scholarly journals The concept of (depth) cues: An exemplification of homuncular language in vision science

2018 ◽  
Vol 29 (1) ◽  
pp. 66-86
Author(s):  
Robert Pagel
Keyword(s):  
Cognitive Science ◽  
Processing System ◽  
Computational Approach ◽  
Vision Science ◽  
Depth Cues ◽  
Logical Flaw ◽  
Depth Cue ◽  
Mereological Fallacy ◽  
The Brain ◽  
Theoretical Foundations

The term “depth cue” is fundamental to and widely used in vision science. However, despite the prevalence and importance of that concept, there is virtually no study on its theoretical foundations and coherence. This article aims at filling that gap by investigating both its historical development and its current use within the predominant computational approach to vision. Against the backdrop of Wittgenstein’s therapeutic approach to philosophy, it is shown that both traditional and current characterizations of depth cues suffer from a serious logical flaw known as “homunculus” or “mereological fallacy.” It is suggested that the problem of homuncular language impedes critical thinking and theorizing in vision science since it obscures the matters at issue by disguising explanatorily empty expressions as explanatory hypotheses. Furthermore, it is argued that homuncular language is not confined to the concept of depth cues but typical of current cognitive science in general since it is linked to its most fundamental assumption of the brain being an information processing system. In conclusion, resulting implications for cognitive science and cognitive scientists are considered.

scholarly journals Neurophysiology of Ethnicity (based on foreign literature)

2017 ◽  
Vol 8 (4) ◽  
pp. 43-54
Author(s):  
E.A. Varshaver
Keyword(s):  
Cognitive Science ◽  
Anterior Cingulate Cortex ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Fusiform Face Area ◽  
Foreign Literature ◽  
Face Area ◽  
The Brain

This article contains a review of research in the realm of neurophysiology of ethnicity. According to this body of research, there are zones of the brain which get active in response to demonstration of ethnic stimuli. Among these zones are amygdala, anterior cingulate cortex, fusiform face area and others. The article describes the research focused on each of these zones, discusses their weaknesses and projects further research on the crossroads of neurophysiology, cognitive science, psychology and sociology.

scholarly journals Distributed Cognition, Neuroprostheses and their Implications to Non-Physicalist Theories of Mind

1970 ◽  
Vol 26 (1) ◽  
pp. 123-142
Author(s):  
Jean Gové
Keyword(s):  
Cognitive Functioning ◽  
Cognitive Science ◽  
Distributed Cognition ◽  
Common Assumption ◽  
Theories Of Mind ◽  
Organic Brain ◽  
The Common ◽  
The Brain

This paper investigates the notion of ‘distributed cognition’ – the idea that entities external to one’s organic brain participate in one’s overall cognitive functioning – and the challenges it poses. Related to this is also a consideration of the ever-increasing ways in which neuroprostheses replace and functionally replicate organic parts of the brain. However, the literature surrounding such issues has tended to take an almost exclusively physicalist approach. The common assumption is that, given that non- physicalist theories (dualism, hylomorphism) postulate some form of immaterial ‘soul’, then they are immune from the challenges that these advances in cognitive science pose. The first aim of this paper, therefore, is to argue that this is not the case. The second aim of this paper is to attempt to elucidate a route available for the non- physicalist that will allow them to accept the notion of distributed cognition. By appealing to an Aristotelian framework, I propose that the non-physicalist can accept the notion of distributed cognition by appeal to the notion of ‘unitary life’ which I introduce as well as Aristotle’s dichotomy between active and passive mind.

scholarly journals New Technologies of Motor Control and Rehabilitation

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Albertas Skurvydas
Keyword(s):  
Motor Control ◽  
New Technologies ◽  
Modern Science ◽  
Computational Approach ◽  
Neural Cells ◽  
Motor Rehabilitation ◽  
Principal Mechanism ◽  
Brain Stroke ◽  
Computational Paradigm ◽  
The Brain

Modern paradigms of motor control and rehabilitation are analyzed in the paper. Two main paradigms, i. e. computational approach and dynamical system approach are engaged in rivalry in motor control and learning research at present. From the standpoint of computational paradigm the principal mechanism of motor control and learning consists in the ability of the brain “to calculate” (acting as some kind of biological computer). According to the paradigm of dynamical systems the mechanism of motor control is time dependent. In other words, it can be different each time. The main principles of motor control and properties of movements are given considerable attention in the paper. Besides, modern methods of motor rehabilitation after stroke are emphasized in the paper. Fitting of neuroprosthesis and restoration of damaged neural cells are significant maiden steps in modern science. The scientists are engaged in search for: a) constraining such mechanism prosthesis that would submit to the efforts of human will and b) restoring neural cells damaged because of the brain stroke suffered.Keywords: motor control, rehabilitation, stroke.

