Abstract
Weber’s law describes the linear drop of discriminative performance with increased base intensity of a stimulus. So far, this phenomenon has been modeled using multistable attractor decision networks based on the principle of biased competition between two mutually inhibiting recurrent neural populations. Due to the sensitive balance in a multistable fluctuation-driven regime, these decision models can only account for Weber’s law in a narrow stimulus range. Psychophysical data shows though that the human exhibits this characteristic for a broad stimulus range. Recent neurophysiological evidence suggests that global feedforward inhibition expands the dynamic range of cortical neuron populations and acts as a gain control. In this paper, we introduce a computational model that exploits this type of inhibition and shows through a fit between simulation results and psychophysical data that it is a potential explanation for the principle mechanism behind Weber’s law.
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Friedl, K.E., Qin, Y., Ostler, D., Peer, A. (2014). Modeling the Weber Fraction of Vibrotactile Amplitudes Using Gain Control Through Global Feedforward Inhibition. In: Auvray, M., Duriez, C. (eds) Haptics: Neuroscience, Devices, Modeling, and Applications. EuroHaptics 2014. Lecture Notes in Computer Science(), vol 8619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44196-1_48
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DOI: https://doi.org/10.1007/978-3-662-44196-1_48
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