There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models (...) and targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

This paper focuses on the ethical dilemmas created by advanced technology that would allow patients with motor neurone disease to be sustained by artificial ventilation. The author attempts to support the patient's right to informed choice, arguing from the perspective of autonomy as a first order principle. The counter arguments of caregiver burden and financial restraints are analysed. In the UK, where active euthanasia is not legalized, the dilemma of commencing ventilation is seen to be outweighed by the problems (...) of withdrawing this treatment. The lack of accurate data and protocols that would clarify the current situation is emphasized and the conclusion takes the form of a recommendation for further research. (shrink)

Impossible Minds: My Neurons, My Consciousness has been written to satisfy the curiosity each and every one of us has about our own consciousness. It takes the view that the neurons in our heads are the source of consciousness and attempts to explain how this happens. Although it talks of neural networks, it explains what they are and what they do in such a way that anyone may understand. While the topic is partly philosophical, the text makes no (...) assumptions of prior knowledge of philosophy; and so contains easy excursions into the important ideas of philosophy that may be missing in the education of a computer scientist. The approach is pragmatic throughout; there are many references to material on experiments that were done in our laboratories. The first edition of the book was written to introduce curious readers to the way that the consciousness we all enjoy might depend on the networks of neurons that make up the brain. In this second edition, it is recognized that these arguments still stand, but that they have been taken much further by an increasing number of researchers. A post-script has now been written for each chapter to inform the reader of these developments and provide an up-to-date bibliography. A new epilogue has been written to summarize the state-of-the art of the search for consciousness in neural automata, for researchers in computation, students of philosophy, and anyone who is fascinated by what is one of the most engaging scientific endeavours of the day. This book also tells a story. A story of a land where people think that they are automata without much in the way of consciousness, a story of cormorants and cliffs by the sea, a story of what it might be like to be a conscious machine... (shrink)

An argument with roots in ancient Greek philosophy claims that only humans are capable of a certain class of thought termed conceptual, as opposed to perceptual thought, which is common to humans, the higher animals, and some machines. We outline the most detailed modern version of this argument due to Mortimer Adler, who in the 1960s argued for the uniqueness of the human power of conceptual thought. He also admitted that if conceptual thought were ever manifested by machines, such an (...) achievement would contradict his conclusion. We revisit Adler’s criterion in the light of the past five decades of artificial-intelligence research, and refine it in view of the classical definitions of perceptual and conceptual thought. We then examine two well-publicized examples of creative works produced by AI systems and show that evidence for conceptual thought appears to be lacking in them. Although clearer evidence for conceptual thought on the part of AI systems may arise in the near future, especially if the global neuronal workspace theory of consciousness prevails over its rival, integrated information theory, the question of whether AI systems can engage in conceptual thought appears to be still open. (shrink)

The evolution in the quality of teaching for preschool education is worth studying. In this article, we solved the qualitative problems in the comprehensive quality evaluation by suggesting a method of quantitative combination and establishing a set of indicators suitable for the comprehensive quality evaluation of students in the kindergarten. According to the experience summed up by previous scholars, the weight of each index is obtained by an analytic hierarchy process. This study analyzed the defects and causes of fuzzy comprehensive (...) evaluation and the neural network model in the construction of early childhood and preschool education's comprehensive quality evaluation model and propose a Feedforward Neural Network model. FNN combined with neural network and fuzzy logic characteristics introduces fuzzy concepts and fuzzy inference rules into neural networks of neurons, the connection power, and network learning. It improves the learning ability of NN and fuzzy evaluation of the power of expression and effectively exerts the advantages of fuzzy logic and neural network to make up for their shortcomings. However, the convergence speed is very slow. To solve this problem, the similarity measure was used to improve the number of hidden layer nodes of the network. The effectiveness and feasibility of the FNN improved hidden layer nodes are verified by an example so as to realize the automation of comprehensive quality evaluation. (shrink)

The main idea -- The functioning of a neuron -- Brain structure and function -- The general structure of the neural network -- Instincts, emotions, free will -- The nature of mental objects -- The rise and essence of (self-)consciousness -- Artificial intelligence -- Cognitive limitations of man.

Different theories have been proposed for explaining the evolution of language. One of this maintains that gestural communication has been the precursor of human speech. Here we present a series of neurophysiological evidences that support this hypothesis. Communication by gestures, defined as the capacity to emit and recognize meaningful actions, may have originated in the monkey motor cortex from a neural system whose basic function was action understanding. This system is made by neurons of monkey’s area F5, named mirror (...) neurons, activated by both execution and observation of goal-related actions. Recently, two new categories of mirror neurons have been described. Neurons of one category respond to the sound of an action, neurons of the other category respond to the observation of mouth ingestive and communicative actions. The properties of these neurons indicate that monkey’s area F5 possesses the basic neural mechanisms for associating gestures and meaningful sounds as a pre-adaptation for the later emergence of articulated speech. The homology and the functional similarities between monkey area F5 and Broca’s area support this evolutionary scenario. (shrink)

