Bayesian perceptual inference in linear Gaussian models

The aim of this paper is to provide perceptual scientists with a quantitative framework for modeling a variety of common perceptual behaviors, and to unify various perceptual inference tasks by exposing their common computational underpinnings. This paper derives a model Bayesian observer for perceptual contexts with linear Gaussian generative processes. I demonstrate the relationship between four fundamental perceptual situations by expressing their corresponding posterior distributions as consequences of the model's predictions under their respective assumptions

The use of optimal object information in fronto-parallel orientation discrimination

AbstractWhen determining an object’s orientation an implicit object axis is formed, based on local contour information. Due to the oblique effect (i.e., the more precise perception of horizontal/vertical orientations than oblique orientations), an object’s orientation will be perceived more precise if the axis is either horizontal or vertical than when the axis is oblique. In this study we investigated which object axis is used to determine orientation for objects containing multiple axes. We tested human subjects in a series of experiments using the method of adjustment. We found that observers always use object axes allowing for the highest object orientation discrimination, namely the axes lying closest to the horizontal/vertical. This implies that the weight the visual system attaches to axial object information is in accordance with the precision with which this information is perceived

A framework for applying the principles of depth perception to information visualization

Cataloged from PDF version of article.During the visualization of 3D content, using the depth cues selectively to support the design goals and enabling a user to perceive the spatial relationships between the objects are important concerns. In this novel solution, we automate this process by proposing a framework that determines important depth cues for the input scene and the rendering methods to provide these cues. While determining the importance of the cues, we consider the user's tasks and the scene's spatial layout. The importance of each depth cue is calculated using a fuzzy logic-based decision system. Then, suitable rendering methods that provide the important cues are selected by performing a cost-profit analysis on the rendering costs of the methods and their contribution to depth perception. Possible cue conflicts are considered and handled in the system. We also provide formal experimental studies designed for several visualization tasks. A statistical analysis of the experiments verifies the success of our framework

Near-optimal combination of disparity across a log-polar scaled visual field

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment

A framework for applying the principles of depth perception to information visualization

During the visualization of 3D content, using the depth cues selectively to support the design goals and enabling a user to perceive the spatial relationships between the objects are important concerns. In this novel solution, we automate this process by proposing a framework that determines important depth cues for the input scene and the rendering methods to provide these cues. While determining the importance of the cues, we consider the user's tasks and the scene's spatial layout. The importance of each depth cue is calculated using a fuzzy logic-based decision system. Then, suitable rendering methods that provide the important cues are selected by performing a cost-profit analysis on the rendering costs of the methods and their contribution to depth perception. Possible cue conflicts are considered and handled in the system. We also provide formal experimental studies designed for several visualization tasks. A statistical analysis of the experiments verifies the success of our framework. © 2013 ACM

Design of virtual reality systems for animal behavior research

Virtual reality (VR) experimental behavior setups enable cognitive neuroscientists to study the integration of visual depth cues and self-motion cues into a single percept of three-dimensional space. Rodents can navigate a virtual environment by running on a spherical treadmill, but simulating locomotion in this way can both bias and suppress the frequency of their behaviors as well as introduce vestibulomotor and vestibulovisual sensory conflict during locomotion. Updating the virtual environment via the subject's own freely-moving head movements solves both the naturalistic behavior bias and vestibular conflict issues. In this thesis, I review elements of self-motion and 3D scene perception that contribute to a sense of immersion in virtual environments and suggest a freely-moving CAVE system as a VR solution for low-artifact neuroscience experiments. The manuscripts describing the 3D graphics Python package and the virtual reality setup are included. In this freely-moving CAVE VR setup, freely-moving rats demonstrate immersion in virtual environments by displaying height aversion to virtual cliffs, exploration preference of virtual objects, and spontaneously modify their locomotion trajectories near virtual walls. These experiments help bridge the classic behavior and virtual reality literature by showing that rats display similar behaviors to virtual environment features without training

Prediction related phenomena of visual perception

Perception is grounded in our ability to optimize predictions about upcoming events. Such predictions depend on both the incoming sensory input and on our previously acquired conceptual knowledge. Correctly predicted or expected sensory stimuli induce reduced responses when compared to incorrectly predicted, surprising inputs. Predictions enable an efficient neuronal encoding so that less energy is invested to interpret redundant sensory stimuli. Several different neuronal phenomena are the consequences of predictions, such as repetition suppression (RS) and mismatch negativity (MMN). RS represents the reduced neuronal response to a stimulus upon its repeated presentation. MMN is the electrophysiological response difference between rare and frequent stimuli in an oddball sequence. While both are currently studied extensively, the underlying mechanisms of RS and MMN as well as their relation to predictions remains poorly understood. In the current thesis, four experiments were devised to investigate prediction related phenomena dependent on the repetition probability of stimuli. Two studies deal with the RS phenomenon, while the other two investigate the MMN response. In Experiment 1 the temporal dynamics underlying prediction and RS effects were tested. Participants were presented with expected and surprising stimulus pairs with two different inter-stimulus intervals (0.5s for Immediate and 1.75 or 3.75s for Delayed target presentation). These pairs could either repeat or alternate. Expectations were contingent on face gender and were manipulated with the repetition probability. We found that the prediction effects do not depend on the length of the ISI period, suggesting that Immediate and Delayed cue-target stimulus arrangements create similar expectation effects. In order to elucidate the neuronal mechanisms underlying these prediction effects (i.e. surprise enhancement or expectation suppression), in our second study, we employed the experimental design of the first experiment with the addition of random events as a control. We found that surprising events elicit stronger Blood Oxygen Level Dependent (BOLD) responses than random events, implying that predictions influence the neuronal responses via surprise enhancement. Similarly, the third experiment was employed to disentangle which neural mechanism underlies the visual MMN (vMMN). We compared the responses to the stimuli (chairs, faces, real and false characters) presented in conventional oddball sequences to the same stimuli in control sequences (Kaliukhovich and Vogels, 2014). We found that the neural mechanisms underlying vMMN are category dependent: the vMMN of faces and chairs was due to RS, while the vMMN response of real and false characters was mainly driven by surprise-related changes. So far, no study used category-specific regions of interest (ROIs) to examine the neuroimaging correlates of the vMMN. Therefore, for the fourth experiment, we recorded electrophysiological and neuroimaging data from the same participants with an oddball paradigm for real and false characters. We found a significant correlation between vMMN (CP1 cluster at 400 ms) and functional magnetic resonance imaging adaptation (in the letter form area for real characters), suggesting their strong relationship. Taking the four studies into consideration, it is clear that surprise has an important role in prediction related phenomena. The role of surprise is discussed in the light of these results and other recent developments reported in the literature. Overall, this thesis suggests the unification of RS and MMN within the framework of predictive coding