dnn_textures.bib

@article{rosenholtz_what_2011,
  title = {What Your Visual System Sees Where You Are Not Looking},
  timestamp = {2014-06-09T12:05:16Z},
  journal = {IS\&T/SPIE Electronic Imaging},
  author = {Rosenholtz, Ruth},
  year = {2011},
  pages = {786510--786514},
  file = {Rosenholtz_2011_IS&TSPIE Electronic Imaging.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2011/Rosenholtz_2011_IS&TSPIE Electronic Imaging.pdf:application/pdf}
}

@book{gelman_data_2007,
  address = {New York, NY},
  title = {Data {{Analysis}} Using Regression and Multilevel/Hierarchical Models},
  timestamp = {2015-01-19T11:08:58Z},
  publisher = {{Cambridge Univ Press}},
  author = {Gelman, Andrew and Hill, Jennifer},
  year = {2007},
  file = {Gelman_Hill_2007.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2007/Gelman_Hill_2007.pdf:application/pdf}
}

@incollection{dakin_seeing_2014,
  title = {Seeing Statistical Regularities: {{Texture}} and Pattern Perception},
  timestamp = {2014-06-09T11:46:39Z},
  booktitle = {Oxford {{Handbook}} of {{Perceptual Organization}}},
  author = {Dakin, Steven},
  year = {2014},
  keywords = {1,averaging,distil a massive amount,global processing,guide behaviour within complex,has evolved to effectively,introduction,local-to-global,natural,of,seeing statistics,statistics,texture,the brain must rapidly,the human visual system,to achieve this goal,visual environments},
  pages = {1--19},
  file = {Dakin_2014.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Dakin_2014.pdf:application/pdf}
}

@article{wickham_tidy_2014,
  title = {Tidy {{Data}}},
  volume = {59},
  issn = {1548-7660},
  timestamp = {2015-02-24T14:57:20Z},
  number = {10},
  journal = {Journal of Statistical Software},
  author = {Wickham, Hadley},
  month = sep,
  year = {2014},
  pages = {??--??},
  file = {Wickham_2014_Journal of Statistical Software.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Wickham_2014_Journal of Statistical Software.pdf:application/pdf},
  day = {12},
  coden = {JSSOBK},
  bibdate = {2014-05-09},
  accepted = {2014-05-09},
  submitted = {2013-02-20}
}

@incollection{rosenholtz_texture_2014,
  title = {Texture Perception},
  timestamp = {2014-06-09T12:05:17Z},
  number = {Gibson 1986},
  booktitle = {Oxford {{Handbook}} of {{Perceptual Organization}}},
  publisher = {{Oxford University Press}},
  author = {Rosenholtz, Ruth},
  year = {2014},
  pages = {1--24},
  file = {Rosenholtz_2014.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Rosenholtz_2014.pdf:application/pdf}
}

@article{freeman_metamers_2011,
  title = {Metamers of the Ventral Stream},
  volume = {14},
  timestamp = {2014-06-09T11:49:31Z},
  number = {9},
  journal = {Nature Neuroscience},
  author = {Freeman, Jeremy and Simoncelli, Eero P},
  year = {2011},
  pages = {1195--1201},
  file = {Freeman_Simoncelli_2011_Nature Neuroscience.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2011/Freeman_Simoncelli_2011_Nature Neuroscience.pdf:application/pdf;Freeman_Simoncelli_2011_Nature Neuroscience4.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2011/Freeman_Simoncelli_2011_Nature Neuroscience4.pdf:application/pdf}
}

@book{wickham_ggplot2_2009,
  address = {New York},
  title = {Ggplot2: {{Elegant Graphics}} for {{Data Analysis}}},
  isbn = {978-0-387-98140-6},
  timestamp = {2014-06-09T12:11:44Z},
  publisher = {{Springer}},
  author = {Wickham, Hadley},
  year = {2009}
}

@article{yamins_performanceoptimized_2014,
  title = {Performance-Optimized Hierarchical Models Predict Neural Responses in Higher Visual Cortex},
  volume = {111},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1403112111},
  language = {en},
  timestamp = {2014-06-24T12:40:04Z},
  number = {23},
  urldate = {2014-06-20},
  journal = {Proceedings of the National Academy of Sciences},
  author = {Yamins, D. L. K. and Hong, H. and Cadieu, C. F. and Solomon, E. A. and Seibert, D. and DiCarlo, J. J.},
  month = jun,
  year = {2014},
  pages = {8619--8624},
  file = {Yamins_etal_2014_Proceedings of the National Academy of Sciences.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Yamins_etal_2014_Proceedings of the National Academy of Sciences.pdf:application/pdf}
}

@inproceedings{freeman_functionally_2013,
  address = {San Diego, CA},
  title = {Functionally Partitioning the Ventral Stream with Controlled Natural Stimuli},
  abstract = {The visual areas of the primate cerebral cortex provide distinct representations of the visual world, each with a distinct function and topographic representation. Neurons in primary visual cortex respond selectively to orientation and spatial frequency, whereas neurons in inferotemporal and lateral occipital areas respond selectively to complex objects. But the areas in between, in particular V2 and V4, have been more difficult to differentiate on functional grounds. In previous work (Freeman, Ziemba et al., Nature Neurosci, 2013), we showed that, in both macaque and human, synthetic stimuli whose higher-order statistical properties emulated natural texture images yielded differential responses in V2, but not V1. We have now examined responses to these stimuli and to photographs of objects and scenes, to better distinguish the properties of V2 from those of the areas that provide its input and receive its output. We used fMRI to measure BOLD responses while human observers viewed rapidly presented images, while performing an attentionally-diverting task at fixation. We measured BOLD responses while presenting stimuli drawn from different stimulus classes in alternating blocks. We used three classes: photographs of scenes and objects, texture images matched to the higher-order statistics of these photographs (specifically, the spatially-averaged distribution and correlations of model V1 outputs, Portilla \& Simoncelli, 2000), and noise images matched only to the overall orientation and frequency content of the photographs. As we found previously, fMRI responses to textures were larger than those to noise in V2, V3, and to some extent V4, but not in V1, and not in inferotemporal and lateral occipital areas thought to be selective for complex objects. In contrast, responses to photographs were larger than those to noise in those higher areas, as well as in V2-V4, but not in V1. The differentiation between V1 and V2, however, was less robust than for the more controlled, homogenous textures. Finally, comparing responses to photographs and textures revealed differential activity in higher areas, but little or no differential responses in V1-V4. Together, these three comparisons offer a simple functional account of the visual cortical cascade. Whereas V1 encodes basic spectral properties, V2, V3, and to some extent V4 represent the higher-order statistics of textures. Downstream areas capture the kinds of global structures that are unique to images of natural scenes and objects.},
  timestamp = {2015-04-08T09:56:54Z},
  booktitle = {Annual {{Meeting}}, {{Neuroscience}}},
  publisher = {{Society for Neuroscience}},
  author = {Freeman, J. and Ziemba, C. M. and Simoncelli, E. P. and Movshon, J. A.},
  month = nov,
  year = {2013}
}

@article{kummerer_deepgaze_2016,
  title = {{{DeepGaze II}}: {{Reading}} Fixations from Deep Features Trained on Object Recognition},
  volume = {1610.01563},
  timestamp = {2017-02-05T13:25:39Z},
  journal = {arXiv},
  author = {K{\"u}mmerer, Matthias and Wallis, T.~S.~A. and Bethge, Matthias},
  month = oct,
  year = {2016},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Quantitative Biology - Neurons and Cognition,Statistics - Applications},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  adsurl = {http://adsabs.harvard.edu/abs/2016arXiv161001563K},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{moscatelli_modeling_2012,
  title = {Modeling Psychophysical Data at the Population-Level: The Generalized Linear Mixed Model.},
  volume = {12},
  abstract = {In psychophysics, researchers usually apply a two-level model for the analysis of the behavior of the single subject and the population. This classical model has two main disadvantages. First, the second level of the analysis discards information on trial repetitions and subject-specific variability. Second, the model does not easily allow assessing the goodness of fit. As an alternative to this classical approach, here we propose the Generalized Linear Mixed Model (GLMM). The GLMM separately estimates the variability of fixed and random effects, it has a higher statistical power, and it allows an easier assessment of the goodness of fit compared with the classical two-level model. GLMMs have been frequently used in many disciplines since the 1990s; however, they have been rarely applied in psychophysics. Furthermore, to our knowledge, the issue of estimating the point-of-subjective-equivalence (PSE) within the GLMM framework has never been addressed. Therefore the article has two purposes: It provides a brief introduction to the usage of the GLMM in psychophysics, and it evaluates two different methods to estimate the PSE and its variability within the GLMM framework. We compare the performance of the GLMM and the classical two-level model on published experimental data and simulated data. We report that the estimated values of the parameters were similar between the two models and Type I errors were below the confidence level in both models. However, the GLMM has a higher statistical power than the two-level model. Moreover, one can easily compare the fit of different GLMMs according to different criteria. In conclusion, we argue that the GLMM can be a useful method in psychophysics.},
  timestamp = {2014-06-09T12:01:04Z},
  number = {11},
  journal = {Journal of Vision},
  author = {Moscatelli, Alessandro and Mezzetti, Maura and Lacquaniti, Francesco},
  year = {2012},
  file = {Moscatelli_etal_2012_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2012/Moscatelli_etal_2012_Journal of Vision.pdf:application/pdf}
}

@incollection{xie_knitr_2013,
  title = {Knitr: {{A}} Comprehensive Tool for Reproducible Research in {{R}}. {{BT}} - {{Implementing Reproducible Computational Research}}},
  isbn = {978-1-4665-6159-5},
  timestamp = {2014-06-09T12:12:21Z},
  booktitle = {Implementing {{Reproducible Computational Research}}},
  publisher = {{Chapman \& Hall/CRC}},
  author = {Xie, Yihui},
  editor = {Stodden, Victoria and Leisch, Frederich and Peng, Roger D},
  year = {2013}
}

@book{knoblauch_modeling_2012,
  address = {New York},
  title = {Modeling {{Psychophysical Data}} in {{R}}},
  isbn = {978-1-4614-4474-9},
  abstract = {Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data.R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods.This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.},
  timestamp = {2014-07-22T13:45:51Z},
  publisher = {{Springer}},
  author = {Knoblauch, Kenneth and Maloney, Laurence T},
  year = {2012}
}

@article{imagenet_2015,
  title = {{{ImageNet Large Scale Visual Recognition Challenge}}},
  volume = {115},
  doi = {10.1007/s11263-015-0816-y},
  timestamp = {2016-06-16T11:56:12Z},
  number = {3},
  journal = {International Journal of Computer Vision (IJCV)},
  author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li},
  year = {2015},
  pages = {211--252}
}