Neurophenomenology (Varela 1996) is not only philosophical but also empirical and experimental. Our purpose in this article is to illustrate concretely the efficiency of this approach in the field of neuroscience and, more precisely here, in epileptology. A number of recent observations have indicated that epileptic seizures do not arise suddenly simply as the effect of random fluctuations of brain activity, but require a process of pre-seizure changes that start long before. This has been reported at two different levels of (...) description: on the one hand, the epileptic patient often experiences some warning symptoms that precede seizures from several minutes to hours in the form of very specific lived events. On the other hand, the analyses of brain electrical activities have provided strong evidence that it is possible to detect a pre-seizure state in the neuronal dynamics several minutes before the electro-clinical onset of a seizure. We review here some of the ongoing work of our research group concerning seizure anticipation. In particular, we discuss experimental evidence of upward (local-to-global) formation of conscious experience and its neural substrate, but also of the downward (global-to-local) determination of local neuronal activity by situated conscious activity and its substrate large-scale neural assemblies. This causal role of conscious experience may lead to new kinds of therapy for epileptic patients. (shrink)

'if AI is outside your field, or you know something of the subject and would like to know more then Artificial Intelligence: The Basics is a brilliant primer.' - Nick Smith, Engineering and Technology Magazine November 2011 Artificial Intelligence: The Basics is a concise and cutting-edge introduction to the fast moving world of AI. The author Kevin Warwick, a pioneer in the field, examines issues of what it means to be man or machine and looks at advances in (...) robotics which have blurred the boundaries. Topics covered include: how intelligence can be defined whether machines can 'think' sensory input in machine systems the nature of consciousness the controversial culturing of human neurons. Exploring issues at the heart of the subject, this book is suitable for anyone interested in AI, and provides an illuminating and accessible introduction to this fascinating subject. (shrink)

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more (...) complicated, going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

I develop a multilevel, holistic view of persons, emphasizing embodiment, emotions, consciousness, and the social self. In successive sections I draw from six sources: 1. Theology. The biblical understanding of the unitary, embodied, social self gave way in classical Christianity to a body‐soul dualism, but it has been recovered by many recent theologians. 2. Neuroscience. Research has shown the localization of mental functions in regions of the brain, the interaction of cognition and emotion, and the importance of social interaction in (...) evolutionary history and child development. 3. Artificial intelligence. Some forms of robotics use embodied systems that learn by interacting with their environment, but the possibilities for emotion, socialization, and consciousness in robots remain problematic. 4. Relations between levels. Concepts that can help us relate studies of neurons and persons include the hierarchy of levels, the communication of information, thebehavior of dynamic systems, and epistemological and ontological emergence. 5. Philosophy of mind. Two‐aspect theories of the mind‐brain relation offer an alternative between the extremes of eliminative materialism and the thesis that consciousness is irreducible. 6. Process philosophy. I suggest that process thought provides a coherent philosophical framework in which these themes can be brought together. It combines dipolar monism with organizational pluralism, and it emphasizes embodiment, emotions, a hierarchy oflevels, and the social character of selfhood. (shrink)

Mirror neurons may play a role in representing not only signs but also their meaning. Because actions are the only aspect of behavior that are inter-individually accessible, interpreting meanings in terms of actions might explain how meanings can be shared. Behavioral evidence and artificial life simulations suggest that seeing objects or processing words referring to objects automatically activates motor actions.

Of all possible future agents with greater‐than‐human intelligence, the one that stands out as the most potentially powerful is sophisticated artificial intelligence (AI). An AI might have millions of processors, all of them hundreds of millions of times faster than biological neurons. Historical focus on molecular nanotechnology in the context of explosive AI growth is unfortunate because the use of nanotechnology for mass manufacturing and the creation of rapid infrastructure are so highly speculative. For an AI on a (...) growth tangent, the primary objective would be to bridge from Internet influence to using humans as tools, and eventually to using robots as tools. Robots connected to the Internet could take as much advantage of the full cognitive resources of the core AI as bandwidth and computing budgets permit. This chapter contributes a concrete model of self‐improving AI that provokes further thought. (shrink)

This paper reports about our investigation on action understanding in the brain. We review recent results of the neurophysiology of the mirror system in the monkey. Based on these observations we propose a model of this brain system which is responsible for action recognition. The link between object affordances and action understanding is considered. To support our hypothesis we describe two experiments where some aspects of the model have been implemented. In the first experiment an action recognition system is trained (...) by using data recorded from human movements. In the second experiment, the model is partially implemented on a humanoid robot which learns to mimic simple actions performed by a human subject on different objects. These experiments show that motor information can have a significant role in action interpretation and that a mirror-like representation can be developed autonomously as a result of the interaction between an individual and the environment. (shrink)

