Marcus, G F
Constructivism is the Piagetian notion that learning leads the child to develop new types of representations. For example, on the Piagetian view, a child is born without knowing that objects persist in time even when they are occluded; through a process of learning, the child comes to know that objects persist in time. The trouble with this view has always been the lack of a concrete, computational account of how a learning mechanism could lead to such a change. Recently, however, in a book entitled Rethinking Innateness. Elman et al. (Elman, J.L., Bates, E., Johnson, M.H., Karmiloff-Smith, A., Parisi, D., Plunkett, K., 1996. Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press) have claimed that connectionist models might provide an account of the development of new kinds of representations that would not depend on the existence of innate representations. I show that the models described in Rethinking Innateness depend on innately assumed representations and that they do not offer a genuine alternative to nativism. Moreover, I present simulation results which show that these models are incapable of deriving genuine abstract representations that are not presupposed. I then give a formal account of why the models fail to generalize in the ways that humans do. Thus, connectionism, at least in its current form, does not provide any support for constructivism. I conclude by sketching a possible alternative.
The work of connectionist researchers is examined in order to understand better the implications for modeling second language learning processes. Connectionism is a biologically-oriented framework for understanding complex behavior, and provides a modeling tool (computer simulation) that behaves and learns without rules being explicitly wired into…
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Usually, the problems in AI may be many times related to Philosophy of Mind, and perhaps because this reason may be in essence very disputable. So, for instance, the famous question: Can a machine think? It was proposed by Alan Turing . And it may be the more decisive question, but for many people it would be a nonsense. So, two of the very fundamental and more confronted positions usually considered according this line include the Connectionism and the Computational Theory of Mind. We analyze here its content, with their past disputes, and current situation.
can use their hidden layers to learn difficult discriminations. such as panty or the Penzias two clumps/three clumps problem, where the output is...sauce." For novel sentences that are similar to the training sentences (e.g., train on "the girl hit the boy," test on -the boy hit the girl "), the...overridden by semantic considerations. as in this example from Wendy Lehnert (personal communicanon): (5) John saw the girl with the telescope in a red
Despite the number of articles devoted to the topic of content-based instruction (CBI), little attempt has been made to link the claims for CBI to research in cognitive science. In this article, I review the CBI model of foreign language (FL) instruction in the context of its close alignment with two emergent frameworks in cognitive science:…
Read, S J; Vanman, E J; Miller, L C
We argue that recent work in connectionist modeling, in particular the parallel constraint satisfaction processes that are central to many of these models, has great importance for understanding issues of both historical and current concern for social psychologists. We first provide a brief description of connectionist modeling, with particular emphasis on parallel constraint satisfaction processes. Second, we examine the tremendous similarities between parallel constraint satisfaction processes and the Gestalt principles that were the foundation for much of modem social psychology. We propose that parallel constraint satisfaction processes provide a computational implementation of the principles of Gestalt psychology that were central to the work of such seminal social psychologists as Asch, Festinger, Heider, and Lewin. Third, we then describe how parallel constraint satisfaction processes have been applied to three areas that were key to the beginnings of modern social psychology and remain central today: impression formation and causal reasoning, cognitive consistency (balance and cognitive dissonance), and goal-directed behavior. We conclude by discussing implications of parallel constraint satisfaction principles for a number of broader issues in social psychology, such as the dynamics of social thought and the integration of social information within the narrow time frame of social interaction.
