arXiv subjects

Birgitta Dresp-Langley

Publications and source records attributed to Birgitta Dresp-Langley.

At least 19 recordsLinked to original sources

arXiv2024

Explainable Self-Organizing Artificial Intelligence Captures Landscape Changes Correlated with Human Impact Data

Novel methods of analysis are needed to help advance our understanding of the intricate interplay between landscape changes, population dynamics, and sustainable development. Self organized machine learning has been highly successful in the analysis of visual data the human expert eye may not be able to see. Thus, subtle but significant changes in fine visual detail in images relating to trending alterations in natural or urban landscapes may remain undetected. In the course of time, such changes may be the cause or the consequence of measurable human impact. Capturing such change in imaging data as early as possible can make critical information readily available to citizens, professionals and policymakers. This promotes change awareness, and facilitates early decision making for action. Here, we use unsupervised Artificial Intelligence (AI) that exploits principles of self-organized biological visual learning for the analysis of imaging time series. The quantization error in the output of a Self Organizing Map prototype is exploited as a computational metric of variability and change. Given the proven sensitivity of this neural network metric to the intensity and polarity of image pixel colour, it is shown to capture critical changes in urban landscapes. This is achieved here on imaging data for two regions of geographic interest in Las Vegas County, Nevada, USA. The SOM analysis is combined with the statistical analysis of demographic data revealing human impacts. These latter are significantly correlated with the structural change trends in the numerical data for the specific regions of interest. By correlating data relative to the impact of human activities with numerical data indicating structural evolution, human footprint related environmental changes can be predictably scaled.

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arXiv2023

Spatiotemporal modeling of grip forces captures proficiency in manual robot control

This paper builds on our previous work by exploiting Artificial Intelligence to predict individual grip force variability in manual robot control. Grip forces were recorded from various loci in the dominant and non dominant hands of individuals by means of wearable wireless sensor technology. Statistical analyses bring to the fore skill specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain inspired neural network model that uses the output metric of a Self Organizing Map with unsupervised winner take all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time t and reliably captures the differences between novice and expert performance in terms of grip force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip force monitoring in real time to permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot interaction in environmental contexts of high sensory uncertainty. Parsimonious Artificial Intelligence (AI) assistance will contribute to the outcome of new types of surgery, in particular single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single Incision Laparoscopic Surgery).

cs.RO↗

arXiv2023

The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Two universal functional principles of Adaptive Resonance Theory simulate the brain code of all biological learning and adaptive intelligence. Low level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom up activation and under the control of top down matching rules that integrate high level long term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are revisited in this paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg's pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.

q-bio.NC↗

arXiv2022

Grip force as a functional window to somatosensory cognition

Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the basis of our ability to act upon and to transform the physical world around us, to recognize objects on the basis of touch alone, and to grasp them with the right amount of force for lifting and manipulating them. Recent technology has permitted the wireless monitoring of grip force signals recorded from biosensors in the palm of the human hand to track and trace human grip forces deployed in cognitive tasks executed under conditions of variable sensory (visual, auditory) input. Non-invasive multi-finger grip force sensor technology can be exploited to explore functional interactions between somatosensory brain mechanisms and motor control, in particular during learning a novel and complex tasks where the planning and strategic execution of hand movements is essential. Under the light of a comprehensive overview of recent discoveries into the functional significance of human grip force variations, perspectives for future studies in cognition, in particular the cognitive control of strategic hand movements in robot-assisted precision tasks, are pointed out.

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arXiv2022

Information And Control: Insights from within the brain

The neural networks of the brain are capable of learning statistical input regularities on the basis of synaptic learning, functional integration into increasingly larger, interconnected neural assemblies, and self organization. This self organizing ability has implications for biologically inspired control structures in robotics. On the basis of signal input from vision, sound, smell, touch and proprioception, multisensory representations for action are generated on the basis of physically specified input from the environment. The somatosensory cortex is a brain hub that delivers a choice example of integration for multifunctional representation and control. All sensory information is in a first instance topologically represented in the biological brain, and thereafter integrated in somatosensory neural networks for multimodal and multifunctional control of complex behaviors. Multisignal input triggers interactions between visual, auditory, tactile, olfactive, and proprioceptive mechanisms, which cooperate or compete during learning, and contribute to the formation of integrated representations for action, reflection, and communication between the agent and the outside world. Interaction fuels complex behavioral strategy deployment and further learning for increasingly coherent representation of intrinsically ambiguous physical environments.

