Social and Behavioral Sciences Commons^™

A Computational Account Of A Class Of Orientation Illusions, Dejan M. Todorovic

MODVIS Workshop

Contrast-dependent orientation illusions are phenomena in which the appearance of the illusion depends not only on geometrical arrangements of the constituents of illusory configurations, but also on their luminance levels. Whereas certain standard configurations may evoke strong illusory effects, their contrast-manipulated variants (configurations in which only the luminance contrast polarity of some of their elements is manipulated, while retaining the geometry of the standard versions) may show weakened or no illusory effects, or even reversed illusions. Although generally rather salient, the contrast-dependent illusions have not been researched in much detail, except for the well-known Münsterberg (Café Wall) illusion. Here I …

Heuristics From Statistics—Modeling The Behavior And Perception Of Non-Rigid Materials, Vivian C. Paulun, Roland W. Fleming

MODVIS Workshop

No abstract provided.

Modeling The Neural Circuitry Underlying The Behavioral And Eeg Correlates Of Attentional Capture, Chloe Callahan-Flintoft, Brad Wyble

MODVIS Workshop

The Reactive-Convergent Gradient Field model (R-CGF) is a unique approach to modeling spatial attention in that it links neural mechanisms to event related potentials (ERPs) from scalp EEG. This model was developed with the aim of explaining different, sometimes conflicting, findings in the attention literature. Specifically, this model address conflicting findings showing both simultaneous and serial deployment of attention. Another argument addressed by the model is whether attention to a location invokes a suppression of the spatial surround, or the selective inhibition of distractors. With the R-CGF, we have found that these results are not as incompatible as they appear …

Spatial-Temporal Visible Contrast Energy Predictions Of Detection Thresholds, Albert Ahumada, Andrew B. Watson, Jihyun Yeonan-Kim

MODVIS Workshop

The Barten (1994) spatial-temporal model was used to predict the Gabor stimulus contrast energy thresholds reported by Carney et al. (2013). The RMS error of fit was 1.6 dB, corrected for the number of parameters (6) estimated. The model has two lowpass spatial-temporal channels, combined by inhibition as in our spatial models (Watson & Ahumada, 2005; Ahumada & Watson, 2013). Computation of models predictions were greatly simplified by the spatial-temporal separability of the stimuli and the simplifications that result from using Gaussian filters in the spatial domain. The best fitting spatial filter frequency cutoffs are 11.4 and 0.88 cpd. The …

Computational Modeling Of Contrast Sensitivity And Orientation Tuning In Schizophrenia, Steven M. Silverstein, Docia L. Demmin, James A. Bednar

MODVIS Workshop

Computational modeling is being increasingly used to understand schizophrenia, but, to date, it has not been used to account for the common perceptual disturbances in the disorder. We manipulated schizophrenia-relevant parameters in the GCAL (gain control, adaptation, laterally connected) model (Stevens et al., 2013), run using the Topographica simulator (Bednar, 2012), to model low-level visual processing changes in the disorder. Our models incorporated: separate sheets for retinal, LGN, and V1 activity; gain control in the LGN; homeostatic adaptation in V1 based on a weighted sum of all inputs and limited by a logistic (sigmoid) nonlinearity; lateral excitation and inhibition in …

Virtual V1sion: A Collaborative Coding Project, Cheryl Olman

MODVIS Workshop

Virtual V1sion is a new idea for fostering modeling collaborations and data sharing. While still in its infancy, the ultimate goal is a website that hosts repositories for (1) interchangeable model elements, (2) datasets that can be fit/predicted by those models, and (3) educational modules that explain the background for both the models and the datasets. The scope of the modeling is limited to predictions of V1 responses, although not all computations represented by model elements in Virtual V1sion are required to be V1-intrinsic: a goal of the project is to provide a framework in which predictions for modulation by …

A Neural Circuit For Visual Information Spreading, Gregory Francis

MODVIS Workshop

I describe a neural circuit made of integrate-and-fire neurons that instantiates a mechanism for the spreading of visual information. The circuit is simple and operates at a reasonable time scale with reasonable parameter values. Slight alterations to the anatomy allow the circuit to spread information across surfaces (constrained by the surface boundaries) as for brightness and color effects or across a group/object (as for some attention effects).

Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd

MODVIS Workshop

Lightness matching data from disk-annulus experiments has the form of a parabolic (2^nd-order polynomial) function when matches are plotted against annulus luminance on log-log axes. Rudd (2010) has proposed a computational cortical model to account for this fact and has subsequently (Rudd, 2013, 2014, 2015) extended the model to explain data from other lightness paradigms, including staircase-Gelb and luminance gradient illusions (Galmonte, Soranzo, Rudd, & Agostini, 2015). Here, I re-analyze parametric lightness matching data from disk-annulus experiments by Rudd and Zemach (2007) and Rudd (2010) for the purpose of further testing the model and to try to constrain …

Figure-Ground Organization Using 3d Symmetry, Aaron Michaux, Vijai Jayadevan, Edward Delp, Zygmunt Pizlo

MODVIS Workshop

We present a novel approach to object localization using mirror symmetry as a general purpose and biologically motivated prior. 3D symmetry leads to good segmentation because (i) almost all objects exhibit symmetry, and (ii) configurations of objects are not likely to be symmetric unless they share some additional relationship. Furthermore, psychophysical evidence suggests that the human vision system makes use symmetry in constructing 3D percepts, indicating that symmetry may be important in object localization. No general purpose approach is known for solving 3D symmetry correspondence in 2D camera images, because few invariants exist. Therefore, to test symmetry as a clustering …

A Mixture Model Demonstrates Use Of Distinct Strategies In A Global Motion Direction Task, Lanya Tianhao Cai, Benjamin T. Backus

MODVIS Workshop

Mixture models are well known in cognitive psychology, less so in vision. Are there cases where the data allow clear testing as to whether different strategies are employed in a task? Most psychophysical measurements manipulate a single staircase variable to map out a monotonic increasing function, but if performance is limited by different mechanisms over the range of the variable, classical fitting could be inappropriate. We present a data set and analyses that confirm the presence of two visual strategies addressing the same task, with the choice of strategies depending on the staircase variable. In a net-motion discrimination task, stimuli …

Choice-Dependent Perceptual Biases, Long Luu, Alan A. Stocker

MODVIS Workshop

The perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination task (Jazayeri and Movshon, 2007). The biases were originally thought to occur because subjects mistakenly reuse the neural read-out optimized for the discrimination task when forming the percept (Fig.1a, Task-dependent model). In a series of experiments, we demonstrated that this explanation is incorrect and that the biases actually result from the conditioning of the percept on the preceding discrimination judgment (Fig1.b, Choice-dependent model). Experiment 1 was aimed at replicating the biases for an orientation stimulus. Subjects first indicated whether the stimulus …

An Image-Based Model For Early Visual Processing, Heiko H. Schütt, Felix A. Wichmann

MODVIS Workshop

No abstract provided.

A Learning Model For L/M Specificity In Ganglion Cells, Albert Ahumada

MODVIS Workshop

An unsupervised learning model for developing L/M specific wiring at the ganglion cell level would support the research indicating L/M specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This …

Focusing On Selection For Fixation, John K. Tsotsos, Calden Wloka, Yulia Kotseruba

MODVIS Workshop

Building on our presentation at MODVIS 2015, we continue in our quest to discover a functional, computational, explanation of the relationship among visual attention, interpretation of visual stimuli, and eye movements, and how these produce visual behavior. Here, we focus on one component, how selection is accomplished for the next fixation. The popularity of saliency map models drives the inference that this is solved; we suggested otherwise at MODVIS 2015. Here, we provide additional empirical and theoretical arguments. We then develop arguments that a cluster of complementary, conspicuity representations drive selection, modulated by task goals and history, leading to a …

Learning Object Representations For Modeling Attention In Real World Scenes, Alex Schwarz, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Models of visual attention have been rarely used in real world tasks as they have been typically developed for psychophysical setups using simple stimuli. Thus, the question remains how objects must be represented to allow such models an operation in real world scenarios. We have previously presented an attention model capable of operating on real-world scenes (Beuth, F., and Hamker, F. H. 2015, NCNC, which is a successor of Hamker, F. H., 2005, Cerebral Cortex), and show here how its object representations have been learned. We have used a learning rule based on temporal continuity (Földiák, P., 1991, Neural Computation) …

Precise Measurements Of Perceptual Attention Filters For Features, Peng Sun, Charles Chubb, Charles E. Wright, Stefanie Drew, George Sperling

MODVIS Workshop

No abstract provided.