How can unity of content be obtained from the diversity of expression; from symbolism to connectionism How can unity of content be obtained from the diversity of expression; from symbolism to connectionism
José Marcelino Poersch
Full Text Available Adopting a symbolic paradigm, reading can be considered as an act of communication leading a reader to intentionally build in his mind, from the perception of printed symbols and from the aid of non-verbal elements, a substance of content similar to the one the writer wanted to express by means of a verbal written message. Nowadays we see that the strictness – typically signalized by the staticity of mental epresentations (symbols and the seriality of the information process (classical artificial intelligence – with which the symbolic paradigm explains the cognitive processes of the reading process in our mind clearly contrasts with the flexibility – characterized by the use of dynamic “ad hoc” configurations obtained by means of parallel distributed information among the interneural connections – with which the connectionist paradigm tries to explain the sequence of processes (hidden units in our brain interpolated between input and output data. In a connectionist paradigm, reading consists of constructing, in the brain of the reader, a network of synaptic connections as answers to individual stimuli and experiences. It follows that the new text built in the reader’s brain, even keeping the cultural unity, will show diversities reflecting the way each reader experiences the world. The diversity in communicative acts can more easily be noticed in a translating Adopting a symbolic paradigm, reading can be considered as an act of communication leading a reader to intentionally build in his mind, from the perception of printed symbols and from the aid of non-verbal elements, a substance of content similar to the one the writer wanted to express by means of a verbal written message. Nowadays we see that the strictness – typically signalized by the staticity of mental epresentations (symbols and the seriality of the information process (classical artificial intelligence – with which the symbolic paradigm explains the cognitive processes of the reading process in our mind clearly contrasts with the flexibility – characterized by the use of dynamic “ad hoc” configurations obtained by means of parallel distributed information among the interneural connections – with which the connectionist paradigm tries to explain the sequence of processes (hidden units in our brain interpolated between input and output data. In a connectionist paradigm, reading consists of constructing, in the brain of the reader, a network of synaptic connections as answers to individual stimuli and experiences. It follows that the new text built in the reader’s brain, even keeping the cultural unity, will show diversities reflecting the way each reader experiences the world. The diversity in communicative acts can more easily be noticed in a translating
Helm, P.A. van der
In Perceptual Dynamics, Sundqvist argues that the early 20th-century Gestaltist ideas gain fresh relevance by recent developments in cognitive science, particularly by approaches that start from either dynamic systems theory or connectionism. In this review, it is argued that Sundqvist's book is a
Brooks, David W.; Shell, Duane F.
Working memory is where we "think" as we learn. A notion that emerges as a synthesis from several threads in the research literatures of cognition, motivation, and connectionism is that motivation in learning is the process whereby working memory resource allocation is instigated and sustained. This paper reviews much literature on motivation and…
eventually be able to appeal to the members of the group on whose behalf they have developed surrogacy . The group in whom surrogate consciousness is...California at San Diego. His book Natural Ethical Facts: Evolution, Connectionism, and Moral Cognition is available from MIT Press; his co-authored book
Hammerton, J; Chen, SH; Cheng, HD; Chiu, DKY; Das, S; Duro, R; Kerre, EE; Leong, HV; Li, Q; Lu, M; Romay, MG; Ventura, D; Wu, J
Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research in connectionism. However much of this work has involved small and/or artificially created data sets, whilst other approaches to language learning are now routinely applied to large real-world
Roč. 4, č. 1 (1997), s. 23 ISSN 1355-8250. [The Brain and Self Workshop: Toward a Science of Consciousness. Elsinore, 21.08.1997-24.08.1997] R&D Projects: GA ČR GA201/95/0992 Keywords : free will and agency * attention * emotion * neural networks and connectionism * nonlinear dynamics
Pontier, M.A.; Hoorn, J.F.; Miyake, N.; Peebles, B.; Cooper, R.P.
With the increasing dependence on autonomous operating agents and robots the need for ethical machine behavior rises. This paper presents a moral reasoner that combines connectionism, utilitarianism and ethical theory about moral duties. The moral decision-making matches the analysis of expert
Knee, Robert Everett
The purpose of the present study was to establish evidence for the suggested integration of the theories of connectionism and leadership. Recent theoretical writings in the field of leadership have suggested that the dynamic representations generated by the connectionist perspective is an appropriate approach to understanding how we perceive leaders. Similarly, implicit leadership theory (ILT) explains that our cognitive understandings of leaders are based on a cognitive structure that we u...
Kymissis, E; Poulson, C L
The concept of imitation has undergone different analyses in the hands of different learning theorists throughout the history of psychology. From Thorndike's connectionism to Pavlov's classical conditioning, Hull's monistic theory, Mowrer's two-factor theory, and Skinner's operant theory, there have been several divergent accounts of the conditions that produce imitation and the conditions under which imitation itself may facilitate language acquisition. In tracing the roots of the concept of...
Wang, Haohan; Raj, Bhiksha
This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...
