This paper is devoted to some generalizations of explanatory potential of connectionist approaches to theoretical problems of the philosophy of mind. Are considered both strong, and weaknesses of neural network models. Connectionism has close methodological ties with modern neurosciences and neurophilosophy. And this fact strengthens its positions, in terms of empirical naturalistic approaches. However, at the same time this direction inherits weaknesses of computational approach, and in this case all system of anticomputational critical arguments becomes applicable to the (...) connectionst models of mind. The last developments in the field of deep learning gave rich empirical material for cognitive sciences. Multilayered networks, mathematical models of associative dynamics of learning, self-organizing neuronets and all that allow to explain the principles of human conceptual organizing and after this to emulate these processes in computer systems. At all engineering achievements of this technology there is a traditional criticism from representatives of cognitive psychology who cannot accept a thesis about learning ability of a neuronet on the basis of redistribution of scales. Process of learning of natural intelligence, according to cognitive models, happens due to attraction of knowledge broadcast in a symbolical form (mental representations, concepts) at the expense of the systems of output knowledge expressed in the propositional contents. Some philosophical aspects of «neural metaphor» in modern cognitive sciences create the problem field which demands comprehensive understanding, the first step towards which is taken in this work. (shrink)

This article proposes a 7E model of the human mind, which was developed within the cognitive paradigm in religious studies and its primary expression, the Cognitive Science of Religion. This study draws on the philosophically most sophisticated currents in the cognitive sciences, which have come to define the human mind through a 4E model as embodied, embedded, enactive, and extended. Introducing Catherine Malabou’s concept of “plasticity,” the study not only confirms the insight of the 4E model (...) of the self as a decentered system, but it also recommends two further traits of the self that have been overlooked in the cognitive sciences, namely the negativity of plasticity and the tension between giving and receiving form. Finally, the article matures these philosophical insights to develop a concrete model of the religious mind, equipping it with three further Es, namely emotional, evolved, and exoconscious. (shrink)

Recent years have witnessed rapidly increasing interests in developing quantum theoretical models of human cognition. Quantum mechanisms have been taken seriously to describe how the mind reasons and decides. Papers in this special issue report the newest results in the field. Here we discuss why the two levels of commitment, treating the human brain as a quantum computer and merely adopting abstract quantum probability principles to model human cognition, should be integrated. We speculate that quantum (...) cognition models gain greater modeling power due to a richer representation scheme. (shrink)

This paper proposes a model for an artificial autonomous moral agent (AAMA), which is parsimonious in its ontology and minimal in its ethical assumptions. Starting from a set of moral data, this AAMA is able to learn and develop a form of moral competency. It resembles an “optimizing predictive mind,” which uses moral data (describing typical behavior of humans) and a set of dispositional traits to learn how to classify different actions (given a given background knowledge) as morally right, (...) wrong, or neutral. When confronted with a new situation, this AAMA is supposedly able to predict a behavior consistent with the training set. This paper argues that a promising computational tool that fits our model is “neuroevolution,” i.e. evolving artificial neural networks. (shrink)

Introduction. During the past two decades philosophers of psychology have considered a large variety of computational models for philosophy of mind and more recently for cognitive science. Among the suggested models are computer programs, Turing machines, pushdown automata, linear bounded automata, finite state automata and sequential machines. Many philosophers have found finite state automata models to be the most appealing, for various reasons, although there has been no shortage of defenders of programs and Turing machines. A (...) paper by Arthur Burks convinced me long ago that “all natural human functions” are, or can be fruitfully modeled to be, finite state automata with output. Further work in the field has reinforced this conviction. There is room, however, for the use of any of the above models in philosophy of mind and in the ongoing development of cognitive science. (shrink)

