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Modeling the Directionality of Attention During Spatial Language Comprehension

机译:空间语言理解过程中注意方向的建模

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It is known that the comprehension of spatial prepositions involves the deployment of visual attention. For example, consider the sentence "The salt is to the left of the stove". Researchers [29,30] have theorized that people must shift their attention from the stove (the reference object, RO) to the salt (the located object, LO) in order to comprehend the sentence. Such a shift was also implicitly assumed in the Atten-tional Vector Sum (AVS) model by [35], a cognitive model that computes an acceptability rating for a spatial preposition given a display that contains an RO and an LO. However, recent empirical findings showed that a shift from the RO to the LO is not necessary to understand a spatial preposition ([3], see also [15,38]). In contrast, these findings suggest that people perform a shift in the reverse direction (i.e., from the LO to the RO). Thus, we propose the reversed AVS (rAVS) model, a modified version of the AVS model in which attention shifts from the LO to the RO. We assessed the AVS and the rAVS model on the data from [35] using three model simulation methods. Our simulations show that the rAVS model performs as well as the AVS model on these data while it also integrates the recent empirical findings. Moreover, the rAVS model achieves its good performance while being less flexible than the AVS model. (This article is an updated and extended version of the paper [23] presented at the 8th International Conference on Agents and Artificial Intelligence in Rome, Italy. The authors would like to thank Holger Schultheis for helpful discussions about the additional model simulation.)
机译:众所周知,空间介词的理解涉及部署视觉关注。例如,考虑“盐在炉子的左侧”句子。研究人员已经理解,人们必须将他们的注意力从炉子(参考物体,RO)转移到盐(所定位的物体,LO)以理解句子。在[35]的衰减 - 调向量和(AVS)模型中也隐含地假设这种移位,这是一种认知模型,其为包括RO和LO的显示器来计算空间介词的可接受性评级。然而,最近的实证发现表明,从RO到LO的偏移是不需要了解空间介词([3],另见[15,38])。相比之下,这些发现表明人们在反向(即,从LO到RO)执行换档。因此,我们提出了反转的AVS(RAVS)模型,修改版的AVS模型,其中注意从LO到RO。我们使用三种模型仿真方法评估了从[35]的数​​据上的AVS和RAVS模型。我们的模拟表明,RAVS模型在这些数据上执行以及AVS模型,同时还集成了最近的实证发现。此外,RAVS模型的性能良好,而不是比AVS模型更柔软。 (本文是罗马,意大利罗马的第8届国际代理商和人工智能国际会议的更新和扩展版本[23]。作者谨此感谢霍尔格·舒尔斯蒂斯有助于讨论额外的模型模拟。)

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