Dr David Noelle's talk at Dream Team meeting
Dr. David C. Noelle[1] is Associate Professor of Cognitive and Information Sciences at the University of California, Merced. He is also a member of the Electrical Engineering and Computer Science graduate group. Dr. Noelle manages the Computational Cognitive Neuroscience Laboratory. He received his Ph.D. in Cognitive Science and Computer Science from the University of California, San Diego, and he completed postdoctoral training at the Center for the Neural Basis of Cognition at Carnegie Mellon University. Dr. Noelle's research largely involves the fabrication, analysis, and testing of computational models of brain function, with a focus on the prefrontal cortex and its role in learning, memory, and the control of behavior. He also develops and applies biologically inspired machine learning methods.
He will talk about his 2013 Proceedings of the National Academy of Sciences paper[2] on modeling "pointers" in the prefrontal cortex.
Indirection and symbol-like processing in the prefrontal cortex and basal ganglia
Trenton Kriete, David C. Noelle, Jonathan D. Cohen, and Randall C. O'Reilly
Abstract
The ability to flexibly, rapidly, and accurately perform novel tasks is a hallmark of human behavior. In our everyday lives we are often faced with arbitrary instructions that we must understand and follow, and we are able to do so with remarkable ease. It has frequently been argued that this ability relies on symbol processing, which depends critically on the ability to represent variables and bind them to arbitrary values. Whereas symbol processing is a fundamental feature of all computer systems, it remains a mystery whether and how this ability is carried out by the brain. Here, we provide an example of how the structure and functioning of the prefrontal cortex/basal ganglia working memory system can support variable binding, through a form of indirection (akin to a pointer in computer science). We show how indirection enables the system to flexibly generalize its behavior substantially beyond its direct experience (i.e., systematicity). We argue that this provides a biologically plausible mechanism that approximates a key component of symbol processing, exhibiting both the flexibility, but also some of the limitations, that are associated with this ability in humans.