Abstract

We investigated audiovisual interactions in motion perception by behavioral experiments testing both, the influence of visual stimuli on auditory apparent motion and the influence of auditory stimuli on visual apparent motion perception. A set of loudspeakers with an LED mounted in the middle of each speaker cone was arranged in a semicircle. Apparent motion streams were presented for each modality alone in the unimodal conditions. In the bimodal conditions, stimuli of the second modality were added to fill the temporal and spatial gaps of the sampled trajectory of the reference stream. The participants' task was to observe the quasi-naturalistic stimulus sequences and to perform a standard classification. The addition of stimuli of the second modality indeed facilitated apparent motion perception. Bimodal presentation increased the upper temporal interval up to which the stimuli could be separated in time while still being perceived as continuous motion. We interpret these results as evidence for an ecologically advantageous audiovisual motion integration mechanism which operates beyond the constraints of strict spatiotemporal coincidence. Functional considerations suggest that this mechanism may represent an amodal stage suited for the processing of both unimodal and bimodal signals.

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Abstract

This paper derives a particle filter algorithm within the Dempster-Shafer framework. Particle filtering is a well-established Bayesian Monte Carlo technique for estimating the current state of a hidden Markov process using a fixed number of samples. When dealing with incomplete information or qualitative assessments of uncertainty, however, Dempster-Shafer models with their explicit representation of ignorance often turn out to be more appropriate than Bayesian models. The contribution of this paper is twofold. First, the Dempster-Shafer formalism is applied to the problem of maintaining a belief distribution over the state space of a hidden Markov process by deriving the corresponding recursive update equations, which turn out to be a strict generalization of Bayesian filtering. Second, it is shown how the solution of these equations can be efficiently approximated via particle filtering based on importance sampling, which makes the Dempster-Shafer approach tractable even for large state spaces. The performance of the resulting algorithm is compared to exact evidential as well as Bayesian inference.

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Abstract

Based on the neurobiological and cognitive principles of human information processing, we develop a system for the automatic visual identification and exploration of scenes. The system architecture consists of three layers: a bottom-up feature extraction stage, a top-down object identification stage and knowledge from a domain ontology for scene analysis. The uncertainty in the latter two stages is managed by Dempster–Shafer belief measures. The system sequentially selects “informative” image regions, identifies the local structure in these regions, and uses this information for drawing efficient conclusions about an object in the scene. The selection process involves low-level, bottom-up processes for sensory feature extraction, and cognitive top-down processes for the generation of active motor commands that control the positioning of the sensors towards the most informative regions. Both processing levels have to deal with uncertain data, and have to take into account learned statistical knowledge. For bottom-up feature extraction this is achieved by integrating a nonlinear filtering stage modeled after the neural computations performed in the early stages of the visual system. The top-down cognitive reasoning strategy operates in an adaptive fashion on a belief distribution. The resulting object hypotheses in combination with knowledge from the domain ontology in the third layer are used for generating a scene hypothesis.

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Abstract

We investigate the relation between the physical world and its mental representation in the ‘cognitive map,’ and test if this representation is image-like and complies with the laws of Euclidean geometry. We have developed a new experimental technique using ‘impossible’ virtual environments (VE) to directly influence the representational development. Subjects explore a number of VEs — some ’normal,’ others with severe violations of Euclidean metrics or planar topology. We check if these manipulated properties cause problems in navigation performance. A consistent VE should be easily represented mentally in a map-like fashion, while a VE with severe violations should prove difficult. Surprisingly, we found no substantial influence of the impossible VEs on navigation performance, and forced-choice tests showed little evidence that subjects were aware of manipulations. This suggests that the representation does not resemble a two-dimensional image-like map. Alternatives to consider are sensorimotor and graph-like representations.