Image Segmentation Using Fuzzy-Spatial Taxon Cut, Lauren Barghout

MODVIS Workshop

Images convey multiple meanings that depend on the context in which the viewer perceptually organizes the scene. This presents a problem for automated image segmentation, because it adds uncertainty to the process of selecting which objects to include or not include within a segment. I’ll discuss the implementation of a fuzzy-logic-natural-vision-processing engine that solves this problem by assuming the scene architecture prior to processing. The scene architecture, a standardized natural-scene-perception-taxonomy comprised of a hierarchy of nested spatial-taxons. Spatial-taxons are regions (pixel-sets) that are figure-like, in that they are perceived as having a contour, are either `thing-like', or a `group of …

‘Edge’ Integration Explains Contrast And Assimilation In A Gradient Lightness Illusion, Michael E. Rudd

MODVIS Workshop

In the ‘phantom’ illusion (Galmonte, Soranzo, Rudd, & Agostini, submitted), either an incremental or a decremental target, when surrounded by a luminance gradient, can to be made to appear as an increment or a decrement, depending on the gradient width. For wide gradients, incremental targets appear as increments and decremental targets appear as decrements. For narrow gradients, the reverse is true. Here, I model these phenomena with a two-stage neural lightness theory (Rudd, 2013, 2014) in which local steps in log luminance are first encoded by oriented spatial filters operating on a log-transformed version of the image; then the filter …

A Linearized Model For Flicker And Contrast Thresholds At Various Retinal Illuminances, Albert Ahumada, Andrew B. Watson

MODVIS Workshop

Watson and Ahumada (1992 SID) predicted flicker thresholds for bright displays using a temporal contrast sensitivity function (TCSF). Under the assumptions that the falling limb of the TCSF is linear at all retinal illuminations and that the Ferry-Porter law can be extended to supra-threshold levels, the thresholds for any of the three variables (frequency in Hz, log10 contrast, and retinal illuminance in log Trolands) can be predicted from the other two from a linear model with four parameters.

The Bounded Log-Odds Model Of Frequency And Probability Distortion, Hang Zhang, Laurence T. Maloney

MODVIS Workshop

No abstract provided.

A Signal Detection Experiment With Limited Number Of Trials, Tadamasa Sawada

MODVIS Workshop

Signal detection theory has been well accepted in vision science to measure human sensitivity to stimuli in a Psychophysical experiment. The theory is formulated so that the measured sensitivity is independent from a response bias (criterion). The formulation is based on an assumption that number of trials in the experiment is infinite but this assumption cannot be satisfied in practice. The assumption came from two normal distributions used in the formulation. The distributions respectively represent a set of signal trial and that of noise trials in the experiment. In this study, I will show how the violation of the assumption …

Testing The Bayesian Confidence Hypothesis, Wei Ji Ma, Ronald Van Den Berg

MODVIS Workshop

Asking subjects to rate their confidence is one of the oldest procedures in psychophysics. Remarkably, quantitative models of confidence ratings have been scarce. The Bayesian confidence hypothesis (BCH) states that an observer’s confidence rating is monotonically related to the posterior probability of their choice. I will report tests of this hypothesis in two visual categorization tasks: one requiring rapid categorization of a single oriented stimulus, the other a deliberative judgment typically made by scientists, namely interpreting scatterplots. We find evidence against the Bayesian confidence hypothesis in both tasks.

