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    GAMIT-Net: retrospective and prospective interval timing in a single neural network

    Addyman, Caspar and Mareschal, Denis (2014) GAMIT-Net: retrospective and prospective interval timing in a single neural network. In: Bello, P. and Guarini, M. and McShane, M. and Scassellati, B. (eds.) Proceedings of the 36th Annual Conference of the Cognitive Science Society. The Cognitive Science Society, pp. 98-103. ISBN 9780991196708.

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    The neural network version of the Gaussian Activation Model of Interval Timing (GAMIT-Net) is a simple recurrent network that unifies retrospective and prospective timing in a single framework. It has two parts. Firstly, a time-dependent signal is generated by a spreading Gaussian activation. Next, a simple recurrent network (SRN) combines information from the Gaussian and its own internal state during a timing task to generate time estimates. This model captures the scalar property of interval timing (Gibbon, 1977). Furthermore, under high cognitive load the Gaussian fades faster while the internal state is updated less often. These factors interact to account for the surprising finding that retrospective estimates increase under cognitive load while prospective estimates decrease (Block, Hancock & Zakay, 2010).


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences
    Research Centres and Institutes: Educational Neuroscience, Centre for, Brain and Cognitive Development, Centre for (CBCD)
    Depositing User: Sarah Hall
    Date Deposited: 24 Mar 2015 11:07
    Last Modified: 02 Aug 2023 17:15


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