This paper describes an application of the second-generation method CAHR (Connectionism Assessment of Human Reliability; Straeter, 1997) that was developed at the Technical University of Munich and the GRS in the years from 1992 to 1998. The method enables to combine event analysis and assessment and therefore to base human reliability assessment on past experience. The term connectionism' was coined by modeling human cognition on the basis of artificial intelligence models. Connectionism is a term describing methods that represent complex interrelations of various parameters (known for pattern recognition, expert systems, modeling of cognition). The method enables to combine event analysis and assessment on past experience. The paper will demonstrate the application of the method to communication aspects in NPPs (Nuclear Power Plants) and will give some outlooks for further developments. Application of the method to the problem of communication failures, for examples, initial work on communication within the low-power and shut down study for Boiling Water Reactors (BWRs), investigation of communication failures, importance of procedural and verbal communication for different error type and causes for failures in procedural and verbal communication are explained. (S.Y.)
Parnas, Josef; Bovet, P
Etiologic research in psychiatry relies on an objectivist epistemology positing that human cognition is specified by the "reality" of the outer world, which consists of a totality of mind-independent objects. Truth is considered as some sort of correspondence relation between words and external...... (connectionism), and developmental psychology (developmental biodynamics) converge in viewing living organisms as self-organizing systems. In this perspective, the organism is not specified by the outer world, but enacts its environment by selecting relevant domains of significance that constitute its world...
Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.
Muhammad Ali Zahidin
Full Text Available Connectionism theory view that learning occurs by experimenting and making mistakes, as well as language learning in early childhood. Each early childhood initially tries to speak and make mistakes before they become accustomed to the pronunciation and correct. Any child's vocabulary has increased and finally to convey the intent and purpose to his interlocutor well. The process can be seen from the process of learning a second language in early childhood. In second language learning is done by younger children show conformity of laws and principles of learning connectionism theory. Giving the prize being a stimulus of children to learn and motivated to recite the words proposed as a form of positive response. Reduced average number of repetitions of the first week as many as seventeen times to three times in the fourth week is a proof of principle trial and error learning proposed by Edward Thorndike. Factors second language learning is strongly influenced by age, first language, language development and motivation.
Full Text Available The Purpose of this study is to determine the achievement and improvement of students’ mathematical connectionability through using outdoor mathematics learning. 64 students from the fifth grade of Primary School at SDN 65 and SDN 67 Bengkulu City were taken as the sample of this study. While the method of the research used in this research is experiment with quasi-experimental designs non-equivalent control group. The results of the study are as follows: (1 There is an increasing ability found in mathematical connection of students whom taught by using outdoors mathematics learning is 0,53; (2 Based on statical computation that achievement of students’ ability of mathematical connection is taught by using outdoor mathematics learning score is 71,25. It is higher than the students score 66,25 which were taught by using the conventional learning. So as to improve students’ mathematical connection, teachers are suggested to use the outdoors mathematics learning
Full Text Available The article discusses how a modern form of AI programming, known as Connectionism in a design known as Distributed Artificial Intelligence (DAI, affects the perception Luhmann has on mass media's role concerning second-order observations. DAI uses nodes to create activity in the systems and not the codes used by the Classic or Symbolic form of AI. Luhmann’s theory can be developed by replacing the systems codes with nodes that change depending on their relations to other nodes. In this way, we can reformulate the concept of communication, so that it includes the systems interactions with the environment. It creates better conditions so that observing opportunities may arise directly from these interactions. Internet and AI-programmed search systems and robots can then act as an artificial semiotic system that creates opportunities for making observations.
