A new preprint is available for a recent paper Martin worked on in collaboration with Nicolas Roth, Olaf Hellwich, and Klaus Obermayer, who work with him through the Cluster of Excellence through the Science of Intelligence.

Through their work, they present a computational framework that can be used to simulate where and when humans decide to move their eyes when observing dynamic real-world scenes. Using their framework, they can assess the influence of objects on the model predictions. Here, they found that including object-based attention using modeling increases the resemblance of simulated eye movements to human gaze behavior, showing that objects play an important role in guiding our gaze when exploring the world around us. 

Roth, N., Rolfs, M., Hellwich, O., & Obermayer, K. (2023). Objects guide human gaze behavior in dynamic real-world scenes [Preprint]. bioRxiv. [doi]

Abstract:
The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by bottom-up saliency, but increasing evidence suggests that objects also play a significant role in guiding attention. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented four different models within this framework: two purely location-based models, where one is based on low-level saliency and one on high-level saliency, and two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information. We optimized each model’s parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and revisiting objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.