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Abstract

We investigate a hybrid system for autonomous exploration and navigation, and implement it in a virtual mobile agent, which operates in virtual spatial environments. The system is based on several distinguishing properties. The representation is not map-like, but based on sensorimotor features, i.e. on combinations of sensory features and motor actions. The system has a hybrid architecture, which integrates a bottom-up processing of sensorimotor features with a top-down, knowledge-based reasoning strategy. This strategy selects the optimal motor action in each step according to the principle of maximum information gain. Two sensorimotor levels with different behavioural granularity are implemented, a macro-level, which controls the movements of the agent in space, and a micro-level, which controls its eye movements. At each level, the same type of hybrid architecture and the same principle of information gain are used for sensorimotor control. The localisation performance of the system is tested with large sets of virtual rooms containing different mixtures of unique and non-unique objects. The results demonstrate that the system efficiently performs those exploratory motor actions that yield a maximum amount of information about the current environment. Localisation is typically achieved within a few steps. Furthermore, the computational complexity of the underlying computations is limited, and the system is robust with respect to minor variations in the spatial environments.

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Abstract

We investigated nonlinear multi-stage networks optimised to reduce statistical dependences in natural images. These networks serve as models for the neural information processing in the higher visual areas of primates (visual cortices V2, V4). We analysed the resulting units with regard to nonlinear selectivity and invariance properties. We show that the proposed network principle leads to units that are highly selective with respect to the input signal space and to units that are invariant with respect to certain stimulus classes. The stimulus selectivity is tested by a set of Cartesian, hyperbolic, and polar gratings as used in several physiological experiments. We compared the population histogram of the stimulus selectivity indices of the network units with the population histogram of macaque V2 units and show that the network can achieve the high selectivity observed in many V2 neurons. We also tested for nonlinear frequency interaction effects, and found substantial interactions for frequencies with the same orientation and for frequencies with different orientations. The former enable a selective encoding of edges and the latter are required for the selective encoding of intrinsically two-dimensional features like corners and terminators

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Abstract

Linear filtering is a basic concept in neural models of early sensory information processing. In particular the visual system has been described to perform a wavelet-like multi-channel decomposition by a set of independent spatial-frequency selective filter mechanisms. Here we suggest that this principle of linear filtering deserves a critical re-evaluation. We propose that an optimal adaptation to natural scene statistics would require AND-like nonlinear interactions between the frequency-selective filter channels. We describe how this hypothesis can be tested by predicted violations of the principle of linearity that should be observable if cortical neurons would actually implement the proposed nonlinearities. We further explain why these effects might have been easily overlooked in earlier tests of the linearity of neurons in primary visual cortex.

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Abstract

We investigated the extent to which auditory and visual motion signals are combined when observers are asked to predict the location of a moving target. In condition 1, the unimodal and bimodal signals were noisy but the target object was continuously visible and audible; in condition 2, the moving object was hidden (invisible and inaudible) for a short period prior to its arrival at the target location. Our main finding is that the facilitation due to simultaneous visual and auditory input is very different for the two conditions: when the target is continuously visible and audible (condition 1), the bimodal performance is twice as good as the unimodal performances thus suggesting a very effective integration mechanism. On the other hand, if the object is hidden for a short period (condition 2) and the task therefore requires the extrapolation of motion speed over a temporal and spatial period, the facilitation due to both sensory inputs is almost absent and the bimodal performance is limited by the visual performance.

