Language comprehension, whether listening or reading, is a rapid skill that requires the coordination of perception (e.g., recognizing printed words on a page), action (e.g., controlling the movement of the eyes), memory (e.g. remembering what came in earlier parts of a sentence to relate them to later parts), and linguistic knowledge (e.g., using the order of words to constrain possible relations among them). Understanding how these processes are combined on a moment-to-moment basis has been a major challenge for cognitive science. This project will develop and test a new theory of language comprehension from an adaptive perspective. The hypothesis is that language processing may be understood as the coordinated, adaptive control of both internal (and limited) cognitive processes and external perceptual-motor actions so as to maximize specific task goals. To apply this idea to language, the investigators will formalize and implement optimal control models of limited Bayesian perception and memory for different reading tasks. These Bounded Optimal Control models will generate predictions that will be tested by monitoring the eye-movements and responses of humans.
Bounded Optimal Control is a new approach to language, thought, and action, with possibly major long-term implications for how we understand and address individual differences and deficits in reading ability. It represents a shift in scientific focus from seeking to describe the perceptual and cognitive strategies that the mind and brain employ during reading, to specifying clearly the problems for which these strategies are the solutions. There is no canonical strategy because there is no canonical problem. Thus, the best cognitive and perceptual strategies are determined by specific task goals and individual constraints. This has the potential to change our approach to reading deficits: rather than asking how an individual's reading strategies deviate from normal, it is possible to ask instead whether those strategies are the best possible strategies, taking into account both task goals and idiosyncratic cognitive constraints. Revealing the true nature of the gaps between actual behavior and "bounded optimal" behavior is an important step in understanding how to go about closing them.