This study aims to predict the shear strength of reinforced concrete deep beams based on artificial neural network using four training algorithms, namely, Levenberg–Marquardt, quasi-Newton method, conjugate gradient, and gradient descent. A database containing 106 results of RC deep beam shear strength tests is collected and used to investigate the performance of the four proposed algorithms. The ANN training phase uses 70% of data, randomly taken from the collected dataset, whereas the remaining 30% of data are used for the (...) algorithms’ evaluation process. The ANN structure consists of an input layer with 9 neurons corresponding to 9 input parameters, a hidden layer of 10 neurons, and an output layer with 1 neuron representing the shear strength of RC deep beams. The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error. The results show that the ANN-CG model has the best prediction performance with R = 0.992, RMSE = 14.02, MAE = 14.24, and MAPE = 6.84. The results of this study show that the ANN-CG model can accurately predict the shear strength of RC deep beams, representing a promising and useful alternative design solution for structural engineers. (shrink)

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has possible states, and a traveling salesperson problem with as little as 82 cities already has more than different tours (similar to chess). Biological (...) class='Hi'>neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving. (shrink)

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease (...) or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

The question about the potential for consciousness of artificial systems has often been addressed using thought experiments, which are often problematic in the philosophy of mind. A more promising approach is to use real experiments to gather data about the correlates of consciousness in humans, and develop this data into theories that make predictions about human and artificial consciousness. A key issue with an experimental approach is that consciousness can only be measured using behavior, which places fundamental limits (...) on our ability to identify the correlates of consciousness. This paper formalizes these limits as a distinction between type I and type II potential correlates of consciousness (PCCs). Since it is not possible to decide empirically whether type I PCCs are necessary for consciousness, it is indeterminable whether a machine that lacks neurons or hemoglobin, for example, is potentially conscious. A number of responses have been put forward to this problem, including suspension of judgment, liberal and conservative attribution of the potential for consciousness and a psychometric scale that models our judgment about the relationship between type I PCCs and consciousness. (shrink)

The implementation of an artificial neural network for predicting induction heating region locations is proposed in this research. Steel plate deformations during the induction heating process are produced using an analytical solution derived from electromagnetic and plate theory. The plate transform following vertical displacements in each divided area was used as input of neural following desired shape of the steel plate and the specified heating areas for induction treatment as output parameters to predict and evaluate the model. A dataset (...) used 90% for training and remaining 10% for testing to implement on the efficient models when changing hidden layer and its neurons relatively. The trial and error for analyzing and predicting heating-affected regions with the ANNs model reached the high average accuracy and lowest mean square error at 98.08% and 0.00913, respectively. Consequently, the feasibility test indicates that the developed approach may be well utilized to identify the heating positions by grid area in order to achieve the desired plate deformation. Moreover, the analysis of vertical displacement during induction heating and its response behaviour of steel plate based on thermo-mechanical are also addressed. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel (...) fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

En las últimas décadas, la neurocomunicación ha emergido como un campo interdisciplinario que explora cómo los procesos neurológicos influyen en la manera en que las personas se comunican y aprenden. En el ámbito educativo, esta disciplina abre nuevas posibilidades para comprender cómo los estudiantes procesan la información y responden a los estímulos cognitivos y emocionales. Con la creciente influencia de la IA, se han desarrollado herramientas que permiten aplicar principios de neurocomunicación en contextos pedagógicos. Este artículo de revisión se centra (...) en la intersección entre neurocomunicación, educación y las nuevas experiencias que ofrece la IA, explorando las teorías clave y los avances tecnológicos que potencian el aprendizaje personalizado y eficiente. La investigación teórica se enmarca en un análisis de las fuentes más relevantes y recientes sobre cómo la IA está transformando la interacción educativa, proporcionando una nueva perspectiva sobre el impacto neuronal en los procesos de enseñanza-aprendizaje. En este artículo se pretende explorar cómo la neurocomunicación y la IA están transformando la educación desde una perspectiva teórica. La revisión se centrará en las principales teorías de neurocomunicación aplicadas a la enseñanza y cómo la IA ha redefinido estas interacciones en el aula. A través del análisis de fuentes clave, se examinarán los avances en neurotecnología educativa que mejoran la personalización del aprendizaje, adaptándose a los patrones neuronales individuales. Así, se busca ofrecer una perspectiva profunda sobre cómo las experiencias y aplicaciones en IA están configurando un nuevo paradigma en la neurocomunicación educativa, planteando preguntas cruciales sobre el futuro del aprendizaje y su relación con la tecnología. Este estudio de naturaleza teórica apunta a la necesidad de comprender cómo estas tecnologías emergentes influyen en los estudiantes, no solo en términos de rendimiento académico, sino también en su desarrollo cognitivo y emocional. Al identificar las áreas clave en las que la IA y la neurocomunicación convergen, se pretende sentar las bases para futuras investigaciones y aplicaciones prácticas en el ámbito educativo. (shrink)

