- Consciousness, Mental Health, Philosophy of Artificial Intelligence, Color Perception, Color (Philosophy), Philosophy of perception, and 8 moreArt and Perception, Visual perception, Psychophysics, Somatosensory Neuroscience, Challenges of the Digital World, Psychology, Biology, and Public Healthedit
- CNRS Directeur de Recherche/CNRS Research Directoredit
Meta-analyses of the pertaining literature have shown that General Anxiety Disorder (GAD) in youth and in particular students, is a prevalent syndrome in mental health issues identified in 2022, in the wake of the COVID-19 pandemic, by... more
Meta-analyses of the pertaining literature have shown that General Anxiety Disorder (GAD) in youth and in particular students, is a prevalent syndrome in mental health issues identified in 2022, in the wake of the COVID-19 pandemic, by the World Health Organization and other national organizations such as Santé Publique France. This study, performed between 2022 and 2023, offers a pinhole view on student anxiety in the post COVID-19 context at a French University. A sample population of 80 undergraduate medical students within the age range from 18 to 24 years was tested for GAD in a survey using an online version of the Hamilton Anxiety Scale (HAM-A). The total test scores indicate a prevalence of severe to very severe GAD in 36% of the sample population, which is consistent with results from studies on larger student populations in other countries. Further statistical analyses reveal a significantly higher number of psychological symptoms by comparison with somatic symptoms of GAD. Reasons why, under the light of the findings placed in the current society context, student anxiety needs to be addressed in terms of a larger society problem beyond the immediate consequences of the COVID-19 pandemic are discussed.
This bookchapter critically questions the claim that there would be possibility of emulating human consciousness and consciousness-dependent activity by Artificial Intelligence to create conscious artificial systems. The analysis is based... more
This bookchapter critically questions the claim that there would be possibility of emulating human consciousness and consciousness-dependent activity by Artificial Intelligence to create conscious artificial systems. The analysis is based on neurophysiological research and theory. In-depth scrutiny of the field and the prospects for converting neuroscience research into the type of algorithmic programs utilized in computer-based AI systems to create artificially conscious machines leads to conclude that such a conversion is unlikely to ever be possible because of the complexity of unconscious and conscious brain processing and their interaction. It is through the latter that the brain opens the doors to consciousness, a property of the human mind that no other living species has developed for reasons that are made clear in this chapter. As a consequence, many of the projected goals of AI will remain forever unrealizable. Although this work does not directly examine the question within a philosophy of mind framework by, for example, discussing why identifying consciousness with the activity of electronic circuits is first and foremost a category mistake in terms of scientific reasoning, the approach offered in the chapter is complementary to this standpoint, and illustrates various aspects of the problem under a monist from-brain-to-mind premise.
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When “hijacked” by compulsive behaviors that affect the reward and stress centers of the brain, functional changes in the dopamine circuitry occur as the consequence of pathological brain adaptation. As a brain correlate of mental health,... more
When “hijacked” by compulsive behaviors that affect the reward and stress centers of the brain, functional changes in the dopamine circuitry occur as the consequence of pathological brain adaptation. As a brain correlate of mental health, dopamine has a central functional role in
behavioral regulation from healthy reward-seeking to pathological adaptation to stress in response to adversity. This narrative review offers a spotlight view of the transition from healthy reward function, under the control of dopamine, to the progressive deregulation of this function in interactions with other brain centers and circuits, producing what may be called an anti-reward brain state. How such deregulation is linked to specific health-relevant behaviors is then explained in relation to pandemic-related adversities and the stresses they engendered. The long lockdown periods where people in social isolation had to rely on drink, food, and digital rewards via the internet may be seen as the major triggers of changes in motivation and reward-seeking behavior worldwide. The pathological adaptation of dopamine-mediated reward circuitry in the brain is discussed. It is argued that, when pushed by fate and circumstance into a physiological brain state of anti-reward, human
behavior changes and mental health is affected, depending on individual vulnerabilities. A unified conceptual account that places dopamine function at the centre of the current global mental health context is proposed.
Keywords: reward; dopamine; brain; addiction; stress; anhedonia; compulsive behavior; COVID-19
pandemic; mental health
behavioral regulation from healthy reward-seeking to pathological adaptation to stress in response to adversity. This narrative review offers a spotlight view of the transition from healthy reward function, under the control of dopamine, to the progressive deregulation of this function in interactions with other brain centers and circuits, producing what may be called an anti-reward brain state. How such deregulation is linked to specific health-relevant behaviors is then explained in relation to pandemic-related adversities and the stresses they engendered. The long lockdown periods where people in social isolation had to rely on drink, food, and digital rewards via the internet may be seen as the major triggers of changes in motivation and reward-seeking behavior worldwide. The pathological adaptation of dopamine-mediated reward circuitry in the brain is discussed. It is argued that, when pushed by fate and circumstance into a physiological brain state of anti-reward, human
behavior changes and mental health is affected, depending on individual vulnerabilities. A unified conceptual account that places dopamine function at the centre of the current global mental health context is proposed.
Keywords: reward; dopamine; brain; addiction; stress; anhedonia; compulsive behavior; COVID-19
pandemic; mental health
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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... more
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 aging, 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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Environmental studies, metabolic research, and state of the art research in neurobiology point towards the reduced amount of natural day and sunlight exposure of the developing child, as a consequence of increasingly long hours spent... more
Environmental studies, metabolic research, and state of the art research in neurobiology point towards the reduced amount of natural day and sunlight exposure of the developing child, as a consequence of increasingly long hours spent indoors online, as the single unifying source of a whole set of health risks identified worldwide, as is made clear in this review of currently available literature. Over exposure to digital environments, from abuse to addiction, now concerns even the youngest (ages 0 to 2) and triggers, as argued on the basis of clear examples herein, a chain of interdependent negative and potentially long-term metabolic changes. This leads to a deregulation of the serotonin and dopamine neurotransmitter pathways in the developing brain, currently associated with online activity abuse and/or internet addiction, and akin to that found in severe substance abuse syndromes. A general functional working model is proposed under the light of evidence brought to the forefront in this review.
