A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture.     

Memory systems within a cognitive architecture

This article addresses the division of memory systems in relation to an overall cognitive architecture. As understanding the architecture is essential to understanding the mind, developing computational cognitive architectures is an important enterprise in computational psychology (computational cognitive modeling). The article proposes a set of hypotheses concerning memory systems from the standpoint of a cognitive architecture, in particular, the four-way division of memory (including explicit and implicit procedural memory and explicit and implicit declarative memory). It then discusses in detail how these hypotheses may be validated through examining qualitatively the literature on memory. A quick review follows of computational simulations of a variety of quantitative data (which are not limited to narrowly conceived “memory tasks”). Results of accounting for both qualitative and quantitative data point to the promise of this approach.     

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision‐making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT‐R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed.     

Using a cognitive architecture to examine what develops

Different theories of development propose alternative mechanisms by which development occurs. Cognitive architectures can be used to examine the influence of each proposed mechanism of development while keeping all other mechanisms constant. An ACT-R computational model that matched adult behavior in solving a 21-block pyramid puzzle was created. The model was modified in three ways that corresponded to mechanisms of development proposed by developmental theories. The results showed that all the modifications (two of capacity and one of strategy choice) could approximate the behavior of 7-year-old children on the task. The strategy-choice modification provided the closest match on the two central measures of task behavior (time taken per layer, r = .99, and construction attempts per layer, r = .73). Modifying cognitive architectures is a fruitful way to compare and test potential developmental mechanisms, and can therefore help in specifying "what develops."     

Criteria for the design and evaluation of cognitive architectures

Cognitive architectures are unified theories of cognition that take the form of computational formalisms. They support computational models that collectively account for large numbers of empirical regularities using small numbers of computational mechanisms. Empirical coverage and parsimony are the most prominent criteria by which architectures are designed and evaluated, but they are not the only ones. This paper considers three additional criteria that have been comparatively undertheorized. (a) Successful architectures possess subjective and intersubjective meaning, making cognition comprehensible to individual cognitive scientists and organizing groups of like-minded cognitive scientists into genuine communities. (b) Successful architectures provide idioms that structure the design and interpretation of computational models. (c) Successful architectures are strange: They make provocative, often disturbing, and ultimately compelling claims about human information processing that demand evaluation.     

脑损伤语言认知障碍研究 ——认知神经心理学研究途径

20世纪90年代以来,从认知神经科学的角度揭示语言及其它认知的脑机制成为科学研究的前沿与热点。研究各种脑损伤导致的特异性语言障碍,为探索语言和其它认知的结构、加工,及大脑不同部位在语言和其它认知活动中的功能增添了一条新的途径。同时也对临床失语症的诊断与康复起了很多积极的作用。汉语在世界语言体系中具有其独特性,对汉语的语言障碍及其脑机制的研究将为完善人类语义知识理论、语言的表征与加工模型有重要贡献,也为发展汉语失语症针对性的康复方案提供理论和实践基础     

Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural "blackboard" architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.     

Are Scientific Research Programmes Applicable to Theology? On Philip Hefner’s Use of Lakatos

This paper focuses on the work of Philip Hefner, who employs Lakatosian scientific research programme methodology in order to develop and articulate certain theological concepts. Lakatos’ methodology has been criticised on several counts, including its dependence on reconstructed history and the risk of prematurely abandoning a scientific theory. Hefner does not address these critiques directly, nor does he modify his own methodology sufficiently to avoid valid criticism. Finally, this paper explores the implications of applying a Lakatosian methodology to theology for the way in which we understand truth in relation to the methodology’s criterion of fruitfulness.     

A Review and Taxonomy of Choice Architecture Techniques

We present a taxonomy of choice architecture techniques that focus on intervention design, as opposed to the underlying cognitive processes that make an intervention work. We argue that this distinction will facilitate further empirical testing and will assist practitioners in designing interventions. The framework is inductively derived from empirically tested examples of choice architecture and consists of nine techniques targeting decision information, decision structure, and decision assistance. An inter‐rater reliability test demonstrates that these techniques can be used in an intersubjectively replicable way to describe sample choice architectures. We conclude by discussing limitations of the framework and key issues concerning the use of the techniques in the development of new choice architectures. Copyright © 2015 John Wiley & Sons, Ltd.     

Turing's Analysis of Computation and Theories of Cognitive Architecture

Turing's analysis of computation is a fundamental part of the background of cognitive science. In this paper it is argued that a re-interpretation of Turing's work is required to underpin theorizing about cognitive architecture. It is claimed that the symbol systems view of the mind, which is the conventional way of understanding how Turing's work impacts on cognitive science, is deeply flawed. There is an alternative interpretation that is more faithful to Turing's original insights, avoids the criticisms made of the symbol systems approach and is compatible with the growing interest in agent-environment interaction. It is argued that this interpretation should form the basis for theories of cognitive architecture.     

The origins of inquiry: inductive inference and exploration in early childhood

Analogies between scientific theories and children's folk theories have been central to the study of cognitive development for decades. In support of the comparison, numerous studies have shown that children have abstract, ontologically committed causal beliefs across a range of content domains. However, recent research suggests that the comparison with science is informative not only about how children represent knowledge but also how they acquire it: many of the epistemic practices essential to and characteristic of scientific inquiry emerge in infancy and early childhood.     