In her seminal text, What Should We Do With Our Brain? (2008), Catherine Malabou gestured towards neuroplasticity to upend Bergson's famous parallel of the brain as a "central telephonic exchange," whereby the function of the brain is simply that of a node where perceptions get in touch with motor mechanisms, the brain as an instrument limited to the transmission and divisions of movements. Drawing from the history of cybernetics one can trace how Bergson's 'telephonic exchange' prefigures the neural 'cybernetic metaphor.' (...) It is elsewhere, however, that What Should We Do With Our Brain? finds its crux: inspired a dialectical-speculative opposition between plasticity and flexibility (wherein plasticity is the way in which time shapes or fashions us, constitutes our subjectivity and at the same time allows for resistance), Malabou invalidates the 'telephonic exchange' metaphor for failing to take into account synaptic and neuronal vitality. Bolstered by neurologist Marc Jeannerod's research in The Nature of Mind (2002), Malabou further demonstrated, in her past work, that the cybernetic metaphor has also had its day. In Morphing Intelligence, by problematizing intelligence as strictly empirical and biologically determined, Malabou also troubles the traditional distinction between intelligence and intuition. This division is perhaps best exemplified by Bergson’s analysis of intellectual measurement magnitudes in his appeal to intensity, and intensity alone. Malabou, drawing from Dewey and the pragmatism mode of thought, characterizes this fetid standstill as little more than provincialism, charging that, ater Bergson, no truly new argument was offered to counter intelligence as defined by psychologists and biologists, including the most recent cognitivist version. Despite I disagree with her claims regarding cognitivism, in echoing Georges Canguilhem, Malabou castigates psychology’s instrumentalist regard for intelligence, making the claim that it is able to measure only the human ability to “become an instrument.” Malabou contends that Alfred Binet (who heavily critiqued Bergson) had it right—intelligence is constituted by intensities and qualities. This book picks up from this critique and works genealogically; in my exegetical review, I engage with Malabou's and its implications, given her past work. (shrink)

The authors present a neurological theory of how cognitive information and emotional information are integrated in the nucleus accumbens during effective decision making. They describe how the nucleus accumbens acts as a gateway to integrate cognitive information from the ventromedial prefrontal cortex and the hippocampus with emotional information from the amygdala. The authors have modeled this integration by a network of spiking artificial neurons organized into separate areas and used this computational model to simulate 2 kinds of cognitive–affective (...) integration. The model simulates successful performance by people with normal cognitive–affective integration. The model also simulates the historical case of Phineas Gage as well as subsequent patients whose ability to make decisions became.. (shrink)

For Ray Kurzweil, artificial intelligence (AI) is not just about making artificial things intelligent; it's also about making humans artificially superintelligent. The author challenges Kurzweil's predictions about the destiny of the human mind. He argues that it is unlikely ever to be rational for human beings to upload their minds completely onto computers. The author uses the term “mind uploading” to describe two processes. Most straightforwardly, it describes the one‐off event when a fully biological being presses a button (...) and instantaneously and completely copies her entire psychology into a computer. It also describes the decisive event in a series of replacements of neurons by electronic chips. Searle's Wager treats the exchanges over the possibility of successful mind uploading in much the same way that Pascal's Wager treats the debate over God's existence. Finally the author explains why nondestructive uploading is prudentially irrational. (shrink)

The notion of affordance has been introduced by Gibson (1977, 1979) as the feature of an object or the environment that allows the observer to perform an action, a set of “environmental supports for an organism’s intentional activities” (Reybrouck 2005). Studied under very different perspectives, this concept has become a crucial issue not only for the ecological psychology, but also for cognitive sciences, artificial intelligence studies, and philosophy of mind. This variety of approaches has widened the already ambiguous definition (...) originally provided by Gibson, contributing to the development of different standpoints in open contrast with each other (see Zipoli Caiani 2011). During the last two decades, moreover, many researchers tried to extend the notion also to musical experience, aiming to draw a coherent theory of musical affordances (e.g. Clarke 2005; Nussbaum 2007; Krueger 2011a; 2011b). In this paper, we will argue for a particular concept of musical affordances, that is, as we see it, one narrower and less ambiguous in scope and more closely related to its original. Taking the discovery of canonical neurons as our starting point, we will (i) introduce the general notion of affordance, (ii) discuss some significant contributions in this area of research, mostly focusing on musical affordances and (iii) propose a motor-based interpretation of musical affordances. (shrink)

