A neural network model for exogenous perceptual alternations of the Necker cube

Research output: Contribution to journalArticle


When a bistable visual image, such as the Necker cube, is continuously viewed, the percept of the image endogenously alternates between one possible percept and the other. However, perceptual alternation can also be induced by an exogenous perturbation. For example, a typical external perturbation is the flashlight, which is expected to pervasively activate many brain regions. Therefore, the neural mechanism related to exogenous perceptual alternation remains to be clarified. As a cue to solving this problem, our recent psychophysiological experiment reported a positive correlation between the enhancement of visual mismatch negativity evoked by breaks in the sequential regularity of the visual stimuli and the proportion of perceptual alternation. To elucidate the mechanism underlying exogenous perceptual alternation induced by visual mismatch negativity, the present study attempted to construct a neural network model for bistable perception of the Necker cube, whose perceptual alternation is facilitated by an increase in visual mismatch negativity. The model consists of both a prediction layer and a prediction error layer, following the predictive coding framework for biologically plausible relationships between the change detection process and the perceptual alternation mechanism. Computer simulations showed that the mean duration of perception decreased as the response increased, which is in concordance with the experimental data. This result suggested that the excitatory feedforward and inhibitory feedback connections play an important role. Additionally, the validity of this model suggests that the visual mismatch signal propagates in the neural systems and affects the visual perceptual mechanism as a prediction error signal.

Original languageEnglish
Pages (from-to)229-237
Number of pages9
JournalCognitive Neurodynamics
Issue number2
Publication statusPublished - 1 Apr 2020



  • Necker cube
  • Neural network model
  • Perceptual alternation
  • Predictive coding

Cite this