cs.RO↗

arXiv2022

Principles of perceptual grouping: implications for image-guided surgery

Gestalt theory has provided perceptual science with a conceptual framework which has inspired researchers ever since, taking the field of perceptual organization into the 21st century. This opinion article discusses the importance of rules of perceptual organization for the testing and design of visual interface technology. It is argued that major Gestalt principles, such as the law of good continuation or the principle of Praegnanz (suggested translation: salience), taken as examples here, are important to our understanding of visual image processing by a human observer. Perceptual integration of contrast information across collinear space, and the organization of objects in the 2D image plane into figure and ground are of a particular importance here. Visual interfaces for image-guided surgery illustrate the criticality of these two types of perceptual processes for reliable decision making and action. It is concluded that Gestalt theory continues to generate powerful concepts and insights for perceptual science placed within the context of major technological challenges of today.

cs.HC↗

arXiv2022

The contribution of local variations in hue or contrast to symmetry of things in a thing

Symmetry contributes to processes of perceptual organization in biological vision and influences the quality and time of goal directed decision making in animals and humans, as discussed in recent work on the examples of symmetry of things in a thing and bilateral shape symmetry. The present study was designed to show that selective chromatic variations in geometric shape configurations with mirror symmetry can be exploited to highlight functional properties of symmetry of things in a thing in human vision. The experimental procedure uses a psychophysical two alternative forced choice technique, where human observers have to decide as swiftly as possible whether two shapes presented simultaneously on a computer screen are symmetrical or not. The stimuli are computer generated 2D shape configurations consisting of multiple elements, with and without systematic variations in local color, color saturation, or achromatic contrast producing variations in symmetry of things in a thing. All stimulus pairs presented had perfect geometric mirror symmetry. The results show that varying the color of local shape elements selectively in multichromatic and monochromatic shapes significantly slows down perceptual response times, which are a direct measure of stimulus uncertainty. It is concluded that local variations in hue or contrast produce functionally meaningful variations in symmetry of things in thing, revealed here as a relevant perceptual variable in symmetry detection. Disturbance of the latter increases stimulus uncertainty and thereby affects the perceptual salience of mirror symmetry in the time course for goal relevant human decisions.

q-bio.NC↗

arXiv2022

From Biological Synapses to Intelligent Robots

This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival or task relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems. Keywords: Hebbian learning, synaptic plasticity, neural networks, self organization, brain, reinforcement, sensory processing, robot control

cs.RO↗

arXiv2022

Brain representation of perceptual stimuli at different levels of awareness

This article questions the widespread assumption that there are brain representations that will always remain unconscious in the sense of being inaccessible to individual awareness under any circumstances. This implies that some part of the knowledge generated by the brain is once and for always excluded from consciousness and, therefore, from being communicated to the outside world. This standpoint neglects the possibility that the human brain might have a capacity for generating metarepresentations of nonconscious knowledge contents at a given moment in time through context sensitive adaptive learning, and is somewhat difficult to reconcile with experimental findings showing that initially subliminal targets can be made available to awareness, or break through to supraliminal levels of processing, when they are embedded in an appropriate perceptual object context (relevance condition). Specific properties of neural network architectures, inspired by the functional organization of the primate cortex, are able to explain how a human brain could generate this kind of perceptual learning. Signals or knowledge processed outside awareness could be made available to awareness through adaptive resonance of bottom-up and top-down signal exchanges in massively parallel neural network architectures; in other words, on the basis of statistically significant signal matches in the domain of time and in the domain of memory content.

q-bio.NC↗

arXiv2022

Consciousness beyond neural fields: expanding the possibilities of what has not yet happened

In the field theories in physics, any particular region of the presumed space-time continuum and all interactions between elementary objects therein can be objectively measured and/or accounted for mathematically. Since this does not apply to any of the field theories, or any other neural theory, of consciousness, their explanatory power is limited. As discussed in detail herein, the matter is complicated further by the facts than any scientifically operational definition of consciousness is inevitably partial, and that the phenomenon has no spatial dimensionality. Under the light of insights from research on meditation and expanded consciousness, chronic pain syndrome, healthy ageing, and eudaimonic well-being, we may conceive consciousness as a source of potential energy that has no clearly defined spatial dimensionality, but can produce significant changes in others and in the world, observable in terms of changes in time. It is argued that consciousness may have evolved to enable the human species to generate such changes in order to cope with unprecedented and/or unpredictable adversity. Such coping could, ultimately, include the conscious planning of our own extinction when survival on the planet is no longer an acceptable option.

