Learning features in a complex and changing environment: A distribution-based framework for visual attention and vision in general.

[1]  A. Chetverikov,et al.  Representing Color Ensembles , 2017, Psychological science.

[2]  J. Wolfe,et al.  Five factors that guide attention in visual search , 2017, Nature Human Behaviour.

[3]  Árni Kristjánsson,et al.  Rapid learning of visual ensembles. , 2017, Journal of vision.

[4]  A. Chetverikov,et al.  Building ensemble representations: How the shape of preceding distractor distributions affects visual search , 2016, Cognition.

[5]  Honghua Chang,et al.  Search performance is better predicted by tileability than presence of a unique basic feature , 2016, Journal of vision.

[6]  Cathleen M Moore,et al.  The time-limited visual statistician. , 2016, Journal of experimental psychology. Human perception and performance.

[7]  Maria Concetta Morrone,et al.  Professor Adriana Fiorentini: 1/11/1926–29/2/2016 , 2016, i-Perception.

[8]  A. Ishiguchi,et al.  Evidence for a Global Sampling Process in Extraction of Summary Statistics of Item Sizes in a Set , 2016, Front. Psychol..

[9]  Jeremy M. Wolfe,et al.  Visual Search Revived: The Slopes Are Not That Slippery: A Reply to Kristjansson (2015) , 2016, i-Perception.

[10]  Michael A. Cohen,et al.  What is the Bandwidth of Perceptual Experience? , 2016, Trends in Cognitive Sciences.

[11]  Janneke F. M. Jehee,et al.  Perceptual learning increases orientation sampling efficiency. , 2016, Journal of vision.

[12]  Chris Oriet,et al.  Incidental statistical summary representation over time. , 2016, Journal of vision.

[13]  Igor S Utochkin,et al.  Similarity and heterogeneity effects in visual search are mediated by "segmentability". , 2016, Journal of experimental psychology. Human perception and performance.

[14]  A. Schubö,et al.  Target discrimination delays attentional benefit for grouped contexts: An ERP study , 2015, Brain Research.

[15]  Árni Kristjánsson,et al.  Reconsidering Visual Search , 2015, i-Perception.

[16]  Wei Ji Ma,et al.  Requiem for the max rule? , 2015, Vision Research.

[17]  Janneke F. M. Jehee,et al.  Sensory uncertainty decoded from visual cortex predicts behavior , 2015, Nature Neuroscience.

[18]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[19]  Deniz Başkent,et al.  Normal-Hearing Listeners’ and Cochlear Implant Users’ Perception of Pitch Cues in Emotional Speech , 2015, i-Perception.

[20]  I. Utochkin,et al.  Ensemble summary statistics as a basis for rapid visual categorization. , 2015, Journal of vision.

[21]  Jacob Feldman,et al.  Probabilistic models of perceptual features , 2015 .

[22]  Bjorn Hubert-Wallander,et al.  Not all summary statistics are made equal: Evidence from extracting summaries across time. , 2015, Journal of vision.

[23]  R. Sekuler,et al.  Obligatory and adaptive averaging in visual short-term memory , 2015 .

[24]  C. Moore,et al.  The capacity limitations of orientation summary statistics , 2015, Attention, perception & psychophysics.

[25]  S. Shergill,et al.  Local and Global Limits on Visual Processing in Schizophrenia , 2015, PloS one.

[26]  David Melcher,et al.  Stable statistical representations facilitate visual search. , 2014, Journal of experimental psychology. Human perception and performance.

[27]  C. Summerfield,et al.  Priming by the variability of visual information , 2014, Proceedings of the National Academy of Sciences.

[28]  Cathleen M Moore,et al.  Summary statistics of size: fixed processing capacity for multiple ensembles but unlimited processing capacity for single ensembles. , 2014, Journal of experimental psychology. Human perception and performance.

[29]  I. Utochkin,et al.  Parallel averaging of size is possible but range-limited: a reply to Marchant, Simons, and De Fockert. , 2014, Acta psychologica.