The article analyzes the role of theories of embodied cognition for the development of emotion research. The role and position of emotions changed as philosophy developed. In classical and modern European philosophy, the idea of the “primacy of reason” prevailed over emotions and physicality, emotions and affective life were described as low-ranking phenomena regarding cognitive processes or were completely eliminated as an unknown quantity. In postmodern philosophy, attention focuses on physicality and sensuality, which are rated higher than rational principle, mind (...) and intelligence. Within the framework of this approach, there is a recently emerged theory of embodied cognition, which allows to take a fresh look at the place of emotions in the architecture of mental processes – thinking, perception, memory, imagination, speech. The article describes and analyzes a number of empirical studies showing the impossibility of excluding emotional processes and the significance of their research for understanding the architecture of embodied cognition. However, the features of the architecture of embodied cognition remain unclear, and some of the discoveries of recent years (mirror neurons or neurons of simulation) rather raise new questions and require further research. The rigorously described and clear architecture of the embodied cognition can grow the theoretical basis that will allow to advance the studies of learning processes, language understanding, psychotherapy techniques, social attitudes and stereotypes, highlight the riddle of consciousness and create new theories of consciousness or even create an anthropomorphic artificial intelligence that is close to “strong artificial intelligence.”. (shrink)

The functioning of a subject in a changing environment is most effective from the point of view of survival if the subject can form, maintain and use internal representations of the external world for decision-making. These representations are also called reflection in a broad sense. Using it, one can win in reflexive games since an internal representation of the enemy allows predicting their future moves. The goal is to assess the reflexive potential of heuristic model objects – artificial neural (...) networks – in the reflexive games “Even-Odd” (or “Matching pennies”) and “Rock-Paper-Scissors”. We used homogeneous fully connected neural networks of small sizes (from 8 to 45 neurons). Games were played between neural networks with different configurations and parameters (size, step size for modifying weight coefficients). A set of reflexivity criteria is presented, corresponding to different levels of consideration: neuronal, behavioral, formal. The transitivity of formal success in the game is shown. The most successful configurations, however, may not meet other criteria of reflexivity. We hypothesize that the best compliance with the criteria and, as a consequence, universal success in reflection tasks is achievable for heterogeneous configurations with a structure in which the formation of hierarchical systems of attractors is possible. (shrink)

With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves “in the image of our machines,” and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against (...) this self-reification of the human being, the present book defends a humanism of embodiment: our corporeality, vitality, and embodied freedom are the foundations of a self-determined existence, which uses the new technologies only as means instead of submitting to them. The book offers an array of interventions directed against a reductionist naturalism in various areas of science and society. As an alternative, it offers an embodied and enactive account of the human person: we are neither pure minds nor brains, but primarily embodied, living beings in relation with others. This general concept is applied to issues such as artificial intelligence (AI), transhumanism and enhancement, virtual reality, neuroscience, embodied freedom, psychiatry, and finally to the accelerating dynamics of current society which lead to an increasing disembodiment of our everyday life. The book thus applies cutting-edge concepts of embodiment and enactivism to current scientific, technological, and cultural tendencies that will crucially influence our society’s development in the twenty-first century. (shrink)

How the Body Shapes the Mind is an interdisciplinary work that addresses philosophical questions by appealing to evidence found in experimental psychology, neuroscience, studies of pathologies, and developmental psychology. There is a growing consensus across these disciplines that the contribution of embodiment to cognition is inescapable. Because this insight has been developed across a variety of disciplines, however, there is still a need to develop a common vocabulary that is capable of integrating discussions of brain mechanisms in neuroscience, behavioural expressions (...) in psychology, design concerns in artificial intelligence and robotics, and debates about embodied experience in the phenomenology and philosophy of mind. Shaun Gallagher's book aims to contribute to the formulation of that common vocabulary and to develop a conceptual framework that will avoid both the overly reductionistic approaches that explain everything in terms of bottom-up neuronal mechanisms, and inflationistic approaches that explain everything in terms of Cartesian, top-down cognitive states. Gallagher pursues two basic sets of questions. The first set consists of questions about the phenomenal aspects of the structure of experience, and specifically the relatively regular and constant features that we find in the content of our experience. If throughout conscious experience there is a constant reference to one's own body, even if this is a recessive or marginal awareness, then that reference constitutes a structural feature of the phenomenal field of consciousness, part of a framework that is likely to determine or influence all other aspects of experience. The second set of questions concerns aspects of the structure of experience that are more hidden, those that may be more difficult to get at because they happen before we know it. They do not normally enter into the content of experience in an explicit way, and are often inaccessible to reflective consciousness. To what extent, and in what ways, are consciousness and cognitive processes, which include experiences related to perception, memory, imagination, belief, judgement, and so forth, shaped or structured by the fact that they are embodied in this way? (shrink)

Francis Lareau Une tâche importante en philosophie est la lecture et l’analyse de textes pour en dégager les concepts. L’objectif de la présente étude est d’explorer la possibilité d’une assistance computationnelle pour effectuer cette tâche. Une méthode classique est le concordancier, mais celle-ci ne permet pas de distinguer les extraits où le concept n’est pas exprimé de manière canonique. Nous proposons une méthode permettant de reconnaître ces extraits, que nous appliquons à un corpus d’articles de la revue Philosophiques. Nous déterminons (...) d’abord les extraits où le concept est exprimé de manière explicite. Ensuite, nous déterminons les extraits les moins susceptibles d’exprimer le concept cible. Enfin, nous utilisons plusieurs classifieurs afin de distinguer les extraits où le concept est exprimé de manière implicite. Les résultats montrent une différence significative entre les classifieurs les plus performants, machines à vecteurs de support et réseaux de neurones, et certains modèles probabilistes classiques. (shrink)