Evolution of the Icarus Cognitive Architecture

Cognitive architectures serve as both unified theories of the mind and as computational infrastructures for constructing intelligent agents. In this article, we review the evolution of one such framework, Icarus, over the three decades of its development. We discuss the representational and processing assumptions made by different versions of the architecture, their relation to alternative theories, and some promising directions for future research.     

A general theoretical framework for the design of artificial emotion systems in Autonomous Agents

Autonomous Agents (AAs) capable of exhibiting emotional behaviors have contributed to the development of natural human-machine interactions in several application domains. In order to provide AAs with emotional mechanisms, their underlying architecture must implement an Artificial Emotion System (AES), a computational model that imitates specific facets of human emotions. Although several AES have been reported in related literature, their design is generally supported on several emotion theories, leading researchers to model and integrate isolated emotion components and mechanisms into the architectures of AES. This theoretical foundation of AES contributes to ambiguities in the analysis and comparison of their underlying architectures, which demands the definition of standards, design guidelines, and integrative frameworks. In this paper, we present a psychologically inspired theoretical framework designed to serve as a platform for the unification of AES' components, the comparison of AES, and the design and implementation of AES in AAs. We analyze common emotion-related requirements of AES, emotion components involved in the design of this type of computational model, and emotion theories that drive the design of most AES. The validation of this framework demonstrates its compatibility with current AES and its feasibility as a model for unifying multiple emotional theories.     

Dual-process theories of reasoning: Contemporary issues and developmental applications

In this paper, I discuss the current state of theorising about dual processes in adult performance on reasoning and decision making tasks, in which Type 1 intuitive processing is distinguished from Type 2 reflective thinking. I show that there are many types of theory some of which distinguish modes rather than types of thinking and that assumptions about underlying cognitive architecture vary. I show that some dual-system theories have been replaced recently by the idea that we have two or more distinct ‘minds’ with different evolutionary histories. I also present the most recent formulation of my own account of dual processing within hypothetical thinking theory, at a level more easily applied to performance on specific tasks. I then consider implications for cognitive development, pointing out that while Type 2 thinking is clearly linked to the development of cognitive ability, it combines and competes with multiple Type 1 processing systems which persist in adult cognition, each of which could have their own developmental time course. Hence, while dual-process theories can and should inspire much research and theory in cognitive development, the derivation of predictions for cognitive development is far from straightforward.     

Does cognitive science show belief in god to be irrational? The epistemic consequences of the cognitive science of religion

The last 15 years or so has seen the development of a fascinating new area of cognitive science: the cognitive science of religion (CSR). Scientists in this field aim to explain religious beliefs and various other religious human activities by appeal to basic cognitive structures that all humans possess. The CSR scientific theories raise an interesting philosophical question: do they somehow show that religious belief, more specifically belief in a god of some kind, is irrational? In this paper I investigate this question and argue that CSR does not show that belief in god is irrational.     

Mathematical progress: Between reason and society

It is shown how the historiographic purport of Lakatosian methodology of mathematics is structured on the theme of analysis and synthesis. This theme is explored and extended to the revolutionary phase around 1800. On the basis of this historical investigation it is argued that major innovations, crucial to the appraisal of mathematical progress, defy reconstruction as irreducibly rational processes and should instead essentially be understood as processes of social-cognitive interaction. A model of conceptual change is developed whose essential ingredients are the variability of rational responses to new intellectual and practical challenges arising in the cultural environment of mathematics, and the shifting selective pressure of society. The resulting view of mathematical development is compared with Kuhn's theory of scientific paradigms in the light of some personal communications.     

EXPERIENTIAL STRUCTURALISM AND NEO-PIAGETIAN THEORIES: TOWARD AN INTEGRATED MODEL

Experiential structuralism is a new theory of cognitive organization and growth. It postulates that the cognitive system is organized into six autonomous capacity spheres: the quantitative-relational, the qualitative-analytic, the imaginal-spatial, the causal-experimental, the verbal-propositional, and the metacognitive-reflecting. These capacities were called experiential because they are experimentally documented, they reflect the organization of the persons' experience and the - subjective - experience the persons have about this organization. Thus it was proposed that a set of specific cognitive abilities may be integrated into a general capacity under the guidance of four principles. Namely, the principles of (1) domain specificity, (2) formal-procedural specificity, (3) symbolic bias, and (4) subjective distinctness of capacities. It was argued that this theory resolves some of the problems related to the competence-performance dispute better than the other neo-Piagetian theories. It seems to succeed in this regard because it postulates mechanisms directly linking performance variations with systematic variations in the organization of the cognitive system. However, the neo-Piagetian theories, though they did not define capacities, did propose notions able to causally explain the construction of autonomous capacities. These notions were integrated into a common model able to explain the generation of capacities. Overall, then, the present article attempts to integrate traditional differential and cognitive-developmental psychology into a common theory.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

Cognitive Developmental Theory and Spiritual Development

Traditional conceptions of cognitive development have failed to account for changes in adult cognition as well as more subjective and intuitive features of human experience. This paper reviews recent theories and research in cognitive development and spirituality with the aim of providing connections between the two domains. Neo-Piagetian and postformal theories of cognitive development suggest that advances in cognition are domain-specific, dependent on individual experience, and can occur at any point in the lifespan. However, theories of spiritual development have not adequately addressed these points. Thus, a novel account of spiritual development is presented that addresses these features with respect to individuals' changing conceptions of their relation to an External Power.