[30]  Zhaoyu Wei,et al.  The plunging cavities formed by the impinged jet after the entry of a sphere into water , 2014, J. Vis..

[31]  Aaron R. Seitz,et al.  Learning what to expect (in visual perception) , 2013, Front. Hum. Neurosci..

[32]  S P Arun,et al.  Does linear separability really matter? Complex visual search is explained by simple search. , 2013, Journal of vision.

[33]  I. Utochkin Visual search with negative slopes: the statistical power of numerosity guides attention. , 2013, Journal of vision.

[34]  Gunter Loffler,et al.  Set-size effects for sampled shapes: experiments and model , 2013, Front. Comput. Neurosci..

[35]  Anna Schubö,et al.  Context homogeneity facilitates both distractor inhibition and target enhancement. , 2013, Journal of vision.

[36]  Stefanie I. Becker,et al.  Higher set sizes in pop-out search displays do not eliminate priming or enhance target selection , 2013, Vision Research.

[37]  Chris Oriet,et al.  Size averaging of irrelevant stimuli cannot be prevented , 2013, Vision Research.

[38]  Aaron R. Seitz,et al.  Complexity and specificity of experimentally induced expectations in motion perception , 2013, BMC Neuroscience.

[39]  Hee Yeon Im,et al.  The effects of sampling and internal noise on the representation of ensemble average size , 2013, Attention, perception & psychophysics.

[40]  D. Newport,et al.  Transient natural convection in a conducting enclosure heated from above , 2013, J. Vis..

[41]  Zhigang Yang,et al.  Visualization of icing process of a water droplet impinging onto a frozen cold plate under free and forced convection , 2013, J. Vis..

[42]  P. Cavanagh,et al.  Different processing strategies underlie voluntary averaging in low and high noise. , 2012, Journal of vision.

[43]  W. Ma Organizing probabilistic models of perception , 2012, Trends in Cognitive Sciences.

[44]  Chris Oriet,et al.  Size and emotion averaging: costs of dividing attention after all. , 2012, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[45]  C. Cierpka,et al.  Particle imaging techniques for volumetric three-component (3D3C) velocity measurements in microfluidics , 2011, Journal of Visualization.

[46]  Aaron R. Seitz,et al.  Changing expectations about speed alters perceived motion direction , 2011, Current Biology.

[47]  Nicolas Robitaille,et al.  When more is less: extraction of summary statistics benefits from larger sets. , 2011, Journal of vision.

[48]  Jennifer E. Corbett,et al.  The whole is indeed more than the sum of its parts: perceptual averaging in the absence of individual item representation. , 2011, Acta psychologica.

[49]  D. Whitney,et al.  Serial dependence in visual perception , 2011, Nature Neuroscience.

[50]  C. Summerfield,et al.  Robust averaging during perceptual judgment , 2011, Proceedings of the National Academy of Sciences.

[51]  K. Nakayama,et al.  Situating visual search , 2011, Vision Research.

[52]  A. Pouget,et al.  Behavior and neural basis of near-optimal visual search , 2011, Nature Neuroscience.

[53]  Eero P. Simoncelli,et al.  Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics , 2011, Nature Neuroscience.

[54]  Chris Oriet,et al.  Rapid averaging? Not so fast! , 2011, Psychonomic bulletin & review.

[55]  G. Alvarez Representing multiple objects as an ensemble enhances visual cognition , 2011, Trends in Cognitive Sciences.

[56]  Aude Oliva,et al.  Estimating perception of scene layout properties from global image features. , 2011, Journal of vision.

[57]  Joshua A Solomon,et al.  Visual discrimination of orientation statistics in crowded and uncrowded arrays. , 2010, Journal of vision.

[58]  Jason M Haberman,et al.  The visual system discounts emotional deviants when extracting average expression , 2010, Attention, perception & psychophysics.

[59]  Karl J. Friston,et al.  Attention, Uncertainty, and Free-Energy , 2010, Front. Hum. Neurosci..

[60]  S. Klein,et al.  Rule-Based Learning Explains Visual Perceptual Learning and Its Specificity and Transfer , 2010, The Journal of Neuroscience.