Research on natural and artificial brains is proceeding at a rapid pace. However, the understanding of the essence of consciousness has changed slightly over the millennia, and only the last decade has brought some progress to the area. Scientific ideas emerged that the soul could be a product of the material body and that calculating machines could imitate brain processes. However, the authors of this book reject the previously common dualism—the view that the material and spiritual-psychic processes are separate (...) and require a completely different substance as their foundation. -/- Reductive Model of the Conscious Mind is a forward-thinking book wherein the authors identify processes that are the essence of conscious thinking and place them in the imagined, simplified structure of cells able to memorize and transmit information in the form of impulses, which they call neurons. The purpose of the study is to explain the essence of consciousness to the degree of development of natural sciences, because only the latter can find a way to embed the concept of the conscious mind in material brains. The book is divided into three parts. Part 1 works to convince readers that the emergence of consciousness does not require detailed knowledge of the structure and morphology of the brain, with the exception of some specific properties of the neural network structure that the authors attempt to point out. Part 2 proves that the biological structure of many natural brains fulfills the necessary conditions for consciousness and intelligent thinking. Similarly, Part 3 shows the ways in which artificial creatures imitating natural brains can meet these conditions, which gives great hopes for building artificially intelligent beings endowed with consciousness. Covering topics that include cognitive architecture, the embodied mind, and machine learning, this book is ideal for cognitive scientists, philosophers of mind, neuroscientists, psychologists, researchers, academicians, and advanced-level students. The book can also help to focus the research of linguists, neurologists, and biophysicists on the biophysical basis of postulated information processing into knowledge structures. (shrink)

What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of individual neurons produce something as extraordinarily complex as consciousness? What is it that guides self-organizing structures like the immune system, the World Wide Web, the global economy, and the human genome? These are just a few of the fascinating and elusive questions that the science of complexity seeks to answer. In this remarkably accessible and companionable book, leading complex (...) systems scientist Melanie Mitchell provides an intimate, detailed tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Comprehending such systems requires a wholly new approach, one that goes beyond traditional scientific reductionism and that re-maps long-standing disciplinary boundaries. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. She explores as well the relationship between complexity and evolution, artificial intelligence, computation, genetics, information processing, and many other fields. Richly illustrated and vividly written, Complexity : A Guided Tour offers a comprehensive and eminently comprehensible overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for the field's contribution to solving some of the most important scientific questions of our time. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

Writings by a thinker—a psychiatrist, a philosopher, a cybernetician, and a poet—whose ideas about mind and brain were far ahead of his time. Warren S. McCulloch was an original thinker, in many respects far ahead of his time. McCulloch, who was a psychiatrist, a philosopher, a teacher, a mathematician, and a poet, termed his work “experimental epistemology.” He said, “There is one answer, only one, toward which I've groped for thirty years: to find out how brains work.” Embodiments of Mind, (...) first published more than fifty years ago, teems with intriguing concepts about the mind/brain that are highly relevant to recent developments in neuroscience and neural networks. It includes two classic papers coauthored with Walter Pitts, one of which applies Boolean algebra to neurons considered as gates, and the other of which shows the kind of nervous circuitry that could be used in perceiving universals. These first models are part of the basis of artificial intelligence. Chapters range from “What Is a Number, that a Man May Know It, and a Man, that He May Know a Number,” and “Why the Mind Is in the Head,” to “What the Frog's Eye Tells the Frog's Brain” (with Jerome Lettvin, Humberto Maturana, and Walter Pitts), “Machines that Think and Want,” and “A Logical Calculus of the Ideas Immanent in Nervous Activity” (with Walter Pitts). Embodiments of Mind concludes with a selection of McCulloch's poems and sonnets. This reissued edition offers a new foreword and a biographical essay by McCulloch's one-time research assistant, the neuroscientist and computer scientist Michael Arbib. (shrink)

The vital data about the electrical activities of the brain are carried by the electroencephalography signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG (...) studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model with Singular Value Decomposition Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset. (shrink)

This is an exciting time for brain science. Recent progress has been such that it now seems realistic to look toward an explanation of mind in terms of the brain's anatomy and physiology. Models based on artificially symmetrical arrays of idealized neurons are now being superseded by ones which properly take into account the brain's actual circuitry. This book presents a comprehensive overview of the current state of brain modeling, containing contributions from many leading researchers in this field. It (...) will be of interest not only to researchers in the fields of brain science and neurobiology, but also to psychologists and those involved in the study of artificial intelligence. (shrink)