Heloísa Pedroso de Moraes Feltes
Full Text Available The aim of this paper is to reflect on the character of embodiment in the framework of Cognitive Linguistics based on Lakoff, collaborators and interlocutors. Initially I characterize the embodied mind, via cognitive experientialism. In these terms, the theory shapes how human beings build and process knowledge structures which regulate their individual and collective lives. Next, the Neural Theory of Language in which embodiment is rebuilt from a five level paradigm, where structured connectionism carries on the very burden of computational description and explanation is discussed. From these assumption, classical problems about computational implementations for models of natural language functioning as reductionist-physicalist approaches, I then conclude by assuming that embodiment, as an investigation phenomenon, shouldn't be formulated in terms of levels, being treated as interfaces instead, at such manner that: (a the epistemological commitments should be synchronically sustained in all interfaces of the investigation paradigm; (b the conventional computational level should be taken as one of the problems which has to be treated in the structured connectionism plan; (c the strategic reduction levels paradigm and the results obtained from it might imply a kind of modularization of the program of research itself; e (d the modules would be interdependent only as a result of the reductionist proposal. As a result, I assume that it is possible to do Cognitive Linguistics without adhering to structured connectionism, or to neurocomputacional simulation, as long as one would operate with interfaces constructions between domains of investigation and not with a reductionist features paradigm treated in terms of "levels".Este artigo é uma reflexão sobre o caráter da corporeidade no quadro da Lingüística Cognitiva associada a Lakoff, colaboradores e interlocutores. Inicia-se com a caracterização de mente corpórea, via experiencialismo
Full Text Available In temporal lobe epilepsy (TLE, the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin–Huxley (HH type neurons and Pinsky–Rinzel (PR type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR, we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.
Barnden, John A.; Fields, Christopher A.
Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.
Dawson, Michael R W; Dupuis, Brian
The contingency between cues and outcomes is fundamentally important to theories of causal reasoning and to theories of associative learning. Researchers have computed the equilibria of Rescorla-Wagner models for a variety of contingency problems, and have used these equilibria to identify situations in which the Rescorla-Wagner model is consistent, or inconsistent, with normative models of contingency. Mathematical analyses that directly compare artificial neural networks to contingency theory have not been performed, because of the assumed equivalence between the Rescorla-Wagner learning rule and the delta rule training of artificial neural networks. However, recent results indicate that this equivalence is not as straightforward as typically assumed, suggesting a strong need for mathematical accounts of how networks deal with contingency problems. One such analysis is presented here, where it is proven that the structure of the equilibrium for a simple network trained on a basic contingency problem is quite different from the structure of the equilibrium for a Rescorla-Wagner model faced with the same problem. However, these structural differences lead to functionally equivalent behavior. The implications of this result for the relationships between associative learning, contingency theory, and connectionism are discussed.
This review deals with how geomagnetic storms can be predicted with the use of Artificial Intelligence (AI) techniques. Today many different Al techniques have been developed, such as symbolic systems (expert and fuzzy systems) and connectionism systems (neural networks). Even integrations of AI techniques exist, so called Intelligent Hybrid Systems (IHS). These systems are capable of learning the mathematical functions underlying the operation of non-linear dynamic systems and also to explain the knowledge they have learned. Very few such powerful systems exist at present. Two such examples are the Magnetospheric Specification Forecast Model of Rice University and the Lund Space Weather Model of Lund University. Various attempts to predict geomagnetic storms on long to short-term are reviewed in this article. Predictions of a month to days ahead most often use solar data as input. The first SOHO data are now available. Due to the high temporal and spatial resolution new solar physics have been revealed. These SOHO data might lead to a breakthrough in these predictions. Predictions hours ahead and shorter rely on real-time solar wind data. WIND gives us real-time data for only part of the day. However, with the launch of the ACE spacecraft in 1997, real-time data during 24 hours will be available. That might lead to the second breakthrough for predictions of geomagnetic storms.
The effort to relate mental and biological functioning has fluctuated between two doctrines: 1) an attempt to explain mental functioning as a collective property of the brain and 2) as one relatied to other mental processes associated with specific regions of the brain. The article reviews the main theories developed over the last 200 years: phrenology, the psuedo study of the brain, mass action, cellular connectionism and distributed processing among others. In addition, approaches have emerged in recent years that allows for an understanding of the biological determinants and individual differences in complex mental processes through what is called cognitive neuroscience. Knowing the definition of neuroscience, the learning of memory, the ways in which learning occurs, the principles of the neural basis of memory and learning and its effects on brain function, among other things, allows us the basic understanding of the processes of memory and learning and is an important requirement to address the best manner to commit to the of training future specialists in Psychiatry. Copyright © 2012 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.
Piccinini, Gualtiero; Scarantino, Andrea
Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both - although others disagree vehemently. Yet different cognitive scientists use 'computation' and 'information processing' to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates' empirical aspects.
Full Text Available Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes. In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe--replaced by the relationships between the maps that transform them.