The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences (...) that are invalid, e.g., Few people ate steak or sole; therefore, few people ate steak. Other crucial experiments show that individuals reject inferences that are not necessary but valid, e.g., He had the anesthetic or felt pain, but not both; therefore, he had the anesthetic or felt pain, or both. Nothing in logic can justify the rejection of a valid inference: a denial of its conclusion is inconsistent with its premises, and inconsistencies yield valid inferences of any conclusions whatsoever including the one denied. So inconsistencies are catastrophic in logic. In contrast, the model theory treats all inferences as defeasible (nonmonotonic), and inconsistencies have the null model, which yields only the null model in conjunction with any other premises. So inconsistences are local. Which allows truth values in natural languages to be much richer than those that occur in the semantics of standard logics; and individuals verify assertions on the basis of both facts and possibilities that did not occur. (shrink)

Building artificial moral agents (AMAs) underscores the fragmentary character of presently available models of human ethical behavior. It is a distinctly different enterprise from either the attempt by moral philosophers to illuminate the “ought” of ethics or the research by cognitive scientists directed at revealing the mechanisms that influence moral psychology, and yet it draws on both. Philosophers and cognitive scientists have tended to stress the importance of particular cognitive mechanisms, e.g., reasoning, moral sentiments, heuristics, intuitions, or a (...) moral grammar, in the making of moral decisions. However, assembling a system from the bottom-up which is capable of accommodating moral considerations draws attention to the importance of a much wider array of mechanisms in honing moral intelligence. Moral machines need not emulate human cognitive faculties in order to function satisfactorily in responding to morally significant situations. But working through methods for building AMAs will have a profound effect in deepening an appreciation for the many mechanisms that contribute to a moral acumen, and the manner in which these mechanisms work together. Building AMAs highlights the need for a comprehensive model of how humans arrive at satisfactory moral judgments. (shrink)

A major challenge for research in artificial intelligence is to develop systems that can infer the goals, beliefs, and intentions of others (i.e., systems that have theory of mind, ToM). In this research, we propose a cognitive ToM framework that uses a well-known theory of decisions from experience to construct a computational representation of ToM. Instance-based learning theory (IBLT) is used to construct a cognitive model that generates ToM from the observation of other agents' behavior. The IBL model of (...) the observer distinguishes itself from previous models of ToM that make unreasonable assumptions about human cognition, are hand-crafted for particular settings, complex, or unable to explain a cognitive development of ToM compared to human's ToM. The IBL model learns from the observation of goal-directed agents' behavior in a gridworld navigation task, and it infers and predicts the behaviors of the agents in new gridworlds across different degrees of decision complexity in similar ways to the way human observers do. We provide evidence for the alignment of the IBL observer's predictions under various levels of decision complexity. We also advance the demonstration of the IBL predictions using a classic test of false beliefs (the Sally–Anne test), which is commonly used to test ToM in humans. We discuss our results and the potential of the IBL observer model to improve human–machine interactions. (shrink)

Technologies of communication condition human sense-making. They do so by creating the social environment we inhabit and extending their structural biases and logics through human use. As such, this essay inquires into the prevailing habits of mind in the digital era. Employing a media ecology of communication, I argue that digital computers and microprocessors are defined by three structural properties and, hence, underlying logics: digitization (binary code), algorithmic execution (input/output), and efficiency (machine logic). Repeated exposure to (...) these logics cultivates a digital mind, a model of thinking, communicating, and sense-making characterized by intransigence, impertinence, and impulsivity. I conclude the essay by exploring the broader implications of a digital mind, paying particular attention to the challenges it poses to democratic politics. (shrink)

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of (...) recent results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant. (shrink)

Ideas about human consciousness and mental functions will be analyzed and developed using cognitive science information available in the Upanishads, Brahmajnaana, Advaita and Dvaita schools of thought. -/- The analysis and development so done will be used to theorize and give scheme of human language acquisition and communication process clubbing with Sabdabrahma Siddhanta/Sphota Vaada which put forward infrasonic wave oscillator issuing pulses in infrasonic range and are reflected as brain waves. -/- Thus a brain-wave modulation/demodulation model of (...) class='Hi'>human language acquisition and communication will be advanced and put forward. Application of this study in mind-machine modeling, natural language comprehension field of artificial intelligence will also be hinted. -/- . (shrink)