[61]  Timothy F. Brady,et al.  Hierarchical Encoding in Visual Working Memory , 2010, Psychological science.

[62]  Aaron R. Seitz,et al.  Rapidly learned stimulus expectations alter perception of motion. , 2010, Journal of vision.

[63]  Alice R. Albrecht,et al.  Perceptually Averaging in a Continuous Visual World , 2010, Psychological science.

[64]  P. Berkes,et al.  Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .

[65]  Masaaki Kawahashi,et al.  Renovation of Journal of Visualization , 2010, J. Vis..

[66]  R. Rosenholtz,et al.  A summary statistic representation in peripheral vision explains visual search. , 2009, Journal of vision.

[67]  Jason M Haberman,et al.  Averaging facial expression over time. , 2009, Journal of vision.

[68]  Alfonso Caramazza,et al.  Attention selection, distractor suppression and N2pc , 2009, Cortex.

[69]  Tom Troscianko,et al.  Optimal feature integration in visual search. , 2009, Journal of vision.

[70]  S. Klein,et al.  Complete Transfer of Perceptual Learning across Retinal Locations Enabled by Double Training , 2008, Current Biology.

[71]  A. Treisman,et al.  Dividing attention across feature dimensions in statistical processing of perceptual groups , 2008, Perception & psychophysics.

[72]  D. Simons,et al.  Better than average: Alternatives to statistical summary representations for rapid judgments of average size , 2008, Perception & psychophysics.

[73]  Árni Kristjánsson,et al.  Priming in visual search: Separating the effects of target repetition, distractor repetition and role-reversal , 2008, Vision Research.

[74]  Michael Lindenbaum,et al.  Predicting visual search performance by quantifying stimuli similarities. , 2008, Journal of vision.

[75]  A. Oliva,et al.  The Representation of Simple Ensemble Visual Features Outside the Focus of Attention , 2008, Psychological science.

[76]  Agnieszka Wykowska,et al.  Detecting pop-out targets in contexts of varying homogeneity: Investigating homogeneity coding with event-related brain potentials (ERPs) , 2007, Brain Research.

[77]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[78]  Fuhui Long,et al.  Spectral statistics in natural scenes predict hue, saturation, and brightness. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Eero P. Simoncelli,et al.  Noise characteristics and prior expectations in human visual speed perception , 2006, Nature Neuroscience.

[80]  Endel Põder,et al.  Crowding, feature integration, and two kinds of "attention". , 2006, Journal of vision.

[81]  Aaron R. Seitz,et al.  A unified model for perceptual learning , 2005, Trends in Cognitive Sciences.

[82]  Allen L. Nagy,et al.  Effects of target and distractor heterogeneity on search for a color target , 2005, Vision Research.

[83]  A. Treisman,et al.  Statistical processing: computing the average size in perceptual groups , 2005, Vision Research.

[84]  Dale Purves,et al.  The statistical structure of natural light patterns determines perceived light intensity. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[85]  A. Reeves,et al.  The roles of distractor noise and target certainty in search: A signal detection model , 2004, Vision Research.

[86]  Allen L. Nagy,et al.  Distractor heterogeneity, attention, and color in visual search , 2003, Vision Research.

[87]  A. Treisman,et al.  Representation of statistical properties , 2003, Vision Research.

[88]  Mieke Donk,et al.  Detection Performance in Pop-Out Tasks: Nonmonotonic Changes with Display Size and Eccentricity , 2002, Perception.

[89]  Rajesh P. N. Rao,et al.  Probabilistic Models of the Brain: Perception and Neural Function , 2002 .

[90]  Ruth Rosenholtz,et al.  Visual search for orientation among heterogeneous distractors: experimental results and implications for signal-detection theory models of search. , 2001, Journal of experimental psychology. Human perception and performance.

[91]  G W Humphreys,et al.  Driving attention with the top down: The relative contribution of target templates to the linear separability effect in the size dimension , 2001, Perception & psychophysics.

