Online research in philosophy

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.

The aim of this paper is to demonstrate a _prima facie_ tension between our commonsense conception of ourselves as thinkers and the connectionist programme for modelling cognitive processes. The language of thought hypothesis plays a pivotal role. The connectionist paradigm is opposed to the language of thought; and there is an argument for the language of thought that draws on features of the commonsense scheme of thoughts, concepts, and inference. Most of the paper (Sections 3-7) is taken up with the argument for the language of thought hypothesis. The argument for an opposition between connectionism and the language of thought comes towards the end (Section 8), along with some discussion of the potential eliminativist consequences (Sections 9 and.

In this paper we defend a position we call radical connectionism. Radical connectionism claims that cognition _never_ implicates an internal symbolic medium, not even when natural language plays a part in our thought processes. On the face of it, such a position renders the human capacity for abstract thought quite mysterious. However, we argue that connectionism is committed to an analog conception of neural computation, and that representation of the abstract is no more problematic for a system of analog vehicles than for a symbol system. Natural language is therefore not required as a representational medium for abstract thought. Since natural language is arguably not a representational medium _at all_, but a conventionally governed scheme of communicative signals, we suggest that the role of internalised (i.e., self- directed) language is best conceived in terms of the coordination and control of cognitive activities within the brain.

��Natural language understanding involves the simul- taneous consideration of a large number of different sources of information. Traditional methods employed in language analysis have focused on developing powerful formalisms to represent syntactic or semantic structures along with rules for transforming language into these formalisms. However, they make use of only small subsets of knowledge. This article will describe how to use the whole range of information through a neurosymbolic architecture which is a hybridization of a symbolic network and subsymbol vectors generated from a connectionist network. Besides initializing the symbolic network with prior knowledge, the subsymbol vectors are used to enhance the system’s capability in disambiguation and provide flexibility in sentence understand- ing. The model captures a diversity of information including word associations, syntactic restrictions, case-role expectations, semantic rules and context. It attains highly interactive processing by representing knowledge in an associative network on which actual semantic inferences are performed. An integrated use of previously analyzed sentences in understanding is another important feature of our model. The model dynamically se- lects one hypothesis among multiple hypotheses. This notion is supported by three simulations which show the degree of disambiguation relies both on the amount of linguistic rules and the semantic-associative information available to support the inference processes in natural language understanding. Unlike many similar systems, our hybrid system is more sophisticated in tackling language disambiguation problems by using linguistic clues from disparate sources as well as modeling context effects into the sentence analysis. It is potentially more powerful than any systems relying on one processing paradigm.