Understanding visual-working-memory encoding as a visual search for multiple targets

将视觉工作记忆编码理解为对多个目标的视觉搜索

• 批准号: 412295130
• 负责人: Privatdozent Dr. Heinrich René Liesefeld
• 金额: --
• 依托单位: Institut für Psychologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/412295130?language=en
• 关键词: Understanding; visual; working; memory; encoding

Experience with the environment influences conscious perception by biasing the selection of information. Most conscious processing of visual information goes via a system called visual working memory (VWM). VWM is severely limited in its capacity so that only a tiny fraction of the information available in the environment is encoded and represented at a time. Furthermore, even among those objects that made it into VWM, representational quality varies considerably. The reasons for this inter-item variability remain largely unknown. I suggest that a huge share of this variability emerges during encoding: instead of passively absorbing all relevant information until VWM capacity is exhausted, VWM encoding might be characterized by an active interaction between the visual scene (e.g., different saliencies of the objects) and the observer (e.g., experience with the environment: predictive coding). By examining this idea, the project aims to close a crucial gap in the VWM literature and to extend the scope of the research unit to the next step in the processing chain: active selection of visual information for further processing (maintenance).The project relates two central attentional mechanisms to VWM encoding: priority maps and focal spatial attention. A priority map is a spatial representation of the visual scene that abstracts from specific features of objects and instead provides a single priority value at each location, reflecting a combination of bottom-up saliency, top-down goals, and previous experiences (task history). The main hypothesis tested in WP1 is that the priority map determines the quality of encoding into VWM. This will be examined via (experimental) variations in the two contributors to the priority map that were so far largely neglected in VWM research, namely the objects’ saliencies (WP1a) and task history (WP1b). WP2 will examine whether focal spatial attention contributes to VWM encoding by upgrading one object into a special state. This special state has been the subject of vigorous debates and extensive empirical investigation, but it is as yet unknown what brings an object into that state if it is not explicitly cued and in how far being in the special state is qualitatively different from having the highest priority.Using elements from various validated models, a new computational model of VWM encoding will be developed to explain the expected complex data patterns in considerable detail and to gain a principled understanding of the cognitive mechanisms involved. Furthermore, electrophysiological markers of attention allocations that were previously validated for visual search will be used to understand the attentional dynamics that influence VWM encoding and will also be integrated into the model. In sum, the present project will examine whether and how various mechanisms of active perception affect object processing beyond attentional selection.

环境经验通过偏向信息选择来影响意识感知。大多数视觉信息的有意识处理都是通过称为视觉工作记忆（VWM）的系统进行的。 VWM 的容量受到严重限制，因此一次只能对环境中可用信息的一小部分进行编码和表示。此外，即使在那些进入 VWM 的对象中，表征质量也存在很大差异。这种项目间差异的原因仍然很大程度上未知。我认为这种可变性的很大一部分是在编码过程中出现的：VWM 编码的特征可能是视觉场景（例如，对象的不同显着性）和观察者（例如，环境体验：预测编码）之间的主动交互，而不是被动地吸收所有相关信息，直到 VWM 容量耗尽。通过研究这个想法，该项目旨在缩小 VWM 文献中的一个关键差距，并将研究单位的范围扩展到处理链的下一步：主动选择视觉信息以进行进一步处理（维护）。该项目将两种中心注意力机制与 VWM 编码联系起来：优先级图和焦点空间注意力。优先级图是视觉场景的空间表示，它从对象的特定特征中抽象出来，并在每个位置提供单个优先级值，反映自下而上的显着性、自上而下的目标和先前经验（任务历史记录）的组合。 WP1 中测试的主要假设是优先级映射决定了 VWM 的编码质量。这将通过优先级图的两个贡献者（即对象的显着性（WP1a）和任务历史记录（WP1b））的（实验）变化进行检查，这两个贡献者迄今为止在 VWM 研究中很大程度上被忽视。 WP2 将通过将一个对象升级为特殊状态来检查焦点空间注意力是否有助于 VWM 编码。这种特殊状态一直是激烈争论和广泛实证研究的主题，但目前尚不清楚如果没有明确提示，是什么使对象进入该状态，以及处于特殊状态与具有最高优先级有多大的质量差异。使用来自各种经过验证的模型的元素，将开发一种新的 VWM 编码计算模型，以相当详细地解释预期的复杂数据模式，并获得对认知机制的原则性理解 涉及。此外，先前针对视觉搜索进行验证的注意力分配的电生理标记将用于理解影响 VWM 编码的注意力动态，并且也将集成到模型中。总之，本项目将研究主动感知的各种机制是否以及如何影响注意力选择之外的对象处理。