Connectionism and three levels of nativism

Along with the increasing popularity of connectionist language models has come a number of provocative suggestions about the challenge these models present to Chomsky's arguments for nativism. The aim of this paper is to assess these claims. We begin by reconstructing Chomsky's argument from the poverty of the stimulus and arguing that it is best understood as three related arguments, with increasingly strong conclusions. Next, we provide a brief introduction to connectionism and give a quick survey of recent efforts to develop networks that model various aspects of human linguistic behavior. Finally, we explore the implications of this research for Chomsky's arguments. Our claim is that the relation between connectionism and Chomsky's views on innate knowledge is more complicated than many have assumed, and that even if these models enjoy considerable success the threat they pose for linguistic nativism is small.     

新联结主义产生的心理学背景

新联结主义虽然是20世纪80年代早期所复兴的认知心理学的一种新的研究范式．但心理学史中早已存在这种思想的萌芽．这为新联结主义的产生提供了重要的心理学背景,本文主要从六个方面对此进行了分析。     

Connectionism and the problem of multiple instantiation

Multiple instantiation is the ability to handle different instances of the same concept simultaneously. For example, from the following two facts: `Pepin the Short was the son of Charles Martel' and `Charlemagne was the son of Pepin the Short', one can infer that Charles Martel was the grandfather of Charlemagne. This inference requires two instantiations of `Pepin the Short', the first in the role of son, the second in the role of father. For a connectionist model that does not use a working area receiving copies of items from a long-term knowledge base, the problem of multiple instantiation is a particularly thorny one. People are able to deal with multiple instances, unlike most connectionist models, but nonetheless their performance when doing so is reduced. On the other hand, there is no decrease in performance for symbolic models doing multiple instantiation. A good cognitive model should reflect both human competence and human limitations. This review proposes several connectionist solutions to the problem of multiple instantiation and examines their merits.     

认知主义与联结主义之比较

认知主义的研究定向和联结主义的研究定向是广义的现代认知心理学的两种主要研究范式。这两种研究范式都各有自己的研究内容和方法论,也取得了各自不同的成就,同时也存在各自不同的问题。通过对两种研究范式的比较,我们可以清楚地认识到二者的相互沟通和融合才是认知心理学未来发展的必然趋势。     

Connectionism and the learning of probabilistic concepts

Three experiments examine the claim of Gluck and Bower (1986, 1988a, 1988b) that the learning of medical concepts can be simulated by a connectionist network in which the symptoms are the input and the diagnosis is the output. The first experiment replicates the main finding of Gluck and Bower. In this experiment, subjects were required to estimate the probability of each of two diseases, given a particular target symptom. In fact these two probabilities were identical, but because one illness was more common than the other, the target symptom was a better predictor of the rare disease than of the common disease. Contrary to a normative probability judgement account, subjects were biased in that they judged the probability of the rare disease given the target symptom to be greater than the probability of the common disease given the target symptom. Gluck and Bower argued that such a result was predicted by a connectionist network using the Rescorla-Wagner learning rule, but it is argued that Gluck and Bower's network simulation was not appropriate for the experiment they had performed. In fact, it appeared that the connectionist network failed to predict the bias in the subjects' probability estimates.

However, this conclusion rests on an assumption that Gluck and Bower implicitly made. They arranged for P(rare disease/target symptom) and P(com-mon disease/target symptom) to be identical across all trials on which the target symptom occurred, both on its own and with other symptoms present. Gluck and Bower assumed that the subjects were estimating these probabilities. But the results of the second experiment showed instead that the subjects were estimating the probability of each disease given only the target symptom. In the final experiment the design was changed so that this problem might be circumvented. In this experiment, again, the subjects were biased in their probability judgements exactly as the connectionist network predicted. Thus, finally, evidence was found which was compatible with the network model but not with a normative account, but this was true only if the network did not include a layer of hidden units.     

Connectionism and the Philosophical Foundations of Cognitive Science

This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: (1) the classical computation conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call "non-sentential computationalism"; and (3) an alternative interpretation of connectionism we call "dynamical cognition." Also discussed are two recent philosophical attempts to enlist connectionism in defense of eliminativism about folk psychology.     

A Defence Of Connectionism Against The "Syntactic' Argument

In "Representations without Rules, Connectionism and the Syntactic Argument', Kenneth Aizawa argues against the view that connectionist nets can be understood as processing representations without the use of representation-level rules, and he provides a positive characterization of how to interpret connectionist nets as following representation-level rules. He takes Terry Horgan and John Tienson to be the targets of his critique. The present paper marshals functional and methodological considerations, gleaned from the practice of cognitive modelling, to argue against Aizawa's characterization of how connectionist nets may be understood as making use of representation-level rules.     

NETWORKS AND THEORIES: The Place of Connectionism to Cognitive Science

This article considers how connectionist modeling can contribute to understanding of human cognition. I argue that connectionist networks should not be thought of as theories or simulations of theories, hut may nevertheless contribute to the development of theories.     

Connectionism, eliminativism, and the semantic view of theories

Recently some philosophers have urged that connectionist artificial intelligence is (potentially) eliminative for the propositional attitudes of folk psychology. At the same time, however, these philosophers have also insisted that since philosophy of science has failed to provide criteria distinguishing ontologically retentive from eliminative theory changes, the resulting eliminativism is not principled. Application of some resources developed within the semantic view of scientific theories, particularly recent formal work on the theory reduction relation, reveals these philosophers to be wrong in this second contention, yet by and large correct in the first.