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Constraining the dynamics of multi-stable perception

机译:限制多稳态感知的动力学

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The dynamics of multi-stable perception is known to reflect several stabilizing and destabilizing factors. This interplay is captured by simplistic computational models (e.g., Laing & Chow, 2002, J. Comp. Neurosci., 'LC-model') with mutual inhibition, adaptation, and noise, at least under restrictive stimulus conditions. Hoping to constrain the parameters of such models, we collected reversal statistics from 24 observers and with 3 multi-stable displays (binocular rivalry, structure-from-motion, and Necker cube). We compared these observations to reversal sequences generated by a reduced LC-model in a 5-dimensional parameter space. As expected, the distribution of dominance periods exhibited a highly consistent shape (Gamma-like with ?±=3.6?±0.5), which has been proposed as an effective constraint for multi-stable dynamics (Shpiro et al., 2009, J. Comp. Neurosci.). However, we found that approximately 66% of the possible parameter volume generated realistic distribution shapes with suitable levels of noise. Thus, distribution shape is not, by itself, an effective constraint. Also as expected, reversal sequences exhibited weak (up to c=0.4) but significant correlations between dominance episodes and an integral measure of prior perceptual history ('cumulative history', Pastukhov & Braun, 2011, J. Vis.). In addition, serial correlations (up to c=0.2) between successive dominance episodes also reached significance occasionally (Van Ee, 2009, J. Opt. Soc. Am. A). We found that both observations are consistent with and, indeed, predicted by, the LC-model with suitable levels of adaptation. When combined, dominance distribution and history-dependence constrained multi-stable dynamics effectively. Approximately 15% of the possible parameter volume of the LC-model generated reversal sequences with realistic statistics in both regards. Between 3% and 6% of the parameter volume reproduced the perceptual dynamics of individual observer/display combinations. We conclude that the combination of dominance distribution and history dependence constrains the perceptual dynamics of individual human observers, revealing the operative balance of mutual inhibition, adaptation, and noise.
机译:已知多稳态感知的动力学反映了一些稳定和不稳定因素。至少在限制性刺激条件下,具有相互抑制,适应和噪声的简化计算模型(例如,Laing&Chow,2002,J.Comp.Neurosci。,“ LC模型”)捕获了这种相互作用。为了限制此类模型的参数,我们收集了来自24位观察者的逆向统计数据,并通过3种多稳态显示器(双目竞争,运动结构和Necker立方体)进行了统计。我们将这些观察结果与在5维参数空间中由简化LC模型生成的反向序列进行了比较。不出所料,支配期的分布呈现出高度一致的形状(γ像样,γ= 3.6?±0.5),已被提议作为多稳态动力学的有效约束(Shpiro et al。,2009,J.比较神经科学。)。但是,我们发现大约66%的可能参数体积生成具有适当噪声水平的实际分布形状。因此,分布形状本身并不是有效的约束。同样如预期的那样,逆转序列显示出较弱的(高达c = 0.4),但在优势事件与先前知觉历史的整体度量之间存在显着相关性(“累积历史”,Pastukhov&Braun,2011,J。Vis。)。此外,连续优势事件之间的序列相关性(最高c = 0.2)偶尔也达到显着性(Van Ee,2009,J.Opt.Soc.Am.A)。我们发现,这两个观察结果都与具有适当适应水平的LC模型一致,并且确实由其预测。当结合在一起时,优势分布和历史依赖关系有效地约束了多稳态动力学。 LC模型的可能参数量的大约15%生成了在两个方面都具有实际统计数据的反转序列。在参数量的3%和6%之间,再现了各个观察者/显示组合的感知动态。我们得出的结论是,优势分布和历史依赖的结合限制了单个人类观察者的知觉动力,揭示了相互抑制,适应和噪音的有效平衡。

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