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Sternad Awarded EAGER Grant
COS & ECE Professor Dagmar Sternad was awarded a $171K NSF EAGER grant for "Challenging the Cognitive-Control Divide".
Abstract Source: NSF
This EArly-concept Grant for Exploratory Research (EAGER) collaborative research project is between an expert in robotics and control theory and an expert in experimental and computational motor neuroscience. It bridges cognitive science, experimental psychology and control engineering. The intellectual premise of the work is that a quantitative theory of human cognition may be built on top of limiting cases of human motor function. This premise lays the foundation for the development of a comprehensive quantitative theory of control-relevant cognition. The result will be an invaluable tool for the human-friendly design of complex motion control systems. Control strategies based on these fundamental objects would be more intuitively understandable by human operators, including prediction of impending failure. Extension of the results beyond motion control provide a new class of knowledge-processing systems capable of more natural interactions with humans.
The objective of this project is to articulate and test a quantitative, control-relevant theory of human cognition, to address a growing divide between cognitive science and control theory. The core hypothesis is that cognitive functions emerged from and are constrained by neural structures used for motor control. Complex motor actions are composed from a limited "library" of dynamic primitives, defined as attractors (e.g. fixed points, limit cycles, etc.). The project postulates that a similar composition of dynamic primitives underlies cognitive processes and that quantitative details may be obtained by re-purposing dynamic primitives found in motor behavior, especially in the manipulation of complex objects such as tools where the link between motor and cognitive function may be strongest. The project is based on a novel series of experiments: A data series is generated by various human participants physically manipulating a complex dynamic object. Alternative data sets are generated by computer simulation of movements to the same targets that minimize mean-squared applied force. Random fluctuations generated by low-pass filtered zero-mean Gaussian white noise and of magnitude comparable to the fluctuations in human performance are added to the simulated force and motion time-series. Without being told the origin, a second set of subjects are presented with the results as evolving abstract time-series and asked to predict their outcome. Subsequently, they are asked to generate a control input for the abstract system, based on their experience, to accomplish a specified task. According to the hypothesis, the subjects will more successfully predict the outcome of human-controlled systems than the synthetic systems, and will generate control inputs that more closely match the human-controlled system inputs.