Christopher Frith is a research professor at the Functional Imaging Laboratory of the Wellcome Department of Imaging Neuroscience at University College, London. He explores, experimentally, using the techniques of functional brain imaging, the relationship between human consciousness and the brain. His research focuses on questions pertaining to perception, attention, control of action, free will, and awareness of our own mental states and those of others. As the following discussion makes clear, Frith investigates brain systems involved in the choice of (...) one action over another and in the understanding of other people. Such investigations are aimed at understanding brain basis of autism and schizophrenia. In his widely cited study of schizophrenia, The Cognitive Neuropsychology of Schizophrenia , Frith argues that many of the positive symptoms of schizophrenia, such as delusions of control, auditory hallucinations, and thought insertion, involve problems of self-monitoring. Patients, in effect, lose track of their own intentions and mistakenly attribute agency for their own actions to someone else. Frith employs models of motor control, involving comparator mechanisms and efference copy, not only to explain delusions that involve movement, but also to develop a neurocognitive explanation of delusional cognition. (shrink)

In this paper I discuss the topics of mechanism and algorithmicity. I emphasise that a characterisation of algorithmicity such as the Turing machine is iterative; and I argue that if the human mind can solve problems that no Turing machine can, the mind must depend on some non-iterative principle — in fact, Cantor's second principle of generation, a principle of the actual infinite rather than the potential infinite of Turing machines. But as there has been (...) theorisation that all physical systems can be represented by Turing machines, I investigate claims that seem to contradict this: specifically, claims that there are noncomputable phenomena. One conclusion I reach is that if it is believed that the human mind is more than a Turing machine, a belief in a kind of Cartesian dualist gulf between the mental and the physical is concomitant. (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)

In the 1860s and 1870s the logic of Boole and the calculating machines of Babbage were key resources in W. S. Jevons’s attempt to construct a mechanical model of the mind, and both therefore played an important role in Jevons’s attempted revolution in economic theory. In this same period both Boole and Babbage were studied within the Cambridge Moral Sciences Tripos, but the Cambridge reading of Boole and Babbage was much more circumspect. Implicitly following the division of the moral (...) sciences into material and ‘real’ as established by the Rev. Grote, John Venn treated Boole’s logic as a purely formal science, while Alfred Marshall based his psychological model of the mechanical part of the human mind upon Babbage’s two-level machine. From the different perspectives of logic and psychology, Venn and Marshall did not simply incorporate their readings of Boole and Babbage, but also attempted to establish the limits to any mechanical explanation of the mind. This comparison of the attitudes to mental science of Jevons and Marshall provides a foundation from which the differing conceptions of economic theory of the two men can be established.Keywords: Alfred Marshall; Charles Babbage; George Boole; John Venn; Logic; Psychology. (shrink)

A leading contributor to artificial intelligence offers insight into the numerous ways in which the mind works to demonstrate how emotions and feelings are just ...

Analogical reminding in humans and machines is a great source for chance discoveries because analogical reminding can produce representational change and thereby produce insights. Here, we present a new kind of representational change associated with analogical reminding called packing. We derived the algorithm in part from human data we have on packing. Here, we explain packing and its role in analogy making, and then present a computer model of packing in a micro-domain. We conclude that packing is likely used (...) in human chance discoveries, and is needed if our machines are to make their own chance discoveries. (shrink)

In this multidisciplinary book, mathematician Matthew He provides integrative perspectives of algebraic biology, cognitive informatics, and poetic expressions of the human mind. Using classical Pythagorean Theorem and contemporary Category Theory, the proposed matrix models of the human mind connect three domains of the physical space of objective matters, mental space of subjective meanings, and emotional space of bijective modes; draws the connections between neural sparks and idea points, between synapses and idea lines, and between action (...) potentials and frequency curves. (shrink)

Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made against strong AI. It is (...) a very extensive review of what building a mind essentially amounts to drawing on insights and results from biology, physics, linguistics, computability, philosophy, and mathematics. (shrink)

The following is a summary of the author’s five-stage model of adult skill acquisition, developed in collaboration with Hubert L. Dreyfus. An earlier version of this article appeared in chapter 1 of Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (1986, Free Press, New York).