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Abstract

The neural mechanisms of early vision can be explained in terms of an information-theoretic optimization of the neural processing with respect to the statistical properties of the natural environment. Recent applications of this approach have been successful in the prediction of the linear filtering properties of ganglion cells and simple cells, but the relations between the environmental statistics and cortical nonlinearities, like those of end-stopped or complex cells, are not yet fully understood. Here we present extensions of our previous investigations of the exploitation of higher-order statistics by nonlinear neurons. We use multivariate wavelet statistics to demonstrate that a strictly linear processing would inevitably leave substantial statistical dependencies between the outputs of the units. We then consider how the basic nonlinearities of cortical neurons--gain control and ON/OFF half-wave rectification--can exploit these higher-order statistical dependencies. We first show that gain control provides an adaptation to the polar separability of the multivariate probability density function (PDF), and, together with an output nonlinearity, enables an overcomplete sparse coding. We then consider how the remaining higher-order dependencies between different units can be exploited by a combination of basic ON/OFF point nonlinearities and subsequent weighted linear combinations. We consider two statistical optimization schemes for the computation of the optimal weights: principal component analysis (PCA) and independent component analysis (ICA). Since the intermediate nonlinearities transform some of the higher-order dependencies into second-order dependencies even the basic PCA approach is able to exploit part of the redundancies. ICA ignores this second-order structure, but can exploit higher-order dependencies. Both schemes yield a variety of nonlinear units which comprise the typical nonlinear processing properties, such as end-stopping, side-stopping, complex-cell properties and extra-classical receptive field properties, but the 'ideal' complex cells seem only to occur with PCA. Thus, a combination of ON/OFF nonlinearities with an integrated PCA-ICA strategy seems necessary to exploit the statistical properties of natural images.

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Abstract

The classical approach in vision research--the derivation of basically linear filter models from experiments with simple artificial test stimuli--is currently undergoing a major revision. Instead of trying to keep the dirty environment out of our clean labs we put it now right into the focus of scientific exploration. An increasing number of scientists are using natural images in their experimental work, and concepts from statistics and information theory are employed for the theoretical modeling of the results. The new approach has a close relation to basic engineering strategies for electronic image processing, since its major concept is that biological sensory systems exploit the statistical redundancies of the environment by appropriate neural transformations. The standard engineering methods are not sufficient, however. Even such a basic biological feature as orientation selectivity requires the consideration of higher-order statistics, like multivariate wavelet statistics, cumulants, or polyspectra. Furthermore, there exists an abundance of nonlinear phenomena in biological vision, for example the phase invariance of complex cells, cortical gain control, end-stopping, and a variety of extra-classical receptive field properties. These amount to nonlinear combinations of linear wavelet filter outputs, which are required to exploit higher-order statistical dependencies, and make it necessary to consider unconventional modeling approaches like differential geometry or Volterra-Wiener systems. By use of such methods we cannot only gain a deeper understanding of the adaptation of the visual system to the complex natural environment, but we can also make the biological system an inspiring source for the design of novel strategies in electronic image processing.

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Abstract

Based on an information theoretical approach, we investigate feature selection processes in saccadic object and scene analysis. Saccadic eye movements of human observers are recorded for a variety of natural and artificial test images. These experimental data are used for a statistical evaluation of the fixated image regions. Analysis of second-order statistics indicates that regions with higher spatial variance have a higher probability to be fixated, but no significant differences beyond these variance effects could be found at the level of power spectra. By contrast, an investigation with higher-order statistics, as reflected in the bispectral density, yielded clear structural differences between the image regions selected by saccadic eye movements as opposed to regions selected by a random process. These results indicate that nonredundant, intrinsically two-dimensional image features like curved lines and edges, occlusions, isolated spots, etc. play an important role in the saccadic selection process which must be integrated with top-down knowledge to fully predict object and scene analysis by human observers.

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Abstract

The inherent structure of the encoding in early stages of the visual system is investigated from a combined information-theoretical, psychophysical, and neurophysiological perspective. We argue that the classical modeling in terms of linear spatial filters is equivalent to the assumption of a Cartesian organization of the feature space of early vision. We show that such a linear Cartesian feature space would be suboptimal for the exploitation of the statistical redundancies of natural images since these have a radially separable probability-density function. Therefore a more efficient representation can be obtained by a nonlinear encoding that yields a feature space with polar organization. This prediction of the information-theoretical approach regarding the organization of the feature space of early vision is confirmed by our psychophysical measurements of basic discrimination capabilities for elementary Gabor patches, and the necessary nonlinear operations are shown to be closely related to cortical gain control and to the phase invariance of complex cells. Finally, we point out some striking similarities between the polar representation in visual cortex and basic image-coding strategies pursued in shape-gain vector quantization schemes.

