Prof. David Noelle, faculty in Cognitive & Information Sciences at UC Merced, just published a journal article appearing this week in the prestigious journal, Proceedings of the National Academy of Sciences (PNAS). The first author of the paper, Dr. Trenton Kriete, was the first student to receive a Ph.D. from the CIS program, in 2010. The work reported in this article was begun while Dr. Kriete was a graduate student working with Dr. Noelle here at UC Merced.
The article reports on a new computer model of brain function that offers some innovative ideas concerning how the brain learns to process novel situations. The model shows how parts of the brain’s prefrontal cortex (PFC) can learn to dynamically reference other parts of the brain, acting much like “pointers” in a computer program, providing a means to flexibly handle new situations that are composed of familiar parts. The article is already winning some broader press attention, even internationally. To see the article on the PNAS website, click here. Here is the paper’s 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.