Benjamin Peters¹ (), Eivinas Butkus², Matthew H. Retchin³, Nikolaus Kriegeskorte^2,3,4; ¹School of Psychology & Neuroscience, University of Glasgow, UK, ²Department of Psychology, Columbia University, ³Zuckerman Mind Brain Behavior Institute, Columbia University, ⁴Department of Neuroscience, Columbia University

Tasks lend direction to modeling and drive progress in both cognitive computational neuroscience and AI. While these disciplines have some shared goals, they have traditionally navigated the space of possible tasks with different intentions in mind, leading to vastly different types of tasks. Cognitive scientists and neuroscientists often prioritize experimental control leading them to use abstract tasks that remove many of the complexities of real-world experience, which are considered unrelated to the question at hand. AI engineers, by contrast, often directly engage the complex structure and dynamism of the real world, trading explainability for performance under natural conditions. However, AI engineers, too, are interested in gaining an abstract understanding of their models and cognitive computational neuroscientists ultimately want to model cognition under real-world conditions. If science and engineering are to provide useful constraints for each other in this area, it will be essential that they engage a shared set of tasks. Here we attempt to bridge the divide for dynamic object vision. We present a conceptual framework and a practical software toolbox called “FlyingObjects” that enables the construction of task generative models that span a vast space of degrees of naturalism, interactive dynamism, and generalization challenge. Task generators enable procedural sampling of interactive experiences ad infinitum, scaling between abstracted toy tasks and real-world appearance of objects and complex dynamics, access to and control over the task-generative variables, and sampling of atypical and out-of-distribution experiences. FlyingObjects connects science and engineering and enables researchers to acquire large-scale human behavioral data through smartphones, web-browsers, or in the lab, and to evaluate the alignment of humans and machines in dynamic object vision.

Acknowledgements: B.P. has received funding from the EU Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 841578.