The myth of visual depth cues IV: Shape-from-shading

2021 ◽  
Author(s):  
Lydia Maniatis
Keyword(s):  
Visual Experience ◽  
Shape From Shading ◽  
Visual Depth ◽  
Depth Cues ◽  
Depth Cue ◽  
Physical Fact

The popular idea that “shading” is a shape and depth “cue” is the result of a failure to appreciate that neither shading as a physical fact nor shading as a perceptual fact can serve to explain the process leading to visual experience, because the description “shading” does not apply to the proximal stimulation, where this process begins. Both perceived shape and perceived illumination are products of figural constraints.

Information-based Analysis of the Coupling between Dynamic Visual Stimuli, Eye Movements, and Brain Signals

2021 ◽  
pp. 2150048
Author(s):  
Hamidreza Namazi ◽  
Avinash Menon ◽  
Ondrej Krejcar
Keyword(s):  
Eye Movements ◽  
Moving Objects ◽  
Brain Activity ◽  
Visual Stimuli ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Vision Science ◽  
Brain Signals ◽  
Information Contents ◽  
The Brain

Our eyes are always in search of exploring our surrounding environment. The brain controls our eyes’ activities through the nervous system. Hence, analyzing the correlation between the activities of the eyes and brain is an important area of research in vision science. This paper evaluates the coupling between the reactions of the eyes and the brain in response to different moving visual stimuli. Since both eye movements and EEG signals (as the indicator of brain activity) contain information, we employed Shannon entropy to decode the coupling between them. Ten subjects looked at four moving objects (dynamic visual stimuli) with different information contents while we recorded their EEG signals and eye movements. The results demonstrated that the changes in the information contents of eye movements and EEG signals are strongly correlated ([Formula: see text]), which indicates a strong correlation between brain and eye activities. This analysis could be extended to evaluate the correlation between the activities of other organs versus the brain.

On Cognitive Computing

2012 ◽  
pp. 83-97
Author(s):  
Yingxu Wang
Keyword(s):  
Computational Intelligence ◽  
Autonomic Computing ◽  
Theoretical Framework ◽  
Autonomous Agent ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Intelligent Computing ◽  
Agent Systems ◽  
The Brain ◽  
Theoretical Foundations

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.

Vision

2018 ◽  
Author(s):  
Nico Orlandi
Keyword(s):  
Visual Perception ◽  
Cognitive Science ◽  
Three Dimensional ◽  
Psychological State ◽  
Psychological Process ◽  
Two Dimensional ◽  
Vision Science ◽  
Psychological Theories ◽  
Black Surface ◽  
Obvious Manner

Why do things look to us as they do? This question, formulated by psychologist Kurt Koffka, identifies the main problematic of vision science. Consider looking at a black cat. We tend to see both the cat and its colour as the same at different times. Despite the ease with which this perception occurs, the process by which we perceive is fairly complex. The initial stimulation that gives rise to seeing, consists in a pattern of light that projects on the retina – a light-sensitive layer of the eye. The so-called ‘retinal image’ is a two-dimensional projection that does not correspond in any obvious manner to the way things look. It is not three-dimensional, coloured and shaped in a similar fashion to the objects of our experience. Indeed the light projected from objects is not just different from what we see, it is also both continuously changing and ambiguous. Because the cat moves around, the light it reflects changes from moment to moment. The cat’s projection on the retina correspondingly changes in size. We do not, however, see the cat as changing in size. We tend to see it as size-constant and uniformly coloured through time. How do we explain this constancy? Along similar lines, the cat’s white paws cause on the retina a patch of light that differs in intensity from the rest. This patch could also be caused by a change in illumination. A black surface illuminated very brightly can look like a white surface illuminated very dimly. This means that the light hitting the retina from the paws is underdetermined – it does not uniquely specify what is present. But, again, we tend to see the paws as consistently white. We do not see them as shifting from being white to being black, but illuminated brightly. How do we explain this stability? A central aim of theories of vision is to answer these questions. The science that attempts to address these queries is interdisciplinary. Traditionally, philosophical theories of vision have influenced psychological theories and vice versa. The collaboration between these disciplines eventually developed into what is now known as cognitive science. Cognitive science includes – in addition to philosophy and psychology – computer science, linguistics and neuroscience. Cognitive scientists aim primarily to understand the process by which we see. Philosophers are interested in this topic particularly as it connects to understanding the nature of our acquaintance with reality. Theories of vision differ along many dimensions. Giving a full survey is not possible in this entry. One useful difference is whether a theory presumes that visual perception involves a psychological process. Psychological theories of vision hold that in achieving perception – which is itself a psychological state – the organism uses other psychological material. Opponents of psychological theories prefer to make reference to physiological, mechanical and neurophysiological explanations.

Could Emotions Be Modelled through Information Processing?

