Attentional control driven by statistical learning

统计学习驱动的注意力控制

• 批准号: RGPIN-2014-05617
• 负责人: Zhao, Jiaying
• 金额: $ 2.26万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=631225
• 关键词: Attentional; control; driven; statistical; learning

The visual environment is highly structured and rich with regularities in terms of how objects co-occur in space (e.g., a chair tends to appear next to a table) and over time (e.g., the yellow light is always followed by the red light). The visual system is extremely efficient at extracting these regularities through the process of statistical learning (Fiser & Aslin, 2001; Saffran et al., 1996; Turk-Browne et al., 2009). Recently, it has been found that attention can be drawn spontaneously and implicitly to regularities (Zhao et al., 2013). Such attentional bias toward regularities may enable the detection of structure and enhance the acquisition of knowledge about stable aspects of the world. The goal of the current proposal is to explore how learning guides the allocation of attention and what consequences on perception are produced as a result. There are three specific aims.Aim 1. What are the consequences of the attentional bias to regularities?The first aim examines how the attentional prioritization of structured stimuli alters the representation of these stimuli, and what kind of error in perception is produced as a result. For example, when two objects reliably co-occur over space or time (e.g., A always appears with B), these objects may be represented more similarly, which may result in the merging of representations of the objects. Aim 2. How does learning alter the spatial scale of attention?The second aim examines how regularities influence the spatial scale of attention. Reliable co-occurrence of objects in a spatial array may draw attention locally to individual objects and bias attention away from the global set. Thus, regularities may induce a local scale of attention to individual objects in an array, impeding global processing of the array. This can further explain the interference between statistical learning and summary perception. Aim 3. What are the temporal dynamics, durability and flexibility of the attentional bias?The modulation on attention by learning may be non-linear. After extensive exposure to regularities, the attentional bias may dissipate or even reverse to less structured sources of information. In other words, having strong expectations after learning might release attention from structured stimuli to elsewhere. In the absence of regularities, the bias may persist over previously structured information. Finally, when regularities shift to a different spatial location, attention may be flexibly re-allocated to the new location.The proposed program of research will offer a comprehensive understanding of how attention is directed by statistical learning. It motivates a shift away from the theoretical dichotomy on exogenous and endogenous control of attention, and encourages the development of a broader framework. It also suggests that when knowledge about relationships among objects has been acquired (after extensive exposure), attention may be disengaged and shifted to other information. This process allows exploration and acquisition of new knowledge in the environment. The work reveals new ways in which learning guides the allocation of attention. The current proposal can also offer new insights on human perception. That is, how objects co-occur in space or time can shape the representations of these objects. Finally, the proposed research provides novel experimental paradigms for examining learning-induced attentional control, which reflect and facilitate the field’s growing interest in understanding how attentional mechanisms interact with learning.

视觉环境是高度结构化的，并且在物体如何在空间中共同出现方面具有丰富的结构（例如，椅子倾向于出现在桌子旁边）并且随着时间的推移（例如，黄灯之后总是跟着红灯）。视觉系统在通过统计学习的过程提取这些信息方面非常有效（Fiser & Aslin，2001; Saffran等人，1996; Turk-Browne等人，2009年）。最近，已经发现注意力可以自发地和含蓄地被吸引到“不”上（Zhao et al.，2013年）。这种对结构的注意偏向可能使结构的检测和增强对世界稳定方面的知识的获取成为可能。目前的建议的目标是探索学习如何引导注意力的分配，以及由此产生的感知后果。有三个具体目标。对规律性的注意力偏差会产生什么后果？第一个目标是研究结构化刺激的注意优先顺序如何改变这些刺激的表征，以及由此产生的感知错误。例如，当两个对象在空间或时间上可靠地共同出现时（例如，A总是与B一起出现），这些对象可以被更类似地表示，这可能导致对象的表示的合并。目标2.学习如何改变注意力的空间尺度？第二个目的是研究注意力的空间尺度是如何被影响的。空间阵列中对象的可靠共现可以将注意力局部地吸引到单个对象，并使注意力偏离全局集合。因此，重复可能会引起对数组中单个对象的局部关注，从而阻碍数组的全局处理。这可以进一步解释统计学习和概括感知之间的干扰。目标3.注意偏向的时间动态性、持久性和灵活性是什么？学习对注意力的调节可能是非线性的。在大量接触信息源后，注意偏向可能会消散，甚至逆转到结构性较低的信息源。换句话说，在学习后有强烈的期望可能会将注意力从结构性刺激释放到其他地方。在没有重复性的情况下，偏见可能会持续存在于先前结构化的信息中。最后，当注意力转移到不同的空间位置时，注意力可以灵活地重新分配到新的位置。它促使人们从关于注意力的外源性和内源性控制的理论二分法转向，并鼓励发展一个更广泛的框架。它还表明，当（在广泛接触之后）获得了有关对象之间关系的知识时，注意力可能会脱离并转移到其他信息上。这个过程允许在环境中探索和获取新知识。这项工作揭示了学习引导注意力分配的新方法。目前的提案还可以为人类感知提供新的见解。也就是说，物体如何在空间或时间中共同出现可以塑造这些物体的表示。最后，拟议的研究提供了新的实验范式检查学习引起的注意力控制，这反映和促进该领域的日益增长的兴趣，了解注意力机制如何与学习相互作用。