Representing and responding in the visual world: a new model of contextual cuing.

在视觉世界中表示和响应：上下文提示的新模型。

• 批准号: ES/J007196/1
• 负责人: David Shanks
• 金额: $ 20.81万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FJ007196%2F1
• 关键词: Representing; responding; visual; world; new

One of the most fundamental psychological functions humans possess is the ability to recognize a familiar scene and perform an action relevant to it or an action relevant to some internal goal. For example, we may want to search for our keys within a room in our house. We know that the actions we perform in completing this simple task will vary, depending on whether we are searching for the keys in the kitchen, the bathroom or the lounge. That is, the context in which we are situated is crucial to the order in which we search locations in the room. We rely on our memory for a specific scene to narrow down the options and make our search as efficient as possible.Of course, this form of learning is not restricted to locating household objects. The same cognitive processes are likely to play an important role when any organism performs an action within many different environments, from driving cars in crowded traffic, walking to the local shop, or even when an animal hunts its prey in a forest. In every case there is a need to process the different features, or cues, within the environment, and then generate predictions about where certain elements will be located using our memory of previous encounters with similar scenes.We currently have a very limited understanding of this fundamental aspect of human behaviour. In experimental tasks developed over the last 10 years, researchers have started to examine how we learn this type of information. In a laboratory task designed to invoke this behaviour, participants view a computer screen which on each trial displays a context of distractor objects (e.g., different coloured "L" shapes) and a unique target object (e.g. a "T" shape). Participants have the goal of locating the target and responding to a particular feature of the shape, such as its orientation. During the task, participant's reaction times are measured. Crucially, some distractor configurations are repeated throughout the task. It has been shown that participants are faster to locate the target in repeating configurations than in completely novel arrangements. This decrease in the time taken to detect targets must be due to participants storing the repeating patterns of context in long-term memory.The current research aims to test a newly proposed computational model of this type of learning. This model learns about repeating scenes by creating memories for how specific objects within the scene are arranged with respect to the target object. Taking the analogy of a familiar kitchen scene, the model predicts that we learn only about the spatial location of a target object relative to other objects in the room (e.g., the toaster is opposite the fridge and to the right of the cooker). This information is enough to explain why the toaster is located more quickly on successive searches, as each object in the kitchen provides some information as to the location of the toaster. However, it seems natural to suppose that we will also learn about other aspects of the kitchen that are not relevant to our search. That is, we will engage in "incidental learning" (unintentional or automatic learning) about the general layout of the room, the objects present and their positions relative to one another (e.g., the cooker is opposite the fridge, the sink is under the window). Recent evidence from our laboratory suggests that these relationships, which are irrelevant to the search task, are also learned. The current project takes these important findings as a starting point and will provide a thorough examination of scene learning processes, allied to the development of a new computational model. In a complimentary strand of research we will monitor and study eye movements during scene learning. This research will provide data which will determine whether attention plays a key role in controlling learning in this behavior. The findings of this research will also inform the development of our new model of scene learning.

人类拥有的最基本的心理功能之一是识别熟悉的场景并执行与之相关的动作或与内在目标相关的动作的能力。例如，我们可能想要在我们房子的一个房间里搜索我们的钥匙。我们知道，我们在完成这项简单任务时所采取的行动会有所不同，这取决于我们是在厨房、浴室还是休息室寻找钥匙。也就是说，我们所处的环境对我们在房间中搜索位置的顺序至关重要。我们依靠对特定场景的记忆来缩小选择范围，使我们的搜索尽可能有效。当然，这种学习形式并不局限于定位家居物品。当任何有机体在许多不同的环境中执行动作时，同样的认知过程可能会发挥重要作用，从在拥挤的交通中开车，到当地商店步行，甚至当动物在森林中狩猎猎物时。在任何情况下，都需要处理环境中不同的特征或线索，然后利用我们对以前遇到类似场景的记忆来预测某些元素将位于何处。目前，我们对人类行为的这一基本方面的了解非常有限。在过去10年开发的实验任务中，研究人员已经开始检查我们是如何学习这类信息的。在为调用这一行为而设计的实验室任务中，参与者观看计算机屏幕，该屏幕在每次实验中显示分心对象(例如，不同颜色的L形状)和独特的目标对象(例如，T形)的上下文。参与者的目标是定位目标并对形状的特定特征做出反应，例如其方向。在任务期间，测量参与者的反应时间。至关重要的是，在整个任务中会重复一些分心装置的配置。研究表明，参与者在重复配置中定位目标的速度比完全新的配置中的要快。发现目标所需时间的减少肯定是因为参与者将重复的背景模式存储在长期记忆中。目前的研究旨在测试一种新提出的这种类型学习的计算模型。该模型通过创建场景内特定对象相对于目标对象如何排列的记忆来学习重复场景。以熟悉的厨房场景为类比，该模型预测我们只了解目标对象相对于房间中其他对象的空间位置(例如，烤面包机在冰箱对面，炉子的右侧)。这些信息足以解释为什么连续搜索会更快地找到烤面包机，因为厨房里的每个物体都提供了一些关于烤面包机位置的信息。然而，我们似乎很自然地认为，我们还会了解与我们的搜索无关的厨房的其他方面。也就是说，我们将对房间的总体布局、存在的物体及其相对于彼此的位置进行“附带学习”(无意或自动学习)(例如，炉子在冰箱对面，水槽在窗户下面)。来自我们实验室的最新证据表明，这些与搜索任务无关的关系也是可以学习的。目前的项目以这些重要的发现为起点，将结合开发新的计算模型，对场景学习过程进行彻底检查。在一系列免费的研究中，我们将监测和研究场景学习期间的眼球运动。这项研究将提供数据，以确定注意力是否在控制这种行为的学习中起关键作用。这项研究的发现也将为我们开发新的情景学习模式提供参考。

项目成果

A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation.

• DOI: 10.3758/s13428-014-0544-1
• 发表时间: 2015-12
• 期刊: Behavior research methods
• 影响因子: 5.4
• 作者: Vadillo MA;Street CNH;Beesley T;Shanks DR
• 通讯作者: Shanks DR

Underpowered samples, false negatives, and unconscious learning.

• DOI: 10.3758/s13423-015-0892-6
• 发表时间: 2016-02
• 期刊: Psychonomic bulletin & review
• 影响因子: 3.5
• 作者: Vadillo MA;Konstantinidis E;Shanks DR
• 通讯作者: Shanks DR