Phillips, Steven; Wilson, William H
Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe--replaced by the relationships between the maps that transform them.
Cognitive science has become the most influential paradigm on mental health in the late 20(th) and the early 21(st) centuries. In few years, the concepts, problem approaches and solutions proper to this science have significantly changed. Introduction and discussion of the fundamental concepts of cognitive science divided in four stages: Start, Classic Cognitivism, Connectionism, and Embodying / Enacting. The 2(nd) Part of the paper discusses the above mentioned fourth stage and explores the clinical setting, especially in terms of cognitive psychotherapy. The embodying/enacting stage highlights the role of the body including a set of determined evolutionary movements which provide a way of thinking and exploring the world. The performance of cognitive tasks is considered as a process that uses environmental resources that enhances mental skills and deploys them beyond the domestic sphere of the brain. On the other hand, body and mind are embedded in the world, thus giving rise to cognition when interacting, a process known as enacting. There is a close connection between perception and action, hence the interest in real-time interactions with the world rather than abstract reasoning. Regarding clinics, specifically the cognitive therapy, there is little conceptual discussion maybe due to good results from practice that may led us to consider that theoretical foundations are firm and not problem-raising. Copyright © 2012 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.
Zhang, Liyuan; Fan, Denggui; Wang, Qingyun
In temporal lobe epilepsy (TLE), the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal diversity and the indeterminacy of network connection. Hence, based on Hodgkin-Huxley (HH) type neurons and Pinsky-Rinzel (PR) type neurons coupling with glutamatergic and GABAergic synaptic connections respectively, we propose a computational framework which contains dentate gyrus (DG) region and CA3 region. By regulating the concentration range of N-methyl-D-aspartate-type glutamate receptor (NMDAR), we demonstrate the pyramidal neuron can generate transitions from interictal to seizure discharges. This suggests that enhanced endogenous activity of NMDAR contributes to excitability in pyramidal neuron. Moreover, we conclude that excitatory discharges in CA3 region vary considerably on account of the excitatory currents produced by the excitatory pyramidal neuron. Interestingly, by changing the backprojection connection, we find that glutamatergic type backprojection can promote the dominant frequency of firings and further motivate excitatory counterpropagation from CA3 region to DG region. However, GABAergic type backprojection can reduce firing rate and block morbid counterpropagation, which may be factored into the terminations of TLE. In addition, neuronal diversity dominated network shows weak correlation with different backprojections. Our modeling and simulation studies provide new insights into the mechanisms of seizures generation and connectionism in local hippocampus, along with the synaptic mechanisms of this disease.
Vyacheslav A. Starodubtsev
Full Text Available The purpose of the paper is to compare the development trends of information and communication environment, the global educational space and pedagogical ideas, which directly or indirectly affect the application of ICT in education. The study has been based on the foresight results and the content of the Internet publications on the prospects for sustainable development of education in the context of UNESCO Incheon Declaration for Education 2030. The content analysis of pedagogical publications has demonstrated that the current understanding of knowledge appeals to its socialization and dissemination in the global network environment, whose technological basis is rapidly growing. The educational process in the Learning Society is regarded as a distributed one among some formal education establishments (“universities of the world for one” and the community of content curators. The function of content curators is to deliver comments, generalize and promote new information that supports learning needs of different global network users. The relation model of the subjects of the informational and educational space has been described. The model includes lecturers and learners as well as the content curators. The necessity of humanitarian potential development in the informational and communicational environment has been argued as well as the development of a creative class of people who share their existential experience, knowledge and wisdom. The development of every society member in the robot-based artificial intelligence environment has been considered to be defective without any interpersonal interaction and learners’ activity in the online community. Thus, the psychological and pedagogical theories of connectionism, social learning, multiple intelligences, and some others are to be recognized in the development of hardware and software base for education technologies.
Alexander, J A.; Mozer, M C.