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Abstract

We suggest that intrinsic-2D (i2D) features, computationally defined as the outputs of nonlinear operators which model the activity of end-stopped neurons, play a role in preattentive texture discrimination. We first show that for discriminable textures with identical power spectra the predictions of traditional models depend on the type of nonlinearity and fail for energy measures. We then argue that the concept of intrinsic dimensionality, and the existence of end-stopped neurons, can help to understand the role of the nonlinearities. Furthermore, we show examples where models without strong i2D selectivity fail to predict the correct ranking order of perceptual segregation. Our arguments regarding the importance of i2D-features resemble the arguments of Julesz and colleagues on behalf of textons like terminators and crossings. However, we provide a computational framework which identifies textons with the outputs of nonlinear operators which are selective to i2D features.

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Abstract

The geometric concept of curvature can help to deal with some important aspects of information processing in natural and artificial vision systems. The paper briefly reviews earlier results regarding the relationship between the notions of curvature, the processing of information, and the modelling of end-stopped neurons in the visual cortex. It then focuses on the difference between Gaussian curvature and the Riemann tensor and reveals the corresponding logical structures. Furthermore, it is shown how multidimensional deviations from flatness can be measured by two-dimensional curvature operators. In the context of image-sequence analysis, a new relationship between the Riemann tensor components and the computation of the optical flow is found.

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Abstract

Orientation selectivity is a basic property of neurones in the visual cortex of higher vertebrates. Such neurones can be seen to act as `feature detectors', which provide an efficient cortical representation of the outside world. More recently, the removal of correlations between the signals of cortical neurones has been suggested as suitable theoretical concept for explaining the development of receptive fields. Corresponding neural network simulations yielded oriented `receptive field' structures resembling those observed by neurophysiologists. The findings suggest that the `decorrelation approach' can provide a causal relationship between characteristics of the physical world and brain function. However, we were able to reveal a basic deficit of the decorrelation approach which we illustrate by the construction of two artificial `worlds', a `Gaussian' one and an `orientation-only' one. We show that, according to the decorrelation approach, oriented environmental features would be neither necessary nor sufficient for the development of oriented receptive fields. Thus the link between environmental structure and cortical orientation selectivity still awaits a theoretical explanation.

Abstract

Local intrinsic dimensionality is shown to be an elementary structural property of multidimensional signals that cannot be evaluated using linear filters. We derive a class of polynomial operators for the detection of intrinsically 2-D image features like curved edges and lines, junctions, line ends, etc. Although it is a deterministic concept, intrinsic dimensionality is closely related to signal redundancy since it measures how many of the degrees of freedom provided by a signal domain are in fact used by an actual signal. Furthermore, there is an intimate connection to multidimensional surface geometry and to the concept of `Gaussian curvature'. Nonlinear operators are inevitably required for the processing of intrinsic dimensionality since linear operators are, by the superposition principle, restricted to OR-combinations of their intrinsically 1-D eigenfunctions. The essential new feature provided by polynomial operators is their potential to act on multiplicative relations between frequency components. Therefore, such operators can provide the AND-combination of complex exponentials, which is required for the exploitation of intrinsic dimensionality. Using frequency design methods, we obtain a generalized class of quadratic Volterra operators that are selective to intrinsically 2-D signals. These operators can be adapted to the requirements of the signal processing task. For example, one can control the "curvature tuning" by adjusting the width of the stopband for intrinsically 1-D signals, or the operators can be provided in isotropic and in orientation-selective versions. We first derive the quadratic Volterra kernel involved in the computation of Gaussian curvature and then present examples of operators with other arrangements of stop and passbands. Some of the resulting operators show a close relationship to the end-stopped and dot-responsive neurons of the mammalian visual cortex