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In 2020, theWorld Health Organization formally recognized addiction to digital technology (connected devices) as a worldwide problem, where excessive online activity and internet use lead to inability to manage time, energy, and attention... more
In 2020, theWorld Health Organization formally recognized addiction to digital technology (connected devices) as a worldwide problem, where excessive online activity and internet use lead to inability to manage time, energy, and attention during daytime and produce disturbed sleep
patterns or insomnia during nighttime. Recent studies have shown that the problem has increased in magnitude worldwide during the COVID-19 pandemic. The extent to which dysfunctional sleep is a consequence of altered motivation, memory function, mood, diet, and other lifestyle variables or results from excess of blue-light exposure when looking at digital device screens for long hours at day and night is one of many still unresolved questions. This article offers a narrative overview of some of the most recent literature on this topic. The analysis provided offers a conceptual basis for understanding digital addiction as one of the major reasons why people, and adolescents in particular, sleep less and less well in the digital age. It discusses definitions as well as mechanistic model accounts in context. Digital addiction is identified as functionally equivalent to all addictions, characterized by the compulsive, habitual, and uncontrolled use of digital devices and an excessively repeated engagement in a particular online behavior. Once the urge to be online has become uncontrollable, it is always accompanied by severe sleep loss, emotional distress, depression, and memory dysfunction. In extreme cases, it may lead to suicide. The syndrome has been linked to the known chronic effects of all drugs, producing disturbances in cellular and molecular mechanisms of the GABAergic and glutamatergic neurotransmitter systems. Dopamine and serotonin synaptic plasticity, essential for impulse control, memory, and sleep function, are measurably altered. The full spectrum of behavioral symptoms in digital addicts include eating disorders and withdrawal from outdoor and social life.
Evidence pointing towards dysfunctional melatonin and vitamin D metabolism in digital addicts should be taken into account for carving out perspectives for treatment. The conclusions offer a holistic account for digital addiction, where sleep deficit is one of the key factors.
patterns or insomnia during nighttime. Recent studies have shown that the problem has increased in magnitude worldwide during the COVID-19 pandemic. The extent to which dysfunctional sleep is a consequence of altered motivation, memory function, mood, diet, and other lifestyle variables or results from excess of blue-light exposure when looking at digital device screens for long hours at day and night is one of many still unresolved questions. This article offers a narrative overview of some of the most recent literature on this topic. The analysis provided offers a conceptual basis for understanding digital addiction as one of the major reasons why people, and adolescents in particular, sleep less and less well in the digital age. It discusses definitions as well as mechanistic model accounts in context. Digital addiction is identified as functionally equivalent to all addictions, characterized by the compulsive, habitual, and uncontrolled use of digital devices and an excessively repeated engagement in a particular online behavior. Once the urge to be online has become uncontrollable, it is always accompanied by severe sleep loss, emotional distress, depression, and memory dysfunction. In extreme cases, it may lead to suicide. The syndrome has been linked to the known chronic effects of all drugs, producing disturbances in cellular and molecular mechanisms of the GABAergic and glutamatergic neurotransmitter systems. Dopamine and serotonin synaptic plasticity, essential for impulse control, memory, and sleep function, are measurably altered. The full spectrum of behavioral symptoms in digital addicts include eating disorders and withdrawal from outdoor and social life.
Evidence pointing towards dysfunctional melatonin and vitamin D metabolism in digital addicts should be taken into account for carving out perspectives for treatment. The conclusions offer a holistic account for digital addiction, where sleep deficit is one of the key factors.
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Technological progress has brought about the emergence of machines that have the capacity to take human lives without human control. These represent an unprecedented threat to humankind. This paper starts from the example of chemical... more
Technological progress has brought about the emergence of machines that have the capacity to take human lives without human control. These represent an unprecedented threat to humankind. This paper starts from the example of chemical weapons, now banned worldwide by the Geneva protocol, to illustrate how technological development initially aimed at the benefit of humankind has, ultimately, produced what is now called the “Weaponization of Artificial Intelligence (AI)”. Autonomous Weapon Systems (AWS) fail the so-called discrimination principle, yet, the wider public is largely unaware of this problem. Given that ongoing scientific research on AWS, performed in the military sector, is generally not made available to the public domain, many of the viewpoints on this subject, expressed across dierent media, invoke common sense rather than scientific evidence. Yet, the implications of a potential weaponization of our work as scientists, especially in the field of AI, are reaching further than some may think. The potential consequences of a deployment of AWS for citizen stakeholders are incommensurable, and it is time to raise awareness in the public domain of the kind of potential threats identified, and to encourage legal policies ensuring that these threats will not materialize.
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Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental... more
Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher 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 re-visited in this concept paper 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. Keywords: multisensory perception; brain representation; contextual modulation; adaptive resonance; biological learning; self-organization; matching rules; winner-take-all principleThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
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 re-visited in this concept paper 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. Keywords: multisensory perception; brain representation; contextual modulation; adaptive resonance; biological learning; self-organization; matching rules; winner-take-all principleThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
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The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including... more
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of "strong" artificial intelligence in robotics are brought forward.
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This paper 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... more
This paper 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-relevant 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.
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-relevant 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.
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New technologies for monitoring grip forces during hand and finger movements in nonstandard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to... more
New technologies for monitoring grip forces during hand and finger movements in nonstandard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force
data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with 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 pap 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 a given moment in time and reliably captures the differences between novice and expert performance in terms of gripforce
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. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with 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 pap 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 a given moment in time and reliably captures the differences between novice and expert performance in terms of gripforce
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. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0).