Why are computers so smart these days? And why are humans apparently still a bit smarter? Does this have something to do with the difference between data and meaning? Does this in turn mean that at least some abstract entities, such as numbers, exist independently of human thought? Wouldn’t that require an expansion of our scientific world view? And would that at all be compatible with what we know about our world from physics and chemistry, philosophy, psychology, neuroscience and the (...) theory of evolution? Finally, what would this tell us about ethical and aesthetic value theories? These and related questions will be discussed in this book. We will find that the difference between data and meaning, i.e. quantitative and qualitative information, does indeed appear to be of central importance for understanding both artificial and natural intelligence. And then the independent existence of abstract entities not only appears to be a particularly promising hypothesis, but also one that is entirely compatible with the sum of our scientific knowledge, especially with regard to value theories. The book thus arrives at the exploration of a scientifically tenable, panpsychistically inspired, objective idealism that can be derived from our most fundamental intuitions as subjects that perceive qualities, but that can also take into account the structuring of the world already at the micro-scale, found in the modern natural sciences. The result is a Platonic, but in a second step also a scientific realism and a naturalism in the sense that it is informed by the natural sciences in terms of an inductive metaphysics. An objective idealism, not in a rationalistic maximum form, but in a pragmatic minimum form; without eternal truths, but dependent on the continued philosophical-scientific and also philosophicalsocial dialog. The proposed model could offer interesting solutions to a number of problems at and near the mind/matter boundary: Proposals are being considered for the interpretation of quantum mechanics, the problem of molecular symmetry, the neuronal code and the binding problem in neuroscience, mental causation, a more holistic understanding of mental processes, and so on and so forth. However, the extent to which the model threatens to promise far too much is also being discussed. In sum, the core question is how we can imagine human thinking beyond physically conceived information processing. An alternative model of human thinking is then put up for discussion, for which not only machine-like cognitive performance, but above all the intentional perception of qualitative information, i.e. of abstract entities, would be central, as well as the free, ultimately creative linking of patterns of quantitative information (signals, data) with such qualities (meanings). (shrink)

There is some complementarity of models for the origin of the electroencephalogram (EEG) and neural network models for information storage in brainlike systems. From the EEG models of Freeman, of Nunez, and of the authors' group we argue that the wavelike processes revealed in the EEG exhibit linear and near-equilibrium dynamics at macroscopic scale, despite extremely nonlinear – probably chaotic – dynamics at microscopic scale. Simulations of cortical neuronal interactions at global and microscopic scales are then presented. The simulations depend (...) on anatomical and physiological estimates of synaptic densities, coupling symmetries, synaptic gain, dendritic time constants, and axonal delays. It is shown that the frequency content, wave velocities, frequency/wavenumber spectra and response to cortical activation of the electrocorticogram (ECoG) can be reproduced by a “lumped” simulation treating small cortical areas as single-function units. The corresponding cellular neural network simulation has properties that include those of attractor neural networks proposed by Amit and by Parisi. Within the simulations at both scales, sharp transitions occur between low and high cell firing rates. These transitions may form a basis for neural interactions across scale. To maintain overall cortical dynamics in the normal low firing-rate range, interactions between the cortex and the subcortical systems are required to prevent runaway global excitation. Thus, the interaction of cortex and subcortex via corticostriatal and related pathways may partly regulate global dynamics by a principle analogous to adiabatic control of artificial neural networks. (shrink)

Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networkscharts the immense progress made in recent years in many specific areas related to two great questions: How does the brain work? and How can we build intelligent machines? While many books have appeared on limited aspects of one subfield or another of brain theory and neural networks, the (...) Handbookcovers the entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. The excitement, and the frustration, of these topics is that they span such a broad range of disciplines including mathematics, statistical physics and chemistry, neurology and neurobiology, and computer science and electrical engineering as well as cognitive psychology, artificial intelligence, and philosophy. Thus, much effort has gone into making the Handbookaccessible to readers with varied backgrounds while still providing a clear view of much of the recent, specialized research in specific topics. The heart of the book, part III, comprises of 267 original articles by leaders in the various fields, arranged alphabetically by title. Parts I and II, written by the editor, are designed to help readers orient themselves to this vast range of material. Part I, Background, introduces several basic neural models, explains how the present study of brain theory and neural networks integrates brain theory, artificial intelligence, and cognitive psychology, and provides a tutorial on the concepts essential for understanding neural networks as dynamic, adaptive systems. Part II, Road Maps, provides entry into the many articles of part III through an introductory "Meta-Map" and twenty-three road maps, each of which tours all the Part III articles on the chosen theme. (shrink)

