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Descripción
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| We developed a new perception model to simulate multistable perception. The model is based on selective adaptation which periodically destabilises one of the perception states. Additive brain noise makes the switches between coexisting perception states random. Although the effects of selective adaptation are well documented in experimental reports, there is a lack of the underlying mechanism that captures the essence of this phenomenon maintaining a minimum number of state variables and assumptions, and having a clear biological correspondence. Keeping this in mind, our perceptual model implies two competitive perceptions which consistently stabilise and destabilise themselves depending on time scales and noise. In our model, each perceptive state has a slowly varying memory state which is connected so as to provide a negative feedback loop with delay. The number of switches between different perception states is found to be monotonically increasing with noise. The dominance time or the duration of a particular perceptual state maximises at a certain intermediate level of the duty cycle of a biased stimulus which prefers that state. The decreasing dominance results from the selective adaptation which causes self-destabilisation. For small duty cycles, increasing brain noise leads to a decrease in the dominance times due to noise-dependent switches. On the other hand, for large duty cycles, where selective adaptation forces are keeping longer biased stimulations from increasing the dominance, increasing brain noise causes an increase in the dominance. This apparently contradictory result can be explained by the fact that the slowly adapting memory cannot follow fast random variations of the perceptual signal due to much a larger time scale of the memory. This leads to decreasing selective adaptation; the higher duty cycle of the biased stimulus makes the dominance higher. The distribution of dominance times has been widely reported to follow a gamma function in humans. Our magnetoencephalography (MEG) experiments with subjects observing an ambiguous Necker cube flickering image with its two perceptional interpretations yield similar results. The results of the simulations with our model are in a good agreement with our MEG experiments. The simulations reveal that increasing brain noise tends to change the distribution of dominance times from Gaussian to Gamma and then finally to Exponential. The range of noise values in our model corresponding to Gamma distribution should only be used to compare the model predictions and experimental observations. In the same range of brain noise, we observe a monotonic decrease in the dominance time with increasing brain noise, both in the model and experiment. The results of this work provide interesting and valuable insights into the human brain. This simple yet versatile mechanism may turn out to be radical in understanding human perception. | |
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Internacional
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Si |
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Entidad
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Lugar
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Max Planck Institute for the Physics of Complex Systems, Dresden, Germany |
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Páginas
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Referencia/URL
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https://www.pks.mpg.de/dymecs19/ |
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Tipo de publicación
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Poster |