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities (...) in their underlying capacities, vs. when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring. (shrink)

The overarching guiding principle of Alan Turing’s work was directed towards modelling the human mind as a machine. It is extraordinary that Turing introduced, in his early papers, ideas that are only now beginning to be investigated. Throughout his life, he considered conjectures to be of great importance because they suggest useful lines of research. In my own conjecture, I am asking the question: what is the brain’s geometry? Can it ever be unravelled, or does its complexity (...) defy any form of visual representation? Recent research reveals that the detailed workings of the brain take place in a ‘global workspace’, where conscious contents appear to be disseminated globally to a great multitude of networks throughout the brain that are unconscious. During conscious tasks, neurons contributing to the global workspace enter into a coherent activation pattern by being tightly connected through long axons. Is it possible, therefore, that my proposed geometric pattern (an Islamic latticework) can be envisaged as a useful means of interconnecting multiple long axons in a unified global workspace dedicated to conscious thought? I share and communicate my idea through a process that combines visual imagery with musical performance (i.e. visual music). It is a method that can assist in clarifying concepts that might otherwise remain elusive or esoteric. (shrink)

Interactions between an intelligent software agent and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality, we frame (...) these interactions as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction, as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings. (shrink)

Is your mind the same thing as your brain, or are there aspects of mind beyond the brain's biology? This is the mind-body problem, and it has captivated curious minds since the dawn of human contemplation. Today many insist that the mind is completely reducible to the brain. But is that claim justified? In this stimulating anthology, twenty-five philosophers and scientists offer fresh insights into the mind-brain debate, drawing on psychology, neurology, philosophy, computer science, (...) and neurosurgery. Their provocative conclusion? The mind is indeed more than the brain. (shrink)

Machine learning (ML) trains itself by discovering patterns of correlations that can be applied to new inputs. That is a very powerful form of generalization, but it is also very different from the sort of generalization that the west has valorized as the highest form of truth, such as universal laws in some of the sciences, or ethical principles and frameworks in moral reasoning. Machine learning’s generalizations synthesize the general and the particular in a new way, creating a (...) multidimensional model that often retains more of the complex differentiating patterns it has uncovered in the training process than the human mind can grasp. Particulars speak louder in these models than they do in traditional generalizing frameworks. This creates an odd analogy with recent movements in moral philosophy, particularly the feminist ethics of care which rejects the application of general moral frameworks in favor of caring responses to the particular needs and interests of those affected by a moral decision. This paper suggests that our current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing our culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care. (shrink)

This paper presented during the Physis: Inhabiting the Earth conference, Florence, Italy, October 28?31,1986 examines how new brain research, by radically expanding our knowledge of the physiological foundation for empirical social science, makes possible a new understanding of the nature of higher mind and the place of the human being in evolution. It reports research supporting a model of right, left and frontal brain interaction in forecasting. It also describes development of measures and methods indicating a primarily frontal (...) brain guidance system for higher mind consisting of futures, systems, social, moral, and managerial sensitivities. Driving and shaping this guidance system, and thereby human cultural evolution, appears to be the function of our dialectical construction of the Image of the Future. (shrink)

The frontal feedback model argues that the sudden appearance of art and advancing technologies around 40,000 years ago in the hominid archaeological record was the end result of a recent fundamental change in the functional properties of the hominid brain, which occurred late in that brain's evolution. This change was marked by the switching of the driving mechanism behind the global, dynamic function of the brain from an "object-centered" bias, reflective of nonhuman primate and early hominid brains, to a "self-centered" (...) bias, reflective of modern Homo sapiens and perhaps late Homo erectus brains. Such a change in the global-functional properties of the brain was provided for by the progressive enlargement of the primate frontal lobe throughout its evolution. In late-developing hominids, this progressive enlargement effectively succeeded in reversing the preferred direction of information flow in the highest association areas of the neocortex from a caudo-rostral bias to a rostro-caudal bias. It was this reversal specifically that provided for the ability of humans to use symbolic thought in the creative expression of art, language, and the development of advancing technologies. Part 2 discusses the specific changes in the brain that occurred as a result of the reversal and how those changes were and are manifested as human abilities and experience. (shrink)