Closed Loop medical devices such as Closed Loop Deep Brain Stimulation and Brain Computer Interface are some of the emerging neurotechnologies. New generations of implantable brain–computer interfaces have recently gained success in human clinical trials. These implants detect specific neuronal patterns and provide the subject with information to respond to these patterns. Further, Closed Loop brain devices give control to the subject so that he can respond and decide on a therapeutic goal. Although the implants have improved subjects’ quality of (...) life, their use has raised varied ethical concerns. The aim of this study is to provide an overview of the ethical implications specific to the therapeutic goals of closed loop brain implants. The authors analyze the related work qualitatively and quantitatively so as to help readers in understanding the various ethical implications of closed loop brain implants. (shrink)

Within theoretical and empirical enquiries, many different meanings associated with consciousness have appeared, leaving the term itself quite vague. This makes formulating an abstract and unifying version of the concept of consciousness – the main aim of this article –into an urgent theoretical imperative. It is argued that consciousness, characterized as dually accessible (cognized from the inside and the outside), hierarchically referential (semantically ordered), bodily determined (embedded in the working structures of an organism or conscious system), and useful in action (...) (pragmatically functional), is a graded rather than an all-or-none phenomenon. A gradational approach, however, despite its explanatory advantages, can lead to some counterintuitive consequences and theoretical problems. In most such conceptions consciousness is extended globally (attached to primitive organisms or artificial systems), but also locally (connected to certain lower-level neuronal and bodily processes). For example, according to information integration theory (as introduced recently by Tononi and Koch, 2014), even such simple artificial systems as photodiodes possess miniscule amounts of consciousness. The major challenge for this article, then, is to establish reasonable, empirically justified constraints on how extended the range of a graded consciousness could be. It is argued that conscious systems are limited globally by the ability to individuate information (where individuated information is understood as evolutionarily embedded, socially altered, and private), whereas local limitations should be determined on the basis of a hypothesis about the action-oriented nature of the processes that select states of consciousness. Using these constraints, an abstract concept of consciousness is arrived at, hopefully contributing to a more unified state of play within consciousness studies itself. (shrink)

I suggest that most discussions of intentional systems have overlooked an important aspect of living organisms: the intrinsic goal-directedness inherent in the behaviour of living eukaryotic cells. This goal directedness is nicely displayed by a normal cell’s ability to rearrange its own local material structure in response to damage, nutrient distribution or other aspects of its individual experience. While at a vastly simpler level than intentionality at the human cognitive level, I propose that this basic capacity of living things provides (...) a necessary building block for cognition and high-order intentionality, because the neurons that make up vertebrate brains, like most cells in our body, embody such capacities. I provisionally dub the capacities in question “nano-intentionality”: a microscopic form of “aboutness”. The form of intrinsic intentionality I propose is thoroughly materialistic, fully compatible with known biological facts, and derived non-mysteriously through evolution. Crucially, these capacities are not shared by any existing computers or computer components, and thus provide a clear, empirically-based distinction between brains and currently existing artificial information processing systems. I suggest that an appreciation of this aspect of living matter provides a potential route out of what may otherwise appear to be a hopeless philosophical quagmire confronting information-processing models of the mind. (shrink)

Most people will agree that if my brain were made to have within it precisely the same pattern of activity that is in it now but through artificial means, as in its being fed all its stimulation through electrodes as it sits in a vat, an experience would result for me that would be subjectively indistinguishable from that I am now having. In ‘The Story of a Brain’ I ask whether the same subjective experience would be maintained in variations (...) like these: The hemispheres are in different vats but interacting with each other through radio transmission. Smaller chunks - and eventually the individual neurons - are interacting in this way. The hemispheres or the smaller chunks undergo internally the same pattern of activity but without interacting with each other. (shrink)

The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting _Hybrid Minds_ focuses on the integration of artificial intelligence into the human brain through intelligent brain-computer interfaces as they enter clinical use. In this contribution we discuss a complementary development: the integration of a functional in vitro network of human neurons into an _in silico_ computing environment. To do so, we draw on a recent experiment reporting the creation of (...) silico-biological intelligence as a case study (Kagan et al., 2022b ). In this experiment, multielectrode arrays were plated with stem cell-derived human neurons, creating a system which the authors call _DishBrain_. By embedding the system into a virtual game-world, neural clusters were able to receive electrical input signals from the game-world and to respond appropriately with output signals from pre-assigned motor regions. Using this design, the authors demonstrate how the _DishBrain_ self-organises and successfully learns to play the computer game ‘Pong’, exhibiting ‘sentient’ and intelligent behaviour in its virtual environment. The creation of such hybrid, silico-biological intelligence raises numerous ethical challenges. Following the neuroscientific framework embraced by the authors themselves, we discuss the arising ethical challenges in the context of Karl Friston’s Free Energy Principle, focusing on the risk of creating synthetic phenomenology. Following the _DishBrain’s_ creator’s neuroscientific assumptions, we highlight how DishBrain’s design may risk bringing about artificial suffering and argue for a congruently cautious approach to such synthetic biological intelligence. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects (...) of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)