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry"], "article_id"=>999256, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Saliency_functions_/999256", "title"=>"Saliency functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464417"], "description"=>"<p>For each category (<i>first</i> row, <i>green</i> background, <b>Natural</b>; <i>second</i> row, <i>red</i> background, <b>Fractal</b>; <i>third</i> row, <i>black</i> background, <b>Urban</b>; <i>last</i> row, <i>magenta</i> background; <b>Pink Noise</b>) a representative stimulus (left upper corner) and its associated low-level feature maps are shown. <b>LM</b>: Mean Luminance; <b>RGM</b>: Mean Red-Green Intensity; <b>YBM</b>: Mean Yellow-Blue Intensity; <b>SATM</b>: Mean Saturation; <b>LC</b>: Luminance Contrast; <b>RGB</b>: Red-Green Contrast; <b>YBC</b>: Yellow-Blue Contrast; <b>SATC</b>: Saturation Contrast; <b>TC</b>: Texture Contrast; <b>S</b>: Surfaceness; <b>C</b>: Cornerness; <b>E</b>: Edgeness; <b>BiSymm</b>: Bilateral Symmetry; <b>Ra-Symm L/H</b>: Radial symmetry with high or low spatial frequency selectivity. In addition to these low-level features, interestingness ratings were collected with the help of a pointer device (see Material and Methods), the topographic distribution of this high-level feature is shown as interestingness maps (<b>iMap</b>: Interestingness Map). Please note that this data is not collected for the case of Pink Noise category (lowest row). In addition to click data, recorded eye-movements for these four images are also presented in the same topographic form (<b>fMap</b>: Fixation Map; <i>second</i> row in each panel, <i>last</i> entry). All these maps were are shown following the histogram equalization step therefore all values occur equally likely.</p>", "links"=>[], "tags"=>["neuroscience", "cognitive science", "Cognitive psychology", "attention", "Sensory perception", "psychology", "behavior", "Mental health and psychiatry", "stimuli", "low-", "high-level"], "article_id"=>999254, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Selim Onat", "Alper Açık", "Frank Schumann", "Peter König"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0093254.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decomposition_of_stimuli_into_a_set_of_low_and_high_level_features_/999254", "title"=>"Decomposition of stimuli into a set of low- and high-level features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:44:38"}

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Relative Metric

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