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arXiv2022

Seeing virtual while acting real: Visual display and strategy effects on the time and precision of eye-hand coordination

Effects of computer generated 2D and 3D views on the time and precision of bare-handed or tool-mediated eye-hand coordination were investigated in a pick-and-place-task with complete novices. All of them scored well above average in spatial perspective taking ability and performed the task with their dominant hand. Two groups of novices, four men and four women in each group, had to place a small object in a precise order on the centre of five targets on a Real-world Action Field (RAF), as swiftly as possible and as precisely as possible, using a tool or not (control). Each individual session consisted of four visual display conditions. The order of conditions was counterbalanced between individuals and sessions. Subjects looked at what their hands were doing 1) directly in front of them (natural top-down view) 2) in topdown 2D fisheye camera view 3) in top-down undistorted 2D view or 4) in 3D stereoscopic top-down view (head-mounted OCULUS DK 2). It was made sure that object movements in all image conditions matched the real-world movements in time and space. One group was looking at the 2D images with the monitor positioned sideways (sub-optimal); the other group was looking at the monitor placed straight ahead of them (near-optimal). All image viewing conditions had significantly detrimental effects on time (seconds) and precision (pixels) of task execution when compared with natural direct viewing.

cs.HC↗

arXiv2021

On Information Processing Limitations In Humans and Machines

Information theory is concerned with the study of transmission, processing, extraction, and utilization of information. In its most abstract form, information is conceived as a means of resolving uncertainty. Shannon and Weaver (1949) were among the first to develop a conceptual framework for information theory. One of the key assumptions of the model is that uncertainty increases linearly with the amount of complexity (in bit units) of information transmitted or generated. A whole body of data from the cognitive neurosciences has shown since that the time of human response or action increases in a similar fashion as a function of information complexity. This paper will discuss some of the implications of what is known about the limitations of human information processing for the development of reliable Artificial Intelligence. It is concluded that novel conceptual frameworks are needed to inspire future studies on this complex problem space.

cs.CY↗

arXiv2021

Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map

This study exploits previously demonstrated properties such as sensitivity to the spatial extent and the intensity of local image contrast of the quantization error in the output of a Self Organizing Map (SOM QE). Here, the SOM QE is applied to double color staining based cell viability data in 96 image simulations. The results show that the SOM QE consistently and in only a few seconds detects fine regular spatial increases in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increases or decreases in the percentage of theoretical cell viability below the critical threshold. Such small changes may carry clinical significance, but are almost impossible to detect by human vision. Moreover, we demonstrate a clear sensitivity of the SOM QE to differences in the relative physical luminance (Y) of the colors, which here translates into a RED GREEN color selectivity. Across differences in relative luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels. Further selective color contrast studies on simulations of biological imaging data will allow generating increasingly larger benchmark datasets and, ultimately, unravel the full potential of fast, economic, and unprecedentedly precise biological data analysis using the SOM QE.

q-bio.QM↗

arXiv2021

Making Sense of Complex Sensor Data Streams

This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non dominant hands of operators performing a robot assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image guided task performed in a real world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task specific characteristics of operator expertise in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is in this case here necessary to decipher the meaning of the full extent of intra and inter individual variance in the sensor data by using the appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio temporal grip force profiles permits detecting expertise specific differences between individual users. It is concluded that these two analytic strategies are complementary. They enable drawing meaning from thousands of biosensor data reflecting human grip performance and its evolution with training, while fully taking into account their considerable inter and intra individual variability.

cs.RO↗

arXiv2021

Surgical task expertise detected by a self-organizing neural network map

Individual grip force profiling of bimanual simulator task performance of experts and novices using a robotic control device designed for endoscopic surgery permits defining benchmark criteria that tell true expert task skills from the skills of novices or trainee surgeons. Grip force variability in a true expert and a complete novice executing a robot assisted surgical simulator task reveal statistically significant differences as a function of task expertise. Here we show that the skill specific differences in local grip forces are predicted by the output metric of a Self Organizing neural network Map (SOM) with a bio inspired functional architecture that maps the functional connectivity of somatosensory neural networks in the primate brain.

q-bio.NC↗

arXiv2021

Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map

Symmetry in biological and physical systems is a product of self organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry based feature extrac-tion or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self Organizing Map, the Quantization Error (SOM QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within or across the shapes in a given pair produce, as shown here, longer choice RT for yes responses relative to symmetry. These data are consistently mirrored by the variations in the SOM QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metrics proven selectivity to local contrast and color variations in large and highly complex image data.

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arXiv2021

Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real world applications.

cs.RO↗

arXiv2021

Wearable Sensors for Spatio-Temporal Grip Force Profiling

Wearable biosensor technology enables real-time, convenient, and continuous monitoring of users behavioral signals. Such include signals relative to body motion, body temperature, biological or biochemical markers, and individual grip forces, which are studied in this paper. A four step pick and drop image guided and robot assisted precision task has been designed for exploiting a wearable wireless sensor glove system. Individual spatio temporal grip forces are analyzed on the basis of thousands of individual sensor data, collected from different locations on the dominant and non-dominant hands of each of three users in ten successive task sessions. Statistical comparisons reveal specific differences between grip force profiles of the individual users as a function of task skill level (expertise) and time.

cs.RO↗