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

人类拥有的最基本的心理功能之一是能够识别熟悉的场景，并采取与之相关的行动，或采取与某些内部目标相关的行动。例如，我们可能想在家里的一个房间里寻找钥匙。我们知道，我们在完成这个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、浴室还是休息室里找钥匙。也就是说，我们所处的环境对我们在房间里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高效。当然，这种形式的学习并不局限于定位家庭物品。当任何生物体在许多不同的环境中执行动作时，从在拥挤的交通中驾驶汽车，到步行到当地的商店，甚至是动物在森林中捕猎猎物时，同样的认知过程可能发挥重要作用。在任何情况下，我们都需要处理环境中的不同特征或线索，然后利用我们之前遇到类似场景的记忆来预测某些元素的位置。我们目前对人类行为的这一基本方面的理解非常有限。在过去10年开发的实验任务中，研究人员开始研究我们是如何学习这类信息的。在一项旨在激发这种行为的实验室任务中，参与者观看计算机屏幕，该屏幕在每次试验中显示分心物体（例如不同颜色的“L”形）和唯一目标物体（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，比如它的方向。在任务过程中，参与者的反应时间被测量。至关重要的是，在整个任务过程中，一些分心物的配置是重复的。实验表明，参与者在重复的布局中比在全新的布局中更快地定位目标。检测目标所需时间的减少一定是由于参与者在长期记忆中储存了上下文的重复模式。目前的研究旨在测试一种新提出的这种学习的计算模型。这个模型通过创建关于场景中特定对象相对于目标对象的排列方式的记忆来学习重复场景。以一个熟悉的厨房场景为例，该模型预测我们只了解目标物体相对于房间中其他物体的空间位置（例如，烤面包机在冰箱对面，在炊具的右边）。这些信息足以解释为什么在连续搜索中可以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，我们还将了解与我们的搜索无关的厨房的其他方面。也就是说，我们会进行“偶然学习”（无意或自动学习），了解房间的总体布局、存在的物体及其彼此之间的相对位置（例如，炊具在冰箱对面，水槽在窗户下面）。我们实验室最近的证据表明，这些与搜索任务无关的关系也是习得的。当前的项目以这些重要的发现为起点，将提供对场景学习过程的彻底检查，并与新的计算模型的开发相结合。在一项免费的研究中，我们将监测和研究场景学习过程中的眼球运动。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的新场景学习模型的发展提供信息。

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.