Western thought since the seventeenth century has been dominated by methodological solipsism (Krieger, 1991). The famous sound-bite of René Descartes 'cogito, ergo sum': 'I think, therefore I am', became the starting point for most discourse on the nature of things. This dictum does not advocate idealism. It does not assert that everything is necessarily a construct of the human mind. But it assumes that the world of things and beings is surveyed and interpreted from the point of view (...) of a single individual. Nowhere is this stance more entrenched than in the philosophy of science. To a remarkable degree the scientist is represented as studying the natural world as if alone in it, served only by mindless assistants who might as well be replaced by machines. Scientific theories are presented as systems of thought conjured up and tested by that same individual in a further series of single-headed operations. Research results are formulated and treated philosophically as the independent findings of lone explorers, each reporting the evidence of their own eyes and their rational inferences concerning the hidden mechanisms by which these personal percepts might be generated. Our epistemological role models are Robinson Crusoe and Sherlock Holmes, self-sufficient intellectuals to whom their human companions, Friday and Watson, are mere stooges. (shrink)

Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training (...) instances and rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. (shrink)

A novel theory of reflective consciousness, will and self is presented, based on modeling each of these entities using self-referential mathematical structures called hypersets. Pattern theory is used to argue that these exotic mathematical structures may meaningfully be considered as parts of the minds of physical systems, even finite computational systems. The hyperset models presented are hypothesized to occur as patterns within the "moving bubble of attention" of the human brain and any roughly human-mind-like AI system. (...) These ideas appear to be compatible with both panpsychist and materialist views of consciousness, and probably other views as well. Their relationship with the CogPrime AI design and its implementation in the OpenCog software framework is elucidated in detail. (shrink)

The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good (...) qualitative and semi-quantitative agreement has been achieved between the trajectories produced by a human subject, and the traces produced by a computer algorithm. The advantage of this reciprocal modelling option, besides facilitating agreement between the algorithm and the empirically observed trajectories, is that the theoretical model provides an explanation, and not just a description, of the active perception of the human subject. (shrink)

Machine generated contents note: -- Preface -- Acknowledgements -- Notes on Contributors -- PART I: COMPLEXITY IN ANIMAL MINDS -- Introduction: M.McGonigle-Chalmers -- Relational and Absolute Discrimination Learning by Squirrel Monkeys: Establishing a Common Ground with Human Cognition; B.T.Jones -- Serial List Retention by Non-Human Primates: Complexity and Cognitive Continuity; F.R.Treichler -- The Use of Spatial Structure in Working Memory: A Comparative Standpoint; C.De Lillo -- The Emergence of Linear Sequencing in Children: A Continuity Account and a (...) Formal Model; M.McGonigle-Chalmers&I.Kusel -- Sensitivity to Quantity: What Counts Across Species?; S.T.Boysen&A.M.Yocom -- PART II: COMPLEXITY IN ROBOTS -- Editorial Introduction; D.McFarland -- Towards Cognitive Robotics: Robotics, Biology and Developmental Psychology; M.Lee, U.Nehmzow&M.Rodriguez -- Structuring Intelligence: The Role of Hierarchy, Modularity and Learning in Generating Intelligent Behaviour; J.J.Bryson -- Epistemology, Access, and Computational Models; G.Luger -- Reasoning About Representations in Autonomous Systems: What P´Olya and Lakatos Have To Say; A.Bundy -- PART III: LANGUAGE, EVOLUTION AND THE COMPLEX MIND -- Editorial Introduction; K.Stenning -- How to Qualify for a Cognitive Upgrade: Executive Control, Glass Ceilings, and the Limits of Simian Success; A.Clark -- Private Codes and Public Structures; C.Allen -- The Emergence of Complex Language; W.Hinzen -- Language Evolution: Enlarging the Picture; K.Stenning&M.Van Lambalgen -- Epilogue: Reminiscences of Brendan McGonigle -- Index. (shrink)

