A model linking immediate serial recall, the Hebb repetition effect and the learning of phonological word forms.

We briefly review the considerable evidence for a common ordering mechanism underlying both immediate serial recall (ISR) tasks (e.g. digit span, non-word repetition) and the learning of phonological word forms. In addition, we discuss how recent work on the Hebb repetition effect is consistent with the idea that learning in this task is itself a laboratory analogue of the sequence-learning component of phonological word-form learning. In this light, we present a unifying modelling framework that seeks to account for ISR and Hebb repetition effects, while being extensible to word-form learning. Because word-form learning is performed in the service of later word recognition, our modelling framework also subsumes a mechanism for word recognition from continuous speech. Simulations of a computational implementation of the modelling framework are presented and are shown to be in accordance with data from the Hebb repetition paradigm.

The irrelevant sound effect: what needs modelling, and a tentative model.

This paper reviews the literature on the irrelevant sound effect and concludes that, contrary to some claims, the data consistently show that irrelevant sound and articulatory suppression are not functionally equivalent. We evaluate the contribution of Larsen and Baddeley (2003 in this issue) and briefly discuss additional data in support of their position. We perform an error analysis on data from their third experiment and simulate detailed aspects of those data using our primacy model of immediate serial recall. Our model is briefly related to a number of findings in the literature on irrelevant sound.

The primacy model: a new model of immediate serial recall.

A new model of immediate serial recall is presented: the primacy model. The primacy model stores order information by means of the assumption that the strength of activation of successive list items decreases across list position to form a primacy gradient. Ordered recall is supported by a repeated cycle of operations involving a noisy choice of the most active item followed by suppression of the chosen item. Word-length and list-length effects are attributed to a decay process that occurs both during input, when effective rehearsal is prevented, and during output. The phonological similarity effect is attributed to a second stage of processing at which phonological confusions occur. The primacy model produces accurate simulations of the effects of word length, list length, and phonological similarity.

Use of neural networks in brain SPECT to diagnose Alzheimer's disease.

UNLABELLED: The usefulness of artificial neural networks in the classification of 99mTc-HMPAO SPECT axial brain scans was investigated in a study group of Alzheimer's disease patients and age-matched normal subjects. METHODS: The cortical circumferential profiling (CCP) technique was used to extract information regarding patterns of cortical perfusion. Traditional analysis of the CCP data, taken from slices at the level of the basal ganglia, indicated significant perfusion deficits for Alzheimer's disease patients relative to normals, particularly in the left temporo-parietal and left posterior frontal areas of the cortex. The compressed profiles were then used to train a neural-network classifier, the performance of which was compared with that of a number of more traditional statistical (discriminant function) techniques and that of two expert viewers. RESULTS: The optimal classification performance of the neural network (ROC area = 0.91) was better than that of the alternative statistical techniques (max. ROC area = 0.85) and that of the expert viewers (max. ROC area = 0.79). CONCLUSION: The CCP produces perfusion profiles which are well suited to automated classification methods, particularly those employing neural networks. The technique has the potential for wide application.