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What is computational intelligence and where is it going?

In Wlodzislaw Duch & Jacek Mandziuk (eds.), Challenges for Computational Intelligence. Springer. pp. 1--13 (2007)

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  1. Through a narrow window: working memory capacity and the detection of covariation.Yaakov Kareev - 1995 - Cognition 56 (3):263-269.
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  • A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.Pascal Fries - 2005 - Trends in Cognitive Sciences 9 (10):474-480.
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  • On levels of cognitive modeling.Ron Sun, L. Andrew Coward & Michael J. Zenzen - 2005 - Philosophical Psychology 18 (5):613-637.
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  • Vision.David Marr - 1982 - W. H. Freeman.
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  • Unified theories of cognition.Allen Newell - 1990 - Cambridge: Harvard University Press.
    In this book, Newell makes the case for unified theories by setting forth a candidate.
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  • The magical number 4 in short-term memory: A reconsideration of mental storage capacity.Nelson Cowan - 2001 - Behavioral and Brain Sciences 24 (1):87-114.
    Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits (...)
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  • The Newell test for a theory of cognition.John R. Anderson & Christian Lebiere - 2003 - Behavioral and Brain Sciences 26 (5):587-601.
    Newell proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories by a (...)
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  • Timing of the brain events underlying access to consciousness during the attentional blink.Claire Sergent, Sylvain Baillet & Stanislas Dehaene - 2005 - Nature Neuroscience 8 (10):1391-1400.
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  • Vision Science: Photons to Phenomenology.Stephen Palmer - 1999 - MIT Press.
    This textbook on vision reflects the integrated computational approach of modern research scientists, combining psychological, computational and neuroscientific perspectives.
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  • A feature integration theory of attention.Anne Treisman - 1980 - Cognitive Psychology 12:97-136.
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  • Desiderata for cognitive architectures.Ron Sun - 2004 - Philosophical Psychology 17 (3):341-373.
    This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking (...)
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  • The Society Of Mind.Marvin Minsky - 1986 - Simon & Schuster.
    Computing Methodologies -- Artificial Intelligence.
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  • Artificial Intelligence: A Modern Approach.Stuart Jonathan Russell & Peter Norvig (eds.) - 1995 - Prentice-Hall.
    Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...)
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  • The Senses Considered as Perceptual Systems.James Jerome Gibson - 1966 - Boston, USA: Houghton Mifflin.
    Describes the various senses as sensory systems that are attuned to the environment. Develops the notion of rich sensory information that specifies the distal environment. Includes a discussion of affordances.
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  • Recognition-by-components: A theory of human image understanding.Irving Biederman - 1987 - Psychological Review 94 (2):115-147.
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  • (1 other version)Implicit learning and tacit knowledge.Arthur S. Reber - 1989 - Journal of Experimental Psychology: General 118 (3):219-235.
    I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative of the structure of (...)
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  • (1 other version)Husserl and Intentionality: A Study of Mind, Meaning, and Language.David Woodruff Smith & Ronald McIntyre - 1982 - Springer.
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  • The Psychology of Attention.Harold Pashler - 1998 - The MIT Press.
    The book develops empirical generalizations about the major issues and suggests possible underlying theoretical principles.
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  • What is the biological basis of consciousness?Greg Miller - 2005 - Science 309 (5731):79.
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  • (1 other version)Computer science as empirical inquiry: Symbols and search.Allen Newell & Herbert A. Simon - 1981 - Communications of the Association for Computing Machinery 19:113-26.
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  • A theory and methodology of inductive learning.Ryszard S. Michalski - 1983 - Artificial Intelligence 20 (2):111-161.
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  • (4 other versions)The Logic of Scientific Discovery.K. Popper - 1959 - British Journal for the Philosophy of Science 10 (37):55-57.
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  • The perceptron: A probabilistic model for information storage and organization in the brain.F. Rosenblatt - 1958 - Psychological Review 65 (6):386-408.
    If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.
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  • Primitive Auditory Segregation Based on Oscillatory Correlation.DeLiang Wang - 1996 - Cognitive Science 20 (3):409-456.
    Auditory scene analysis is critical for complex auditory processing. We study auditory segregation from the neural network perspective, and develop a framework for primitive auditory scene analysis. The architecture is a laterally coupled two‐dimensional network of relaxation oscillators with a global inhibitor. One dimension represents time and another one represents frequency. We show that this architecture, plus systematic delay lines, can in real time group auditory features into a stream by phase synchrony and segregate different streams by desynchronization. The network (...)
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  • Computational cognitive modeling the source of power and other related issues.Ron Sun - unknown
    Computational cognitive models hypothesize internal mental processes of human cognitive activities and express such activities by computer programs Such computational models often consist of many components and aspects Claims are often made that certain aspects of the models play a key role in modeling but such claims are sometimes not well justi ed or explored In this paper we rst review some fundamental distinctions and issues in computational modeling We then discuss in principle systematic ways of identifying the source of (...)
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  • The what and why of binding: The modeler's perspective.Christoph von der Malsburg - 1999 - Neuron 24:95-104.
    In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects (...)
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  • Paying attention to consciousness.John G. Taylor - 2002 - Trends in Cognitive Sciences 6 (5):206-210.
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  • Connectionist learning procedures.Geoffrey E. Hinton - 1989 - Artificial Intelligence 40 (1-3):185-234.
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  • Robust reasoning: integrating rule-based and similarity-based reasoning.Ron Sun - 1995 - Artificial Intelligence 75 (2):241-295.
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  • (1 other version)The Modularity of Mind.Robert Cummins & Jerry Fodor - 1983 - Philosophical Review 94 (1):101.
