To navigate changes within a highly dynamic and complex environment, it is crucial to compare current visual representations of a scene to previously formed representations stored in memory. This process of mental comparison requires integrating information from multiple sources to inform decisions about changes within the environment. In the present article, we combine a novel systems factorial technology change detection task (Blunden et al., 2022) with a set size manipulation. Participants were required to detect 0, 1, or 2 changes of low and high detectability between a memory and probe array of 1-4 spatially separated luminance discs. Analyses using systems factorial technology indicated that the processing architecture was consistent across set sizes but that capacity was always limited and decreased as the number of distractors increased. We developed a novel model of change detection based on the statistical principles of basic sampling theory (Palmer, 1990; Sewell et al., 2014). The sample size model, instantiated parametrically, predicts the architecture and capacity results a priori and quantitatively accounted for several key results observed in the data: (a) increasing set size acted to decrease sensitivity (d') in proportion to the square root of the number of items in the display; (b) the effect of redundancy benefited performance by a factor of the square root of the number of changes; and (c) the effect of change detectability was separable and independent of the sample size costs and redundancy benefits. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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了解变化检测能力的统一方法。


要在高度动态和复杂的环境中导航变化，将场景的当前视觉表示与存储在内存中的先前形成的表示进行比较至关重要。这个心理比较过程需要整合来自多个来源的信息，以告知有关环境变化的决策。在本文中，我们将一种新的系统因子技术变化检测任务 （Blunden et al.， 2022） 与一组大小操作相结合。参与者需要检测 1-4 个空间分离的亮度盘的内存和探针阵列之间的 0、1 或 2 个低和高可检测性变化。使用系统因子技术的分析表明，处理架构在集合大小之间是一致的，但该容量总是有限的，并且随着干扰项数量的增加而减少。我们基于基本抽样理论的统计原理开发了一种新的变化检测模型（Palmer，1990;Sewell et al.， 2014）。参数实例化的样本量模型先验地预测架构和容量结果，并定量地解释了在数据中观察到的几个关键结果：（a） 增加集合大小的作用是降低灵敏度 （d'），与显示中项目数的平方根成比例;（b） 冗余的效果使性能受益，其效果是更改次数的平方根的系数;（c） 变化可检测性的影响是可分离的，并且与样本量成本和冗余收益无关。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。