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Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provides unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the... more
Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provides unprecedented functional insight into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world around us, to recognize them 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 cognitive task where the planning and strategic execution of hand movements is essential. Sensorial and cognitive processes underlying manual skills and/or hand-specific (dominant versus non-dominant hand) behaviors can be studied in a variety of contexts by probing selected measurement loci in the fingers and palm of the human hand. Thousands of sensor data recorded from multiple spatial locations can be approached statistically to breathe functional sense into the forces measured under specific task constraints. Grip force patterns in individual performance profiling may reveal the evolution of grip force control as a direct result of cognitive changes during task learning. Grip forces can be functionally mapped to from-global-to-local coding principles in brain networks governing somatosensory processes for motor control in cognitive tasks leading to a specific task expertise or skill. 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 and task relevant hand movements in complex real-world precision task, are pointed out.
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Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors... more
Symmetry in nature is a result of biological self-organization, driven by evolutionary processes. Detected by the visual systems of various species, from invertebrates to primates, symmetry determines survival relevant choice behaviors and supports adaptive function by reducing stimulus uncertainty. Symmetry also provides a major structural key to bio-inspired artificial vision and shape or movement simulations. In this psychophysical study, local variations in color covering the whole spectrum of visible wavelengths are compared to local variations in luminance contrast across an axis of geometrically perfect vertical mirror symmetry. The chromatic variations are found to delay response time to shape symmetry to a significantly larger extent than achromatic variations. This effect depends on the degree of variability, i.e., stimulus complexity. In both cases, we observe linear increase in response time as a function of local color variations across the vertical axis of symmetry.
These results are directly explained by the difference in computational complexity between the two major (magnocellular vs. parvocellular) visual pathways involved in filtering the contrast (luminance vs. luminance and color) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics
exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property.
These results are directly explained by the difference in computational complexity between the two major (magnocellular vs. parvocellular) visual pathways involved in filtering the contrast (luminance vs. luminance and color) of the shapes. It is concluded that color variability across an axis of symmetry proves detrimental to the rapid detection of symmetry, and, presumably, other structural shape regularities. The results have implications for vision-inspired artificial intelligence and robotics
exploiting functional principles of human vision for gesture and movement detection, or geometric shape simulation for recognition systems, where symmetry is often a critical property.
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Symmetry in biological and physical systems is a product of Self-Organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel... more
Symmetry in biological and physical systems is a product of Self-Organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction 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 Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (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 metric’s proven selectivity to local contrast and color variations in large and highly complex image data.
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 Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (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 metric’s proven selectivity to local contrast and color variations in large and highly complex image data.
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We exploit the previously demonstrated properties (i.e., sensitivity to spatial extent and intensity of local image contrasts) of the quantization error in the output of a Self-Organizing Map (SOM-QE). Here, the SOM-QE is applied to... more
We exploit the previously demonstrated properties (i.e., sensitivity to spatial extent and intensity of local image contrasts) 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 from this study show that, as expected, SOM-QE consistently and in only a few seconds detects fine regular spatial increase in relative amounts of RED or GREEN pixel staining across the test images, reflecting small, systematic increase or decrease in the percentage of theoretical cell viability below a critical threshold. While such small changes may carry clinical significance, they are almost impossible to detect by human vision. Moreover, here we demonstrate an expected sensitivity of the SOM-QE to differences in the relative physical luminance (Y) of the colors, which translates into a RED–GREEN color selectivity. Across differences in relative luminance, the SOM-QE exhibits consistently greater sensitivity to the smallest spatial increase in RED image pixels compared with smallest increases of the same spatial magnitude 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 predictive imaging data analysis based on SOM-QE.
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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... more
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.
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.
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The analyses draw from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip... more
The analyses draw from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand 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 necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of 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 allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable us to draw meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability.
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Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices... more
Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices enable convenient, continuous, and/or unobtrusive monitoring of a user's behavioral signals in real time. Examples include signals relative to body motion, body temperature, blood flow parameters and a variety of biological or biochemical markers and, as will be shown in this chapter here, individual grip force data that directly translate into spatiotemporal grip force profiles for different locations on the fingers and/or palm of the hand. Wearable sensor systems combine innovation in sensor design, electronics, data transmission, power management, and signal processing for statistical analysis, as will be further shown herein. The first section of this chapter will provide an overview of the current state of the art in grip force profiling to highlight important functional aspects to be considered. In the next section, the contribution of wearable sensor technology in the form of sensor glove systems for the real-time monitoring of surgical task skill evolution in novices training in a simulator task will be described on the basis of recent examples. In the discussion, advantages and limitations will be weighed against each other. Finally, although a lot of research is currently devoted to this area, many technological aspects still remain to be optimized, and new
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Victor Vasarely's (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a... more
Victor Vasarely's (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a large-scale pictorial plane. Current perception science explains such effects by invoking brain mechanisms for the processing of monocular (2D) depth cues. Here in this study, we illustrate and explain local effects of 2D color and contrast cues on the perceptual organization in terms of figure-ground assignments, i.e. which local surfaces are likely to be seen as "nearer" or "bigger" in the image plane. Paired configurations are embedded in a larger, structurally ambivalent pictorial context inspired by some of Vasarely's creations. The figure-ground effects these configurations produce reveal a significant correlation between perceptual solutions for "nearer" and "bigger" when other geometric depth cues are missing. In consistency with previous findings on similar, albeit simpler visual displays, a specific color may compete with luminance contrast to resolve the planar ambiguity of a complex pattern context at a critical point in the hierarchical resolution of figure-ground uncertainty. The potential role of color temperature in this process is brought forward here. Vasarely intuitively understood and successfully exploited the subtle context effects accounted for in this paper, well before empirical investigation had set out to study and explain them in terms of information processing by the visual brain.
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Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of... more
Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey's "detective work" principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person's product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward.