To the age-old debate over what it means to be human, the relatively new fields of sociobiology and artificial intelligence bring new, if not necessarily compatible, insights. What have these two fields in common? Have they affected the way we define humanity? These and other timely questions are addressed with colorful individuality by the authors of _The Boundaries of Humanity_. Leading researchers in both sociobiology and artificial intelligence combine their reflections with those of philosophers, historians, and social scientists, while (...) the editors explore the historical and contemporary contexts of the debate in their introductions. The implications of their individual arguments, and the often heated controversies generated by biological determinism or by mechanical models of mind, go to the heart of contemporary scientific, philosophical, and humanistic studies. (shrink)

In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.

The currently leading cognitive theory of consciousness, Global Workspace Theory,1,2 postulates that the primary functions of consciousness include a global broadcast serving to recruit internal resources with which to deal with the current situation and to modulate several types of learning. In addition, conscious experiences present current conditions and problems to a "self" system, an executive interpreter that is identifiable with brain structures like the frontal lobes and precuneus.1Be it human, animal or artificial, an autonomous agent3 is said to (...) be functionally conscious if its control structure (mind) implements Global Workspace Theory and the LIDA Cognitive Cycle, which includes unconscious memory and control functions needed to integrate the conscious component of the system. We would therefore consider humans, many animals4 and even some virtual or robotic agents5,6 to be functionally conscious. Such entities may approach phenomenal consciousness, as found in human and other biological brains, as additional brain-like features are added.Here we argue that adding mechanisms to produce a stable, coherent perceptual field7 in a LIDA controlled mobile robot might provide a small but significant step toward phenomenal consciousness in machines.>8 We also propose that implementing several of the various notions of self in such a LIDA controlled robot may well prove another step toward phenomenal consciousness in machines. (shrink)

A model of human consciousness is presented here in terms of physics and electronics using Upanishadic awareness. The form of Atman proposed in the Upanishads in relation to human consciousness as oscillating psychic energy-presence and its virtual or unreal energy reflection maya, responsible for mental energy and mental time-space are discussed. Analogy with Fresnel’s bi-prism experimental set up in physical optics is used to state, describe and understand the form, structure and function of Atman and maya, the ingredients (...) of human consciousness. A description of phases of mind in terms of conscious states and transformation of mental energy is given. Four states of consciousness and four modes of language communication and understanding processes are also given. Implications of above scientific awareness of Upanishadic wisdom to the modern scientific fields of physiological psychology, cognitive sciences, mind-machine modeling and natural language comprehension are suggested. -/- . (shrink)

Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can (...) be derived from the demands of Rawlsian public reason. In the second part of the paper, I try to show that the argument from the limitations of human cognition fails to get AI off the hook of public reason. Against a growing trend in AI ethics, my main argument is that the analogy between human minds and artificial neural networks fails because it suffers from an atomistic bias which makes it blind to the social and institutional dimension of human reasoning processes. I suggest that developing interpretive AI algorithms is not the only possible answer to the explainability problem; social and institutional answers are also available and in many cases more trustworthy than techno-scientific ones. (shrink)

The comprehensive nature of information and insight available in the Upanishads, the Indian philosophical systems like the Advaita Philosophy, Sabdabrahma Siddhanta, Sphota Vaada and the Shaddarsanas, in relation to the idea of human consciousness, mind and its functions, cognitive science and scheme of human cognition and communication are presented. All this is highlighted with vivid classification of conscious-, cognitive-, functional- states of mind; by differentiating cognition as a combination of cognitive agent, cognizing element, cognized element; formation; (...) form and structure of cognition, instruments and means of cognition, validity of cognition and the nature of energy/matter which facilitates and also is the medium of cognition- cognizing process; and also as the container and content of cognition. The human communication process which is just the reverse of cognizing process is also presented with necessary description and detail. The sameness of cognitive / communicative process during language acquisition and communication processes and the modes of language acquisition and communication are also given. The hardware and software of human cognition, language acquisition and communication as envisaged in Indian spiritual and philosophical expressions are given. In the light of the information and insight obtained as above, the axioms for human cognition / communication are formed, framed and presented. A brain-wave frequency modulation / demodulation model of human cognition, communication and language acquisition and communication process based on Upanishadic expressions, Shaddarsanas and Sabdabrahma Siddhhanta is defined and discussed here. The use of these theories and models and axioms in mind-machine modelling and natural language comprehension branch of artificial intelligence will be put forward and highlighted. (shrink)