@article{hoffman_nouturn_2014,
  title = {The {{No}}-{{U}}-{{Turn Sampler}}: {{Adaptively Setting Path Lengths}} in {{Hamiltonian Monte Carlo}}},
  volume = {15},
  timestamp = {2014-09-06T09:30:55Z},
  number = {Apr},
  journal = {Journal of Machine Learning Research},
  author = {Hoffman, Matthew D. and Gelman, Andrew},
  year = {2014},
  pages = {1593--1623},
  file = {Hoffman_Gelman_Journal of Machine Learning Research2.pdf:/Users/tsawallis/Dropbox/Zotero_Library/Hoffman_Gelman_Journal of Machine Learning Research2.pdf:application/pdf}
}

@inproceedings{gatys_texture_2015-1,
  title = {Texture {{Synthesis Using Convolutional Neural Networks}}},
  timestamp = {2016-05-03T14:50:04Z},
  booktitle = {Advances in {{Neural Information Processing Systems}} 28},
  author = {Gatys, L. A. and Ecker, A. S. and Bethge, M.},
  month = may,
  year = {2015},
  keywords = {convolutional neural networks,deep learning,texture synthesis,ventral stream},
  file = {Gatys_etal_2015_Advances in Neural Information Processing Systems 28.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Gatys_etal_2015_Advances in Neural Information Processing Systems 28.pdf:application/pdf}
}

@book{jones_scipy_2001,
  title = {{{SciPy}}: {{Open}} Source Scientific Tools for {{Python}}},
  timestamp = {2016-10-07T12:53:07Z},
  author = {Jones, Eric and Oliphant, Travis and Peterson, Pearu},
  year = {2001},
  note = {[Online; accessed 2014-09-12]}
}

@article{balas_texture_2006,
  title = {Texture Synthesis and Perception: Using Computational Models to Study Texture Representations in the Human Visual System.},
  volume = {46},
  issn = {0042-6989},
  doi = {10.1016/j.visres.2005.04.013},
  abstract = {Traditionally, texture perception has been studied using artificial textures made of random dots or repeated shapes. At the same time, computer algorithms for natural texture synthesis have improved dramatically. We seek to unify these two fields through a psychophysical assessment of a particular computational model, providing insight into which statistics are most vital for natural texture perception. We employ Portilla and Simoncelli's texture synthesis algorithm, a parametric model that mimics computations carried out in human vision. We find an intriguing interaction between texture type (periodic, structured, or 3-D textures) and image statistics (autocorrelation function and filter magnitude correlations), suggesting different representations may be employed for these texture families under pre-attentive viewing.},
  timestamp = {2014-06-09T10:53:16Z},
  number = {3},
  journal = {Vision research},
  author = {Balas, Benjamin J},
  month = feb,
  year = {2006},
  keywords = {Adult,Algorithms,Analysis of Variance,Computer Simulation,Contrast Sensitivity,Contrast Sensitivity: physiology,Humans,Models,Psychological,Psychological Tests,Psychophysics},
  pages = {299--309},
  file = {Balas_2006_Vision research.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2006/Balas_2006_Vision research.pdf:application/pdf}
}

@article{wilkinson_lateral_1997,
  title = {Lateral Interactions in Peripherally Viewed Texture Arrays},
  volume = {14},
  abstract = {A horizontal array of vertically oriented Gabor elements was used to examine lateral masking in the near periphery (1.9$^\circ$\textendash{}5.7$^\circ$ eccentricity). Thresholds were assessed for detecting changes in the contrast, the spatial frequency, and the orientation of the central ...},
  timestamp = {2014-06-09T12:11:53Z},
  number = {9},
  journal = {Journal of the Optical Society of America A},
  author = {Wilkinson, Frances and Wilson, Hugh R and Ellemberg, Dave},
  year = {1997},
  pages = {2057},
  file = {Wilkinson_etal_1997_Journal of the Optical Society of America A.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1997/Wilkinson_etal_1997_Journal of the Optical Society of America A.pdf:application/pdf}
}

@article{cheung_nonlinear_2008,
  title = {Nonlinear Mixed-Effects Modeling of {{MNREAD}} Data.},
  volume = {49},
  abstract = {PURPOSE:It is often difficult to estimate parameters from individual clinical data because of noisy or incomplete measurements. Nonlinear mixed-effects (NLME) modeling provides a statistical framework for analyzing population parameters and the associated variations, even when individual data sets are incomplete. The authors demonstrate the application of NLME by analyzing data from the MNREAD, a continuous-text reading-acuity chart. METHODS:The authors analyzed MNREAD data (measurements of reading speed vs. print size) for two groups: 42 adult observers with normal vision and 14 patients with age-related macular degeneration (AMD). Truncated sets of MNREAD data were generated from the individual observers with normal vision. The MNREAD data were fitted with a two-limb function and an exponential-decay function using an individual curve-fitting approach and an NLME modeling approach. RESULTS:The exponential-decay function provided slightly better fits than the two-limb function. When the parameter estimates from the truncated data sets were used to predict the missing data, NLME modeling gave better predictions than individual fitting. NLME modeling gave reasonable parameter estimates for AMD patients even when individual fitting returned unrealistic estimates. CONCLUSIONS:These analyses showed that (1) an exponential-decay function fits MNREAD data very well, (2) NLME modeling provides a statistical framework for analyzing MNREAD data, and (3) NLME analysis provides a way of estimating MNREAD parameters even for incomplete data sets. The present results demonstrate the potential value of NLME modeling for clinical vision data.},
  timestamp = {2014-06-09T11:23:34Z},
  number = {2},
  journal = {Investigative Ophthalmology \& Visual Science},
  author = {Cheung, Sing-Hang and Kallie, Christopher S and Legge, Gordon E and Cheong, Allen M Y},
  year = {2008},
  pages = {828--835},
  file = {Cheung_etal_2008_Investigative Ophthalmology & Visual Science.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2008/Cheung_etal_2008_Investigative Ophthalmology & Visual Science.pdf:application/pdf}
}

@article{gelman_prior_2006,
  title = {Prior Distributions for Variance Parameters in Hierarchical Models},
  volume = {1},
  timestamp = {2014-06-09T11:50:14Z},
  number = {3},
  journal = {Bayesian Analysis},
  author = {Gelman, A},
  year = {2006},
  pages = {515--533},
  file = {Gelman_2006_Bayesian Analysis.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2006/Gelman_2006_Bayesian Analysis.pdf:application/pdf}
}

@article{freeman_functional_2013,
  title = {A Functional and Perceptual Signature of the Second Visual Area in Primates},
  volume = {16},
  abstract = {There is no generally accepted account of the function of the second visual cortical area (V2), partly because no simple response properties robustly distinguish V2 neurons from those in primary visual cortex (V1). We constructed synthetic stimuli replicating the higher-order statistical dependencies found in natural texture images and used them to stimulate macaque V1 and V2 neurons. Most V2 cells responded more vigorously to these textures than to control stimuli lacking naturalistic structure; V1 cells did not. Functional magnetic resonance imaging (fMRI) measurements in humans revealed differences between V1 and V2 that paralleled the neuronal measurements. The ability of human observers to detect naturalistic structure in different types of texture was well predicted by the strength of neuronal and fMRI responses in V2 but not in V1. Together, these results reveal a particular functional role for V2 in the representation of natural image structure.},
  timestamp = {2014-06-09T11:49:33Z},
  number = {7},
  journal = {Nature Neuroscience},
  author = {Freeman, Jeremy and Ziemba, CM and Heeger, David J and Simoncelli, Eero P and Movshon, J Anthony},
  year = {2013},
  pages = {974--981},
  file = {Freeman_etal_2013_Nature Neuroscience.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Freeman_etal_2013_Nature Neuroscience.pdf:application/pdf}
}

@book{kruschke_doing_2011,
  address = {Burlington MA},
  title = {Doing {{Bayesian Data Analysis}}},
  isbn = {978-0-12-381485-2},
  timestamp = {2015-05-26T13:13:44Z},
  publisher = {{Academic Press / Elsevier}},
  author = {Kruschke, J K},
  year = {2011},
  file = {Kruschke_2011.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2011/Kruschke_2011.pdf:application/pdf}
}

@article{balas_attentive_2008,
  title = {Attentive Texture Similarity as a Categorization Task: {{Comparing}} Texture Synthesis Models},
  volume = {41},
  timestamp = {2014-06-09T10:53:23Z},
  number = {3},
  journal = {Pattern Recognition},
  author = {Balas, Benjamin J},
  year = {2008},
  pages = {972--982},
  file = {Balas_2008_Pattern Recognition.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2008/Balas_2008_Pattern Recognition.pdf:application/pdf}
}

@article{adelson_plenoptic_1991,
  title = {The Plenoptic Function and the Elements of Early Vision},
  volume = {1},
  timestamp = {2014-06-09T07:56:20Z},
  journal = {Computational models of visual processing},
  author = {Adelson, Edward H and Bergen, JR R},
  editor = {Landy, Michael S and Movshon, J Anthony},
  year = {1991},
  pages = {3--20},
  file = {Adelson_Bergen_1991_Computational models of visual processing.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1991/Adelson_Bergen_1991_Computational models of visual processing.pdf:application/pdf;Adelson_Bergen_1991_Computational models of visual processing3.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1991/Adelson_Bergen_1991_Computational models of visual processing3.pdf:application/pdf}
}

@article{gelman_inference_1992,
  title = {Inference from Iterative Simulation Using Multiple Sequences},
  volume = {7},
  timestamp = {2014-06-09T11:50:19Z},
  number = {4},
  journal = {Statistical Science},
  author = {Gelman, A and Rubin, D B},
  year = {1992},
  pages = {457--472},
  file = {Gelman_Rubin_1992_Statistical Science.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1992/Gelman_Rubin_1992_Statistical Science.pdf:application/pdf}
}

@article{rosenholtz_summary_2012,
  title = {A Summary Statistic Representation in Peripheral Vision Explains Visual Search.},
  volume = {12},
  abstract = {Vision is an active process: We repeatedly move our eyes to seek out objects of interest and explore our environment. Visual search experiments capture aspects of this process, by having subjects look for a target within a background of distractors. Search speed often correlates with target-distractor discriminability; search is faster when the target and distractors look quite different. However, there are notable exceptions. A given discriminability can yield efficient searches (where the target seems to "pop-out") as well as inefficient ones (where additional distractors make search significantly slower and more difficult). Search is often more difficult when finding the target requires distinguishing a particular configuration or conjunction of features. Search asymmetries abound. These puzzling results have fueled three decades of theoretical and experimental studies. We argue that the key issue in search is the processing of image patches in the periphery, where visual representation is characterized by summary statistics computed over a sizable pooling region. By quantifying these statistics, we predict a set of classic search results, as well as peripheral discriminability of crowded patches such as those found in search displays.},
  timestamp = {2014-06-09T12:05:19Z},
  number = {4},
  journal = {Journal of Vision},
  author = {Rosenholtz, Ruth and Huang, Jie and Raj, Alvin and Balas, Benjamin J and Ilie, Livia},
  year = {2012},
  file = {Rosenholtz_etal_2012_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2012/Rosenholtz_etal_2012_Journal of Vision.pdf:application/pdf}
}