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Cellular and molecular imaging techniques and models have been developed to characterize single stages of viral proliferation after focal infection of cells in vitro. The fast and automatic classification of cell imaging data may prove... more
Cellular and molecular imaging techniques and models have been developed to characterize single stages of viral proliferation after focal infection of cells in vitro. The fast and automatic classification of cell imaging data may prove helpful prior to any further comparison of representative experimental data to mathematical models of viral propagation in host cells. Here, we use computer generated images drawn from a reproduction of an imaging model from a previously published study of experimentally obtained cell imaging data representing progressive viral particle proliferation in host cell monolayers. Inspired by experimental time-based imaging data, here in this study viral particle increase in time is simulated by a one-by-one increase, across images, in black or gray single pixels representing dead or partially infected cells, and hypothetical remission by a one-by-one increase in white pixels coding for living cells in the original image model. The image simulations are submitted to unsupervised learning by a Self-Organizing Map (SOM) and the Quantization Error in the SOM output (SOM-QE) is used for automatic classification of the image simulations as a function of the represented extent of viral particle proliferation or cell recovery. Unsupervised classification by SOM-QE of 160 model images, each with more than three million pixels, is shown to provide a statistically reliable, pixel precise, and fast classification model that outperforms human computer-assisted image classification by RGB image mean computation. The automatic classification procedure proposed here provides a powerful approach to understand finely tuned mechanisms in the infection and proliferation of virus in cell lines in vitro or other cells.
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Scientific studies have shown that non-conscious stimuli and representations influence information processing during conscious experience. In the light of such evidence, questions about potential functional links between non-conscious... more
Scientific studies have shown that non-conscious stimuli and representations influence information processing during conscious experience. In the light of such evidence, questions about potential functional links between non-conscious brain representations and conscious experience arise. This article discusses neural model capable of explaining how statistical learning mechanisms in dedicated resonant circuits could generate specific temporal activity traces of non-conscious representations in the brain. How reentrant signaling, top-down matching, and statistical coincidence of such activity traces may lead to the progressive consolidation of temporal patterns that constitute the neural signatures of conscious experience in networks extending across large distances beyond functionally specialized brain regions is then explained
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STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis ® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently... more
STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis ® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for 'expertise' need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable-driven subsystems , one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee (complete novice) during all the steps of surgical task execution in a simulator task (4-step-pick-and-drop). Analysis of grip-force profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master/slave system.
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Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data... more
Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam's razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.
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The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-takeall learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical... more
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-takeall learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE is akin to functional characteristics of a specific class of retinal ganglion cells (the so-called Y-cells) in the visual systems of the primate and the cat, the sensitivity of the QE surpasses the capacity limits of human visual detection. Here, the quantization error in the SOM is found to reliably signal changes in contrast or colour when contrast information is removed from or added to the image, but not when the amount and relative weight of contrast information is constant and only the local spatial position of contrast elements in the pattern changes. While the RGB Mean reflects coarser changes in colour or contrast well enough, the SOM-QE is shown to outperform the RGB Mean in the detection of single-pixel changes in images with up to five million pixels. This could have important implications in the context of unsupervised image learning and computational building block approaches to large sets of image data (big data), including deep learning blocks, and automatic detection of contrast change at the nanoscale in Transmission or Scanning Electron Micrographs (TEM, SEM), or at the subpixel level in multispectral and hyper-spectral imaging data.
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Although symmetry has been discussed in terms of a major law of perceptual organization since the early conceptual efforts of the Gestalt school (Wertheimer, Metzger, Koffka and others), the first quantitative measurements testing for... more
Although symmetry has been discussed in terms of a major law of perceptual organization since the early conceptual efforts of the Gestalt school (Wertheimer, Metzger, Koffka and others), the first quantitative measurements testing for effects of symmetry on processes of Gestalt formation have seen the day only recently. In this study, a psychophysical rating study and a "foreground"-"background" choice response time experiment were run with human observers to test for effects of bilateral symmetry on the perceived strength of figure-ground in triangular Kanizsa configurations. Displays with and without bilateral symmetry, identical physically-specified-to-total contour ratio, and constant local contrast intensity within and across conditions, but variable local contrast polarity and variable orientation in the plane, were presented in a random order to human observers. Configurations with bilateral symmetry produced significantly stronger figure-ground percepts reflected by greater subjective magnitudes and consistently higher percentages of "foreground" judgments accompanied by significantly shorter response times. These effects of symmetry depend neither on the orientation of the axis of symmetry, nor on the contrast polarity of the physical inducers. It is concluded that bilateral symmetry, irrespective of orientation, significantly contributes to the, largely sign-invariant, visual mechanisms of figure-ground segregation that determine the salience of figure-ground in perceptually ambiguous configurations.
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Pieron's and Chocholle's seminal psychophysical work predicts that human response time to information relative to visual contrast and/or sound frequency decreases when contrast intensity or sound frequency increases. The goal of this... more
Pieron's and Chocholle's seminal psychophysical work predicts that human response time to information relative to visual contrast and/or sound frequency decreases when contrast intensity or sound frequency increases. The goal of this study is to bring to the forefront the ability of individuals to use visual contrast intensity and sound frequency in combination for faster perceptual decisions of relative depth ("nearer") in planar (2D) object configurations based on physical variations in luminance contrast. Computer controlled images with two abstract patterns of varying contrast intensity, one on the left and one on the right, preceded or not by a pure tone of varying frequency, were shown to healthy young humans in controlled experimental sequences. Their task (two-alternative, forced-choice) was to decide as quickly as possible which of two patterns, the left or the right one, in a given image appeared to "stand out as if it were nearer" in terms of apparent (subjective) visual depth. The results showed that the combinations of varying relative visual contrast with sounds of varying frequency exploited here produced an additive effect on choice response times in terms of facilitation, where a stronger visual contrast combined with a higher sound frequency produced shorter forced-choice response times. This new effect is predicted by audiovisual probability summation.
