Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps

Abstract

Semantic information is important in eye-movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven towards inconsistent objects because they are semantically more informative. We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive ‘meaning’ in images. In Experiment 1, we compared gaze data and contextualized meaning maps for images, in which objects-scene consistency was manipulated. Observers fixated more on inconsistent vs. consistent objects. However, contextualized meaning maps did not assigned higher meaning to image regions that contained semantic inconsistencies. In Experiment 2, a large number of raters evaluated the meaningfulness of a set of carefully selected image-regions. The results suggest that the same scene locations were experienced as slightly less meaningful when they contained inconsistent compared to consistent objects. In summary, we demonstrated that – in the context of our rating task – semantically inconsistent objects are experienced as less meaningful than their consistent counterparts, and that contextualized meaning maps do not capture prototypical influences of image meaning on gaze guidance.

Matthias Kümmerer
Matthias Kümmerer
Postdoc

I’m interested in understanding how we use eye movements to gather information about our environment. This includes building saliency models and models of eye movement prediction such as my line of DeepGaze models. I also work on the question of how to evaluate model quality and benchmarking and I’m the main organizer of the MIT/Tuebingen Saliency Benchmark.

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.