Baek Jongsoo, Chong Sang Chul
Yonsei Institute of Convergence Technology, Yonsei University, Incheon, Korea.
Graduate Program in Cognitive Science, Yonsei University, Seoul, Korea.
Atten Percept Psychophys. 2020 Jan;82(1):63-79. doi: 10.3758/s13414-019-01827-z.
The visual system efficiently processes complex and redundant information in a scene despite its limited capacity. One strategy for coping with the complexity and redundancy of a scene is to summarize it by using average information. However, despite its importance, the mechanism of averaging is not well understood. Here, a distributed attention model of averaging is proposed. Human percept for an object can be disturbed by various sources of internal noise, which can occur either before (early noise) or after (late noise) forming an ensemble perception. The model assumes these noises and reflects noise cancellation by averaging multiple items. The model predicts increased precision for more items with decelerated increments for large set-sizes resulting from late noise. Importantly, the model incorporates mechanisms of attention, which modulate each item's contribution to the averaging process. The attention in the model also results in saturation of performance increments for small set-sizes because the amount of attention allocated to each item is greater for small set-sizes than for large set-sizes. To evaluate the proposed model, a psychophysical experiment was conducted in which observers' ability to discriminate average sizes of two displays was measured. The observers' averaging performance increased at a decreasing rate with small set-sizes and it approached an asymptote for large set-sizes. The model accurately predicted the observed pattern of data. It provides a theoretical framework for interpreting behavioral data and leads to an understanding of the characteristics of ensemble perception.
尽管视觉系统的能力有限，但它能有效地处理场景中的复杂和冗余信息。应对场景复杂性和冗余性的一种策略是通过使用平均信息来对其进行总结。然而，尽管平均机制很重要，但其原理尚未得到很好的理解。在此，我们提出了一种分布式平均注意力模型。人类对物体的感知可能会受到各种内部噪声源的干扰，这些噪声可能在形成整体感知之前（早期噪声）或之后（晚期噪声）出现。该模型假设存在这些噪声，并通过对多个项目进行平均来反映噪声消除。该模型预测，对于更多的项目，精度会提高，而对于由晚期噪声导致的大集合大小，增量会减速。重要的是，该模型纳入了注意力机制，该机制会调节每个项目对平均过程的贡献。模型中的注意力还会导致小集合大小的性能增量饱和，因为对于小集合大小，分配给每个项目的注意力量比大集合大小更大。为了评估所提出的模型，我们进行了一项心理物理学实验，测量了观察者区分两个显示器平均大小的能力。观察者的平均性能在小集合大小时以递减的速率增加，并在大集合大小时接近渐近线。该模型准确地预测了观察到的数据模式。它为解释行为数据提供了一个理论框架，并有助于理解整体感知的特征。
