Looking away from a moving target does not disrupt the way in which the movement toward the target is guided.

People usually follow a moving object with their gaze if they intend to interact with it. What would happen if they did not? We recorded eye and finger movements while participants moved a cursor toward a moving target. An unpredictable delay in updating the position of the cursor on the basis of that of the invisible finger made it essential to use visual information to guide the finger's ongoing movement. Decreasing the contrast between the cursor and the background from trial to trial made it difficult to see the cursor without looking at it. In separate experiments, either participants were free to hit the target anywhere along its trajectory or they had to move along a specified path. In the two experiments, participants tracked the cursor rather than the target with their gaze on 13% and 32% of the trials, respectively. They hit fewer targets when the contrast was low or a path was imposed. Not looking at the target did not disrupt the visual guidance that was required to deal with the delays that we imposed. Our results suggest that peripheral vision can be used to guide one item to another, irrespective of which item one is looking at.

Eye movements in interception with delayed visual feedback.

The increased reliance on electronic devices such as smartphones in our everyday life exposes us to various delays between our actions and their consequences. Whereas it is known that people can adapt to such delays, the mechanisms underlying such adaptation remain unclear. To better understand these mechanisms, the current study explored the role of eye movements in interception with delayed visual feedback. In two experiments, eye movements were recorded as participants tried to intercept a moving target with their unseen finger while receiving delayed visual feedback about their own movement. In Experiment 1, the target randomly moved in one of two different directions at one of two different velocities. The delay between the participant's finger movement and movement of the cursor that provided feedback about the finger movements was gradually increased. Despite the delay, participants followed the target with their gaze. They were quite successful at hitting the target with the cursor. Thus, they moved their finger to a position that was ahead of where they were looking. Removing the feedback showed that participants had adapted to the delay. In Experiment 2, the target always moved in the same direction and at the same velocity, while the cursor's delay varied across trials. Participants still always directed their gaze at the target. They adjusted their movement to the delay on each trial, often succeeding to intercept the target with the cursor. Since their gaze was always directed at the target, and they could not know the delay until the cursor started moving, participants must have been using peripheral vision of the delayed cursor to guide it to the target. Thus, people deal with delays by directing their gaze at the target and using both experience from previous trials (Experiment 1) and peripheral visual information (Experiment 2) to guide their finger in a way that will make the cursor hit the target.

Spectral imaging using consumer-level devices and kernel-based regression.

Hyperspectral reflectance factor image estimations were performed in the 400-700 nm wavelength range using a portable consumer-level laptop display as an adjustable light source for a trichromatic camera. Targets of interest were ColorChecker Classic samples, Munsell Matte samples, geometrically challenging tempera icon paintings from the turn of the 20th century, and human hands. Measurements and simulations were performed using Nikon D80 RGB camera and Dell Vostro 2520 laptop screen as a light source. Estimations were performed without spectral characteristics of the devices and by emphasizing simplicity for training sets and estimation model optimization. Spectral and color error images are shown for the estimations using line-scanned hyperspectral images as the ground truth. Estimations were performed using kernel-based regression models via a first-degree inhomogeneous polynomial kernel and a Matérn kernel, where in the latter case the median heuristic approach for model optimization and link function for bounded estimation were evaluated. Results suggest modest requirements for a training set and show that all estimation models have markedly improved accuracy with respect to the DE00 color distance (up to 99% for paintings and hands) and the Pearson distance (up to 98% for paintings and 99% for hands) from a weak training set (Digital ColorChecker SG) case when small representative training data were used in the estimation.