@article{ho_conjoint_2008,
  title = {Conjoint Measurement of Gloss and Surface Texture},
  volume = {19},
  timestamp = {2014-06-09T11:52:48Z},
  number = {2},
  journal = {Psychological Science},
  author = {Ho, Yun-Xian and Landy, Michael S and Maloney, Laurence T},
  year = {2008},
  pages = {196--204},
  file = {Ho_etal_2008_Psychological Science.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2008/Ho_etal_2008_Psychological Science.pdf:application/pdf}
}

@inproceedings{yamins_hierarchical_2013,
  title = {Hierarchical {{Modular Optimization}} of {{Convolutional Networks Achieves Representations Similar}} to {{Macaque IT}} and {{Human Ventral Stream}}},
  timestamp = {2016-09-07T12:01:18Z},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Yamins, Daniel L K and Hong, H and Cadieu, C and DiCarlo, James J},
  year = {2013}
}

@article{brainard_psychophysics_1997,
  title = {The {{Psychophysics Toolbox}}},
  volume = {10},
  abstract = {The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.},
  timestamp = {2014-06-09T11:11:43Z},
  number = {4},
  journal = {Spatial Vision},
  author = {Brainard, David H},
  year = {1997},
  pages = {433--436},
  annote = {9176952},
  file = {Brainard_1997_Spatial Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1997/Brainard_1997_Spatial Vision.pdf:application/pdf}
}

@article{kingdom_sensitivity_2001,
  title = {Sensitivity to Contrast Histogram Differences in Synthetic Wavelet-Textures.},
  volume = {41},
  abstract = {Recent research on texture synthesis suggests that characterisation of those properties of textures to which human observers are sensitive may be provided by the histograms of the coefficients of a wavelet decomposition. In this study we examined the properties of wavelet histograms that affect texture discrimination by measuring observer sensitivity to differences in the wavelet histograms of synthetic textures. The textures, generated via Gabor micropattern synthesis, were broadband, with amplitude spectra that are characteristic of natural images, i.e. 1/f. We measured texture-difference thresholds for three moments of the wavelet histograms \textendash{} variance, skew and kurtosis \textendash{} by manipulating the contrast, phase, and density, of the Gabor elements used to construct the textures. Observers discriminated more efficiently between textures that had differences in kurtosis, than between textures that had differences in either variance or skew. Performance was compared to two model observers; one used the pixel-luminance histogram, the other used the histogram of the output of wavelet-filters. The results support the idea that the visual system is relatively sensitive to the kurtosis, or 4th moment, of the wavelet histogram of textures. We argue that higher than 4th-order moments will, in practice, become increasingly difficult for the visual system to represent because the lack of a perfect match between the elements and the receptive fields effectively blurs the response histogram, thereby attenuating higher moments.},
  timestamp = {2014-06-09T11:55:26Z},
  number = {5},
  journal = {Vision Research},
  author = {Kingdom, F A and Hayes, A and Field, D J},
  year = {2001},
  pages = {585--598},
  file = {Kingdom_etal_2001_Vision Research.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2001/Kingdom_etal_2001_Vision Research.pdf:application/pdf}
}

@article{thaler_what_2013,
  title = {What Is the Best Fixation Target? {{The}} Effect of Target Shape on Stability of Fixational Eye Movements},
  volume = {76},
  abstract = {Vision Research, 76 (2013) 31-42. doi:10.1016/j.visres.2012.10.012},
  timestamp = {2016-02-18T10:56:05Z},
  journal = {Vision Research},
  author = {Thaler, L and Sch{\"u}tz, A C and Goodale, M A and Gegenfurtner, K R},
  year = {2013},
  pages = {31--42},
  file = {Thaler_etal_2013_Vision Research.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Thaler_etal_2013_Vision Research.pdf:application/pdf}
}

@article{portilla_parametric_2000,
  title = {A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients},
  volume = {40},
  timestamp = {2014-06-09T12:04:02Z},
  number = {1},
  journal = {International Journal of Computer Vision},
  author = {Portilla, J and Simoncelli, Eero P},
  year = {2000},
  pages = {49--70},
  annote = {11620918327101739510related:9tG58qXLRaEJ},
  file = {Portilla_Simoncelli_2000_International Journal of Computer Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2000/Portilla_Simoncelli_2000_International Journal of Computer Vision.pdf:application/pdf}
}

@article{arsenault_higher_2011,
  title = {Higher Order Texture Statistics Impair Contrast Boundary Segmentation.},
  volume = {11},
  abstract = {Texture boundary segmentation is conventionally thought to be mediated by global differences in Fourier energy, i.e., low-order texture statistics. Here, we have examined the importance of higher order statistical structure of textures in a simple second-order segmentation task. We measured modulation depth thresholds for contrast boundaries imposed on texture samples extracted from natural scene photographs, using forced-choice judgments of boundary orientation (left vs. right oblique). We compared segmentation thresholds for contrast boundaries whose constituent textures were either intact or phase scrambled. In the intact condition, all the texture statistics were preserved, while in the phase-scrambled condition the higher order statistics of the same texture were randomized, but the lower order statistics were unchanged. We found that (1) contrast boundary segmentation is impaired by the presence of higher order statistics; (2) every texture shows impairment but some substantially more than others; and (3) our findings are not related to scrambling-induced changes in detectability. The magnitude of phase-scrambling effect for individual textures was uncorrelated with variations in their amplitude spectra, but instead we suggest that it might be related to differences in local edge structure or sparseness.},
  timestamp = {2014-06-09T08:41:49Z},
  number = {10},
  journal = {Journal of Vision},
  author = {Arsenault, Elizabeth and Yoonessi, Ahmad and Baker, Curtis L},
  year = {2011},
  pages = {14},
  file = {Arsenault_etal_2011_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2011/Arsenault_etal_2011_Journal of Vision.pdf:application/pdf}
}

@article{balas_summary-statistic_2009,
  title = {A Summary-Statistic Representation in Peripheral Vision Explains Visual Crowding},
  volume = {9},
  abstract = {Peripheral vision provides a less faithful representation of the visual input than foveal vision. Nonetheless, we can gain a lot of information about the world from our peripheral vision, for example in order to plan eye movements. The phenomenon of crowding shows that the reduction of information available in the periphery is not merely the result of reduced resolution. Crowding refers to visual phenomena in which identification of a target stimulus is significantly impaired by the presence of nearby stimuli, or flankers. What information is available in the periphery? We propose that the visual system locally represents peripheral stimuli by the joint statistics of responses of cells sensitive to different position, phase, orientation, and scale. This "textural" representation by summary statistics predicts the subjective "jumble" of features often associated with crowding. We show that the difficulty of performing an identification task within a single pooling region using this representation of the stimuli is correlated with peripheral identification performance under conditions of crowding. Furthermore, for a simple stimulus with no flankers, this representation can be adequate to specify the stimulus with some position invariance. This provides evidence that a unified neuronal mechanism may underlie peripheral vision, ordinary pattern recognition in central vision, and texture perception. A key component of our methodology involves creating visualizations of the information available in the summary statistics of a stimulus. We call these visualizations "mongrels" and show that they are highly useful in examining how the early visual system represents the visual input. Mongrels enable one to study the "equivalence classes" of our model, i.e., the sets of stimuli that map to the same representation according to the model.},
  timestamp = {2014-06-09T10:53:30Z},
  number = {12},
  journal = {Journal of Vision},
  author = {Balas, Benjamin J and Nakano, L and Rosenholtz, R},
  year = {2009},
  pages = {13},
  annote = {http://www.ncbi.nlm.nih.gov/pubmed/20053104},
  file = {Balas_etal_2009_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2009/Balas_etal_2009_Journal of Vision.pdf:application/pdf}
}

@article{fleming_visual_2014,
  title = {Visual Perception of Materials and Their Properties},
  volume = {94},
  abstract = {VISION RESEARCH, 94 (2014) 62-75. doi:10.1016/j.visres.2013.11.004},
  timestamp = {2014-06-09T11:49:09Z},
  number = {C},
  journal = {Vision research},
  author = {Fleming, RW},
  year = {2014},
  pages = {62--75},
  file = {Fleming_2014_Vision research.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Fleming_2014_Vision research.pdf:application/pdf}
}

@book{r_core_development_team_r:_2016,
  address = {Vienna, Austria},
  title = {R: {{A Language}} and {{Environment}} for {{Statistical Computing}}},
  timestamp = {2017-03-30T09:22:38Z},
  publisher = {{R Foundation for Statistical Computing}},
  author = {\{R Core Development Team\}},
  year = {2016}
}

@book{macmillan_detection_2005,
  address = {Mahwah, NJ},
  title = {Detection {{Theory}}: {{A User}}'s {{Guide}}},
  isbn = {0-8058-4230-6},
  timestamp = {2015-03-04T12:25:54Z},
  publisher = {{Lawrence Erlbaum}},
  author = {Macmillan, N A and Creelman, C D},
  year = {2005}
}

@article{malik_preattentive_1990,
  title = {Preattentive Texture Discrimination with Early Vision Mechanisms.},
  volume = {7},
  abstract = {We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers.},
  timestamp = {2014-06-09T11:59:10Z},
  number = {5},
  journal = {Journal of the Optical Society of America A},
  author = {Malik, J and Perona, P},
  year = {1990},
  pages = {923--932},
  file = {Malik_Perona_1990_Journal of the Optical Society of America A.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1990/Malik_Perona_1990_Journal of the Optical Society of America A.pdf:application/pdf}
}

@article{donahue_decaf_2013,
  title = {Decaf: {{A}} Deep Convolutional Activation Feature for Generic Visual Recognition},
  timestamp = {2014-11-03T15:33:10Z},
  journal = {arXiv},
  author = {Donahue, J and Jia, Y and Vinyals, O},
  year = {2013},
  pages = {1--10},
  file = {Donahue_etal_2013_arXiv.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Donahue_etal_2013_arXiv.pdf:application/pdf}
}

@article{wickham_splitapplycombine_2011,
  title = {The {{Split}}-{{Apply}}-{{Combine Strategy}} for {{Data Analysis}}},
  volume = {40},
  timestamp = {2014-06-09T12:11:45Z},
  number = {1},
  journal = {Journal of Statistical Software},
  author = {Wickham, Hadley},
  year = {2011},
  pages = {1--29}
}

@article{kleiner_whats_2007,
  title = {What's New in {{Psychtoolbox}}-3?},
  volume = {36},
  timestamp = {2014-06-09T11:55:31Z},
  number = {ECVP Abstract Supplement},
  journal = {Perception},
  author = {Kleiner, M and Brainard, David H and Pelli, Denis G},
  year = {2007}
}

