Social and Behavioral Sciences Commons^™

Euclidean Coordinates Are The Wrong Prior For Models Of Primate Vision, Garrison W. Cottrell

MODVIS Workshop

Convolutional Neural Networks (CNNs) are currently the best models we have of the ventral temporal lobe – the part of cortex engaged in recognizing objects. They have been effective at predicting the firing rates of neurons in monkey cortex, as well as fMRI and MEG responses in human subjects. They are based on several observations concerning the visual world: 1) pixels are most correlated with nearby pixels, leading to local receptive fields; 2) stationary statistics – the statistics of image pixels are relatively invariant across the visual field, leading to replicated features 3) objects do not change identity depending on …

Extreme Image Transformations Improve Latent Representations In Machines, Girik Malik, Ennio Mingolla

MODVIS Workshop

Shuffling pixels in an image helps machines to learn a more robust object representation. To probe the strategies used by humans and machines for object recognition, we introduce Extreme Image Transformations (EITs). Machines rely heavily on exploiting low-level features like color and texture, so their performance degrades on out-of-distribution and adversarial inputs. Humans depend on high-level features like shapes and contours, making them relatively robust to image distortions. EITs systematically shuffle the pixels in an image, parameterized by the size of grids, probability of shuffle and binary block movement, distorting the structure of objects at both local and global levels. …

Perceptual Grouping With Latent Noise, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Humans effortlessly group elements into objects and segment them from the background and other objects without supervision. For example, the black and white stripes of a zebra are grouped together despite vastly different colors. A thorough theoretical and empirical account of perceptual grouping is still missing – Deep Neural Networks (DNNs), which are considered leading models of the visual system still regularly fail at simplistic perceptual grouping tasks. Here, we propose a counterintuitive unsupervised computational approach to perceptual grouping and segmentation: that they arise because of neural noise, rather than in spite of it. We show that adding noise in …

Anisotropy In Non-Rigidity Perception: The Role Of Anisotropies In Neural Populations, Akihito Maruya, Qasim Zaidi

MODVIS Workshop

No abstract provided.

Do Mechanisms Of Sinusoidal Contrast Sensitivity Account For Edge Sensitivity?, Lynn Schmittwilken, Felix A. Wichmann, Marianne Maertens

MODVIS Workshop

No abstract provided.

Local Geometry Of Elementary Visual Computations, Peter Neri

MODVIS Workshop

Visual operators (e.g. edge detectors) are classically modelled using small circuits involving canonical computations, such as template-matching and gain control. Circuit models explain many aspects of the empirical descriptors that are used to characterize local visual operators, from sensitivity to classification images. Notwithstanding their utility, these models fail to provide a unified framework encompassing the variety of effects observed experimentally, such as the impact of contrast, SNR, and attention on the above descriptors. My goal is to start with a simple, plausible geometrical representation of the perceptual operation carried out by the observer, and to show that this representation is …

Feature Integration And Spatial Localization For Attention Across The Visual Hierarchy, Joyce Tam, Chloe Callahan-Flintoft, Brad Wyble

MODVIS Workshop

The featural and spatial specificity of visual representations broadly decrease along the ventral visual stream. The selection of behaviorally relevant information, or attention, must therefore establish spatial correspondence across the visual hierarchy while maintaining behavioral guidance to relevant visual features. Moreover, selection is often accompanied by an inhibitory surround as well as selective inhibition of distractor items. Considering these key functions, we describe a biologically realistic computational theory of visual selection and inhibition through feedforward and feedback signals along the ventral visual stream, complemented by feature-agnostic spatial competition in the pulvinar nuclei. Our model simulates signature visual search behaviors and …

Extracting Edges In Space And Time During Visual Fixations, Lynn Schmittwilken, Marianne Maertens

MODVIS Workshop

No abstract provided.

A Signal Detection Model For The Analysis Of Continuous Response Gradients And An Application To Confidence Rating Data, Fabian A. Soto

MODVIS Workshop

see attached

Modelling Pairwise Comparisons For Thurstonian Scaling And Kendall Rank Correlation, Maarten Wijntjes

MODVIS Workshop

Pairwise comparisons are a simple and effective way to measure the relative ordering of two samples. For more than two samples, a global ordering can be constructed and with sufficient data it is possible to construct a metric scale, for example by using Thurstonian scaling. We will discuss the concept of Number of Distinguishable Levels (NDLs) that emerges from a Thurstonian scaling procedure. The NDL is the attribute range in terms of Just Noticeable Differences (JNDs), for example the number of grayscale values. The NDL is either limited by the visual system or by the range of stimuli. The latter …

The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer

MODVIS Workshop

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!

