Spatial neglect is characterized by a spatial bias in response to stimuli. The disorder is often assessed with a cancellation task, where several measures can be used to quantify the spatial bias of cancellation responses (e.g., the difference between cancellations on the left and right side, the average location of cancelled targets, and the total number of omissions). Typically, measures of cancellation performance are validated by studying the correlation with measures derived from other tasks to assess neglect (e.g., the directional bisection error derived from performance on the line bisection task). However, the foundation of cancellation performance measures is often more intuitive than theoretical. For instance, it is assumed that measures of cancellation performance isolate the spatial (e.g., the ipsilesional preference typical of spatial neglect) from the non‐spatial (e.g., deficits in working memory or sustained attention) sources of error, but this assumption has not been tested yet. Here we formulated a simple model with conceptually meaningful parameters to predict cancellation performance. Our model parameterizes the spatial and non‐spatial components of cancellation responses. This model allowed us to study the construct representation of commonly used measures of spatial neglect through the use of Monte Carlo simulations. The results showed that most of the cancellation performance measures are also dependent on non‐spatial error sources. The results deepen our understanding of the construct representation of cancellation performance measures, while also having implications for studies focused on the relationship between the spatial and non‐spatial attention deficits in spatial neglect.

中文翻译：

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用取消任务量化以自我为中心的空间疏忽：理论上的验证。

空间忽略的特征在于响应刺激而产生的空间偏向。通常用取消任务来评估疾病，其中可以使用多种措施来量化取消响应的空间偏差（例如，左侧和右侧的取消之间的差异，被取消目标的平均位置以及遗漏）。通常，通过研究与其他任务得出的度量的相关性来评估抵消性能的度量，以评估疏忽（例如，从线性二等分任务的性能中得出的有向二等分误差）。但是，取消绩效衡量标准的基础通常比理论上更直观。例如，假设抵消效果的量度将空间（例如，来自非空间（例如，工作记忆不足或持续注意）的错误源的典型的空间忽略的同病异化偏好，但这一假设尚未得到验证。在这里，我们制定了一个简单的模型，该模型具有概念上有意义的参数来预测抵消效果。我们的模型对抵消响应的空间和非空间成分进行了参数化。该模型使我们能够通过使用蒙特卡洛模拟研究常用的空间忽略量度的结构表示。结果表明，大多数抵消性能指标还取决于非空间误差源。结果加深了我们对取消绩效衡量指标的结构表示的理解，