@article{pelli_videotoolbox_1997,
  title = {The {{VideoToolbox}} Software for Visual Psychophysics: Transforming Numbers into Movies.},
  volume = {10},
  abstract = {The VideoToolbox is a free collection of two hundred C subroutines for Macintosh computers that calibrates and controls the computer-display interface to create accurately specified visual stimuli. High-level platform-independent languages like MATLAB are best for creating the numbers that describe the desired images. Low-level, computer-specific VideoToolbox routines control the hardware that transforms those numbers into a movie. Transcending the particular computer and language, we discuss the nature of the computer-display interface, and how to calibrate and control it.},
  timestamp = {2014-06-09T12:03:05Z},
  number = {4},
  journal = {Spatial Vision},
  author = {Pelli, Denis G},
  year = {1997},
  pages = {437--442},
  file = {Pelli_1997_Spatial Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1997/Pelli_1997_Spatial Vision.pdf:application/pdf}
}

@article{balas_contrast_2012,
  title = {Contrast Negation and Texture Synthesis Differentially Disrupt Natural Texture Appearance.},
  volume = {3},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2012.00515},
  abstract = {Natural textures have characteristic image statistics that make them discriminable from unnatural textures. For example, both contrast negation and texture synthesis alter the appearance of natural textures even though each manipulation preserves some features while disrupting others. Here, we examined the extent to which contrast negation and texture synthesis each introduce or remove critical perceptual features for discriminating unnatural textures from natural textures. We find that both manipulations remove information that observers use for distinguishing natural textures from transformed versions of the same patterns, but do so in different ways. Texture synthesis removes information that is relevant for discrimination in both abstract patterns and ecologically valid textures, and we also observe a category-dependent asymmetry for identifying an "oddball" real texture among synthetic distractors. Contrast negation exhibits no such asymmetry, and also does not impact discrimination performance in abstract patterns. We discuss our results in the context of the visual system's tuning to ecologically relevant patterns and other results describing sensitivity to higher-order statistics in texture patterns.},
  timestamp = {2016-02-24T10:16:23Z},
  number = {November},
  journal = {Frontiers in psychology},
  author = {Balas, Benjamin J},
  month = jan,
  year = {2012},
  keywords = {image s,image statistics,texture discrimination,texture synthesis},
  pages = {515},
  file = {Balas_2012_Frontiers in psychology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2012/Balas_2012_Frontiers in psychology.pdf:application/pdf}
}

@article{rosenholtz_rethinking_2012,
  title = {Rethinking the Role of Top-down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision.},
  volume = {3},
  abstract = {According to common wisdom in the field of visual perception, top-down selective attention is required in order to bind features into objects. In this view, even simple tasks, such as distinguishing a rotated T from a rotated L, require selective attention since they require feature binding. Selective attention, in turn, is commonly conceived as involving volition, intention, and at least implicitly, awareness. There is something non-intuitive about the notion that we might need so expensive (and possibly human) a resource as conscious awareness in order to perform so basic a function as perception. In fact, we can carry out complex sensorimotor tasks, seemingly in the near absence of awareness or volitional shifts of attention ("zombie behaviors"). More generally, the tight association between attention and awareness, and the presumed role of attention on perception, is problematic. We propose that under normal viewing conditions, the main processes of feature binding and perception proceed largely independently of top-down selective attention. Recent work suggests that there is a significant loss of information in early stages of visual processing, especially in the periphery. In particular, our texture tiling model (TTM) represents images in terms of a fixed set of "texture" statistics computed over local pooling regions that tile the visual input. We argue that this lossy representation produces the perceptual ambiguities that have previously been as ascribed to a lack of feature binding in the absence of selective attention. At the same time, the TTM representation is sufficiently rich to explain performance in such complex tasks as scene gist recognition, pop-out target search, and navigation. A number of phenomena that have previously been explained in terms of voluntary attention can be explained more parsimoniously with the TTM. In this model, peripheral vision introduces a specific kind of information loss, and the information available to an observer varies greatly depending upon shifts of the point of gaze (which usually occur without awareness). The available information, in turn, provides a key determinant of the visual system's capabilities and deficiencies. This scheme dissociates basic perceptual operations, such as feature binding, from both top-down attention and conscious awareness.},
  timestamp = {2014-06-09T12:05:18Z},
  journal = {Frontiers in Psychology},
  author = {Rosenholtz, Ruth and Huang, Jie and Ehinger, Krista A},
  year = {2012},
  pages = {13},
  file = {Rosenholtz_etal_2012_Frontiers in Psychology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2012/Rosenholtz_etal_2012_Frontiers in Psychology.pdf:application/pdf}
}

@article{szegedy_intriguing_2013,
  title = {Intriguing Properties of Neural Networks},
  timestamp = {2014-10-23T13:34:47Z},
  urldate = {2014-10-23},
  journal = {arXiv preprint arXiv:1312.6199},
  author = {Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
  year = {2013},
  file = {1312.6199.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/4ZATEDKB/1312.6199.pdf:application/pdf}
}

@article{khaligh-razavi_deep_2014,
  title = {Deep {{Supervised}}, but {{Not Unsupervised}}, {{Models May Explain IT Cortical Representation}}},
  volume = {10},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1003915},
  language = {en},
  timestamp = {2014-11-07T13:50:40Z},
  number = {11},
  urldate = {2014-11-07},
  journal = {PLoS Computational Biology},
  author = {Khaligh-Razavi, Seyed-Mahdi and Kriegeskorte, Nikolaus},
  editor = {Diedrichsen, J{\"o}rn},
  month = nov,
  year = {2014},
  pages = {e1003915},
  file = {Khaligh-Razavi_Kriegeskorte_2014_PLoS Computational Biology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Khaligh-Razavi_Kriegeskorte_2014_PLoS Computational Biology.pdf:application/pdf}
}

@article{clarke_visual_2014,
  title = {Visual Crowding Illustrates the Inadequacy of Local vs. Global and Feedforward vs. Feedback Distinctions in Modeling Visual Perception},
  volume = {5},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2014.01193},
  timestamp = {2014-11-11T12:32:37Z},
  urldate = {2014-11-11},
  journal = {Frontiers in Psychology},
  author = {Clarke, Aaron M. and Herzog, Michael H. and Francis, Gregory},
  month = oct,
  year = {2014},
  file = {Clarke_etal_2014_Frontiers in Psychology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Clarke_etal_2014_Frontiers in Psychology.pdf:application/pdf}
}

@book{burnham_model_2002,
  address = {New York},
  title = {Model Selection and Multimodel Inference a Practical Information-Theoretic Approach},
  isbn = {0-387-22456-4 978-0-387-22456-5},
  abstract = {This book is unique in that it covers the philosophy of model-based data analysis and a strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These are relatively simple and easy to use in practice. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems. Model selection, under the information theoretic approach presented here, attempts to identify the (likely) best model, orders the models from best to worst, and measures the plausibility ("calibration") that each model is really the best as an inference. Model selection methods are extended to allow inference from more than a single "best" model. The book presents several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians using models for making inferences from empirical data. People interested in the empirical sciences will find this material useful as it offers an alternative to hypothesis testing and Bayesian.},
  language = {English},
  timestamp = {2015-02-10T14:23:06Z},
  urldate = {2015-02-10},
  publisher = {{Springer}},
  author = {Burnham, Kenneth P and Anderson, David Raymond},
  year = {2002},
  file = {Burnham_2002.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2002/Burnham_2002.pdf:application/pdf}
}

@article{okazawa_image_2015,
  title = {Image Statistics Underlying Natural Texture Selectivity of Neurons in Macaque {{V4}}},
  volume = {112},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1415146112},
  language = {en},
  timestamp = {2015-04-22T21:03:14Z},
  number = {4},
  urldate = {2015-04-08},
  journal = {Proceedings of the National Academy of Sciences},
  author = {Okazawa, Gouki and Tajima, Satohiro and Komatsu, Hidehiko},
  month = jan,
  year = {2015},
  pages = {E351--E360},
  file = {Okazawa_etal_2015_Proceedings of the National Academy of Sciences.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Okazawa_etal_2015_Proceedings of the National Academy of Sciences.pdf:application/pdf;Okazawa_etal_2015_Proceedings of the National Academy of Sciences.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Okazawa_etal_2015_Proceedings of the National Academy of Sciences2.pdf:application/pdf}
}

@book{standevelopmentteam_stan_2015-1,
  title = {Stan {{Modeling Language Users Guide}} and {{Reference Manual}}, {{Version}} 2.10.0},
  timestamp = {2016-10-07T15:05:03Z},
  author = {{Stan Development Team}},
  year = {2015},
  file = {Stan Development Team_2015.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Stan Development Team_2015.pdf:application/pdf}
}

@article{herzog_crowding_2015,
  title = {Crowding, Grouping, and Object Recognition: {{A}} Matter of Appearance},
  volume = {15},
  shorttitle = {Crowding, Grouping, and Object Recognition},
  timestamp = {2015-05-23T13:28:45Z},
  number = {6},
  urldate = {2015-05-23},
  journal = {Journal of Vision},
  author = {Herzog, Michael H. and Sayim, Bilge and Chicherov, Vitaly and Manassi, Mauro},
  year = {2015},
  file = {Herzog_etal_2015_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Herzog_etal_2015_Journal of Vision.pdf:application/pdf}
}

@article{vanderwalt_scikitimage_2014,
  title = {Scikit-Image: Image Processing in {{Python}}},
  volume = {2},
  issn = {2167-8359},
  doi = {10.7717/peerj.453},
  timestamp = {2015-09-23T12:10:16Z},
  journal = {PeerJ},
  author = {{van der Walt}, St{\'e}fan and Sch{\"o}nberger, Johannes L. and Nunez-Iglesias, Juan and Boulogne, Fra{\c n}cois and Warner, Joshua D. and Yager, Neil and Gouillart, Emmanuelle and Yu, Tony and {the scikit-image contributors}},
  month = jun,
  year = {2014},
  keywords = {Education,Image Processing,Open source,Python,Reproducible research,Scientific programming,Visualization},
  pages = {e453},
  file = {van der Walt_etal_2014_PeerJ.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/van der Walt_etal_2014_PeerJ.pdf:application/pdf}
}

@article{lecun_deep_2015,
  title = {Deep Learning},
  volume = {521},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nature14539},
  timestamp = {2015-05-29T12:38:03Z},
  number = {7553},
  urldate = {2015-05-29},
  journal = {Nature},
  author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
  month = may,
  year = {2015},
  pages = {436--444},
  file = {LeCun_etal_2015_Nature.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/LeCun_etal_2015_Nature.pdf:application/pdf}
}

@article{standevelopmentteam_stan_2015,
  title = {Stan: {{A C}}++ {{Library}} for {{Probability}} and {{Sampling}}, {{Version}} 2.14.0},
  timestamp = {2017-03-30T09:24:23Z},
  author = {{Stan Development Team}},
  year = {2017}
}