We look forward to the group’s suggestions for these projects.

How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; …

Constraining The Binding Problem Using Maps, Zhixian Han, Anne Sereno

MODVIS Workshop

We constrained the binding problem by creating maps of different attributes. We compared the performance of different models with different maps in our current study. Our preliminary results showed that the performance of the model is the highest when location maps were used. These results suggest that the optimal way to constrain the binding problem is to create location maps of different attributes.

Evaluating Models Of Scanpath Prediction, Matthias Kümmerer, Matthias Bethge

MODVIS Workshop

No abstract provided.

Modeling The Spread Of Object-Based Attention During Free Viewing, Nicolas Roth, Olga Shurygina, Flora Marleen Muscinelli, Klaus Obermayer, Martin Rolfs

MODVIS Workshop

No abstract provided.

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …

Object Rigidity: Competition And Cooperation Between Motion-Energy And Feature- Tracking Mechanisms And Shape-Based Priors, Akihito Maruya, Qasim Zaidi Dr.

MODVIS Workshop

No abstract provided.

Efficient Perception Of Physical Object Properties With Visual Heuristics, Vivian C. Paulun, Florian S. Bayer, Joshua B. Tenenbaum, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Validity Of Neural Distance Measures In Representational Similarity Analysis, Fabian A. Soto, Emily R. Martin, Hyeonjeong Lee, Nafiz Ahmed, Juan Estepa, Kianoosh Hosseini, Olivia A. Stibolt, Valentina Roldan, Alycia Winters, Mohammadreza Bayat

MODVIS Workshop

No abstract provided.

Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke

MODVIS Workshop

No abstract provided.

Visual Expertise In An Anatomically-Inspired Model Of The Visual System, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni

MODVIS Workshop

We report on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. Contrary to the generally accepted wisdom, our hypothesis is that the face inversion effect can be accounted for by the representation in V1 combined with the reliance on the configuration of features due to face expertise. We take two features of the primate visual system into account: 1) The foveated retina; and 2) The log-polar mapping from retina to V1. We simulate acquisition of faces, etc., by gradually increasing the number of identities the network learns. …

Feature Tracking And Geometrical Priors Counteract Illusory Non-Rigidities From Outputs Of Motion-Energy Cells, Akihito Maruya, Qasim Zaidi

MODVIS Workshop

No abstract provided.

Perceived Object Motion Variance Across Optical Contexts, Jan Jaap R. Van Assen, Mitchell J.P. Van Zuijlen, Shin'ya Nishida

MODVIS Workshop

No abstract provided.

Monocular 3d Reconstruction Of Polyhedral Shapes Via Neural Network, Mark Beers, Zygmunt Pizlo

MODVIS Workshop

No abstract provided.

Modeling The Impacts Of Inter-Display And Inter-Lens Separation On Perceived Slant In Virtual Reality Head-Mounted Displays, Jonathan Tong, Laurie M. Wilcox, Robert S. Allison

MODVIS Workshop

No abstract provided.

Fixational Eye Movements, Perceptual Filling-In, And Perceptual Fading Of Grayscale Images, Michael E. Rudd

MODVIS Workshop

No abstract provided.

Constraining Computational Models Of Brightness Perception: What’S The Right Psychophysical Data?, Guillermo Aguilar, Joris Vincent, Marianne Maertens

MODVIS Workshop

No abstract provided.

Identifying And Localizing Multiple Objects Using Artificial Ventral And Dorsal Visual Cortical Pathways, Zhixian Han, Anne Sereno

MODVIS Workshop

We concluded in our previous study that model cortical visual pathways actively retained information differently according to the different goals of the training tasks. One limitation of our study was that there was only one object in each input image whereas in reality there may be multiple objects in a scene. In our current study, we try to find a brain-like algorithm that can recognize and localize multiple objects.

Modeling Perceptual Grouping Strategies In Visual Search Tasks, Maria Kon, Gregory Francis

MODVIS Workshop

No abstract provided.

Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos

MODVIS Workshop

No abstract provided.