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Modeling the Directionality of Attention During Spatial Language Comprehension

Authors :
Kluth, Thomas
Burigo, Michele
Knoeferle, Pia
van den Herik, Jaap
Filipe, Joaquim
Source :
Lecture Notes in Computer Science ISBN: 9783319533537, ICAART (Revised Selected Papers)
Publication Year :
2017
Publisher :
Springer International Publishing, 2017.

Abstract

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 com- prehend the sentence. Such a shift was also implicitly assumed in the Attentional 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.)

Details

Language :
English
ISBN :
978-3-319-53353-7
ISBNs :
9783319533537
Database :
OpenAIRE
Journal :
Lecture Notes in Computer Science ISBN: 9783319533537, ICAART (Revised Selected Papers)
Accession number :
edsair.doi.dedup.....de314b731299dd0a858d201c27d08ca3