[92]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[93]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[94]  S. Dakin Information limit on the spatial integration of local orientation signals. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[95]  D. Ariely Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[96]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[97]  Martin Arguin,et al.  Conjunction and linear non-separability effects in visual shape encoding , 2000, Vision Research.

[98]  H. Nothdurft Salience from feature contrast: variations with texture density , 2000, Vision Research.

[99]  D Purves,et al.  The distribution of oriented contours in the real world. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[100]  M. Landy,et al.  Examining edge- and region-based texture analysis mechanisms , 1998, Vision Research.

[101]  R. Watt,et al.  The computation of orientation statistics from visual texture , 1997, Vision Research.

[102]  W. Cowan,et al.  Distractor Heterogeneity versus Linear Separability in Colour Visual Search , 1996 .

[103]  W. Cowan,et al.  Visual search for colour targets that are or are not linearly separable from distractors , 1996, Vision Research.

[104]  T. Poggio,et al.  Fast perceptual learning in hyperacuity , 1995, Vision Research.

[105]  K. Nakayama,et al.  Priming of pop-out: I. Role of features , 1994, Memory & cognition.

[106]  A. Karni,et al.  The time course of learning a visual skill , 1993, Nature.

[107]  M. Bravo,et al.  The role of attention in different visual-search tasks , 1992, Perception & psychophysics.

[108]  J. Wolfe “Effortless” texture segmentation and “parallel” visual search are not the same thing , 1992, Vision Research.

[109]  J. Wolfe,et al.  The role of categorization in visual search for orientation. , 1992, Journal of experimental psychology. Human perception and performance.

[110]  Michael D'Zmura,et al.  Color in visual search , 1991, Vision Research.

[111]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[112]  H E Egeth,et al.  Local processes in preattentive feature detection. , 1991, Journal of experimental psychology. Human perception and performance.

[113]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[114]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[115]  C Bundesen,et al.  Color segregation and visual search , 1983, Perception & psychophysics.

[116]  B. Julesz Textons, the elements of texture perception, and their interactions , 1981, Nature.

[117]  E. Farmer,et al.  Visual search through color displays: Effects of target-background similarity and background uniformity , 1980, Perception & psychophysics.

[118]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[119]  William Prinzmetal,et al.  Configurational effects in visual information processing , 1976 .

[120]  ohn,et al.  Accurate rapid averaging of multihue ensembles is due to a limited capacity subsampling mechanism , 2019 .

[121]  K. May,et al.  Inefficiency of orientation averaging: Evidence for hybrid serial/parallel temporal integration. , 2016, Journal of vision.

[122]  Krista A. Ehinger,et al.  A general account of peripheral encoding also predicts scene perception performance. , 2016, Journal of vision.

[123]  M. Stefanova,et al.  Global orientation estimation in noisy conditions. , 2015, Acta neurobiologiae experimentalis.

[124]  R. Rosenholtz 1 Texture perception , 2013 .

[125]  Michael S. Landy,et al.  Texture analysis and perception , 2013 .

[126]  David Whitney,et al.  Ensemble perception: Summarizing the scene and broadening the limits of visual processing. , 2012 .

[127]  G. Campana,et al.  Where perception meets memory: A review of repetition priming in visual search tasks , 2010, Attention, perception & psychophysics.

[128]  Masahiro Takei,et al.  Human resource development and visualization , 2009, J. Vis..

[129]  Dominique Lamy,et al.  Priming of Pop-out provides reliable measures of target activation and distractor inhibition in selective attention , 2008, Vision Research.

[130]  Norio Izumi,et al.  Broadening the visualization frontier , 2007, J. Vis..

[131]  A. Mizuno,et al.  A change of the leading player in flow Visualization technique , 2006, J. Vis..

[132]  J. Driver,et al.  Priming in visual search: Context effects, target repetition effects, and role-reversal effects , 2005 .

[133]  D. Purves,et al.  Why we see what we do : an empirical theory of vision , 2003 .

[134]  B. Julesz,et al.  Short-range limitation on detection of feature differences. , 1987, Spatial vision.