UC Santa Barbara

One defining characteristic of human behavior is the ability to select an appropriate action in an entirely novel situation. How abstract rules are represented in the brain, and how these representations operate on internal models of the world to generate flexible rapidly optimized behaviors remains an open question in neuroscience as well as computer science. The hallmark of quickly optimized flexibility inherent to explicit behavioral rules has lead to the assumption that these rules are based on high level abstractions. In the recent past, behavioral measurements in the human psychophysics literature were linked to predictions at the processing level and the convenient mathematical construct of a decision criterion has precipitated in several cognitive process models. However, the assumption that higher order decision processes involve comparisons to some internal criterion is not trivial and was investigated. This thesis provides evidence to falsify the criterion as a processing component in human decision making and offers fundamental insights to reverse engineer decision processes in the brain. Cognitive flexibility and rule guided behavior appear to rely on phylogenetically advanced extrapolation processes that are mediated by dynamic feed-forward and feed-back circuits, which continually update internal and external information to support goal directed behavior.