Modeling mindsets with kalman filter

Abstract

Mathematical models have played an essential role in interface design. This study focused on "mindsets"-people's tacit beliefs about attributes-and investigated the extent to which: (1) mindsets can be extracted in a motion trajectory in target selection, and (2) a dynamic state-space model, such as the Kalman filter, helps quantify mindsets. Participants were experimentally manipulated to hold fixed or growth mindsets in a "mock" memory test, and later performed a concept-learning task in which the movement of the computer cursor was recorded in every trial. By inspecting motion trajectories of the cursor, we observed clear disparities in the impact of mindsets; participants who were induced with a fixed mindset moved the cursor faster as compared to those who were induced with a growth mindset. To examine further the mechanism of this influence, we fitted a Kalman filter model to the trajectory data; we found that system-level error-covariance in the Kalman filter model could effectively separate motion trajectories gleaned from the two mindset conditions. Taken together, results from the experiment suggest that people's mindsets can be captured in motor trajectories in target selection and the Kalman filter helps quantify mindsets. It is argued that people's personality, attitude, and mindset are embodied in motor behavior underlying target selection and these psychological variables can be studied mathematically with a feedback control system.