The effect of continuous, nonlinearly transformed visual feedback on rapid aiming movements

We investigated the ability to adjust to nonlinear transformations that allow people to control external systems like machines and tools. Earlier research (Verwey and Heuer 2007) showed that in the presence of just terminal feedback participants develop an internal model of such transformations that operates at a relatively early processing level (before or at amplitude specification). In this study, we investigated the level of operation of the internal model after practicing with continuous visual feedback. Participants executed rapid aiming movements, for which a nonlinear relationship existed between the target amplitude seen on the computer screen and the required movement amplitude of the hand on a digitizing tablet. Participants adjusted to the external transformation by developing an internal model. Despite continuous feedback, explicit awareness of the transformation did not develop and the internal model still operated at the same early processing level as with terminal feedback. Thus with rapid aiming movements, the type of feedback may not matter for the locus of operation of the internal model

A Silicon Model of Early Visual Processing

Many of the most striking phenomena known from perceptual psychology are a direct result of the first levels of neural processing. In the visual systems of higher animals, the well-known center-surround response to local stimuli is responsible for some of the strongest visual illusions. For example, Mach bands, the Hermann-Hering grid illusion, and the Craik-O'Brian-Comsweet illusion can all be traced to simple inhibitory interactions between elements of the retina (Ratliff 1965). The high degree to which a perceived image is independent of the absolute illumination level can be viewed as a property of the mechanism by which incident light is transduced into an electrical signal. We present a model of the first stages of retinal processing in which these phenomena are viewed as natural by-products of the mechanism by which the system adapts to a wide range of viewing conditions. Our retinal model is implemented as a single silicon chip, which contains integrated photoreceptors and processing elements; this chip generates, in real time, outputs that correspond directly to signals observed in the corresponding levels of biological retinas

The visual processing of text

The results of an investigation into the nature of the visual information obtained from pages of text and used in the visual processing of text during reading are reported. An initial investigation into the visual processing of text by applying a computational model of early vision (MIRAGE: Watt & Morgan, 1985; Watt, 1988) to pages of text (Computational Analysis 1) is shown to extract a range of features from a text image in the representation it delivers, which are organised across a range of spatial scales similar to those spanning human vision. The features the model extracts are capable of supporting a structured set of text processing tasks of the type required in reading. From the findings of this analysis, a series of psychophysical and computational studies are reported which exan-dne whether the type of information used in the human visual processing of text can be described by this modelled representation of information in text images. Using a novel technique to measure the 'visibility' of the information in text images, a second stage of investigation (Experiments 1-3) shows that information used to perform different text processing tasks of the type performed in reading is contained at different spatial scales of visual analysis. A second computational analysis of the information in text demonstrates how the spatial scale dependency of these text processing tasks can be accounted for by the model of early vision. In a third stage, two further experiments (Experiments 4-5) show how the pattern of text processing performance is determined by typographical parameters, and a third computational analysis of text demonstrates how changes in the pattern of text processing performance can be modelled by changes in the pattern of information represented by the model of vision. A fourth stage (Experiments 6-7 and Computational Analysis 4) examines the time-course of the visual processing of text. The experiments show how the duration required to reach a level of visual text processing performance varies as a function of typographical parameters, and comparison of these data with the model shows that this is consistent with a time-course of visual analysis based on a coarse-to-fine spatial scale of visual processing. A final experiment (Experiment 8) examines how reading performance varies with typographical parameters. It is shown how the pattern of reading performance and the pattern of visual text processing performance are related, and how the model of early vision might describe the visual processing of text in reading. The implications of these findings for theories of reading and theories of vision are finally discussed

The Role of Early Recurrence in Improving Visual Representations

This dissertation proposes a computational model of early vision with recurrence, termed as early recurrence. The idea is motivated from the research of the primate vision. Specifically, the proposed model relies on the following four observations. 1) The primate visual system includes two main visual pathways: the dorsal pathway and the ventral pathway; 2) The two pathways respond to different visual features; 3) The neurons of the dorsal pathway conduct visual information faster than that of the neurons of the ventral pathway; 4) There are lower-level feedback connections from the dorsal pathway to the ventral pathway. As such, the primate visual system may implement a recurrent mechanism to improve visual representations of the ventral pathway. Our work starts from a comprehensive review of the literature, based on which a conceptualization of early recurrence is proposed. Early recurrence manifests itself as a form of surround suppression. We propose that early recurrence is capable of refining the ventral processing using results of the dorsal processing. Our work further defines a set of computational components to formalize early recurrence. Although we do not intend to model the true nature of biology, to verify that the proposed computation is biologically consistent, we have applied the model to simulate a neurophysiological experiment of a bar-and-checkerboard and a psychological experiment involving a moving contour illusion. Simulation results indicated that the proposed computation behaviourally reproduces the original observations. The ultimate goal of this work is to investigate whether the proposal is capable of improving computer vision applications. To do this, we have applied the model to a variety of applications, including visual saliency and contour detection. Based on comparisons against the state-of-the-art, we conclude that the proposed model of early recurrence sheds light on a generally applicable yet lightweight approach to boost real-life application performance

A Model of the Ventral Visual System Based on Temporal Stability and Local Memory

The cerebral cortex is a remarkably homogeneous structure suggesting a rather generic computational machinery. Indeed, under a variety of conditions, functions attributed to specialized areas can be supported by other regions. However, a host of studies have laid out an ever more detailed map of functional cortical areas. This leaves us with the puzzle of whether different cortical areas are intrinsically specialized, or whether they differ mostly by their position in the processing hierarchy and their inputs but apply the same computational principles. Here we show that the computational principle of optimal stability of sensory representations combined with local memory gives rise to a hierarchy of processing stages resembling the ventral visual pathway when it is exposed to continuous natural stimuli. Early processing stages show receptive fields similar to those observed in the primary visual cortex. Subsequent stages are selective for increasingly complex configurations of local features, as observed in higher visual areas. The last stage of the model displays place fields as observed in entorhinal cortex and hippocampus. The results suggest that functionally heterogeneous cortical areas can be generated by only a few computational principles and highlight the importance of the variability of the input signals in forming functional specialization

Hierarchical Bayesian Inference in the Visual Cortex

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.Mathematic

A Neural Network Model for the Spatial and Temporal Response of Retinal Ganglion Cells

This article introduces a quantitative model of early visual system function. The model is formulated to unify analyses of spatial and temporal information processing by the nervous system. Functional constraints of the model suggest mechanisms analogous to photoreceptors, bipolar cells, and retinal ganglion cells, which can be formally represented with first order differential equations. Preliminary numerical simulations and analytical results show that the same formal mechanisms can explain the behavior of both X (linear) and Y (nonlinear) retinal ganglion cell classes by simple changes in the relative width of the receptive field (RF) center and surround mechanisms. Specifically, an increase in the width of the RF center results in a change from X-like to Y-like response, in agreement with anatomical data on the relationship between α- and -cell RF profiles. Simulations of model response to various spatio-temporal input patterns replicate many of the classical properties of X and Y cells, including transient (Y) versus sustained (X) responses, null-phase responses to alternating gratings in X cells, on-off or frequency doubling responses in Y cells, and phase-independent on-off responses in Y cells at high spatial frequencies. The model's formal mechanisms may be used in other portions of the visual system and more generally in nervous system structures involved with spatio-temporal information processing