Interestingly, our model's decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a "bias" in human gap acceptance behavior.
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Affordance refers to the perception of possible actions allowed by an object.
Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user.
The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans.
For graphical user interface (UI) design, it is important to understand what attracts visual attention.
To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function.
The results support hierarchical RL as a plausible model of task interleaving.
An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data.
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth.
In this paper, we propose a new approach that tracks the full skeleton motion of the hand from multiple RGB cameras in real-time.