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Short Term Memory and Selection Processes in a Frontal-Lobe Model

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Connectionist Models in Cognitive Neuroscience

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

We present a neural model that addresses the capacity of a frontal lobe system to hold up information for short periods of time and to perform response selection. In the model, reverberation states are sustained after stimulus offset, due to loops of recurrent excitation in neural cell assemblies and lateral inhibition is necessary to block an uncontrolled spread of activation. At high levels of inhibition the system performs response selection, and at lower levels it retains a number of units in active states, after stimulus offset. It is shown formally that such a system has capacity limitations: only a limited number of cell assemblies can be retained. Sequential presentation of a list of items is simulated and serial position curves characterised by recency are obtained. The model explains recency, list-length and presentation rate effects in immediate cued recall, as well as semantic effects and patterns of forgetting in Brown-Peterson type of experiments. A reduction in the strength of recurrent excitations explains the absence of lexical effects in tests of immediate memory for frontal lobe patients [1] and more extreme reductions result in impairments of response selection in dynamic aphasia [2]

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Author information

Authors and Affiliations

  1. University of Kent, UK

    Marius Usher

  2. Princeton University, USA

    Jonathan D. Cohen

Authors
  1. Marius Usher
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  2. Jonathan D. Cohen
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Editor information

Editors and Affiliations

  1. School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK

    Dietmar Heinke PhD , Glynn W. Humphreys PhD  & Andrew Olson PhD ,  & 

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© 1999 Springer-Verlag London Limited

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Usher, M., Cohen, J.D. (1999). Short Term Memory and Selection Processes in a Frontal-Lobe Model. In: Heinke, D., Humphreys, G.W., Olson, A. (eds) Connectionist Models in Cognitive Neuroscience. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0813-9_7

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  • DOI: https://doi.org/10.1007/978-1-4471-0813-9_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-052-1

  • Online ISBN: 978-1-4471-0813-9

  • eBook Packages: Springer Book Archive

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