Although neural networks often achieve impressive learning and generalization performance, their internal workings are typically all but impossible to decipher. This characteristic of the networks, their opacity, is one of the disadvantages of connectionism compared to more traditional, rule-oriented approaches to artificial intelligence. Without a thorough understanding of the network behavior, confidence in a system's results is lowered, and the transfer of learned knowledge to other processing systems - including humans - is precluded. Methods that address the opacity problem by casting network weights in symbolic terms are commonly referred to as rule extraction techniques. This work describes a principled approach to symbolic rule extraction from standard multilayer feedforward networks based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to the actual weights of a unit. Depending on the requirements of the application domain, the approach can accommodate n-ary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k(2)) complexity, or more general classes of recursive n-of-m expressions with O(k(L+2)) complexity, where k is the number of inputs to an unit and L the recursion level of the expression class. Compared to other approaches in the literature, our method of rule extraction offers benefits in simplicity, computational performance, and overall flexibility. Simulation results on a variety of problems demonstrate the application of our procedures as well as the strengths and the weaknesses of our general approach.
Biological psychiatry has been dominated by a psychopharmacologically-driven neurotransmitter dysfunction paradigm. The objective of this paper is to explore a reductionist assumption underlying this paradigm, and to suggest an improvement on it. The methods used are conceptual analysis with a comparative approach, particularly using illustrations from the history of both biological psychiatry and molecular biology. The results are that complete reduction to physicochemical explanations is not fruitful, at least in the initial stages of research in the medical and life sciences, and that an appropriate (non-reducible) integrative principle--addressing a property of the whole system under study--is required for each domain of research. This is illustrated in Pauling's use of a topological integrative principle for the discovery of the functioning of proteins and in Watson and Crick's use of the notion of a genetic code as an integrative principle for the discovery of the structure of genes. The neurotransmitter dysfunction paradigm addresses single molecules and their neural pathways, yet their interactions within the CNS as a whole seem most pertinent to mental disorders such as schizophrenia. The lack within biological psychiatry of an integrative principle addressing a property of the CNS as a whole may be responsible for the empirical failure of orthomolecular psychiatry, as well as for the central role that serendipity has played in the study of mental disorders, which is dominated by the neurotransmitter paradigm. The conclusion is that research in biological psychiatry may benefit from using, at least initially, some integrative principle(s) addressing a property of the CNS as a whole, such as connectionism or a hierarchical notion.
Carlucci, Lorenzo; Case, John
A U-shaped curve in a cognitive-developmental trajectory refers to a three-step process: good performance followed by bad performance followed by good performance once again. U-shaped curves have been observed in a wide variety of cognitive-developmental and learning contexts. U-shaped learning seems to contradict the idea that learning is a monotonic, cumulative process and thus constitutes a challenge for competing theories of cognitive development and learning. U-shaped behavior in language learning (in particular in learning English past tense) has become a central topic in the Cognitive Science debate about learning models. Antagonist models (e.g., connectionism versus nativism) are often judged on their ability of modeling or accounting for U-shaped behavior. The prior literature is mostly occupied with explaining how U-shaped behavior occurs. Instead, we are interested in the necessity of this kind of apparently inefficient strategy. We present and discuss a body of results in the abstract mathematical setting of (extensions of) Gold-style computational learning theory addressing a mathematically precise version of the following question: Are there learning tasks that require U-shaped behavior? All notions considered are learning in the limit from positive data. We present results about the necessity of U-shaped learning in classical models of learning as well as in models with bounds on the memory of the learner. The pattern emerges that, for parameterized, cognitively relevant learning criteria, beyond very few initial parameter values, U-shapes are necessary for full learning power! We discuss the possible relevance of the above results for the Cognitive Science debate about learning models as well as directions for future research. Copyright © 2013 Cognitive Science Society, Inc.