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Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed-precision strategies by providing effective automatic... more
Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed-precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at the task. This may, as shown here, compromise the evolution of their precision scores, sometimes irreparably, if it is not controlled for as early as possible. Artificial intelligence could help make sure that a trainee reaches her/his optimal individual speed-accuracy trade-off by monitoring individual performance criteria, detecting critical trends at any given moment in time, and alerting the trainee as early as necessary when to slow down and focus on precision, or when to focus on getting faster. It is suggested that, for effective benchmarking, individual training statistics of novices are compared with the statistics of an expert surgeon. The speed-accuracy functions of novices trained in a large number of experimental sessions reveal differences in individual speed-precision strategies, and clarify why such strategies should be automatically detected and controlled for before further training on specific surgical task models, or clinical models, may be envisaged. How expert benchmark statistics may be exploited for automatic performance control is explained.
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In image-guided surgical tasks, the precision and timing of hand movements depend on the effectiveness of visual cues relative to specific target areas in the surgeon's peri-personal space. Two-dimensional (2D) image views of real-world... more
In image-guided surgical tasks, the precision and timing of hand movements depend on the effectiveness of visual cues relative to specific target areas in the surgeon's peri-personal space. Two-dimensional (2D) image views of real-world movements are known to negatively affect both constrained (with tool) and unconstrained (no tool) hand movements compared with direct action viewing. Task conditions where virtual 3D would generate and advantage for surgical eye-hand coordination are unclear. Here, we compared effects of 2D and 3D image views on the precision and timing of surgical hand movement trajectories in a simulator environment. Eight novices had to pick and place a small cube on target areas across different trajectory segments in the surgeon's peri-personal space, with the dominant hand, with and without a tool, under conditions of: (1) direct, (2) 2D fisheye camera and (3) virtual 3D viewing (head-mounted). Significant effects of the location of trajectories in the surgeon's peri-personal space on movement times and precision were found. Subjects were faster and more precise across specific target locations, depending on the viewing modality. The significant interactions between
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Colour information not only helps sustain the survival of animal species by guiding sexual selection and foraging behaviour but also is an important factor in the cultural and technological development of our own species. This is... more
Colour information not only helps sustain the survival of animal species by guiding sexual selection and foraging behaviour but also is an important factor in the cultural and technological development of our own species. This is illustrated by examples from the visual arts and from state-of-the-art imaging technology, where the strategic use of colour has become a powerful tool for guiding the planning and execution of interventional procedures. The functional role of colour information in terms of its potential benefits to behavioural success across the species is addressed in the introduction here to clarify why colour perception may have evolved to generate behavioural success. It is argued that evolutionary and environmental pressures influence not only colour trait production in the different species but also their ability to process and exploit colour information for goal-specific purposes. We then leap straight to the human primate with insight from current research on the facilitating role of colour cues on performance training with precision technology for image-guided surgical planning and intervention. It is shown that local colour cues in two-dimensional images generated by a surgical fisheye camera help individuals become more precise rapidly across a limited number of trial sets in simulator training for specific manual gestures with a tool. This facilitating effect of a local colour cue on performance evolution in a video-controlled simulator (pick-and-place) task can be explained in terms of colour-based figure-ground segregation facilitating attention to local image parts when more than two layers of subjective surface depth are present, as in all natural and surgical images.
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Effects of different visual displays 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... more
Effects of different visual displays 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 Realworld 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 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. More importantly, we find significant trade-offs between time and precision between and within groups, and significant interactions between viewing conditions and manipulation conditions. The results shed new light on controversial findings relative to visual display effects on eye-hand coordination, and lead to conclude that differences in camera systems and adaptive strategies of novices are likely to explain these.
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Radiologists use time-series of medical images to monitor the progression of a patient's conditions. They compare information gleaned from sequences of images to gain insight on progression or remission of the lesions, thus evaluating the... more
Radiologists use time-series of medical images to monitor the progression of a patient's conditions. They compare information gleaned from sequences of images to gain insight on progression or remission of the lesions, thus evaluating the progress of a patient's condition or response to therapy. Visual methods of determining differences between one series of images to another can be subjective or fail to detect very small differences. We propose the use of quantization errors obtained from self-organizing maps (SOM) for image content analysis. We tested this technique with MRI images to which we progressively added synthetic lesions. We used a global approach that considers changes on the entire image as opposed to changes in segmented lesion regions only. We claim that this approach does not suffer from the limitations imposed by segmentation, which may compromise the results. Results show that the Quantization Error increases with the increase in lesions in image contents. The results are consistent with previous studies using alternative approaches. We then compare the detection ability of our method to that of human novice observers having to detect very small
local differences in random-dot images. The quantization errors of the SOM outputs compared with correct positive rates, after subtraction of false positive rates (“guesses”), increased noticeably and consistently with
small increases in local dot size that were not detectable by humans. We conclude that our method detects very small changes in complex images and suggest that it could be implemented to assist human operators in image-based decision making.
local differences in random-dot images. The quantization errors of the SOM outputs compared with correct positive rates, after subtraction of false positive rates (“guesses”), increased noticeably and consistently with
small increases in local dot size that were not detectable by humans. We conclude that our method detects very small changes in complex images and suggest that it could be implemented to assist human operators in image-based decision making.
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The 18th-century Scottish 'common sense' philosopher Thomas Reid argued that perception can be distinguished on several dimensions from other categories of experience, such as sensation, illusion, hallucination, mental images, and what he... more
The 18th-century Scottish 'common sense' philosopher Thomas Reid argued that perception can be distinguished on several dimensions from other categories of experience, such as sensation, illusion, hallucination, mental images, and what he called 'fancy.' We extend his approach to eleven mental categories, and discuss how these distinctions, often ignored in the empirical literature, bear on current research. We also score each category on five properties (ones abstracted from Reid) to form a 5 × 11 matrix, and thus can generate statistical measures of their mutual dependencies, a procedure that may have general interest as illustrating what we can call 'computational philosophy.'