The central idea is that the cerebral cortex is a model building machine, where regularities in the world serve as templates for the models it builds. First it is shown how this idea can be naturalized, and how the representational contents of our internal models depend upon the evolutionarily endowed design principles of our model building machine. Current neuroscience suggests a powerful form that these design principles may take, allowing our brains to uncover deep structures of (...) the world hidden behind surface sensory stimulation, the individuals, kinds, and properties that form the objects of human perception and thought. It is then shown how this account solves various problems that arose for previous attempts at naturalizing intentionality, and also how it supports rather than undermines folk psychology. As in the parable of the blind men and the elephant, the seemingly unrelated pieces of earlier theories (information, causation, isomorphism, success, and teleology) emerge as different aspects of the evolved model-building mechanism that explains the intentional features of our kind of mind. (shrink)

Our first tools, sticks and stones, were very different from ourselves. But many tools now resemble us, in function or form, and they are beginning to have minds. A loose parallel with our own evolution suggests how they may develop in future. Computerless industrial machinery exhibits the behavioral flexibility of single-celled organisms. Today's best computer-controlled robots are like the simpler invertebrates. A thousand-fold increase in computer power in this decade should make possible machines with reptile-like sensory and motor competence. Growing (...) computer power over the next half century will allow robots that learn like mammals, model their world like primates and eventually reason like humans. Depending on your point of view, humanity will then have produced a worthy successor, or transcended inherited limitations and transformed itself into something quite new. No longer limited by the slow pace of human learning and even slower biological evolution, intelligent machinery will conduct its affairs on an ever faster, ever smaller scale, until coarse physical nature has been converted to fine-grained purposeful thought. (shrink)

"Making the Human Mind" is an attack on the widespread assumption that the mind has parts and that it is the interaction between these parts which accounts for some of the most characteristic human behaviour, the sorts of irrational behaviour displayed in self-deception and weakness of will. The implications of this attack are considerable: Professor Sharpe contests a realism about the mind, the belief that there is an inventory which an all-seeing deity could compile and (...) which could contain answers to all the questions we could ask about people. With this goes a hermeneutic approach to the understanding of human behaviour: these forms of understanding are markedly different from that suggested by the scientific model and favoured by those who partition the mind. Finally, the author undermines eliminative materialism and the idea that the way we talk about the mind constitutes a "folk psychology", arguing that what is distinctively human about the human mind has been created by self-consciousness and is self-created. (shrink)

We investigate the mathematics of a model of the human mind which has been proposed by the psychologist Jens Mammen. Mathematical realizations of this model consists of what the first author (A.T.) has called Mammen spaces, where a Mammen space is a triple in the Baumgartner–Laver model.Finally, consequences for psychology are discussed.

Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived (...) from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, (...) I first contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

Making the Human Mind is an attack on the widespread assumption that the mind has parts, that the interaction between these parts accounts for some of the most characteristic human behavior, the sorts of irrational behavior displayed in self-deception and weakness of will. The implications of this attack are considerable: Sharpe contests a realism about the mind, the belief that there is an inventory which an all-seeing deity could compile containing answers to all the questions (...) we ask about people, whether they are deceitful or in love, or what their motivation is. With this goes the hermeneutic approach to the understanding of human behavior. These forms of understanding are markedly different from that suggested by the scientific model and favored by those who partition the mind. (shrink)

