In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.

中文翻译：

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认知惯性：注意力和记忆形状学习之间的周期性相互作用

在解释人们如何有选择地参与时，常见框架通常侧重于相对于基于任务属性的理想化分配如何分配注意力。然而，这些观点往往忽略了不同类型的约束，这些约束可以帮助解释为什么注意力以特定方式分配。例如，许多学习计算模型都能够很好地解释如何理想地分配注意力以最小化任务中的错误，但这些模型通常假设所有特征都被完美编码，或者唯一的学习目标是最大化准确性。在本文中，我们通过使用计算模型来理解选择性注意和记忆之间发生的复杂相互作用，从而提出更全面的观点。我们的中心论点是，尽管选择性注意力将注意力引导到相关维度，但相关性只能通过对先前经历的记忆来建立。因此，注意力最初用于编码特征并创建记忆，但此后，注意力会根据记忆中保存的内容选择性地进行操作。从这个角度来看，与理想表现的偏差仍然可以被视为目标导向的选择性注意力，但注意力的方向受到个体学习者的限制。