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Perceptual neuroscience has identified mechanisms of perceptual grouping which account for the ways in which visual sensitivity to order and structural regularities in the environment expresses itself in behavior and in the brain.The need... more
Perceptual neuroscience has identified mechanisms of perceptual grouping which account for the ways in which visual sensitivity to order and structural regularities in the environment expresses itself in behavior
and in the brain.The need to actively construct ordered representations of objects and their trajectories in depth is mandated as soon as visual signals reach the retina, given the occlusion of such signals by retinal veins or blur. Multiple stage of neural processing transform fragmented signals into visual key representations of 3D scenes that can be used to control behaviors effectively. Since behvioral success, i.e. our survival, depends on our ability to pick up order in the physical world, and since we conceive the latter as an ordered one, perception must somehow be sensitive to (i.e. able to sense) structural regularities in the environment.
and in the brain.The need to actively construct ordered representations of objects and their trajectories in depth is mandated as soon as visual signals reach the retina, given the occlusion of such signals by retinal veins or blur. Multiple stage of neural processing transform fragmented signals into visual key representations of 3D scenes that can be used to control behaviors effectively. Since behvioral success, i.e. our survival, depends on our ability to pick up order in the physical world, and since we conceive the latter as an ordered one, perception must somehow be sensitive to (i.e. able to sense) structural regularities in the environment.
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Background: The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what... more
Background: The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning. Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor. Previous research has shown slower task execution times and lower performance scores under image-guidance compared with situations of direct action viewing. The cognitive processes for overcoming this drawback by training are not yet understood. Methods: We investigated the effects of training on the time and precision of direct view versus image guided object positioning on targets of a Real-world Action Field (RAF). Two men and two women had to learn to perform the task as swiftly and as precisely as possible with their dominant hand, using a tool or not and wearing a glove or not. Individuals were trained in sessions of mixed trial blocks with no feedback. Results: As predicted, image-guidance produced significantly slower times and lesser precision in all trainees and sessions compared with direct viewing. With training, all trainees get faster in all conditions, but only one of them gets reliably more precise in the image-guided conditions. Speed-accuracy trade-offs in the individual performance data show that the highest precision scores and steepest learning curve, for time and precision, were produced by the slowest starter. Fast starters produced consistently poorer precision scores in all sessions. The fastest starter showed no sign of stable precision learning, even after extended training. Conclusions: Performance evolution towards optimal precision is compromised when novices start by going as fast as they can. The findings have direct implications for individual skill monitoring in training programmes for image-guided technology applications with human operators.
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The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the... more
The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which
figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies on polarity-specific and polarity-invariant properties are reveiwed here. The FACADE and 3D LAMINART models are fit to explain all these data.
figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies on polarity-specific and polarity-invariant properties are reveiwed here. The FACADE and 3D LAMINART models are fit to explain all these data.
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Following the pioneering studies of the receptive field (RF), the RF concept gained further significance for visual perception by the discovery of input effects from beyond the classical RF. These studies demonstrated that neuronal... more
Following the pioneering studies of the receptive field (RF), the RF concept gained further significance for visual perception by the discovery of input effects from beyond the classical RF. These studies demonstrated that neuronal responses could be modulated by stimuli outside their RFs, consistent with the perception of induced brightness, color, orientation, and motion. Lesion scotomata are similarly modulated perceptually from the surround by RFs that have migrated from the interior to the outer edge of the scotoma and in this way provide filling-in of the void. Large RFs are advantageous to this task. In higher visual areas, such as the middle temporal and inferotemporal lobe, RFs increase in size and lose most of their retinotopic organization while encoding increasingly complex features. Whereas lowerlevel RFs mediate perceptual filling-in, contour integration, and figure-ground segregation, RFs at higher levels serve the perception of grouping by common fate, biological motion, and other biologically relevant stimuli, such as faces. Studies in alert monkeys while freely viewing natural scenes showed that classical and nonclassical RFs cooperate in forming representations of the visual world. Today, our understanding of the mechanisms underlying the RF is undergoing a quantum leap. What had started out as a hierarchical feedforward concept for simple stimuli, such as spots, lines, and bars, now refers to mechanisms involving ascending, descending, and lateral signal flow. By extension of the bottom-up paradigm, RFs are nowadays understood as adaptive processors, enabling the predictive coding of complex scenes. Top-down effects guiding attention and tuned to task-relevant information complement the bottom-up analysis.
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Evolution and geometry generate complexity in similar ways. Evolution drives natural selection while geometry may capture the logic of this selection and express it visually, in terms of specific generic properties representing some kind... more
Evolution and geometry generate complexity in similar ways. Evolution drives natural selection while geometry may capture the logic of this selection and express it visually, in terms of specific generic properties representing some kind of advantage. Geometry is ideally suited for expressing the logic of evolutionary selection for symmetry, which is found in the shape curves of vein systems and other natural objects such as leaves, cell membranes, or tunnel systems built by ants. The topology and geometry of symmetry is controlled by numerical parameters, which act in analogy with a biological organism's DNA. The introductory part of this paper reviews findings from experiments illustrating the critical role of two-dimensional (2D) design parameters, affine geometry and shape symmetry for visual or tactile shape sensation and perception-based decision making in populations of experts and non-experts. It will be shown that 2D fractal symmetry, referred to herein as the "symmetry of things in a thing", results from principles very similar to those of affine projection. Results from experiments on aesthetic and visual preference judgments in response to 2D fractal trees with varying degrees of asymmetry are presented. In a first experiment (psychophysical scaling procedure), non-expert observers had to rate (on a scale from 0 to 10) the perceived beauty of a random series of 2D fractal trees with varying degrees of fractal symmetry. In a second experiment (two-alternative forced choice procedure), they had to express their preference for one of two shapes from the series. The shape pairs were presented successively in random order. Results show that the smallest possible fractal deviation from "symmetry of things in a thing" significantly reduces the perceived attractiveness of such shapes. The potential of future studies where different levels of complexity of fractal patterns are weighed against different degrees of symmetry is pointed out in the conclusion.
