Visual exploration and object recognition by lattice deformation

Mechanisms of explicit object recognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called "Dots"), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating object recognition under a naturalistic setting--free visual exploration--enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for object recognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand.     

Visual perception: understanding visual cues to depth

A new study shows that, in vision, object blur can be a more accurate depth cue than stereo disparity.     

Visual immersion for cultural understanding and multimodal literacy

ABSTRACT

When considering inclusive art curriculum that accommodates all learners, including English language learners, two distinct yet inseparable issues come to mind. The first is that English language learner students can use visual language and visual literacy skills inherent in visual arts curriculum to scaffold learning in and through the arts. Second, in facilitating a sense of belonging for students whose home language and cultural aesthetic may be different from those of the dominant school culture, an authentically developed multicultural art curriculum can guide self-efficacy and inclusiveness. Both aspects of teaching art for English language learners can have the added benefits of facilitating collaborative learning opportunities and increasing worldviews for all students.     

Feature blindness: A challenge for understanding and modelling visual object recognition

Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal that CNNs provide good mechanistic models of shape-bias and, more generally, human visual processing. However, it is also possible that humans and CNNs show a shape-bias for very different reasons, namely, shape-bias in humans may be a consequence of architectural and cognitive constraints whereas CNNs show a shape-bias as a consequence of learning the statistics of the environment. We investigated this question by exploring shape-bias in humans and CNNs when they learn in a novel environment. We observed that, in this new environment, humans (i) focused on shape and overlooked many non-shape features, even when non-shape features were more diagnostic, (ii) learned based on only one out of multiple predictive features, and (iii) failed to learn when global features, such as shape, were absent. This behaviour contrasted with the predictions of a statistical inference model with no priors, showing the strong role that shape-bias plays in human feature selection. It also contrasted with CNNs that (i) preferred to categorise objects based on non-shape features, and (ii) increased reliance on these non-shape features as they became more predictive. This was the case even when the CNN was pre-trained to have a shape-bias and the convolutional backbone was frozen. These results suggest that shape-bias has a different source in humans and CNNs: while learning in CNNs is driven by the statistical properties of the environment, humans are highly constrained by their previous biases, which suggests that cognitive constraints play a key role in how humans learn to recognise novel objects.     

Visual object detection biases escape trajectories following acoustic startle in larval zebrafish

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Experimental-neuromodeling framework for understanding auditory object processing: integrating data across multiple scales.

In this article, we review a combined experimental-neuromodeling framework for understanding brain function with a specific application to auditory object processing. Within this framework, a model is constructed using the best available experimental data and is used to make predictions. The predictions are verified by conducting specific or directed experiments and the resulting data are matched with the simulated data. The model is refined or tested on new data and generates new predictions. The predictions in turn lead to better-focused experiments. The auditory object processing model was constructed using available neurophysiological and neuroanatomical data from mammalian studies of auditory object processing in the cortex. Auditory objects are brief sounds such as syllables, words, melodic fragments, etc. The model can simultaneously simulate neuronal activity at a columnar level and neuroimaging activity at a systems level while processing frequency-modulated tones in a delayed-match-to-sample task. The simulated neuroimaging activity was quantitatively matched with neuroimaging data obtained from experiments; both the simulations and the experiments used similar tasks, sounds, and other experimental parameters. We then used the model to investigate the neural bases of the auditory continuity illusion, a type of perceptual grouping phenomenon, without changing any of its parameters. Perceptual grouping enables the auditory system to integrate brief, disparate sounds into cohesive perceptual units. The neural mechanisms underlying auditory continuity illusion have not been studied extensively with conventional neuroimaging or electrophysiological techniques. Our modeling results agree with behavioral studies in humans and an electrophysiological study in cats. The results predict a particular set of bottom-up cortical processing mechanisms that implement perceptual grouping, and also attest to the robustness of our model.     

Visual analytics for epidemiologists: understanding the interactions between age, time, and disease with multi-panel graphs

Background

Visual analytics, a technique aiding data analysis and decision making, is a novel tool that allows for a better understanding of the context of complex systems. Public health professionals can greatly benefit from this technique since context is integral in disease monitoring and biosurveillance. We propose a graphical tool that can reveal the distribution of an outcome by time and age simultaneously.

Methodology/Principal Findings

We introduce and demonstrate multi-panel (MP) graphs applied in four different settings: U.S. national influenza-associated and salmonellosis-associated hospitalizations among the older adult population (≥65 years old), 1991–2004; confirmed salmonellosis cases reported to the Massachusetts Department of Public Health for the general population, 2004–2005; and asthma-associated hospital visits for children aged 0–18 at Milwaukee Children''s Hospital of Wisconsin, 1997–2006. We illustrate trends and anomalies that otherwise would be obscured by traditional visualization techniques such as case pyramids and time-series plots.

Conclusion/Significance

MP graphs can weave together two vital dynamics—temporality and demographics—that play important roles in the distribution and spread of diseases, making these graphs a powerful tool for public health and disease biosurveillance efforts.     