@article{guclu_deep_2015,
  title = {Deep {{Neural Networks Reveal}} a {{Gradient}} in the {{Complexity}} of {{Neural Representations}} across the {{Ventral Stream}}},
  volume = {35},
  issn = {0270-6474, 1529-2401},
  doi = {10.1523/JNEUROSCI.5023-14.2015},
  language = {en},
  timestamp = {2015-07-10T08:17:45Z},
  number = {27},
  urldate = {2015-07-10},
  journal = {Journal of Neuroscience},
  author = {Guclu, U. and {van Gerven}, M. A. J.},
  month = jul,
  year = {2015},
  pages = {10005--10014},
  file = {Guclu_van Gerven_2015_Journal of Neuroscience.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Guclu_van Gerven_2015_Journal of Neuroscience.pdf:application/pdf}
}

@inproceedings{kummerer_deep_2015,
  title = {Deep {{Gaze I}}: {{Boosting Saliency Prediction}} with {{Feature Maps Trained}} on {{ImageNet}}},
  timestamp = {2015-07-21T09:56:54Z},
  booktitle = {{{ICLR Workshop}}},
  author = {K{\"u}mmerer, M. and Theis, L. and Bethge, M.},
  month = may,
  year = {2015},
  keywords = {deep learning,saliency},
  file = {Kümmerer_etal_2015_ICLR Workshop.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Kümmerer_etal_2015_ICLR Workshop.pdf:application/pdf}
}

@article{wagenmakers_bayesian_2010,
  title = {Bayesian Hypothesis Testing for Psychologists: {{A}} Tutorial on the {{Savage}}\textendash{}{{Dickey}} Method},
  volume = {60},
  issn = {00100285},
  shorttitle = {Bayesian Hypothesis Testing for Psychologists},
  doi = {10.1016/j.cogpsych.2009.12.001},
  language = {en},
  timestamp = {2015-09-04T07:32:23Z},
  number = {3},
  urldate = {2015-09-04},
  journal = {Cognitive Psychology},
  author = {Wagenmakers, Eric-Jan and Lodewyckx, Tom and Kuriyal, Himanshu and Grasman, Raoul},
  month = may,
  year = {2010},
  pages = {158--189},
  file = {Wagenmakers_etal_2010_Cognitive Psychology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2010/Wagenmakers_etal_2010_Cognitive Psychology.pdf:application/pdf}
}

@article{cadieu_deep_2014,
  title = {Deep {{Neural Networks Rival}} the {{Representation}} of {{Primate IT Cortex}} for {{Core Visual Object Recognition}}},
  volume = {10},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1003963},
  language = {en},
  timestamp = {2015-09-09T11:20:05Z},
  number = {12},
  urldate = {2015-09-09},
  journal = {PLoS Computational Biology},
  author = {Cadieu, Charles F. and Hong, Ha and Yamins, Daniel L. K. and Pinto, Nicolas and Ardila, Diego and Solomon, Ethan A. and Majaj, Najib J. and DiCarlo, James J.},
  editor = {Bethge, Matthias},
  month = dec,
  year = {2014},
  pages = {e1003963},
  file = {journal.pcbi.1003963.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/FW9VG5V5/journal.pcbi.1003963.pdf:application/pdf}
}

@article{simonyan_very_2015,
  title = {Very {{Deep Convolutional Networks}} for {{Large}}-{{Scale Image Recognition}}},
  volume = {abs/1409.1556},
  timestamp = {2016-05-03T19:52:48Z},
  journal = {ICLR},
  author = {Simonyan, Karen and Zisserman, Andrew},
  year = {2015},
  file = {Simonyan_Zisserman_2014_CoRR.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Simonyan_Zisserman_2014_CoRR.pdf:application/pdf}
}

@article{morey_fallacy_2015,
  title = {The Fallacy of Placing Confidence in Confidence Intervals},
  issn = {1069-9384, 1531-5320},
  doi = {10.3758/s13423-015-0947-8},
  language = {en},
  timestamp = {2015-10-19T09:22:58Z},
  urldate = {2015-10-19},
  journal = {Psychonomic Bulletin \& Review},
  author = {Morey, Richard D. and Hoekstra, Rink and Rouder, Jeffrey N. and Lee, Michael D. and Wagenmakers, Eric-Jan},
  month = oct,
  year = {2015},
  file = {Morey_etal_2015_Psychonomic Bulletin & Review.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Morey_etal_2015_Psychonomic Bulletin & Review.pdf:application/pdf}
}

@article{balas_invariant_2015,
  title = {Invariant Texture Perception Is Harder with Synthetic Textures: {{Implications}} for Models of Texture Processing},
  volume = {115},
  issn = {00426989},
  shorttitle = {Invariant Texture Perception Is Harder with Synthetic Textures},
  doi = {10.1016/j.visres.2015.01.022},
  language = {en},
  timestamp = {2016-02-24T10:16:33Z},
  urldate = {2015-11-17},
  journal = {Vision Research},
  author = {Balas, Benjamin J and Conlin, Catherine},
  month = oct,
  year = {2015},
  pages = {271--279},
  file = {Balas_Conlin_2015_Vision Research.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Balas_Conlin_2015_Vision Research.pdf:application/pdf}
}

@article{baayen_mixed-effects_2008,
  title = {Mixed-Effects Modeling with Crossed Random Effects for Subjects and Items},
  volume = {59},
  issn = {0749596X},
  doi = {10.1016/j.jml.2007.12.005},
  language = {en},
  timestamp = {2016-05-05T14:32:00Z},
  number = {4},
  urldate = {2016-05-05},
  journal = {Journal of Memory and Language},
  author = {Baayen, R.H. and Davidson, D.J. and Bates, D.M.},
  month = nov,
  year = {2008},
  pages = {390--412},
  file = {baayenDavidsonBates.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/W43PP36A/baayenDavidsonBates.pdf:application/pdf}
}

@article{morey_continued_2015,
  title = {Continued Misinterpretation of Confidence Intervals: Response to {{Miller}} and {{Ulrich}}},
  issn = {1069-9384, 1531-5320},
  shorttitle = {Continued Misinterpretation of Confidence Intervals},
  doi = {10.3758/s13423-015-0955-8},
  language = {en},
  timestamp = {2016-01-13T09:11:56Z},
  urldate = {2016-01-13},
  journal = {Psychonomic Bulletin \& Review},
  author = {Morey, Richard D. and Hoekstra, Rink and Rouder, Jeffrey N. and Wagenmakers, Eric-Jan},
  month = nov,
  year = {2015},
  file = {Morey_etal_2015_Psychonomic Bulletin & Review3.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Morey_etal_2015_Psychonomic Bulletin & Review3.pdf:application/pdf}
}

@article{miller_interpreting_2015,
  title = {Interpreting Confidence Intervals: {{A}} Comment on {{Hoekstra}}, {{Morey}}, {{Rouder}}, and {{Wagenmakers}} (2014)},
  issn = {1069-9384, 1531-5320},
  shorttitle = {Interpreting Confidence Intervals},
  doi = {10.3758/s13423-015-0859-7},
  language = {en},
  timestamp = {2016-01-13T09:11:58Z},
  urldate = {2016-01-13},
  journal = {Psychonomic Bulletin \& Review},
  author = {Miller, Jeff and Ulrich, Rolf},
  month = nov,
  year = {2015},
  file = {Miller_Ulrich_2015_Psychonomic Bulletin & Review.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Miller_Ulrich_2015_Psychonomic Bulletin & Review.pdf:application/pdf}
}

@inproceedings{heeger_pyramidbased_1995,
  address = {New York, NY, USA},
  series = {SIGGRAPH '95},
  title = {Pyramid-Based {{Texture Analysis}}/{{Synthesis}}},
  isbn = {0-89791-701-4},
  doi = {10.1145/218380.218446},
  timestamp = {2016-02-17T15:38:33Z},
  booktitle = {Proceedings of the {{22Nd Annual Conference}} on {{Computer Graphics}} and {{Interactive Techniques}}},
  publisher = {{ACM}},
  author = {Heeger, David J. and Bergen, James R.},
  year = {1995},
  pages = {229--238},
  file = {Heeger_Bergen_1995.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1995/Heeger_Bergen_1995.pdf:application/pdf}
}

@inproceedings{judd_learning_2009,
  address = {Kyoto},
  title = {Learning to Predict Where Humans Look},
  doi = {10.1109/ICCV.2009.5459462},
  timestamp = {2016-02-17T15:49:58Z},
  booktitle = {{{IEEE}} 12th {{International Conference}} on {{Computer Vision}}},
  author = {Judd, T. and Ehinger, K. and Durand, F. and Torralba, A.},
  month = sep,
  year = {2009},
  keywords = {Application software,Biological system modeling,Biology computing,Computer Graphics,Context modeling,eye tracking data,feature extraction,high-level image features,human computer interaction,Image databases,Layout,Predictive models,saliency approaches,Spatial databases,top-down image semantics,tracking},
  pages = {2106--2113},
  file = {Judd_etal_2009.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2009/Judd_etal_2009.pdf:application/pdf}
}

@article{movshon_representation_2014,
  title = {Representation of {{Naturalistic Image Structure}} in the {{Primate Visual Cortex}}},
  volume = {79},
  issn = {0091-7451, 1943-4456},
  doi = {10.1101/sqb.2014.79.024844},
  language = {en},
  timestamp = {2016-02-17T15:58:49Z},
  urldate = {2016-02-17},
  journal = {Cold Spring Harbor Symposia on Quantitative Biology},
  author = {Movshon, J. Anthony and Simoncelli, Eero P.},
  year = {2014},
  pages = {115--122},
  file = {Movshon_Simoncelli_2014_Cold Spring Harbor Symposia on Quantitative Biology.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2014/Movshon_Simoncelli_2014_Cold Spring Harbor Symposia on Quantitative Biology.pdf:application/pdf}
}

@article{yamins_using_2016,
  title = {Using Goal-Driven Deep Learning Models to Understand Sensory Cortex},
  volume = {19},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.4244},
  timestamp = {2016-02-24T08:54:28Z},
  number = {3},
  urldate = {2016-02-24},
  journal = {Nature Neuroscience},
  author = {Yamins, Daniel L K and DiCarlo, James J},
  month = feb,
  year = {2016},
  pages = {356--365},
  file = {Yamins_DiCarlo_2016_Nature Neuroscience.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Yamins_DiCarlo_2016_Nature Neuroscience.pdf:application/pdf}
}