A Conceptual Framework Of Computations In Mid-Level Vision, Jonas Kubilius, Johan Wagemans, Hans P. Op De Beeck

MODVIS Workshop

The goal of visual processing is to extract information necessary for a variety of tasks, such as grasping objects, navigating in scenes, and recognizing them. While ultimately these tasks might be carried out by separate processing pathways, they nonetheless share a common root in the early and intermediate visual areas. What representations should these areas develop in order to facilitate all of these higher-level tasks? Several distinct ideas have received empirical support in the literature so far: (i) boundary feature detection, such as edge, corner, and curved segment extraction; (ii) second-order feature detection, such as the difference in orientation or …

Metacognition: Using Confidence Ratings For Type 2 And Type 1 Roc Curves, S A. Klein

MODVIS Workshop

In the past five years there has been a surge of renewed interest in metacognition ("thinking about thinking"). The typical experiment involves a binary judgment followed by a multilevel confidence rating. It is a confusing topic because the rating could be made either on one's confidence in the binary response (standard rating Type 1 ROC) or on one's confidence sorted by whether the response was correct (Type 2 ROC). Both are metacognition. After a few remarks on challenging aspects of the Type 2 approach, I will present some interesting results for Type 1 ROC for both memory and vision research. …

Two Correspondence Problems Easier Than One, Aaron Michaux, Zygmunt Pizlo

MODVIS Workshop

Computer vision research rarely makes use of symmetry in stereo reconstruction despite its established importance in perceptual psychology. Such stereo reconstructions produce visually satisfying figures with precisely located points and lines, even when input images have low or moderate resolution. However, because few invariants exist, there are no known general approaches to solving symmetry correspondence on real images. The problem is significantly easier when combined with the binocular correspondence problem, because each correspondence problem provides strong non-overlapping constraints on the solution space. We demonstrate a system that leverages these constraints to produce accurate stereo models from pairs of binocular images …

Binocular 3d Motion Perception As Bayesian Inference, Martin Lages, Suzanne Heron

MODVIS Workshop

The human visual system encodes monocular motion and binocular disparity input before it is integrated into a single 3D percept. Here we propose a geometric-statistical model of human 3D motion perception that solves the aperture problem in 3D by assuming that (i) velocity constraints arise from inverse projection of local 2D velocity constraints in a binocular viewing geometry, (ii) noise from monocular motion and binocular disparity processing is independent, and (iii) slower motions are more likely to occur than faster ones. In two experiments we found that instantiation of this Bayesian model can explain perceived 3D line motion direction under …

Computational Modeling Of Depth-Ordering In Occlusion 
Through Accretion Or Deletion Of Texture, Harald Ruda, Gennady Livitz, Guillaume Riesen, Ennio Mingolla

MODVIS Workshop

Understanding the depth-ordering of surfaces in the natural world is one of the most fundamental operations of the primate visual system. Surfaces that undergo accretion or deletion (AD) of texture are always perceived to behind an adjacent surface.

An updated ForMotionOcclusion (FMO) model (Barnes & Mingolla, 2013) includes two streams for computing motion signals and boundary signals. The two streams generate depth percepts such that AD signals together with boundary signals generate a farther depth on the occluded side of the boundary. The model fits the classical data (Kaplan, 1969) as well as the observation that moving surfaces tend to …

Spatially-Global Integration Of Closed Contours By Means Of Shortest-Path In A Log-Polar Representation, Terry Kwon, Kunal Agrawal, Yunfeng Li, Zygmunt Pizlo

MODVIS Workshop

See the one page PDF with abstract and images.

Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh

MODVIS Workshop

Human depth comparisons in line drawings reflect the underlying uncertainty of perceived 3D shape. We propose a Bayesian model that estimates the 3D shape from line drawings based on the local and non-local contour cues. This model estimates the posterior distribution over depth differences at two points on a line drawing. The likelihood is numerically computed by assuming a generative model, which generates random 3D surfaces and, via projection, random line drawings. The 3D surfaces are inflated from random skeletons and projected into line drawings. Given a novel line drawing, the model samples probable local surfaces based on the relations …

Formal Aspects Of Non-Rigid-Shape-From-Motion Perception, Vicky Froyen, Qasim Zaidi

MODVIS Workshop

Our world is full of objects that deform over time, for example animals, trees and clouds. Yet, the human visual system seems to readily disentangle object motions from non-rigid deformations, in order to categorize objects, recognize the nature of actions such as running or jumping, and even to infer intentions. A large body of experimental work has been devoted to extracting rigid structure from motion, but there is little experimental work on the perception of non-rigid 3-D shapes from motion (e.g. Jain, 2011). Similarly, until recently, almost all formal work had concentrated on the rigid case. In the last fifteen …