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The laws and principles which predict how perceptual structure and object qualities can be extracted from the most elementary visual signals were discovered by the Gestalt psychologists (e.g., Wertheimer,1923; Metzger,1930, translated and... more
The laws and principles which predict how perceptual structure and object qualities can be extracted from the most elementary visual signals were discovered by the Gestalt psychologists (e.g., Wertheimer,1923; Metzger,1930, translated and re-edited by Spillmann in 2009 and 2012, respectively). Their seminal work has inspired visual science ever since, and it has led to exciting discoveries which have confirmed the Gestalt idea that the human brain would have the capacity of selecting and combining the most relevant local visual signals to generate holistic output representations of structure , shape, and meaning for decision making and action. Perrceptual grouping enables the correct estimation of relative position, object trajectories, and distances from and between objects represented in the 2D plane.The Gestalt laws and principles were initially aimed at answering the single all-encompassing question: “Why does the world look the way it does?” They have subsequently been made operational in psychophysical studies aimed at deepening our insight into the ways in which specific characteristics and qualities of visual configuration determine perceptual organization and behavioral success at various levels of processing. Perceptual organization directly determines the human ability to assess(1)which parts of an image belong together to form a unified visual object or shape, and (2) which parts should be nearer and which further away from the observer if the represented objects were seen in the real world. This concept paper argues that the Gestalt principle of Prägnanz and the Gestalt law of good continuation in particular address specific problems of perceptual organization with critical implications for visual interface design. The case of perceptually augmented image guided surgery platforms is discussed as a particularly relevant example of such.
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Planar geometry was exploited for the computation of symmetric visual curves in the image plane, with consistent variations in local parameters such as sagitta, chordlength, and the curves' height-to-width ratio, an indicator of the... more
Planar geometry was exploited for the computation of symmetric visual curves in the image plane, with consistent variations in local parameters such as sagitta, chordlength, and the curves' height-to-width ratio, an indicator of the visual area covered by the curve, also called aspect ratio. Image representations of single curves (no local image context) were presented to human observers to measure their visual sensation of curvature magnitude elicited by a given curve. Nonlinear regression analysis was performed on both the individual and the average data using two types of model: (1) a power function where (sensation) tends towards infinity as a function of (stimulus input), most frequently used to model sensory scaling data for sensory continua, and (2) an "exponential rise to maximum" function, which converges towards an asymptotically stable level of as a function of. Both models provide satisfactory fits to subjective curvature magnitude as a function of the height-to-width ratio of single curves. The findings are consistent with an in-built sensitivity of the human visual system to local curve geometry, a potentially essential ground condition for the perception of concave and convex objects in the real world.
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Generic properties of curvature representations formed on the basis of vision and touch were examined as a function of mathematical properties of curved objects. Virtual representations of the curves were shown on a computer screen for... more
Generic properties of curvature representations formed on the basis of vision and touch were examined as a function of mathematical properties of curved objects. Virtual representations of the curves were shown on a computer screen for visual scaling by sighted observers (experiment 1). Their physical counterparts were placed in the two hands of blindfolded and congenitally blind observers for tactile scaling. The psychophysical data show that curvature representations in congenitally blind individuals, who never had any visual experience, and in sighted observers, who rely on vision most of the time, are statistically linked to the same mathematical properties of the curves. The perceived magnitude of object curvature, sensed through either vision or touch, is related by a mathematical power law, with similar exponents for the two sensory modalities, to the aspect ratio of the curves, a scale invariant geometric property. This finding supports biologically motivated models of senso...
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The laws which predict how the perceptual quality of figure-ground can be extracted from the most elementary visual signals were discovered by the Gestaltists, and form an essential part of their movement (see especially Metzger,1930, and... more
The laws which predict how the perceptual quality of figure-ground can be
extracted from the most elementary visual signals were discovered by the
Gestaltists, and form an essential part of their movement (see especially Metzger,1930, and Wertheimer, 1923 translated and re-edited by Lothar Spillmann, 2009 and 2012, respectively). Distinguishing figure from ground is a prerequisite for perception of both form and space (the relative positions, trajectories, and distances of objects in the visual field). This book chapter focuses on principles of Gestalt psychology and the key issues which surround them, providing an up-to-date survey of some of the most interesting and highly debated topics in visual neuroscience, perception and object recognition. The book is divided into three main parts: Gestalt and perceptual organisation, attention aftereffects and illusions, and color vision and art perception, which includes our chapter here.
extracted from the most elementary visual signals were discovered by the
Gestaltists, and form an essential part of their movement (see especially Metzger,1930, and Wertheimer, 1923 translated and re-edited by Lothar Spillmann, 2009 and 2012, respectively). Distinguishing figure from ground is a prerequisite for perception of both form and space (the relative positions, trajectories, and distances of objects in the visual field). This book chapter focuses on principles of Gestalt psychology and the key issues which surround them, providing an up-to-date survey of some of the most interesting and highly debated topics in visual neuroscience, perception and object recognition. The book is divided into three main parts: Gestalt and perceptual organisation, attention aftereffects and illusions, and color vision and art perception, which includes our chapter here.