@article{barr_random_2013,
  title = {Random Effects Structure for Confirmatory Hypothesis Testing: {{Keep}} It Maximal},
  volume = {68},
  issn = {0749596X},
  shorttitle = {Random Effects Structure for Confirmatory Hypothesis Testing},
  doi = {10.1016/j.jml.2012.11.001},
  language = {en},
  timestamp = {2016-02-19T12:10:56Z},
  number = {3},
  urldate = {2016-02-19},
  journal = {Journal of Memory and Language},
  author = {Barr, Dale J. and Levy, Roger and Scheepers, Christoph and Tily, Harry J.},
  month = apr,
  year = {2013},
  pages = {255--278},
  file = {Barr_etal_2013_Journal of Memory and Language.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Barr_etal_2013_Journal of Memory and Language.pdf:application/pdf;Barr_etal_2013_Journal of Memory and Language.txt:/Users/tsawallis/Dropbox/Zotero_Library/2013/Barr_etal_2013_Journal of Memory and Language.txt:text/plain}
}

@article{keshvari_pooling_2016,
  title = {Pooling of Continuous Features Provides a Unifying Account of Crowding},
  volume = {16},
  issn = {1534-7362},
  doi = {10.1167/16.3.39},
  language = {en},
  timestamp = {2016-03-08T13:54:35Z},
  number = {3},
  urldate = {2016-03-08},
  journal = {Journal of Vision},
  author = {Keshvari, Shaiyan and Rosenholtz, Ruth},
  month = feb,
  year = {2016},
  pages = {39},
  file = {Keshvari_Rosenholtz_2016_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Keshvari_Rosenholtz_2016_Journal of Vision.pdf:application/pdf}
}

@article{bates_fitting_2015,
  title = {Fitting {{Linear Mixed}}-{{Effects Models Using}} Lme4},
  volume = {67},
  doi = {10.18637/jss.v067.i01},
  timestamp = {2016-04-04T11:07:08Z},
  number = {1},
  journal = {Journal of Statistical Software},
  author = {Bates, Douglas and M{\"a}chler, Martin and Bolker, Ben and Walker, Steve},
  year = {2015},
  pages = {1--48},
  file = {Bates_etal_2015_Journal of Statistical Software.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2015/Bates_etal_2015_Journal of Statistical Software_2.pdf:application/pdf}
}

@inproceedings{safranek_perceptually_1989,
  title = {A Perceptually Tuned Sub-Band Image Coder with Image Dependent Quantization and Post-Quantization Data Compression},
  doi = {10.1109/ICASSP.1989.266837},
  timestamp = {2016-09-07T11:54:42Z},
  urldate = {2016-06-23},
  publisher = {{IEEE}},
  author = {Safranek, Robert J and Johnston, J.D.},
  year = {1989},
  pages = {1945--1948}
}

@article{wallis_testing_2016,
  title = {Testing Models of Peripheral Encoding Using Metamerism in an Oddity Paradigm},
  volume = {16},
  issn = {1534-7362},
  doi = {10.1167/16.2.4},
  language = {en},
  timestamp = {2016-03-14T10:22:18Z},
  number = {2},
  urldate = {2016-03-14},
  journal = {Journal of Vision},
  author = {Wallis, Thomas S. A. and Bethge, Matthias and Wichmann, Felix A.},
  month = mar,
  year = {2016},
  pages = {4},
  file = {Wallis_etal_2016_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Wallis_etal_2016_Journal of Vision.pdf:application/pdf}
}

@article{vehtari_practical_2016,
  title = {Practical {{Bayesian}} Model Evaluation Using Leave-One-out Cross-Validation and {{WAIC}}${_\ast}$},
  timestamp = {2016-09-28T11:21:39Z},
  urldate = {2016-03-17},
  journal = {arXiv preprint arXiv:1507.04544},
  author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  year = {2016},
  file = {Vehtari_etal_2016_arXiv preprint arXiv1507.04544.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Vehtari_etal_2016_arXiv preprint arXiv1507.04544.pdf:application/pdf}
}

@article{kehrer_central_1989,
  title = {Central Performance Drop on Perceptual Segregation Tasks},
  volume = {4},
  timestamp = {2016-10-26T12:06:38Z},
  number = {1},
  urldate = {2016-10-26},
  journal = {Spatial vision},
  author = {Kehrer, Lothar},
  year = {1989},
  pages = {45--62},
  file = {Kehrer_1989_Spatial vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1989/Kehrer_1989_Spatial vision.pdf:application/pdf}
}

@book{buerkner_brms_2016,
  title = {Brms: {{Bayesian Regression Models}} Using {{Stan}}},
  timestamp = {2016-06-20T13:12:14Z},
  author = {Buerkner, Paul-Christian},
  year = {2016},
  file = {Buerkner_2016.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Buerkner_2016.pdf:application/pdf}
}

@article{landy_texture_2013,
  title = {Texture Analysis and Perception},
  timestamp = {2016-06-16T14:36:43Z},
  urldate = {2016-06-16},
  journal = {The new visual neurosciences (ed. Werner JS, Chalupa LM)},
  author = {Landy, Michael S.},
  year = {2013},
  pages = {639--652},
  file = {Landy_2013_The new visual neurosciences (ed. Werner JS, Chalupa LM).pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Landy_2013_The new visual neurosciences (ed. Werner JS, Chalupa LM).pdf:application/pdf}
}

@inproceedings{efros_image_2001,
  title = {Image Quilting for Texture Synthesis and Transfer},
  timestamp = {2016-06-20T09:49:16Z},
  urldate = {2016-06-20},
  booktitle = {Proceedings of the 28th Annual Conference on {{Computer}} Graphics and Interactive Techniques},
  publisher = {{ACM}},
  author = {Efros, Alexei A. and Freeman, William T.},
  year = {2001},
  pages = {341--346},
  file = {efros-siggraph01.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/R4NWVECM/efros-siggraph01.pdf:application/pdf}
}

@inproceedings{pappas_rough_2013,
  title = {The Rough Side of Texture: Texture Analysis through the Lens of {{HVEI}}},
  shorttitle = {The Rough Side of Texture},
  timestamp = {2016-06-17T09:00:50Z},
  urldate = {2016-06-17},
  booktitle = {{{IS}}\&{{T}}/{{SPIE Electronic Imaging}}},
  publisher = {{International Society for Optics and Photonics}},
  author = {Pappas, Thrasyvoulos N.},
  year = {2013},
  pages = {86510P--86510P},
  file = {Pappas_2013_IS&TSPIE Electronic Imaging.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Pappas_2013_IS&TSPIE Electronic Imaging.pdf:application/pdf}
}

@article{liu_visual_2015,
  title = {Visual {{Perception}} of {{Procedural Textures}}: {{Identifying Perceptual Dimensions}} and {{Predicting Generation Models}}},
  volume = {10},
  issn = {1932-6203},
  shorttitle = {Visual {{Perception}} of {{Procedural Textures}}},
  doi = {10.1371/journal.pone.0130335},
  language = {en},
  timestamp = {2016-06-17T09:09:38Z},
  number = {6},
  urldate = {2016-06-17},
  journal = {PLOS ONE},
  author = {Liu, Jun and Dong, Junyu and Cai, Xiaoxu and Qi, Lin and Chantler, Mike},
  editor = {Bensmaia, Sliman J.},
  month = jun,
  year = {2015},
  pages = {e0130335},
  file = {Liu_etal_2015_PLOS ONE.PDF:/Users/tsawallis/Dropbox/Zotero_Library/2015/Liu_etal_2015_PLOS ONE.PDF:application/pdf}
}

@inproceedings{adelson_seeing_2001,
  title = {On Seeing Stuff: The Perception of Materials by Humans and Machines},
  shorttitle = {On Seeing Stuff},
  timestamp = {2016-06-20T09:59:23Z},
  urldate = {2016-06-20},
  booktitle = {Photonics {{West}} 2001-Electronic Imaging},
  publisher = {{International Society for Optics and Photonics}},
  author = {Adelson, Edward H.},
  year = {2001},
  pages = {1--12},
  file = {adelson_spie_01.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/C468N36B/adelson_spie_01.pdf:application/pdf}
}

@inproceedings{safranek_perceptually_1990,
  title = {Perceptually Tuned Sub-Band Image Coder},
  doi = {10.1117/12.19678},
  timestamp = {2016-06-23T13:26:02Z},
  urldate = {2016-06-23},
  author = {Safranek, Robert J. and Johnston, James D. and Rosenholtz, Ruth E.},
  editor = {Rogowitz, Bernice E. and Allebach, Jan P.},
  month = oct,
  year = {1990},
  pages = {284--293}
}

@article{julesz_textons_1981,
  title = {Textons, the Elements of Texture Perception, and Their Interactions},
  volume = {290},
  issn = {0028-0836},
  doi = {10.1038/290091a0},
  timestamp = {2016-06-20T13:10:40Z},
  number = {5802},
  urldate = {2016-06-20},
  journal = {Nature},
  author = {Julesz, Bela},
  month = mar,
  year = {1981},
  pages = {91--97},
  file = {Julesz_1981_Nature.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1981/Julesz_1981_Nature.pdf:application/pdf}
}

@article{julesz_visual_1978,
  title = {Visual Discrimination of Textures with Identical Third-Order Statistics},
  volume = {31},
  timestamp = {2016-06-20T13:11:47Z},
  number = {3},
  urldate = {2016-06-20},
  journal = {Biological Cybernetics},
  author = {Julesz, Bela and Gilbert, E. N. and Victor, Jonathan D.},
  year = {1978},
  pages = {137--140},
  file = {Julesz_etal_1978_Biological Cybernetics.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1978/Julesz_etal_1978_Biological Cybernetics.pdf:application/pdf}
}

@article{julesz_visual_1962,
  title = {Visual {{Pattern Discrimination}}},
  volume = {8},
  issn = {0018-9448},
  doi = {10.1109/TIT.1962.1057698},
  language = {en},
  timestamp = {2016-06-20T13:15:54Z},
  number = {2},
  urldate = {2016-06-20},
  journal = {IEEE Transactions on Information Theory},
  author = {Julesz, B.},
  month = feb,
  year = {1962},
  pages = {84--92}
}

@article{cichy_comparison_2016,
  title = {Comparison of Deep Neural Networks to Spatio-Temporal Cortical Dynamics of Human Visual Object Recognition Reveals Hierarchical Correspondence},
  volume = {6},
  issn = {2045-2322},
  doi = {10.1038/srep27755},
  timestamp = {2016-06-23T13:47:00Z},
  urldate = {2016-06-23},
  journal = {Scientific Reports},
  author = {Cichy, Radoslaw Martin and Khosla, Aditya and Pantazis, Dimitrios and Torralba, Antonio and Oliva, Aude},
  month = jun,
  year = {2016},
  pages = {27755},
  file = {Cichy_etal_2016_Scientific Reports.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Cichy_etal_2016_Scientific Reports.pdf:application/pdf}
}

@article{cichy_dynamics_2016,
  title = {Dynamics of Scene Representations in the Human Brain Revealed by Magnetoencephalography and Deep Neural Networks},
  issn = {10538119},
  doi = {10.1016/j.neuroimage.2016.03.063},
  language = {en},
  timestamp = {2016-06-23T13:47:03Z},
  urldate = {2016-06-23},
  journal = {NeuroImage},
  author = {Cichy, Radoslaw Martin and Khosla, Aditya and Pantazis, Dimitrios and Oliva, Aude},
  month = apr,
  year = {2016},
  file = {Cichy_etal_2016_NeuroImage.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Cichy_etal_2016_NeuroImage.pdf:application/pdf}
}

