This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

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 (...) and non-action-centered knowledge. The results provide a new perspective on skill learning. (shrink)

Most psychological theories of problem solving have focused on modeling explicit processes that gradually bring the solver closer to the solution in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. In the present paper, we propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative problem solving that involves integrating (...) the results of implicit and explicit processing. In this paper, the new model is used to simulate insight in creative problem solving and the overshadowing effect. (shrink)

Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domaingeneric computational cognitive model that may be used for a broad, multiple-level, multipledomain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will (...) be undertaken, and an example cognitive architecture (CLARION) will be described. (shrink)

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...) integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning. Ó 2003 Elsevier B.V. All rights reserved. (shrink)

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule- based reasoning and whether they involve distributed or localist representations. The bene®ts and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used (...) for cognitive modelling. q 2001 Elsevier Science Ltd. All rights reserved. (shrink)

This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the (...) functional role of consciousness, in the form of the synergy between the conscious and the unconscious. The fit between the model and various cognitive phenomena and data (documented in the psychological literatures) is discussed to accentuate the plausibility of the model and its explanation of consciousness. Comparisons with existing models of consciousness are made in the end. (shrink)

This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the (...) functional role of consciousness on the basis of the aforementioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional role of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model and various cognitive data that it captures. © 2001 Elsevier Science B.V. All rights reserved. (shrink)

Although computational models of cognitive agents that incorporate a wide range of cognitive functionalities have been developed in cognitive science, most of the work in social simulation still assumes rudimentary cognition on the part of the agents. In contrast, in this work, the interaction of cognition and social structures/processes is explored, through simulating survival strategies of tribal societies. The results of the simulation demonstrate interactions between cognitive and social factors. For example, we show that cognitive capabilities and tendencies may be (...) relevant to what social institutions may be adopted. This work points to a cognitively based approach towards social simulation, as well as a new area of research—exploring the cognitive–social interaction through cognitively based social simulation. (shrink)

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments (...) made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration. (shrink)

_role, especially in learning, and through devising hybrid neural network models that (in a qualitative manner) approxi-_ _mate characteristics of human consciousness. In doing so, the paper examines explicit and implicit learning in a variety_ _of psychological experiments and delineates the conscious/unconscious distinction in terms of the two types of learning_ _and their respective products. The distinctions are captured in a two-level action-based model C_larion_. Some funda-_ _mental theoretical issues are also clari?ed with the help of the model. Comparisons with (...) existing models of conscious-_. (shrink)

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods (...) to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed. (shrink)

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data (...) can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved). (shrink)

This paper explicates the interaction between the implicit and explicit learning processes in skill acquisition, contrary to the common tendency in the literature of studying each type of learning in isolation. It highlights the interaction between the two types of processes and its various effects on learning, including the synergy effect. This work advocates an integrated model of skill learning that takes into account both implicit and explicit processes; moreover, it embodies a bottom-up approach (first learning implicit knowledge and then (...) explicit knowledge on its basis) towards skill learning. The paper shows that this approach accounts for various effects in the process control task data, in addition to accounting for other data reported elsewhere. (shrink)

This work explores the importance of similarity-based processes in human everyday reasoning, beyond purely rule-based processes prevalent in AI and cognitive science. A unified framework encompassing both rulebased and similarity-based reasoning may provide explanations for a variety of human reasoning data.

In the physical sciences a rigorous theory is a hierarchy of descriptions in which causal relationships between many general types of entity at a phenomenological level can be derived from causal relationships between smaller numbers of simpler entities at more detailed levels. The hierarchy of descriptions resembles the modular hierarchy created in electronic systems in order to be able to modify a complex functionality without excessive side effects. Such a hierarchy would make it possible to establish a rigorous scientific theory (...) of consciousness. The causal relationships implicit in definitions of access consciousness and phe- nomenal consciousness are made explicit, and the corresponding causal relationships at the more detailed levels of perception, memory, and skill learning described. Extension of these causal relationships to physiological and neural levels is discussed. The general capability of a range of current consciousness models to support a modular hierarchy which could generate these causal relationships is reviewed, and the specific capabilities of two models with good general capabilities are compared in some detail. Ó 2003 Elsevier Inc. All rights reserved. (shrink)

The goal of this research is to understand the interaction of implicit and explicit psychological processes in dealing with emotional distractions and meta-cognitive control of such distractions. The questions are how emotional and meta-cognitive processes can be separated into implicit and explicit components, and how such a separation can be utilized to improve self-regulation of emotion, which can have significant theoretical and practical implications.

This paper describes how meta-cognitive processes (i.e., the self monitoring and regulating of cognitive processes) may be captured within a cognitive architecture Clarion. Some currently popular cognitive architectures lack sufficiently complex built-in meta-cognitive mechanisms. However, a sufficiently complex meta-cognitive mechanism is important, in that it is an essential part of cognition and without it, human cognition may not function properly. We contend that such a meta-cognitive mechanism should be an integral part of a cognitive architecture. Thus such a mechanism has (...) been developed within the Clarion cognitive architecture. The paper demonstrates how human data of two meta-cognitive experiments are simulated using Clarion. The simulations show that the meta-cognitive processes represented by the experimental data (and beyond) can be adequately captured within the Clarion framework. (shrink)

I argue that SHRUTl's ontology is heavily committed to a representational view of mind. This is best seen when one thinks of how SHRUTI could be developed to account for psychological data on deductive reasoning.

Lange & Dyer misunderstand what is meant by an “entity” and confuse a medium of representation with the content being represented. This leads them to the erroneous conclusion that SHRUTI will run out of phases and that its representation of bindings lacks semantic content. It is argued that the limit on the number of phases suffices, and SHRUTI can be interpreted as using “dynamic signatures” that offer significant advantages over fixed preexisting signatures. Bonatti refers to three levels of commitment to (...) a representational theory of the mind and states that SHRUTI is committed to RTM at levels 1 and 2. He acknowledges that SHRUTI is not committed at level 3, but argues that an extended SHRUTI would have to make such a commitment. We agree that SHRUTI is committed to RTM at level 1 and, in a sense, also at level 2. SHRUTI, however, is not committed to RTM at level 3 and, even though it has been extended in several ways, it has retained its original representational character. (shrink)

Most of the work in agent-based social simulation has assumed highly simplified agent models, with little attention being paid to the details of individual cognition. Here, in an effort to counteract that trend, we substitute a realistic cognitive agent model (CLARION) for the simpler models previously used in an organizational design task. On that basis, an exploration is made of the interaction between the cognitive parameters that govern individual agents, the placement of agents in different organizational structures, and the performance (...) of the organization. It is suggested that the two disciplines, cognitive modeling and social simulation, which have so far been pursued in relative isolation from each other, can be profitably integrated. (shrink)

In: E. Vargios (ed.), Educational Psychology Research Focus, pp.1-26. Nova Science Publishers, Hauppauge, NY. 2007.

Variable binding has long been a challenge to connectionists. Attempts to perform variable binding using localist and distributed connectionist representations are discussed, and problems inherent in each type of representation are outlined.

Shastri & Ajjanagadde argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence (...) systems trivially solve the dynamic binding problem using computers' registers and pointers, it has remained a difficult problem for connectionist systems. S&A's temporal synchrony solution to the dynamic binding problem using synchronous firing of argument units and the entities that are bound to them illustrates one way in which connectionist networks can do thisusing a constrained but important class of long-term knowledge rules. Their structured connectionist solution allows dynamic inferencing to proceed in parallel and therefore has a number of advantages for reflexive reasoning over most other connectionist and symbolic systems. (shrink)