Dynamic Models of Simple Judgments: I. Properties of a Self-Regulating Accumulator Module

This is the first of two papers comparing connectionist and traditional stochastic latency mechanisms with respect to their ability to account for simple judgments. In this paper, we show how the need to account for additional features of judgment has led to the formulation of progressively more sophisticated models. One of these, a self-regulating, generalized accumulator process, is treated in detail, and its simulated performance across a sample of tasks is described. Since an adaptive decision module of this kind possesses all the ingredients of intelligent behavior, it is eminently suited as a basic computing element in more complex networks.     

The Representational Relation Between Environmental Structures and Neural Systems: Autonomy and Environmental Dependency in Neural Knowledge Representation

In this paper it will be shown that in neural systems with a recurrent architecture, the traditional concepts of knowledge representation cannot be applied any more; no stable representational relationship of reference can be found. That is why a redefinition of the relationship between the states of the environment and the internal representational states is proposed. Studying the dynamics of recurrent neural systems reveals that the goal of representation is no longer to map the environment as accurately as possible to the representation system (e.g., to symbols). It is suggested that it is more appropriate to look at neural systems as physical dynamical devices embodying the (transformation) knowledge for sensorimotor integration and for generating adequate behavior enabling the organism's survival. As an implication the representation is determined not only by the environment, but highly depends on the organization, structure, and constraints of the representation system as well as the sensory/motor systems which are embedded in a particular body structure. This leads to a system relative concept of representation. By transforming recurrent neural networks into the domain of finite automata, the dynamics as well as the epistemological implications become more clear. In recurrent neural systems a type of balance between the autonomy of the representation and the environmental dependence/influence emerges. This not only affects the traditional concept of knowledge representation, but has also implications for the understanding of semantics, language, communication, and even science.     

Dynamic Models of Simple Judgments: II. Properties of a Self-Organizing PAGAN (Parallel, Adaptive, Generalized Accumulator Network) Model for Multi-Choice Tasks

This is the second of two papers comparing connectionist and traditional stochastic latency mechanisms with respect to their ability to account for simple judgments. In the first, we reviewed evidence for a self-regulating accumulator module for two- and three-category discrimination. In this paper, we examine established neural network models that have been applied to predicting response time measures, and discuss their representational and adaptational limitations. We go on to describe and evaluate the network implementation of a Parallel Adaptive Generalized Accumulator Network (PAGAN), based on the interconnection of a number of self-regulating, generalized accumulator modules. The enhancement of PAGAN through the incorporation of distributed connectionist representation is briefly discussed.     

神经网络系统

本文介绍了神经网络系统的基本概念和主要特征,讨论了此项研究的目的意义,对该研究的发展历史,特别是近年来关于神经网络的各种理论模型的发展作了简要评述。      

Causal emergentism

In this paper I describe basic features of traditional (British) emergentism and Popper’s emergentist theory of consciousness and compare them to the contemporary versions of emergentism present in connectionist approach in cognitive sciences. I argue that despite their similarities, the traditional form, as well as Popper’s theory belong to strong causal emergentism and yield radically different ontological consequences compared to the weaker, contemporary version present in cognitive science. Strong causal emergentism denies the causal closure of the physical domain and introduces genuine new mental causal powers and genuine downward causation, while weak emergentism provides new insights in understanding the mechanisms and explanation that is compatible with physicalism.