Models of human visual attention should consider trial-by-trial variability in preparatory neural signals.
Models of human visual attention should consider trial-by-trial variability in preparatory neural signals.
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人类视觉注意力模型应考虑准备神经信号中逐次试验的变异性。
DOI：
10.1016/j.neunet.2006.09.010
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Corbetta,Maurizio
中科院分区：
文献类型：
--
作者：
Sylvester,ChadM;d'Avossa,Giovanni;Corbetta,Maurizio
几种注意力模型解释了关于刺激特征的先验知识如何在刺激呈现时导致神经活动的调制，进而增强对刺激的感知(例如，Desimone&Duncan，1995；Tsotsos等人，1995)。然而，很少有人关注刺激呈现之前的神经信号如何编码这一先验信息，以及这些信号如何影响知觉。我们认为，心理物理任务中很大一部分行为变异性可以由感觉目标之前的神经信号的变异性来解释，而不是跟随感觉目标的神经信号的变异性。建模人员应该考虑行为可变性的这一重要来源。目前，观察者对视觉刺激感知的可变性被认为反映了由该刺激引起的神经信号的可变性--要么是由于刺激本身的噪声(外部噪声)，要么是由于所诱发的神经信号固有的噪声(内部噪声)。许多单个单位的研究都讨论了影响视觉诱发反应的内部噪声的统计性质，以及它如何影响刺激的内部表征的清晰度(例如，Britten，Shadlen，Newome，&Movshon，1992；Newome，Britten，&Movshon，1989)。由于在相同刺激的重复呈现上，尖峰计数的泊松分布，神经信号中存在的信息与平均神经信号成正比。在图1a中，我们重新绘制了Heuer和Britten(2004)的数据，说明了刺激触发的脉冲序列的平均调制和信息量之间的关系。蓝线表示单个MST神经元在呈现运动刺激后的平均尖峰频率，而红线表示理想观察者从MST神经元的活动中区分每个时间点的运动方向的能力。显然，在内部噪声限制感知的条件下，携带感觉信息的信号的时间过程遵循平均信号。有人提出，注意力可以通过增加平均神经反应的增益来减少内部噪音的影响(Hillard，Vogel，&Luck，1998；Reynolds，Pasternak，&Desimone，2000)。
Several models of attention explain how a priori knowledge about stimulus characteristics results in modulations of neural activity at the time of stimulus presentation and, in turn, enhanced perception of the stimulus (eg, Desimone & Duncan, 1995; Tsotsos et al., 1995). There has been little focus, however, on how neural signals preceding stimulus presentation encode this a priori information and how these signals affect perception. We argue that a significant portion of behavioral variability on psychophysical tasks may be explained by variability in the neural signals preceding, rather than following, the sensory target. Modelers should consider this important source of behavioral variability.Currently, variability in the observer's percept of a visual stimulus is thought to reflect variability in the neural signals evoked by that stimulus–due to either noise in the stimulus itself (‘external noise’) or noise inherent to the evoked neural signals (‘internal noise’). Many single unit studies have addressed the statistical nature of the internal noise affecting visually evoked responses and how it affects the clarity of the internal representation of the stimulus (eg, Britten, Shadlen, Newsome, & Movshon, 1992; Newsome, Britten, & Movshon, 1989). Because of the Poisson-like distribution of spike counts over repeated presentations of the same stimulus, the information present in neural signals is proportional to the mean neural signal. In Figure 1A, we re-plot data from Heuer and Britten (2004) illustrating the relation between average modulations and information content of stimulus-triggered spike trains. The blue line represents average spike rates in a single MST neuron following presentation of a motion stimulus, while the red line portrays the ability of an ideal observer to discriminate the direction of motion at each time point from the activity of the MST neuron. Clearly, the timecourse of signals carrying sensory information follows the mean signal under conditions in which internal noise limits perception. It has been suggested that attention can reduce the effects of internal noise by increasing the gain of the mean neural response (Hillyard, Vogel, & Luck, 1998; Reynolds, Pasternak, & Desimone, 2000).