2015 ◽  
pp. 63-82
Author(s):  
Ebrahim Oshni Alvandi
Keyword(s):  
Information Processing ◽  
Cognitive Processes ◽  
Environmental Variables ◽  
Emotional Processing ◽  
Processing System ◽  
Human Beings ◽  
Context Sensitive ◽  
Emotional Agents ◽  
The Brain

One way to evaluate cognitive processes in living or nonliving systems is by using the notion of “information processing”. Emotions as cognitive processes orient human beings to recognize, express and display themselves or their wellbeing through dynamical and adaptive form of information processing. In addition, humans behave or act emotionally in an embodied environment. The brain embeds symbols, meaning and purposes for emotions as well. So any model of natural or autonomous emotional agents/systems needs to consider the embodied features of emotions that are processed in an informational channel of the brain or a processing system. This analytical and explanatory study described in this chapter uses the pragmatic notion of information to develop a theoretical model for emotions that attempts to synthesize some essential aspects of human emotional processing. The model holds context-sensitive and purpose-based features of emotional pattering in the brain. The role of memory is discussed and an idea of control parameters that have roles in processing environmental variables in emotional patterning is introduced.

5 Models of the world: from perception to hallucination

2020 ◽  
Vol 91 (8) ◽  
pp. e2.3-e2
Author(s):  
Paul Fletcher
Keyword(s):  
Model Building ◽  
Processing System ◽  
Human Perception ◽  
Predictive Processing ◽  
External Reality ◽  
Consultant Psychiatrist ◽  
University Of Cambridge ◽  
The World ◽  
The University ◽  
The Brain

Paul Fletcher is Wellcome Investigator and Bernard Wolfe Professor of Health Neuroscience at the University of Cambridge. He is also Director of Studies for Preclinical Medicine at Clare College and Honorary Consultant Psychiatrist with the Cambridgeshire and Peterborough NHS Foundation Trust. He studied Medicine, before carrying out specialist training in Psychiatry and taking a PhD in cognitive neuroscience. He researches human perception, learning and decision-making in health and mental illness.We do not have direct contact with external reality. We must rely on messages from the sense organs, conveying information about the state of the world and our bodies. These messages are not easy to decipher, being noisy and ambiguous, but from them we have to construct models of the world. I will discuss this challenge and how we are very adept at creating a model of reality based on achieving a balance between what our senses are telling us and our expectations of what should be the case. This is often referred to as the predictive processing framework.Relying on this balance comes at a cost, rendering us vulnerable to illusions and biases and, in more extreme cases, to creating a reality that diverges from that experienced by others. This can arise for a variety of reasons but, at the root, I suggest, lies the nature of the brain as a model-building organ. Though this divergence from reality – psychosis – often seems inexplicable and incomprehensible, I suggest that a few core principles can help us to understand it and offers ways of thinking about how phenomena like hallucinations can be understood. Interestingly, the framework suggests ways in which apparently similar phenomena like hallucinations can arise from distinct alterations to the function of a predictive processing system.

Computational Knowledge Representation in Cognitive Science

2019 ◽  
Vol 56 (3) ◽  
pp. 138-152
Author(s):  
Igor F. Mikhailov ◽  
Keyword(s):  
Knowledge Representation ◽  
Cognitive Science ◽  
Network Architecture ◽  
Mathematical Theory ◽  
Computational Approach ◽  
Formal Epistemology ◽  
The Social ◽  
Parallel Network ◽  
Substrate Independent ◽  
Computational Systems

Cognitive research can contribute to the formal epistemological study of knowledge representation inasmuch as, firstly, it may be regarded as a descriptive science of the very same subject as that, of which formal epistemology is a normative one. And, secondly, the notion of representation plays a constitutive role in both disciplines, though differing therein in shades of its meaning. Representation, in my view, makes sense only being paired with computation. A process may be viewed as computational if it adheres to some algorithm and is substrate-independent. Traditionally, psychology is not directly determined by neuroscience, sticking to functional or dynamical analyses in the what-level and skipping mechanistic explanations in the how-level. Therefore, any version of computational approach in psychology is a very promising move in connecting the two scientific realms. On the other hand, the digital and linear computational approach of the classical cognitive science is of little help in this way, as it is not biologically realistic. Thus, what is needed there on the methodological level, is a shift from classical Turing-style computationalism to a generic computational theory that would comprehend the complicated architecture of neuronal computations. To this end, the cutting-edge cognitive neuroscience is in need of а satisfactory mathematical theory applicable to natural, particularly neuronal, computations. Computational systems may be construed as natural or artificial devices that use some physical processes on their lower levels as atomic operations for algorithmic processes on their higher levels. A cognitive system is a multi-level mechanism, in which linguistic, visual and other processors are built on numerous levels of more elementary operations, which ultimately boil down to atomic neural spikes. The hypothesis defended in this paper is that knowledge derives not only from an individual computational device, such as a brain, but also from the social communication system that, in its turn, may be presented as a kind of supercomputer of the parallel network architecture. Therefore, a plausible account of knowledge production and exchange must base on some mathematical theory of social computations, along with that of natural, particularly neuronal, ones.

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