Shifts of Attention During Spatial Language Comprehension - A Computational Investigation

Regier and Carlson (2001) have investigated the processing of spatial prepositions and developed a cognitive model that formalizes how spatial prepositions are evaluated against depicted spatial relations between objects. In their Attentional Vector Sum (AVS) model, a population of vectors is weighted with visual attention, rooted at the reference object and pointing to the located object. The deviation of the vector sum from a reference direction is then used to evaluate the goodness-of-fit of the spatial preposition. Crucially, the AVS model assumes a shift of attention from the reference object to the located object. The direction of this shift has been challenged by recent psycholinguistic and neuroscientific findings. We propose a modified version of the AVS model (the rAVS model) that integrates these findings. In the rAVS model, attention shifts from the located object to the reference object in contrast to the attentional shift from the reference object to the located object implemented in the AVS model. Our model simulations show that the rAVS model accounts for both the data that inspired the AVS model and the most recent findings.a aThis work was presented at the 8th International Conference on Agents and Artificial Intelligence 2016 (ICAART 2016) that took place in Rome, Italy, from February 24 to 26, 2016 (http://www.icaart.org). Unfortunately, in the version of this paper published in the conference proceedings the citations are messed up. This version is easier to read.