Full Text Available The analysis of the reason-emotion dynamics intersects several disciplinary fields, such as psychology, medicine, informatics, linguistics, neuroscience, with a specific relevance for Education Sciences, as it offers interesting perspectives over its influence on the learning process. Such issues are rooted in philosophical reflections by Plato, Aristotle and later by Descartes, Vico and Kant. These dualistic perspectives will be definitively abandoned in favour of a globalist vision of the mind-body relationship, during the first half of the XX century, particularly thanks to Dewey (1933 who, inspired by Darwin’s theories, was the first to support this unity by recognizing an intersection among physical, mental and environmental processes. Over the last decades, an imperatively anti-dualistic analysis has been developing in the field of neurosciences and cognitive linguistics: on the one hand, cognitivism, considering the mind in its function of symbolic manipulation; on the other hand, connectionism, studying neural networks. Furthermore, recent scientific research has allowed mapping in a detailed - albeit admittedly incomplete manner - the complex activity of the brain and highlighting analogies between elementary connections and complex interactions. The systemic perspective is hence considering “mind and body”, “reason and emotion” as two interconnected and essential aspects of human complexity. In this regard, Damasio’s research shows how participation of the organism to conscious experience returns to the consciousness itself those biological requirements which are essential to legitimate it as an object of scientific study. Knowledge is generated by socio-experiential relationships that play a crucial role within knowledge representation. The mind takes therefore an active role in shaping the representation of the world: understanding does not just consist in a mere reproduction of the external world in our mind; instead, it
Luhmanns masmedieteori och Internet som ett artificiellt intelligent semiotiskt system Luhmanns massmedieteori och Internet som ett artificiellt intelligent semiotiskt system [Luhmann’s mass-media theory and Internet as an artificial intelligent semiotic system
Full Text Available Artikeln diskuterar hur en modern form av AI-programmering, som kallas Konnektionism i en design som kallas Distribuerad AI (DAI, påverkar den uppfattning Luhmann har om massmediernas roll för den andra ordningens observationer. DAI använder noder för att skapa aktivitet i systemen och inte de koder som styr processerna i den klassiska eller symboliska formen av AI. Luhmanns teori kan utvecklas genom att ersätta systemens koder med noder som förändras beroende på i vilken relation de står till andra noder. På så sätt kan kommunikationsbegreppet utvecklas så att det också omfattar systemens interaktioner med omvärlden. Det skapar en bättre förutsättning för att observationsmöjligheter direkt uppstår genom systemens relationer till omvärlden. Internet och AI-programmerade söksystem och robotar kan då fungera som ett artificiellt semiotiskt system som skapar möjligheter att göra observationer.The article discusses how a modern form of AI programming, known as Connectionism in a design known as Distributed Artificial Intelligence (DAI, affects the perception Luhmann has on mass media's role concerning second-order observations. DAI uses nodes to create activity in the systems and not the codes used by the Classic or Symbolic form of AI. Luhmann’s theory can be developed by replacing the systems codes with nodes that change depending on their relations to other nodes. In this way, we can reformulate the concept of communication, so that it includes the systems interactions with the environment. It creates better conditions so that observing opportunities may arise directly from these interactions. Internet and AI-programmed search systems and robots can then act as an artificial semiotic system that creates opportunities for making observations.
Assessment of the human factor in the quantification of technical system reliability taking into consideration cognitive-causal aspects. Partial project 2. Modeling of the human behavior for reliability considerations. Final report
Jennerich, Marco; Imbsweiler, Jonas; Straeter, Oliver; Arenius, Marcus
This report presents the findings of the project for the consideration of human factor in the quantification of the reliability of technical systems, taking into account cognitive-causal aspects concerning the modeling of human behavior of reliability issues (funded by the Federal Ministry of Economics and Technology; grant number 15014328). This project is part of a joint project with the University of Applied Sciences Zittau / Goerlitz for assessing the human factor in the quantification of the reliability of technical systems. The concern of the University of Applied Sciences Zittau / Goerlitz is the mathematical modeling of human reliability by means of a fuzzy set approach (grant number 1501432A). The part of the project presented here provides the necessary data basis for the evaluation of the mathematical modeling using fuzzy set approach. At the appropriate places in this report, the interfaces and data bases between the two projects are outlined accordingly. HRA-methods (Human Reliability Analysis) are an essential component to analyze the reliability of socio-technical systems. Various methods have been established and are used in different areas of application. The established HRA methods have been checked on their congruence. In particular the underlying models and their parameters such as performance-influencing factors and situational influences have been investigated. The elaborated parameters were combined into a hierarchical class structure. Cross-domain incidents were studied. The specific performance-influencing factors have been worked out and have been integrated into a cross-domain database. The dominant (critical) situational factors and their interactions within the event data were identified using the CAHR method (connectionism Assessment of Human Reliability). Task dependent cognitive load profiles have been defined. Within these profiles qualitative and quantitative data of the possibility of emergence of errors have been acquired. This