Modeling the Weber Fraction of Vibrotactile Amplitudes Using Gain Control Through Global Feedforward Inhibition

  • Ken E. FriedlEmail author
  • Yao Qin
  • Daniel Ostler
  • Angelika Peer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8619)


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.


Weber’s law Decision making Attractor dynamics  Global inhibition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ken E. Friedl
    • 1
    Email author
  • Yao Qin
    • 1
  • Daniel Ostler
    • 1
  • Angelika Peer
    • 1
  1. 1.Institute of Automatic Control Engineering, Technische Universität MünchenMunichGermany

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