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Abstract

A new interband vector quantization of a human vision-based image representation is presented. The feature specific vector quantizer (FVQ) is suited for data compression beyond second-order decorrelation. The scheme is derived from statistical investigations of natural images and the processing principles of biological vision systems, the initial stage of the coding algorithm is a hierarchical, and orientation-selective, analytic bandpass decomposition, realized by even- and odd-symmetric filter pairs that are modeled after the simple cells of the visual cortex. The outputs of each even- and odd-symmetric filter pair are interpreted as real and imaginary parts of an analytic bandpass signal, which is transformed into a local amplitude and a local phase component according to the operation of cortical complex cells. Feature-specific multidimensional vector quantization is realized by combining the amplitude/phase samples of all orientation filters of one resolution layer. The resulting vectors are suited for a classification of the local image features with respect to their intrinsic dimensionality, and enable the exploitation of higher order statistical dependencies between the subbands. This final step is closely related to the operation of cortical hypercomplex or end-stopped cells. The codebook design is based on statistical as well as psychophysical and neurophysiological considerations, and avoids the common shortcomings of perceptually implausible mathematical error criteria. The resulting perceptual quality of compressed images is superior to that obtained with standard vector quantizers of comparable complexity

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Abstract

Human amblyopes display reduced contrast sensitivities, suffer from perceptual distortion, and their letter acuities are worse than is predicted from grating visibility. We sought the origin of these dysfunctions by measuring normal and amblyopic sensitivities to various forms of well-defined image distortion, namely band-limited phase quantization, phase quantization with additional amplitude modulation, and grey-scale modification. Our results prove the existence of an amblyopic quasi-blindness to image structure, that cannot be explained in terms of contrast detection. We discuss these findings within the computational scheme of image decomposition into local amplitude and local phase values. They are consistent with the assumption of amblyopic eyes being impaired in processing local phase but having the local amplitude (or energy, possibly at reduced gain) at their disposal. Phrased in physiological terms, we propose a scheme of complex-cells-only vision in amblyopia. We also provide a demonstration of how amblyopic eyes may see the test stimuli and natural images by generating local amplitude and phase representations at limited phase resolution.

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Abstract

With a minimal set of assumptions resulting from considerations about the perception of temporal structure, we argue for the existence of a spatio-temporal memory established by the mapping of time into simultaneous physical properties. The important point of this model is the distinction between external, physical time and the internal representation of time. An immediate consequence of such a structure is the emergence of properties usually associated with the concept of iconic memory or informational persistence. Some of these properties may hence be regarded as epiphenomena produced by the testing of a spatio-temporal system with tachistoscopic spatial stimuli. The model can explain properties of the immediate memory span, the lack of effect of exposure duration on tachistoscopic report, the partial-report superiority, the decay of iconic memory, and effects of a backward mask. It does not only avoid the incompatibility problems of the frozen-image concept in dynamic vision, but also provides an adequate basis for the processing of time-varying scenes.

Abstract

In this paper we consider how the representation of images as surfaces, and their characterizations via surface differential forms, can be related to the concept of redundancy in the intensity signal. In contrast to common approaches, the basic surface types (planar, parabolic, elliptic/hyperbolic) are not seen as equal-priority classes, but as corresponding to different degrees of redundancy. This leads to a new approach to image representation and region labeling based upon generalized curvature measures. Furthermore, we employ different reconstruction algorithms to show that elliptic surface patches carry the significant information in natural images. Based upon deterministic and stochastic relaxation techniques, these algorithms allow one to reconstruct the original image from (i) "elliptic intensities" only and (ii) curvature measures which are zero for nonelliptic regions.

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Abstract

Intrahemispheric learning in a paradigm of choice-reaction time shows multimodal response histograms where a later component gradually disappears, the first one being locked to a specific temporal window. Choice appears to be embedded in a temporally discrete process in non-automated situations.