@article{porat_localized_1989,
  title = {Localized Texture Processing in Vision: Analysis and Synthesis in the {{Gaborian}} Space},
  volume = {36},
  issn = {0018-9294, 1558-2531},
  shorttitle = {Localized Texture Processing in Vision},
  doi = {10.1109/10.16457},
  timestamp = {2016-06-20T13:30:25Z},
  number = {1},
  urldate = {2016-06-20},
  journal = {IEEE Transactions on Biomedical Engineering},
  author = {Porat, M. and Zeevi, Y.Y.},
  month = jan,
  year = {1989},
  pages = {115--129}
}

@article{cano_texture_1988,
  title = {Texture Synthesis Using Hierarchical Linear Transforms},
  volume = {15},
  issn = {01651684},
  doi = {10.1016/0165-1684(88)90066-7},
  language = {en},
  timestamp = {2016-06-20T13:31:48Z},
  number = {2},
  urldate = {2016-06-20},
  journal = {Signal Processing},
  author = {Cano, D. and Minh, T.Ha},
  month = sep,
  year = {1988},
  pages = {131--148}
}

@article{zhu_filters_1998,
  title = {Filters, Random Fields and Maximum Entropy ({{FRAME}}): {{Towards}} a Unified Theory for Texture Modeling},
  volume = {27},
  shorttitle = {Filters, Random Fields and Maximum Entropy ({{FRAME}})},
  timestamp = {2016-06-20T13:37:12Z},
  number = {2},
  urldate = {2016-06-20},
  journal = {International Journal of Computer Vision},
  author = {Zhu, Song Chun and Wu, Yingnian and Mumford, David},
  year = {1998},
  pages = {107--126},
  file = {ijcv.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/WDVI3V94/ijcv.pdf:application/pdf}
}

@inproceedings{simoncelli_texture_1998,
  title = {Texture Characterization via Joint Statistics of Wavelet Coefficient Magnitudes},
  volume = {1},
  isbn = {978-0-8186-8821-8},
  doi = {10.1109/ICIP.1998.723417},
  timestamp = {2016-06-20T13:37:42Z},
  urldate = {2016-06-20},
  publisher = {{IEEE Comput. Soc}},
  author = {Simoncelli, E.P. and Portilla, J.},
  year = {1998},
  pages = {62--66},
  file = {Simoncelli_Portilla_1998.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1998/Simoncelli_Portilla_1998.pdf:application/pdf}
}

@article{ziemba_selectivity_2016,
  title = {Selectivity and Tolerance for Visual Texture in Macaque {{V2}}},
  volume = {113},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1510847113},
  language = {en},
  timestamp = {2016-06-20T13:44:57Z},
  number = {22},
  urldate = {2016-06-20},
  journal = {Proceedings of the National Academy of Sciences},
  author = {Ziemba, Corey M. and Freeman, Jeremy and Movshon, J. Anthony and Simoncelli, Eero P.},
  month = may,
  year = {2016},
  pages = {E3140--E3149},
  file = {Ziemba_etal_2016_Proceedings of the National Academy of Sciences.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Ziemba_etal_2016_Proceedings of the National Academy of Sciences.pdf:application/pdf}
}

@book{venables_modern_2002,
  address = {New York},
  edition = {Fourth},
  title = {Modern {{Applied Statistics}} with {{S}}},
  timestamp = {2016-07-13T14:37:46Z},
  publisher = {{Springer}},
  author = {Venables, W. N. and Ripley, B. D.},
  year = {2002},
  note = {ISBN 0-387-95457-0},
  file = {Venables_Ripley_2002.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2002/Venables_Ripley_2002.pdf:application/pdf}
}

@article{hong_explicit_2016,
  title = {Explicit Information for Category-Orthogonal Object Properties Increases along the Ventral Stream},
  volume = {19},
  issn = {1097-6256, 1546-1726},
  doi = {10.1038/nn.4247},
  timestamp = {2016-06-23T14:00:51Z},
  number = {4},
  urldate = {2016-06-23},
  journal = {Nature Neuroscience},
  author = {Hong, Ha and Yamins, Daniel L K and Majaj, Najib J and DiCarlo, James J},
  month = feb,
  year = {2016},
  pages = {613--622},
  file = {Hong_etal_2016_Nature Neuroscience.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Hong_etal_2016_Nature Neuroscience.pdf:application/pdf}
}

@article{kubilius_deep_2016,
  title = {Deep {{Neural Networks}} as a {{Computational Model}} for {{Human Shape Sensitivity}}},
  volume = {12},
  timestamp = {2016-09-07T12:01:50Z},
  number = {4},
  urldate = {2016-06-23},
  journal = {PLoS Comput Biol},
  author = {Kubilius, Jonas and Bracci, Stefania and {Op de Beeck}, Hans P.},
  year = {2016},
  pages = {e1004896},
  file = {Kubilius_etal_2016_PLoS Comput Biol.PDF:/Users/tsawallis/Dropbox/Zotero_Library/2016/Kubilius_etal_2016_PLoS Comput Biol.PDF:application/pdf}
}

@unpublished{ustyuzhaninov_texture_2016,
  title = {Texture {{Synthesis Using Shallow Convolutional Networks}} with {{Random Filters}}},
  timestamp = {2016-06-28T09:37:27Z},
  author = {Ustyuzhaninov, I. and Brendel, W. and Gatys, L. A. and Bethge, M.},
  month = jun,
  year = {2016},
  keywords = {convolutional neural networks,deep learning,random filters,texture synthesis},
  file = {Ustyuzhaninov_etal_2016.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Ustyuzhaninov_etal_2016.pdf:application/pdf}
}

@article{liu_texture_2016,
  title = {Texture {{Synthesis Through Convolutional Neural Networks}} and {{Spectrum Constraints}}},
  timestamp = {2016-08-03T11:22:48Z},
  urldate = {2016-08-03},
  journal = {arXiv preprint arXiv:1605.01141},
  author = {Liu, Gang and Goussau, Yann and Xia, Gui-Song},
  year = {2016},
  file = {Liu_etal_2016_arXiv preprint arXiv1605.01141.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Liu_etal_2016_arXiv preprint arXiv1605.01141.pdf:application/pdf}
}

@book{xie_dynamic_2015,
  edition = {2nd},
  title = {Dynamic {{Documents}} with {{R}} and {{Knitr}}, {{Second Edition}}},
  isbn = {1-4987-1696-2 978-1-4987-1696-3},
  timestamp = {2016-08-03T14:48:19Z},
  publisher = {{Chapman \& Hall/CRC}},
  author = {Xie, Yihui},
  year = {2015}
}

@book{auguie_gridextra:_2016,
  title = {{{gridExtra}}: {{Miscellaneous Functions}} for "{{Grid}}" {{Graphics}}},
  timestamp = {2016-08-18T08:56:38Z},
  author = {Auguie, Baptiste},
  year = {2016},
  note = {R package version 2.2.1}
}

@book{wickham_dplyr:_2016,
  title = {Dplyr: {{A Grammar}} of {{Data Manipulation}}},
  timestamp = {2016-08-17T15:34:48Z},
  author = {Wickham, Hadley and Francois, Romain},
  year = {2016},
  note = {R package version 0.5.0}
}

@book{arnold_ggthemes:_2016,
  title = {Ggthemes: {{Extra Themes}}, {{Scales}} and {{Geoms}} for 'Ggplot2'},
  timestamp = {2016-08-18T08:58:36Z},
  author = {Arnold, Jeffrey B.},
  year = {2016},
  note = {R package version 3.0.3}
}

@article{kehrer_perceptual_1987,
  title = {Perceptual Segregation and Retinal Position},
  volume = {2},
  timestamp = {2016-10-26T12:26:10Z},
  number = {4},
  urldate = {2016-10-26},
  journal = {Spatial Vision},
  author = {Kehrer, Lothar},
  year = {1987},
  pages = {247--261},
  file = {Kehrer_1987_Spatial Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1987/Kehrer_1987_Spatial Vision.pdf:application/pdf}
}

@article{lewandowski_generating_2009,
  title = {Generating Random Correlation Matrices Based on Vines and Extended Onion Method},
  volume = {100},
  timestamp = {2016-10-07T15:03:24Z},
  number = {9},
  journal = {Journal of Multivariate Analysis},
  author = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
  year = {2009},
  pages = {1989--2001}
}

@article{balas_seeing_2016,
  title = {Seeing Number Using Texture: {{How}} Summary Statistics Account for Reductions in Perceived Numerosity in the Visual Periphery},
  issn = {1943-3921, 1943-393X},
  shorttitle = {Seeing Number Using Texture},
  doi = {10.3758/s13414-016-1204-6},
  language = {en},
  timestamp = {2016-10-10T15:01:23Z},
  urldate = {2016-10-10},
  journal = {Attention, Perception, \& Psychophysics},
  author = {Balas, Benjamin},
  month = sep,
  year = {2016},
  file = {Balas_2016_Attention, Perception, & Psychophysics.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Balas_2016_Attention, Perception, & Psychophysics.pdf:application/pdf}
}

@article{gurnsey_texture_1996,
  title = {Texture Segmentation along the Horizontal Meridian: Nonmonotonic Changes in Performance with Eccentricity.},
  volume = {22},
  shorttitle = {Texture Segmentation along the Horizontal Meridian},
  timestamp = {2016-10-26T12:54:35Z},
  number = {3},
  urldate = {2016-10-26},
  journal = {Journal of Experimental Psychology: Human Perception and Performance},
  author = {Gurnsey, Rick and Pearson, Pauline and Day, Duncan},
  year = {1996},
  pages = {738},
  file = {Gurnsey_etal_1996_Journal of Experimental Psychology Human Perception and Performance.pdf:/Users/tsawallis/Dropbox/Zotero_Library/1996/Gurnsey_etal_1996_Journal of Experimental Psychology Human Perception and Performance.pdf:application/pdf}
}

@book{mcelreath_statistical_2016,
  address = {Boca Raton London New York},
  series = {Texts in statistical science},
  title = {Statistical Rethinking: A {{Bayesian}} Course with Examples in {{R}} and {{Stan}}},
  isbn = {978-1-4822-5344-3},
  shorttitle = {Statistical Rethinking},
  language = {eng},
  timestamp = {2016-12-10T15:08:24Z},
  number = {122},
  publisher = {{CRC Press, Taylor \& Francis Group}},
  author = {McElreath, Richard},
  year = {2016},
  note = {OCLC: 936134364},
  annote = {Literaturverzeichnis: Seite 457-464},
  file = {McElreath_2016.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/McElreath_2016_2.pdf:application/pdf}
}

