this paper, we examine the idea of lexical chains as such a representation. We show how they can be constructed by means of WordNet, and how they can be applied in one particular linguistic task: the detection and correction of malapropisms.

A connectionist model of human short-term memory is presented that extends the 'phonological loop' (A.D. Baddeley, 1986) to encompass serial order and learning. Psychological and neuropsychological data motivate separate layers of lexical, timing and input and output phonemic information. Connection weights between layers show Hebbian learning and decay over short and long time scales. At recall, the timing signal is rerun, phonemic information feeds back from output to input and lexical nodes compete to be selected. The selected node then receives decaying inhibition. The model provides an explanatory mechanism for the phonological loop, and for the effects of serial position, presentation modality, lexicality, grouping and Hebb repetition. It makes new psychological and neuropsychological predictions and is a starting point for understanding the role of the phonological loop in vocabulary acquisition and for interpreting data from functional neuroimaging.

Three experiments are described which use RSVP (rapid serial visual presentation) to demonstrate a new cognitive phenomenon called “repetition blindness”. Subjects have difficulty detecting repeated words-even when the two occurrences are nonconsecutive and differ in case (Experiment 1). In immediate verbatim recall of sentences (Experiment 2), subjects selectively omitted second instances of repeated words, sacrificing the meaning and grammaticality of the sentence. In Experiment 3, recognition threshold for the last word in a list was lowered, not elevated, when that word had also occurred earlier in the same list. Thus, repetition blindness does not result from a refractory period for recognition of second occurrences. These findings support a distinction between the perceptual processes of (i) recognizing a word as’being of a certain type, and (ii) individuating a word as a particular token of that type: repetition blindness occurs when words are recognized as types but not individuated as tokens.

this article are also gratefully acknowledged

The control of routine action is a complex process subject both to minor lapses in normals and to more severe breakdown followingcertain forms of neurological damage. A number of recent empirical studies (e.g. Humphreys & Ford, 1998; Schwartz et al., 1991, 1995, 1998) have examined the details of breakdown in certain classes of patient, and attempted to relate the findings to existing psychological theory. This paper complements those studies by presenting a computational model of the selection of routine actions based on competitive activation within a hierarchically organised network of action schemas (cf. Norman & Shallice, 1980, 1986). Simulations are reported which demonstrate that the model is capable of organised sequential action selection in a complex naturalistic domain. It is further demonstrated that, after lesioning, the model exhibits behaviour qualitatively equivalent to that observed by Schwartz et al., in their action disorganisation syndrome patients.

A micro-world is described, in which many analogies involving strikingly different concepts and levels of subtlety can be made. The question "What differentiates the good ones from the bad ones?" is discussed, and then the problem of how to implement a computational model of the human ability to come up with such analogies (and to have a sense for their quality) is consicered. A key part of the proposed system, now under development, is its dependence on statistically emergent properties of stochastically interacting "codelets" (small pieces of ready-to-run code created by the system, and selected at random to run with probability proportional to heuristically assigned "urgencies"). Another key element is a network of linked concepts of varying levels of "semanticity", in which activation spreads and indirectly controls the urgencies of new codelets. There is pressure in the syste.rn toward maximizing the degree of "semanticity" or "intensionality" of descriptions of structures, but many such pressures, often conflicting, m.,u$ interact with one another, and compromises must be made. The shifting of (1) percei/v'bd boundaries insi,d,e stru,c, tures, (2) descriptive concepts chosen to apply to structures, and (3)4eatures perceived as salient or not, is called "slippage". What can slip, and how, are emergent consequences of the interaction of (1) the temporary ("cytoplasmic") structures involved in the analogy with (2) the permanent ("Platonic") concepts and links in the conceptual proximity network, or "slippability network". The architecture of this system is postulated as a general architecture suitable for dealing not only with fluid analogies, but also with other types of abstract perception and categorization tasks, such as musical perception, scientific theorizing,...

In everyday tasks, selecting actions in the proper sequence requires a continuously updated representation of temporal context. Many existing models address this problem by positing a hierarchy of processing units, mirroring the roughly hierarchical structure of naturalistic tasks themselves. Such an approach has led to a number of difficulties, including a reliance on overly rigid sequencing mechanisms, an inability to account for context sensitivity in behavior, and a failure to address learning. We consider here an alternative framework, according to which the representation of temporal context is facilitated by recurrent connections within a network mapping from environmental inputs to actions. Applying this approach to a specific, and in many ways prototypical, everyday task (coffee-making), we examine its ability to account for several central characteristics of normal and impaired human performance. The model we consider learns to deal flexibly with a complex set of sequencing constraints, encoding contextual information at multiple time-scales within a single, distributed internal representation. Mildly degrading this context representation leads

Abstract not found

This article describes a theory of the computations underlying the selection of coordinated motion patterns, especially in reaching tasks. The central idea is that when a spatial target is selected as an object to be reached, stored postures are evaluated for the contributions they can make to the task. Weights are assigned to the stored postures, and a single target posture is found by taking a weighted sum of the stored postures. Movement is achieved by reducing the distance between the starting angle and target angle of each joint. The model explains compensation for reduced joint mobility, tool use, practice effects, performance errors, and aspects of movement kinematics. Extensions of the model can account for anticipation and coarticulation effects, movement through via points, and hierarchical control of series of movements. The goal of this research is a unified theory of the planning and control of physical action. Such a theory, as several authors have noted (Jeannerod, in press; Rosenbaum, 1991; Wing, 1993), has been lacking. Instead, specialized models have been designed to account for data from different tasks. The sentiment

In 1951, Lashley highlighted the importance of serial order for the brain and behavioural sciences. He considered the response chaining account untenable and proposed an alternative employing parallel response activation and "schemata for action". Subsequently, much has been learned about sequential behaviour, particularly in the linguistic domain. We argue that these developments support Lashley's picture, and recent computational models compatible with it are described. The models are developed in a series of steps, beginning with the basic problem of parallel response competition and its possible resolution into serial action. At each stage, important limitations of the previous models are identified and simple additions proposed to overcome them, including the provision of learning mechanisms. Each type of model is compared with relevant data, and the importance of error data is emphasized. Taken together, the models constitute a unified approach to serial order which has achieved considerable explanatory success across disparate domains.