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Abstract

The ultimate goal in the development of decision support systems is to reach the competence and flexibility set by the human standard. We present an approach which is aimed at the efficient handling of situations with incomplete and partially inconsistent data. Its static structure is derived from a hierarchical implementation of the Dempster/Shafer Belief theory, which is extended towards a multi-layered representation by a set of hierarchies. The dynamic behavior is controlled by an adaptive strategy which can reduce the specific problems which may arise due to the predetermined strategies like "best hypotheses", "establish-refine-techniques", "hypothetic-deductive strategies". The suggested strategy is based on the principle of maximum information gain and is able to take the complete "activation pattern" of the representation into account. Acting together, both components can provide reasonable reactions even in ambiguous and ill-defined situations.

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Abstract

We present a solution to the problem of simultaneous localization and mapping (SLAM) based on Dempster-Shafer theory. While several works on the mapping problem based on belief functions exist, none of these approaches deal with the full SLAM problem. In this paper, we derive an evidential version of the FastSLAM algorithm based on a Rao-blackwellized particle filter where belief functions are used for representing a grid map of the robot's environment. The resulting algorithm includes the probabilistic FastSLAM solution as a special case while preserving its low computational complexity. Due to the additional dimensions of uncertainty provided by belief functions, we obtain maps that explicitly show missing information and conflicting sensor measurements. We evaluate our approach using simulation and a real robot equipped with sonar sensors by comparing maps generated by different combination rules, including Bayesian updating.

Abstract

Virtual reality systems provide a convenient means of studying human cognition and performance on a wide range of tasks for which real-world testing is cost prohibitive or difficult to control. For the results of such studies to be valid, it is important to ensure that aspects of the virtual experience do not alter a participant’s behavior or performance on the experimental tasks. This can be particularly difficult when using novel locomotion interfaces that require training. Training procedures should not be completed until movement tasks can be performed at a high level of ability and they do not interfere with concurrent cognitive tasks. A study is described in which subjects were trained to locomote in the Virtusphere, an interface resembling a “human-sized hamster ball.” The effectiveness of training is discussed in terms of both movement abilities and performance on a concurrent cognitive task. Movement performance was tracked as subjects learned to travel through a virtual environment. Additionally, subjects simultaneously completed cognitive tasks in a dual-task selective-interference paradigm. Results showed very rapid improvement on movement measures, including distance traveled and the ratio of collisions to distance traveled, with performance improvement becoming gradual within a few minutes. However, results also highlight persistent problems with concurrent spatial memory tasks, indicating that the training is not really done when performance on the movement metrics levels off.

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Abstract

The evaluation of printing machines poses the problem of how distortions like streaks caused by the machine can be detected and assessed automatically. Although luminance variations in prints can be measured quite precisely, the measured functions bear little relevance for the lightness of streaks and other distortions of prints as perceived by human observers. First, the measurements sometimes indicate changes of luminance in regions which are perceived as homogeneous by humans. Second, the measured strength of a distortion correlates often weakly with its perceived strength, which is influenced by a variety of factors, like the shape of a streak's luminance profile and the distribution of luminance variations in its spatial surround. We have used a model of human perception, based on fundamental neurophysiological and psychophysical properties of the visual system, in order to predict the strength of streak distortions as perceived by human observers from a measured luminance signal. For the evaluation of the model, tests with naive and expert observers have been conducted. They showed that the model has a good correlation (>0.8) to the assessments of human observers and is therefore suited for use in an automatic evaluation system.

Abstract

What are the ingredients required for vision-based place recognition? Pattern recognition models for localization must fulfill invariance requirements different from those of object recognition. We propose a method to evaluate the suitability of existing image processing techniques by testing their outputs against these invariances. The method is applied to several holistic and one local model. We generalize our findings and identify model properties of locality, spatial configuration and generalization as key factors for applicability to localization tasks.

