An integrative computational architecture for object-driven cortex. (April 2019)

• Record Type: Journal Article
• Authors: Yildirim, Ilker; Wu, Jiajun; Kanwisher, Nancy; Tenenbaum, Joshua
• Is Part Of: Current opinion in neurobiology. Volume 55(2019)
• Page Start: 73
• Page End: 81
• Publication Date: 2019-04
• Subjects: Neurobiology -- Periodicals; 573.8
• DOI: 10.1016/j.conb.2019.01.010
• Languages: English
• ISSNs: 0959-4388

Abstract

Highlights: Objects in motion activate multiple cortical regions in every lobe of the human brain. We outline an integrative computational architecture for this 'object-driven' cortex. Architecture components derive from recent advances in machine learning and AI. Points toward a neurally grounded, functional account of dynamic object cognition. Abstract : Computational architecture for object-driven cortex Objects in motion activate multiple cortical regions in every lobe of the human brain. Do these regions represent a collection of independent systems, or is there an overarching functional architecture spanning all of object-driven cortex? Inspired by recent work in artificial intelligence (AI), machine learning, and cognitive science, we consider the hypothesis that these regions can be understood as a coherent network implementing an integrative computational system that unifies the functions needed to perceive, predict, reason about, and plan with physical objects—as in the paradigmatic case of using or making tools. Our proposal draws on a modeling framework that combines multiple AI methods, including causal generative models, hybrid symbolic-continuous planning algorithms, and neural recognition networks, with object-centric, physics-based representations. We review evidence relating specific components of our proposal to the specific regions that comprise object-driven cortex, and lay out future research directions with the goal of building a complete functional and mechanistic account of this system.