@article{beck_relation_1955,
  title = {The Relation of Apparent Shape to Apparent Slant in the Perception of Objects.},
  volume = {50},
  issn = {0022-1015},
  doi = {10.1037/h0045219},
  language = {en},
  timestamp = {2017-02-11T14:47:46Z},
  number = {2},
  urldate = {2017-02-11},
  journal = {Journal of Experimental Psychology},
  author = {Beck, Jacob and Gibson, James J.},
  year = {1955},
  pages = {125--133}
}

@article{gibson_perception_1950,
  title = {The {{Perception}} of {{Visual Surfaces}}},
  volume = {63},
  issn = {00029556},
  doi = {10.2307/1418003},
  timestamp = {2017-02-11T14:48:05Z},
  number = {3},
  urldate = {2017-02-11},
  journal = {The American Journal of Psychology},
  author = {Gibson, James J.},
  month = jul,
  year = {1950},
  pages = {367},
  file = {1418003.pdf:/Users/tsawallis/Dropbox/Zotero_Library/zotero/storage/ZBWQE9C7/1418003.pdf:application/pdf}
}

@article{victor_perception_2013,
  title = {Perception of Second- and Third-Order Orientation Signals and Their Interactions},
  volume = {13},
  issn = {1534-7362},
  doi = {10.1167/13.4.21},
  abstract = {Orientation signals, which are crucial to many aspects of visual function, are more complex and varied in the natural world than in the stimuli typically used for laboratory investigation. Gratings and lines have a single orientation, but in natural stimuli, local features have multiple orientations, and multiple orientations can occur even at the same location. Moreover, orientation cues can arise not only from pairwise spatial correlations, but from higher-order ones as well. To investigate these orientation cues and how they interact, we examined segmentation performance for visual textures in which the strengths of different kinds of orientation cues were varied independently, while controlling potential confounds such as differences in luminance statistics. Second-order cues (the kind present in gratings) at different orientations are largely processed independently: There is no cancellation of positive and negative signals at orientations that differ by 45$^\circ$. Third-order orientation cues are readily detected and interact only minimally with second-order cues. However, they combine across orientations in a different way: Positive and negative signals largely cancel if the orientations differ by 90$^\circ$. Two additional elements are superimposed on this picture. First, corners play a special role. When second-order orientation cues combine to produce corners, they provide a stronger signal for texture segregation than can be accounted for by their individual effects. Second, while the object versus background distinction does not influence processing of second-order orientation cues, this distinction influences the processing of third-order orientation cues.},
  language = {eng},
  timestamp = {2017-02-11T15:06:51Z},
  number = {4},
  journal = {Journal of Vision},
  author = {Victor, Jonathan D. and Thengone, Daniel J. and Conte, Mary M.},
  month = mar,
  year = {2013},
  pages = {21},
  file = {Victor_etal_2013_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Victor_etal_2013_Journal of Vision.pdf:application/pdf},
  pmid = {23532909},
  pmcid = {PMC3616601}
}

@inproceedings{wichmann_methods_2017,
  title = {Methods and Measurements to Compare Men against Machines},
  timestamp = {2017-02-15T14:24:36Z},
  booktitle = {Proc. {{SPIE}}},
  author = {Wichmann, F A and Janssen, David A. and Geirhos, Robert and Aguilar, Guillermo and Sch{\"u}tt, Heiko H. and Maertens, Marianne and Bethge, Matthias},
  year = {2017},
  file = {Wichmann_etal_2017_Proc. SPIE.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Wichmann_etal_2017_Proc. SPIE.pdf:application/pdf}
}

@article{kruschke_bayesian_2017,
  title = {The {{Bayesian New Statistics}}: {{Hypothesis}} Testing, Estimation, Meta-Analysis, and Power Analysis from a {{Bayesian}} Perspective},
  issn = {1069-9384, 1531-5320},
  shorttitle = {The {{Bayesian New Statistics}}},
  doi = {10.3758/s13423-016-1221-4},
  language = {en},
  timestamp = {2017-03-01T12:05:58Z},
  urldate = {2017-03-01},
  journal = {Psychonomic Bulletin \& Review},
  author = {Kruschke, John K. and Liddell, Torrin M.},
  month = feb,
  year = {2017},
  file = {Kruschke_Liddell_2017_Psychonomic Bulletin & Review.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Kruschke_Liddell_2017_Psychonomic Bulletin & Review.pdf:application/pdf}
}

@article{kokkinos_ubernet:_2016,
  title = {{{UberNet}}: {{Training}} a `{{Universal}}' {{Convolutional Neural Network}} for {{Low}}-, {{Mid}}-, and {{High}}-{{Level Vision}} Using {{Diverse Datasets}} and {{Limited Memory}}},
  volume = {abs/1609.02132},
  timestamp = {2017-03-15T10:38:20Z},
  journal = {CoRR},
  author = {Kokkinos, Iasonas},
  year = {2016},
  biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/Kokkinos16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@book{wandell_foundations_1995,
  title = {Foundations of Vision.},
  timestamp = {2017-03-16T16:38:31Z},
  publisher = {{Sinauer Associates}},
  author = {Wandell, Brian A},
  year = {1995}
}

@article{geirhos_comparing_2017,
  title = {Comparing {{Deep Neural Networks}} against Humans: {{Object}} Recognition When the Signal Gets Weaker},
  timestamp = {2017-06-22T09:35:13Z},
  journal = {arXiv:1706.06969},
  author = {Geirhos, Robert and Janssen, David H. J. and Sch{\"u}tt, Heiko H. and Rauber, Jonas and Bethge, Matthias and Wichmann, Felix A},
  year = {2017},
  file = {Geirhos_etal_2017_arXiv1706.06969.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Geirhos_etal_2017_arXiv1706.06969.pdf:application/pdf}
}

@article{koenderink_eidolons:_2017,
  title = {Eidolons: {{Novel}} Stimuli for Vision Research},
  volume = {17},
  issn = {1534-7362},
  shorttitle = {Eidolons},
  doi = {10.1167/17.2.7},
  language = {en},
  timestamp = {2017-06-18T14:30:29Z},
  number = {2},
  urldate = {2017-06-18},
  journal = {Journal of Vision},
  author = {Koenderink, Jan and Valsecchi, Matteo and {van Doorn}, Andrea and Wagemans, Johan and Gegenfurtner, Karl},
  month = mar,
  year = {2017},
  pages = {7},
  file = {Koenderink_etal_2017_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Koenderink_etal_2017_Journal of Vision.pdf:application/pdf}
}

@incollection{koenderink_metamerism_1996,
  address = {Los Alamitos, Calif. [u.a.]},
  title = {Metamerism in Complete Sets of Image Operators},
  isbn = {0-8186-7644-2},
  timestamp = {2017-07-10T20:48:49Z},
  booktitle = {Advances in Image Understanding : A {{Festschrift}} for {{Azriel Rosenfeld}}},
  publisher = {{IEEE Computer Society Press}},
  author = {Koenderink, Jan and {van Doorn}, Andrea J},
  editor = {Bowyer, Kevin and Ahuja, Narendra},
  year = {1996},
  size = {VIII, 349 S.},
  bestand = {KIT, Campus Süd, Bibliothek der Fakultät für Informatik <90/148>;}
}

@inproceedings{gatys_image_2016,
  title = {Image {{Style Transfer Using Convolutional Neural Networks}}},
  timestamp = {2017-07-03T14:19:55Z},
  booktitle = {Proceedings of the {{IEEE Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Gatys, L. A. and Ecker, A. S. and Bethge, M.},
  month = jun,
  year = {2016},
  keywords = {artistic style,convolutional neural networks,separating content from style,texture transfer},
  file = {Gatys_etal_2016_Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Gatys_etal_2016_Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.pdf:application/pdf}
}

@article{kietzmann_deep_2017,
  title = {Deep {{Neural Networks In Computational Neuroscience}}},
  timestamp = {2017-07-11T08:19:20Z},
  urldate = {2017-07-11},
  journal = {bioRxiv},
  author = {Kietzmann, Tim Christian and McClure, Patrick and Kriegeskorte, Nikolaus},
  year = {2017},
  pages = {133504},
  file = {Kietzmann_etal_2017_bioRxiv.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Kietzmann_etal_2017_bioRxiv.pdf:application/pdf}
}

@article{bruna_invariant_2013,
  title = {Invariant {{Scattering Convolution Networks}}},
  volume = {35},
  issn = {0162-8828, 2160-9292},
  doi = {10.1109/TPAMI.2012.230},
  timestamp = {2017-07-11T14:17:55Z},
  number = {8},
  urldate = {2017-07-11},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  author = {Bruna, Joan and Mallat, S.},
  month = aug,
  year = {2013},
  pages = {1872--1886},
  file = {Bruna_Mallat_2013_IEEE Transactions on Pattern Analysis and Machine Intelligence.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2013/Bruna_Mallat_2013_IEEE Transactions on Pattern Analysis and Machine Intelligence.pdf:application/pdf}
}

@article{burkner_brms:_????-1,
  title = {Brms: {{An R Package}} for {{Bayesian Multilevel Models}} Using {{Stan}}},
  timestamp = {2017-07-18T12:57:36Z},
  journal = {Journal of Statistical Software},
  author = {B{\"u}rkner, Paul-Christian},
  year = {in press}
}

@article{agaoglu_can_2016,
  title = {Can (Should) Theories of Crowding Be Unified?},
  volume = {16},
  issn = {1534-7362},
  doi = {10.1167/16.15.10},
  language = {en},
  timestamp = {2017-07-26T13:20:55Z},
  number = {15},
  urldate = {2017-07-26},
  journal = {Journal of Vision},
  author = {Agaoglu, Mehmet N. and Chung, Susana T. L.},
  month = dec,
  year = {2016},
  pages = {10},
  file = {Agaoglu_Chung_2016_Journal of Vision.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2016/Agaoglu_Chung_2016_Journal of Vision.pdf:application/pdf}
}

@article{zwaan_participant_2017,
  title = {Participant {{Nonnaivet{\'e}}} and the Reproducibility of Cognitive Psychology},
  issn = {1069-9384, 1531-5320},
  doi = {10.3758/s13423-017-1348-y},
  language = {en},
  timestamp = {2017-07-26T14:15:18Z},
  urldate = {2017-07-26},
  journal = {Psychonomic Bulletin \& Review},
  author = {Zwaan, Rolf A. and Pecher, Diane and Paolacci, Gabriele and Bouwmeester, Samantha and Verkoeijen, Peter and Dijkstra, Katinka and Zeelenberg, Ren{\'e}},
  month = jul,
  year = {2017},
  file = {Zwaan_etal_2017_Psychonomic Bulletin & Review.pdf:/Users/tsawallis/Dropbox/Zotero_Library/2017/Zwaan_etal_2017_Psychonomic Bulletin & Review.pdf:application/pdf}
}