Electrophysiological correlates of object location and object identity processing in spatial scenes

The ability to quickly detect changes in our surroundings has been crucial to human adaption and survival. In everyday life we often need to identify whether an object is new and if an object has changed its location. In the current event-related potential (ERP) study we investigated the electrophysiological correlates and the time course in detecting different types of changes of an objecṫs location and identity. In a delayed match-to-sample task participants had to indicate whether two consecutive scenes containing a road, a house, and two objects, were either the same or different. In six randomly intermixed conditions the second scene was identical, one of the objects had changed its identity, one of the objects had changed its location, or the objects had switched locations. The results reveal different time courses for the processing of identity and location changes in spatial scenes. Whereas location changes elicited a posterior N2 effect, indicating early mismatch detection, followed by a P3 effect reflecting post-perceptual processing, identity changes elicited an anterior N3 effect, which was delayed and functionally distinct from the N2 effect found for the location changes. The condition in which two objects switched position elicited a late ERP effect, reflected by a P3 effect similar to that obtained for the location changes. In sum, this study is the first to cohesively show different time courses for the processing of location changes, identity changes, and object switches in spatial scenes, which manifest themselves in different electrophysiological correlates.     

Low-level correlations between object properties and viewpoint can cause viewpoint-dependent object recognition

Viewpoint-dependent recognition performance of 3-D objects has often been taken as an indication of a viewpoint-dependent object representation. This viewpoint dependence is most often found using metrically manipulated objects. We aim to investigate whether instead these results can be explained by viewpoint and object property (e.g. curvature) information not being processed independently at a lower level, prior to object recognition itself. Multidimensional signal detection theory offers a useful framework, allowing us to model this as a low-level correlation between the internal noise distributions of viewpoint and object property dimensions. In Experiment 1, we measured these correlations using both Yes/No and adjustment tasks. We found a good correspondence across tasks, but large individual differences. In Experiment 2, we compared these results to the viewpoint dependence of object recognition through a Yes/No categorization task. We found that viewpoint-independent object recognition could not be fully reached using our stimuli, and that the pattern of viewpoint dependence was strongly correlated with the low-level correlations we measured earlier. In part, however, the viewpoint was abstracted despite these correlations. We conclude that low-level correlations do exist prior to object recognition, and can offer an explanation for some viewpoint effects on the discrimination of metrically manipulated 3-D objects.     

Analysis of object clustering

Objects of a given type lie on a plane or in a thin layer. In the analysis of the arrangement of such objects, the question may arise as to whether these objects form clusters or whether they are randomly distributed. This question can be answered by the calculation of the coefficient of dispersion (CD) of the objects. Some statistical properties of this coefficient, i.e., its sensitivity and its chi-square approximation, are analyzed in this paper. The proposed approach for calculating sensitivity is a mathematical simulation that is not generally applicable but is valid in various biologic applications. Assuming that the objects are much smaller than the expected clusters and that no knowledge exists about interactions among the objects or between the objects and their surroundings, the objects can be assumed to be distributed randomly on the plane. Such situations may occur in various fields, i.e., biology, sociology, archaeology and ecology. The authors have applied this approach successfully in investigating the densities of T-cell lymphocytes in renal transplants treated with Cyclosporin A or conventional immunosuppressive therapy.     

Neural mechanisms of object recognition

Single-unit recordings from behaving monkeys and human functional magnetic resonance imaging studies have continued to provide a host of experimental data on the properties and mechanisms of object recognition in cortex. Recent advances in object recognition, spanning issues regarding invariance, selectivity, representation and levels of recognition have allowed us to propose a putative model of object recognition in cortex.     

Bayesian models of object perception

The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.     

Disruptive camouflage impairs object recognition

Whether hiding from predators, or avoiding battlefield casualties, camouflage is widely employed to prevent detection. Disruptive coloration is a seemingly well-known camouflage mechanism proposed to function by breaking up an object''s salient features (for example their characteristic outline), rendering objects more difficult to recognize. However, while a wide range of animals are thought to evade detection using disruptive patterns, there is no direct experimental evidence that disruptive coloration impairs recognition. Using humans searching for computer-generated moth targets, we demonstrate that the number of edge-intersecting patches on a target reduces the likelihood of it being detected, even at the expense of reduced background matching. Crucially, eye-tracking data show that targets with more edge-intersecting patches were looked at for longer periods prior to attack, and passed-over more frequently during search tasks. We therefore show directly that edge patches enhance survivorship by impairing recognition, confirming that disruptive coloration is a distinct camouflage strategy, not simply an artefact of background matching.     

Neuropsychological studies of object recognition

It is well established that disorders of visual perception are associated with lesions in the right hemisphere. Performances on tasks as disparate as the identification of objects from unusual views of objects drawn so as to overlap, of fragmented letters, of familiar faces, and of anomalous features in drawings, have been shown to be impaired in patients with focal right posterior lesions. A series of investigations are reviewed, directed towards analysing the basis of these deficits. Explanations in terms of primary visual impairment can be rejected, as can an account in terms of faulty figure-ground organization. It is argued that a wide variety of such perceptual deficits--all of which are concerned with meaningful visual stimuli--can be encompassed by the notion of faulty perceptual categorization at an early post-sensory stage of object recognition. Moreover, there is evidence suggesting that some of these various perceptual deficits can be mutually dissociated. The concept of perceptual categorization is discussed in the wider context of tentative model of object recognition.     

Models of object recognition.

Progress in the understanding of visual recognition in the past year has been signified by the demonstration of computational feasibility of and psychophysical support for two-dimensional view-interpolation methods.