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We explore the potential of model identification adaptive controllers (MIAC) within a biofeedback system. Through the application of an adaptive control algorithm, the system performance could be optimised with respect to time. In a series of experiments the galvanic skin resistance of test subjects playing a computer game was recorded. On this data, a system identification was performed, utilising an Adaptive-Network-based Fuzzy Inference System (ANFIS). The results serve as a basis for the development of the adaption laws of the MIAC and allow conclusions about suitable controllers

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Abstract

Perception in natural environments is inseparably linked to motor action. In fact, we consider action an essential component of perceptual representation. But these representations are inherently difficult to investigate: Traditional experimental setups are limited by the lack of flexibility in manipulating spatial features. To overcome these problems, virtual reality (VR) experiments seem to be a feasible alternative, but these setups typically lack ecological realism due to the use of "unnatural" interface-devices (joystick). Thus, we propose an experimental apparatus which combines multisensory perception and action in an ecologically realistic way. The basis is a 10-foot hollow sphere (VirtuSphere) placed on a platform that allows free rotation. A subject inside can walk in any direction for any distance immersed into virtual environment. Both the rotation of the sphere and movement of the subject's head are tracked to process the subject's view within the VR-environment presented on a head-mounted display. Moreover, auditory features are dynamically processed taking greatest care of exact alignment of sound-sources and visual objects using ambisonic-encoded audio processed by a HRTF-filterbank. We present empirical data that confirm ecological realism of this setup and discuss its suitability for multi-sensory-motor research.

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Abstract

One of the reasons why humans are so successful at interpreting everyday situations is that they are able to combine disparate forms of knowledge. Most artificial systems, by contrast, are restricted to a single representation and hence fail to utilize the complementary nature of multiple sources of information. In this paper, we introduce an information-driven scene categorization system that integrates common sense knowledge provided by a domain ontology with a learned statistical model in order to infer a scene class from recognized objects. We show how the unspecificity of coarse logical constraints and the uncertainty of statistical relations and the object detection process can be modeled using Dempster-Shafer theory and derive the resulting belief update equations. In addition, we define an uncertainty minimization principle for adaptively selecting the most informative object detectors and present classification results for scenes from the LabelMe image database.

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Abstract

Apparent motion (AM) is the illusion of a single moving stimulus when discrete stimuli are presented in rapid succession. In previous investigations we found that visual stimuli influence auditory AM perception. In the present investigation we tested the complete range auditory-visual interactions in AM perception by investigating both the influence of visual stimuli on auditory AM and of auditory stimuli on visual AM perception. The experimental setup consisted of stationary sound-light sources, on which AM sequences were presented. The participants had to perform a standard classification task after each stimulus sequence. We found both a facilitation of auditory AM perception by complementing the auditory sequence with visual stimuli and a facilitation of visual AM perception by complementing the visual sequence with auditory stimuli: if stimuli of one modality are added to fill the spatial and temporal gaps of the other modality’s trajectory, we find an increase of the range of SOAs at which continuous motion is perceived. We discuss different models of neural organization. In contrast to the often observed dominance of one sensory system over the other, the effect occurs symmetrically. This supports the notion of an amodal AM interpolation subsystem, which processes visual and auditory stimuli on par.

Abstract

Determining one's position within the environment is a basic feature of spatial behavior and spatial cognition. This task is of inherently sensorimotor nature in that it results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. Biological agents achieve this in a robust and effortless fashion, which prompted us to investigate a bio-inspired architecture to study the localization process of an artificial agent which operates in virtual spatial environments. The spatial representation in this architecture is based on sensorimotor features that comprise sensory sensory features as well as motor actions. It is hierarchically organized and its structure can be learned in an unsupervised fashion by an appropriate clustering rule. In addition, the architecture has a temporal belief update mechanism which explicitly utilizes the statistical correlations of actions and locations. The architecture is hybrid in integrating bottom-up processing of sensorimotor features with top-down reasoning which is able to select optimal motor actions based on the principle of maximum information gain. The architecture operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and on a micro-level, which controls its eye movements. As a result, the virtual mobile agent is able to localize itself within an environment using a minimum number of exploratory actions.