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Poorly saturated colors are closer to a pure gray than strongly saturated ones and, therefore, appear less "colorful." Color saturation is effectively manipulated in the visual arts for balancing conflicting sensations and moods and for... more
Poorly saturated colors are closer to a pure gray than strongly saturated ones and, therefore, appear less "colorful." Color saturation is effectively manipulated in the visual arts for balancing conflicting sensations and moods and for inducing the perception of relative distance in the pictorial plane. While perceptual science has proven quite clearly that the luminance contrast of any hue acts as a self-sufficient cue to relative depth in visual images, the role of color saturation in such figure-ground organization has remained unclear. We presented configurations of colored inducers on gray "test" backgrounds to human observers. Luminance and saturation of the inducers was uniform on each trial, but varied across trials. We ran two separate experimental tasks. In the relative background brightness task, perceptual judgments indicated whether the apparent brightness of the gray test background contrasted with, assimilated to, or appeared equal (no effect) to that of a comparison background with the same luminance contrast. Contrast polarity and its interaction with color saturation affected response proportions for contrast, assimilation and no effect. In the figure-ground task, perceptual judgments indicated whether the inducers appeared to lie in front of, behind, or in the same depth with the background. Strongly saturated inducers produced significantly larger proportions of foreground effects indicating that these inducers stand out as figure against the background. Weakly saturated inducers produced significantly larger proportions of background effects, indicating that these inducers are perceived as lying behind the backgrounds. We infer that color saturation modulates figure-ground organization, both directly by determining relative inducer depth, and indirectly, and in interaction with contrast polarity, by affecting apparent background brightness. The results point toward a hitherto undocumented functional role of color saturation in the genesis of form, and in particular figure-ground percepts in the absence of chromatostereopsis.
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We show that true colors as defined by Chevreul (1839) produce unsuspected simultaneous brightness induction effects on their immediate grey backgrounds when these are placed on a darker (black) general background surrounding two... more
We show that true colors as defined by Chevreul (1839) produce unsuspected simultaneous brightness induction effects on their immediate grey backgrounds when these are placed on a darker (black) general background surrounding two spatially separated configurations. Assimilation and apparent contrast may occur in one and the same stimulus display. We examined the possible link between these effects and the perceived depth of the color patterns which induce them as a function of their luminance contrast. Patterns of square-shaped inducers of a single color (red, green, blue, yellow, or grey) were placed on background fields of a lighter and a darker grey, presented on a darker screen. Inducers were always darker on one side of the display and brighter on the other in a given trial. The intensity of the grey backgrounds varied between trials only. This permitted generating four inducer luminance contrasts, presented in random order, for each color. Background fields were either spatially separated or consisted of a single grey field on the black screen. Experiments were run under three environmental conditions: dark-adaptation, daylight, and rod-saturation after exposure to bright light. In a first task, we measured probabilities of contrast, assimilation, and no effect in a three-alternative forced-choice procedure (background appears brighter on the 'left', on the 'right' or the 'same'). Visual adaptation and inducer contrast had no significant influence on the induction effects produced by colored inducers. Achromatic inducers produced significantly stronger contrast effects after dark-adaptation, and significantly stronger assimilation in daylight conditions. Grouping two backgrounds into a single one was found to significantly decrease probabilities of apparent contrast. Under the same conditions, we measured probabilities of the inducers to be perceived as nearer to the observer (inducers appear nearer on 'left', on 'right' or the 'same'). These, as predicted by Chevreul's law of contrast, were determined by the luminance contrast of the inducers only, with significantly higher probabilities of brighter inducers to be seen as nearer, and a marked asymmetry between effects produced by inducers of opposite sign. Implications of these findings for theories which attempt to link simultaneous induction effects to the relative depth of object surfaces in the visual field are discussed.
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In 1609 an optical instrument consisting of two lenses mounted at the extremities of a long tube, which permitted seeing things far up in the sky so clearly that they appeared as if they were right in front of the observer, was delivered... more
In 1609 an optical instrument consisting of two lenses mounted at the extremities of a long tube, which permitted seeing things far up in the sky so clearly that they appeared as if they were right in front of the observer, was delivered to the city of Venice. Although this strange new object was much admired by the Venetian socialites, essentially for the beauty of its design, nobody seemed to see much more in it, or would have guessed what was going to happen. One man saw the object’s potential, took it,
and set it to work. He began observing the moon and the stars through it for long hours. The man’s name was Galileo, a professor of mathematics from Padua who was about to change the course of history and, in the process, was to become a heretic in the eyes of the Church. Through that optical instrument, the telescope, Galileo would gain knowledge that was to shatter the established vision of the cosmos and give way to a new world order, one where man was to occupy a much more peripheral
place.
and set it to work. He began observing the moon and the stars through it for long hours. The man’s name was Galileo, a professor of mathematics from Padua who was about to change the course of history and, in the process, was to become a heretic in the eyes of the Church. Through that optical instrument, the telescope, Galileo would gain knowledge that was to shatter the established vision of the cosmos and give way to a new world order, one where man was to occupy a much more peripheral
place.
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The design of tools and procedures for the responsible and effective management of risks to humans and their environment is an important topic in modern environmental engineering. This article places the ethical ground clauses of a... more
The design of tools and procedures for the responsible and effective management of risks to humans and their environment is an important topic in modern environmental engineering. This article places the ethical ground clauses of a communication contract in the particular context of early hazard warnings. How respecting ethical ground clauses of communication may help avoid that the short-term economic interests of a few are placed before the long-term interests of society as a whole is explained on the basis of examples from disaster case studies. The need for rules which ensure that relevant information is effectively transmitted, received, and taken into account promptly is highlighted. Why successfully implementing such rules involves the individual responsibility of all stakeholders, from witnesses or victims to scientific experts and policy makers, is made clear. The ethical ground clauses of the communication contract introduced here provide universal rules for responsible communication, defined in terms of general guidelines for sincere, transparent, prompt, and cooperative information sharing, in particular in risk management. Earlier work has shown that implementing such a communication contract in corporate decision making helps promote stakeholder responsibility awareness, and triggers a learning process for initiating and fostering individual and collective behavior that will ultimately lead to responsible decisions and actions. These latter are the prerequisite for mitigating the potentially disastrous consequences of non-action in response to early warnings, when relevant scientific data and/or expert knowledge are not adequately taken into account because of faulty communication, identified as the major cause of delayed action in numerous case studies. Limitations of the communication contract theory are pointed out.