De Montfort University, UK and Durham University, UK.
University of Plymouth, UK.
Cogn Psychol. 2019 Feb;108:22-41. doi: 10.1016/j.cogpsych.2018.11.001. Epub 2018 Dec 11.
A number of influential spatial learning theories posit that organisms encode a viewpoint independent (i.e. allocentric) representation of the global boundary shape of their environment in order to support spatial reorientation and place learning. In contrast to the trial and error learning mechanisms that support domain-general processes, a representation of the global-shape of the environment is thought to be encoded automatically as part of a cognitive map, and without interference from other spatial cues. To date, however, this core theoretical assumption has not been appropriately examined. This is because previous attempts to address this question have failed to employ tasks that fully dissociate reorientation based on an allocentric representation of global-shape from egocentric reorientation strategies. Here, we address this issue in two experiments. Participants were trained to navigate to a hidden goal on one side of a virtual arena (e.g. the inside) before being required to find the same point on the alternative side (e.g. the outside). At test, performing the correct search behaviour requires an allocentric representation of the global boundary-shape. Using established associative learning procedures of overshadowing and blocking, we find that search behaviour at test is disrupted when participants were able to form landmark-goal associations during training. These results demonstrate that encoding of an allocentric representation of boundary information is susceptible to interference from landmark cues, and is not acquired through special means. Instead, the results suggest that allocentric representations of environmental boundaries are acquired through the same kind of error-correction mechanisms that support domain-general non-spatial learning.
一些有影响力的空间学习理论假设,生物为了支持空间重新定向和位置学习,会对环境的全局边界形状进行一种独立于视点(即无参照点)的编码。与支持一般领域过程的试错学习机制不同,环境全局形状的表示被认为是作为认知地图的一部分自动编码的,并且不受其他空间线索的干扰。然而,到目前为止,这一核心理论假设尚未得到适当的检验。这是因为以前试图解决这个问题的尝试未能采用完全区分基于全局形状的无参照点定向和自我定向的重新定向策略的任务。在这里,我们在两个实验中解决了这个问题。参与者被训练在虚拟竞技场的一侧(例如内部)导航到一个隐藏的目标,然后被要求在另一侧(例如外部)找到相同的点。在测试中,正确的搜索行为需要全局边界形状的无参照点表示。使用遮蔽和阻断的既定联想学习程序,我们发现当参与者在训练期间能够形成地标-目标关联时,测试中的搜索行为会受到干扰。这些结果表明,边界信息的无参照点表示的编码容易受到地标线索的干扰,并且不是通过特殊手段获得的。相反,结果表明,环境边界的无参照点表示是通过支持一般非空间学习的相同类型的纠错机制获得的。