In this essay I argue that one of the things that matters most to Descartes' account of mind is that we use our minds actively. This is because for him only an active mind is able to re-organize its passionate experiences in such a way that a genuinely human, self-governed life of virtue and true contentment becomes possible. To bring out this connection, I will read the Meditations against the backdrop of Descartes' correspondence with Elisabeth. This will (...) reveal that in Descartes' writings there is a crucial connection between the conception of ourselves as dreamers and the idea that we fail to realize our true potential as self-determined, active agents. Dreams, as Descartes conceives them, are passively received mental states that inhibit our freedom to use reason at will. To awaken here takes the form of activating our thoughts, which holds the key to freeing ourselves from the stimulus-response patterns that Descartes takes to be at work in animal conduct. Applied to the Meditations, these insights suggest that this work engages in questions far beyond the epistemological agenda. By activating the mind, the Meditations teach us how to realize our true human potential as virtuous thinkers and passionate agents. (shrink)

After more than 60 years, Shannon’s research continues to raise fundamental questions, such as the one formulated by R. Luce, which is still unanswered: “Why is information theory not very applicable to psychological problems, despite apparent similarities of concepts?” On this topic, S. Pinker, one of the foremost defenders of the widespread computational theory of mind, has argued that thought is simply a type of computation, and that the gap between human cognition and computational models may be (...) illusory. In this context, in his latest book, titled Thinking Fast and Slow, D. Kahneman provides further theoretical interpretation by differentiating the two assumed systems of the cognitive functioning of the human mind. He calls them intuition (system 1) determined to be an associative (automatic, fast and perceptual) machine, and reasoning (system 2) required to be voluntary and to operate logical-deductively. In this paper, we propose a mathematical approach inspired by Ausubel’s meaningful learning theory for investigating, from the constructivist perspective, information processing in the working memory of cognizers. Specifically, a thought experiment is performed utilizing the mind of a dual-natured creature known as Maxwell’s demon: a tiny “man–machine” solely equipped with the characteristics of system 1, which prevents it from reasoning. The calculation presented here shows that the Ausubelian learning schema, when inserted into the creature’s memory, leads to a Shannon-Hartley-like model that, in turn, converges exactly to the fundamental thermodynamic principle of computation, known as the Landauer limit. This result indicates that when the system 2 is shut down, both an intelligent being, as well as a binary machine, incur the same minimum energy cost per unit of information (knowledge) processed (acquired), which mathematically shows the computational attribute of the system 1, as Kahneman theorized. This finding links information theory to human psychological features and opens the possibility to experimentally test the computational theory of mind by means of Landauer’s energy cost, which can pave a way toward the conception of a multi-bit reasoning machine. (shrink)

In this presentation we shall discuss the nature of mind vis-a-vis the brain and computers. Such a comparison presumes a general equivalence of brains and computers and models the brain as a huge biological computer, with consciousness added. The uniqueness of Mind in the lines of ancient Indian thought has been accpted as the basic concept in the analysis. Regarding the chief difference between mind and brain, material of the mind is taken to be subtle (...) matter.The brain is made of gross matter and is a part of the physical body. Considering the brain and the computer, the brain is a biological structure made of organic molecules, whereas computer chips are inorganic objects manufactured by etching circuits on the surface of silica chips. Thus the human brain, occupying volume, is a volumetric entity whereas a computer, as electronic circuitry on a silica chip, is an areal entity. This explains the vast processing power andexceptional capabilities of the human brain. (shrink)

Despite the fact that the number of neurons in the human brain has been identified in cognitive and neural sciences, the magnitude of human memory capacity is still unknown. This paper reports the discovery of the memory capacity of the human brain, which is on the order of 10 8432 bits. A cognitive model of the brain is created, which shows that human memory and knowledge are represented by relations, i.e., connections of synapses between neurons, rather (...) than by the neurons themselves as the traditional container metaphor described. The determination of the magnitude of human memory capacity is not only theoretically significant in cognitive science, but also practically useful to unveil the human potential, as well as the gap between the natural and machine intelligence. (shrink)