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Abstract

We introduce an information-driven scene classification system that combines different types of knowledge derived from a domain ontology and a statistical model in order to analyze scenes based on recognized objects. The domain ontology structures and formalizes which kind of scene classes exist and which object classes occur in them. Based on this structure, an empirical analysis of annotations from the LabelMe image database results in a statistical domain description. Both forms of knowledge are utilized for determining which object class detector to apply to the current scene according to the principle of maximum information gain. All evidence is combined in a belief-based framework that explicitly takes into account the uncertainty inherent to the statistical model and the object detection process as well as the ignorance associated with the coarse granularity of ontological constraints. Finally, we present preliminary classification performance results for scenes from the LabelMe database.

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Abstract

Humans utilize region-based hierarchical representations in the context of navigation. We propose a computational model for representing region hierarchies and define criteria for automatically generating them. We devise a cognitively plausible wayfinding algorithm exploiting the hierarchical decomposition given by regions. The algorithm allows to derive plans with decreasing detail level along paths, enabling an agent to obtain the next action in logarithmic time and complete solutions in almost linear time. The resulting paths are reasonable approximations of optimal shortest paths.

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Abstract

We investigate the hypothesis that the basic representation of space which underlies human navigation does not resemble an image-like map and is not restricted by the laws of Euclidean geometry. For this we developed a new experimental technique in which we use the properties of a virtual environment (VE) to directly influence the development of the representation. We compared the navigation performance of human observers under two conditions. Either the VE is consistent with the geometrical properties of physical space and could hence be represented in a map-like fashion, or it contains severe violations of Euclidean metric and planar topology, and would thus pose difficulties for the correct development of such a representation. Performance is not influenced by this difference, suggesting that a map-like representation is not the major basis of human navigation. Rather, the results are consistent with a representation which is similar to a non-planar graph augmented with path length information, or with a sensorimotor representation which combines sensory properties and motor actions. The latter may be seen as part of a revised view of perceptual processes due to recent results in psychology and neurobiology, which indicate that the traditional strict separation of sensory and motor systems is no longer tenable.

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Abstract

The perception of an image by a human observer is usually modeled as a parallel process in which all parts of the image are treated more or less equivalently, but in reality the analysis of scenes is a highly selective procedure, in which only a small subset of image locations is processed by the precise and efficient neural machinery of foveal vision. To understand the principles behind this selection of the "informative" regions of images, we have developed a hybrid system that consists of a combination of a knowledge-based reasoning system with a low-level preprocessing by linear and nonlinear neural operators. This hybrid system is intended as a first step towards a complete model of the sensorimotor system of saccadic scene analysis. In the analysis of a scene, the system calculates in each step which eye movement has to be made to reach a maximum of information about the scene. The possible information gain is calculated by means of a parallel strategy which is suitable for adaptive reasoning. The output of the system is a fixation sequence, and finally, a hypothesis about the scene.

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Abstract

The identification of objects is a major function of vision, but provision of information about one's localization within a larger spatial environment is important as well (what is there vs. where am I). This task is of an inherently sensorimotor nature, i.e. results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. To study the localization process we have designed an artificial agent which operates in virtual spatial environements. Its representation is realized in a hybrid architecture which integrates a bottom-up processing of sensorimotor features with a top-down reasoning that is based on the principle of maximum information gain. The system operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and a micro-level which controls its eye movements. As a result, the virtual mobile agent is able to orient itself within an environment using only a minimum number of exploratory actions. We evaluate its performance with a test suite consisting of different spatial environments and discuss the relation between the representation used by the agent and the properties of place cells in the hippocampus.

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