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  • A model for visual shape recognition.Peter M. Milner - 1974 - Psychological Review 81 (6):521-535.
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  • Neuronal Integration of Synaptic Input in the Fluctuation- Driven Regime.Alexandre Kuhn - unknown
    During sensory stimulation, visual cortical neurons undergo massive synaptic bombardment. This increases their input conductance, and action potentials mainly result from membrane potential fluctuations. To understand the response properties of neurons operating in this regime, we studied a model neuron with synaptic inputs represented by transient membrane conductance changes. We show that with a simultaneous increase of excitation and inhibition, the firing rate first increases, reaches a maximum, and then decreases at higher input rates. Comodulation of excitation and inhibition, therefore, (...)
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  • Constraining computational models of cognition.Terry Regier - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group. pp. 611--615.
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  • Constraints and Preferences in Inductive Learning: An Experimental Study of Human and Machine Performance.Douglas L. Medin, William D. Wattenmaker & Ryszard S. Michalski - 1987 - Cognitive Science 11 (3):299-339.
    The paper examines constraints and preferences employed by people in learning decision rules from preclassified examples. Results from four experiments with human subjects were analyzed and compared with artificial intelligence (AI) inductive learning programs. The results showed the people's rule inductions tended to emphasize category validity (probability of some property, given a category) more than cue validity (probability that an entity is a member of a category given that it has some property) to a greater extent than did the AI (...)
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  • Computational Intelligence: A Logical Approach.David Poole, Alan Mackworth & Randy Goebel - 1998 - Oxford University Press.
    Provides an integrated introduction to artificial intelligence. Develops AI representation schemes and describes their uses for diverse applications, from autonomous robots to diagnostic assistants to infobots. DLC: Artificial intelligence.
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  • Visual feature integration and the temporal correlation hypothesis.Wolf Singer & Charles M. Gray - 1995 - Annual Review of Neuroscience 18:555-86.
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  • Duality of the mind.Ron Sun - manuscript
    Synthesizing situated cognition, reinforcement learning, and hybrid connectionist modeling, a generic cognitive architecture focused on situated involvement and interaction with the world is developed in this book. The architecture notably incorporates the distinction of implicit and explicit processes.
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  • Autonomous Learning of Sequential Tasks: Experiments and Analyses.Todd Peterson - unknown
    This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...)
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  • Rule-plus-exception model of classification learning.Robert M. Nosofsky, Thomas J. Palmeri & Stephen C. McKinley - 1994 - Psychological Review 101 (1):53-79.
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  • Fuzzy Sets.Lofti A. Zadeh - 1965 - Information and Control 8 (1):338--53.
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  • Accessibility versus action-centeredness in the representation of cognitive skills.Ron Sun & Xi Zhang - unknown
    We believe that the distinction between procedural and declarative knowledge unnecessarily confounds two issues: action-centeredness and accessibility, and can be made clearer through separating the two aspects. The work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both action-centered and non-action-centered knowledge. We examine and simulate human data in the Letter Counting task. The work shows how the data may be captured using either the action-centered knowledge alone or the combined action-centered (...)
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  • (1 other version)Implicit learning: News from the front.Axel Cleeremans, Arnaud Destrebecqz & Maud Boyer - 1998 - Trends in Cognitive Sciences 2 (10):406-416.
    69 Thompson-Schill, S.L. _et al. _(1997) Role of left inferior prefrontal cortex 59 Buckner, R.L. _et al. _(1996) Functional anatomic studies of memory in retrieval of semantic knowledge: a re-evaluation _Proc. Natl. Acad._ retrieval for auditory words and pictures _J. Neurosci. _16, 6219–6235 _Sci. U. S. A. _94, 14792–14797 60 Buckner, R.L. _et al. _(1995) Functional anatomical studies of explicit and 70 Baddeley, A. (1992) Working memory: the interface between memory implicit memory retrieval tasks _J. Neurosci. _15, 12–29 and cognition (...)
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  • Visual search and stimulus similar¬ity.John Duncan & Glyn W. Humphreys - 1989 - Psychological Review 96 (3):433-458.
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  • Neural Network Learning as an Inverse Problem.Věra Kůrková - 2005 - Logic Journal of the IGPL 13 (5):551-559.
    Capability of generalization in learning of neural networks from examples can be modelled using regularization, which has been developed as a tool for improving stability of solutions of inverse problems. Such problems are typically described by integral operators. It is shown that learning from examples can be reformulated as an inverse problem defined by an evaluation operator. This reformulation leads to an analytical description of an optimal input/output function of a network with kernel units, which can be employed to design (...)
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  • Brains, Machines, and Mathematics.Michael A. Arbib - 1970 - Journal of Symbolic Logic 35 (3):482-483.
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  • The Race for Consciousness.John G. Taylor - 2001 - MIT Press.
    MIT Press, 1999 Review by Paul Bohan Broderick, Ph.D. on May 26th 2002 Volume: 6, Number: 21.
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  • Integration of cognitive systems across disciplinary boundaries.Ron Sun & Gregg C. Oden - unknown
    The present issue is the beginning of a new journal from various sub-disciplines and paradigms in order – Cognitive Systems Research – which we have to construct a coherent picture of how the various developed in response to what we perceive to be an pieces fit together overall. Such a synthesis is unfilled niche in the current literature in the areas of essential to the discovery of designs for general Cognitive Science and Artificial Intelligence.
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  • Short-term memory capacity: Limitation or optimization?James N. MacGregor - 1987 - Psychological Review 94 (1):107-108.
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