This paper presents a new Retinotopic Reasoning (R2) cognitive architecture that is inspired by studies of visual mental imagery in people. R2 is a hybrid symbolic-connectionist architecture, with certain components of the system represented in propositional, symbolic form, but with a primary working memory store that contains visual mental'' images that can be created and manipulated by the system. R2 is not intended to serve as a full-fledged, stand-alone cognitive architecture, but rather is a specialized system focusing on how visual mental imagery can be represented, learned, and used in support of intelligent behavior. Examples illustrate how R2 can be used to model human visuospatial cognition on several different standardized cognitive tests, including the Raven's Progressive Matrices test, the Block Design test, the Embedded Figures test, and the Paper Folding test.

With the explosive potential of Artificial Intelligence, we ask: How do society and culture shape A.I.? And how does A.I. impact our sense of humanity? Can A.I. help us understand better the human mind? One powerful aspect of human intelligence not well captured in current AI systems is visual mental imagery. DoSER is pleased to feature Dr. Maithilee Kunda, associate professor of computer science and engineering at Vanderbilt University, as the speaker of our annual Holiday Lecture, where she will highlight how algorithms for visual mental imagery can improve the performance and robustness of AI systems. In this presentation, Dr. Kunda will discuss the way mental imagery can advance the capacity of AI for physical reasoning (visualizing how objects move, slide, and fall), object recognition (picturing the hidden face of an object in view), and big data visualization (comparing a current data plot to composited versions of previously seen plots).

This paper presents a new Retinotopic Reasoning (R2) cognitive architecture that is inspired by studies of visual mental imagery in people. R2 is a hybrid symbolic-connectionist architecture, with certain components of the system represented in propositional, symbolic form, but with a primary working memory store that contains visual ``mental'' images that can be created and manipulated by the system. R2 is not intended to serve as a full-fledged, stand-alone cognitive architecture, but rather is a specialized system focusing on how visual mental imagery can be represented, learned, and used in support of intelligent behavior. Examples illustrate how R2 can be used to model human visuospatial cognition on several different standardized cognitive tests, including the Raven's Progressive Matrices test, the Block Design test, the Embedded Figures test, and the Paper Folding test.