Turner Brandon M, Sloutsky Vladimir M
Department of Psychology, The Ohio State University.
Curr Dir Psychol Sci. 2024 Apr;33(2):79-86. doi: 10.1177/09637214231217989. Epub 2024 Jan 19.
In understanding 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 only be established through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively based on 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.
在理解人类如何进行选择性注意时，常见的框架通常关注注意力是如何相对于基于任务属性的理想化分配方式进行分配的。然而，这些观点往往忽略了不同类型的限制因素，而这些因素有助于解释注意力为何以特定方式分配。例如，许多学习的计算模型能够很好地解释注意力理想情况下应如何分配以将任务中的错误最小化，但这些模型常常假定所有特征都被完美编码，或者唯一的学习目标是使准确性最大化。在本文中，我们主张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，从而获得更全面的观点。我们的核心论点是，尽管选择性注意将注意力引向相关维度，但相关性只能通过对先前经验的记忆来确立。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所保留的内容进行选择性运作。通过这个视角，与理想表现的偏差仍可被视为目标导向的选择性注意，但注意力的指向受制于个体学习者的限制因素。
