Proceedings of the Annual Meeting of the Cognitive Science Society

We propose a fully recurrent neural network to model low-level auditory memory in a task to discriminate intensities of sequentially presented tones across a range of varying interstimulus intervals. In this model, memory represents a sensory-trace of the stimulus and takes the form of slow relaxation of a number of units to a globally attractive equilibrium value near zero. The same-different judgment is based on a derivative of the output of the dynamic memory. Gaussian noise added to unit activations was found to improve the resilience of stored information although at the cost of decreased sensitivity. The model exhibits many qualitative properties of human performance on a roving-standard intensity discrimination task.