Pointing on a computer display

Abstract

Four experiments were performed to study planning and control of pointing movements in human-computer interaction (HCI). We examined how theories and models derived from motor controI research on natural movements apply to HCI, in order to understand why mediated pointing is difficult, and to suggest strategies for improvinginterfaces. An OPTOTRAK sensor and computer system mapped 3-D hand movements to a 2-D display with less than 25 ms lag. Subjects viewed targets on a graphics display and showed evidence for fast corrective processes when target position was perturbed at movement c set, even when gaze was directed away from the target location for the hand. Experiment 4 revealed that even when gain is perturbed during a pointing movement, adaptation is achieved through adjusting kinematic parameters of the same underlying plan.Results indicate that modelling and analysis of performance in mediated pointing must be based on hand actions required for a task, regardless of the size of the display.Pointing performance will be improved by reducing the scale of hand movements; thus future effort should be focused on improving the sensing resolution of input devices for manual interaction. Emerging from this research is a refined two-part model for predicting movement time, providing a more detailed view than Fitts' law. In addition, the model's ... lll parameter space predicts performance changes when hand movements are scaled, and characterises tasks and devices in terms of difficulties in the two movement phases. The experiments provide a model of performance when pointing with an unencumbered hand, a benchmark against which pointing devices can be compared. The measurement and analysis techniques, and the identification of separable direction, amplitude, and width effects may prove useful for predicting target location while a movement is ongoing, and can be applied to the design of an intelligent interface, employing precomputation to respond to object selection.

Reaction times and the decision-making process in endoscopic surgery

“…Previous studies have found that superimposed displays are an ideal way to facilitate performance in remote environments [3,7,8,16]. The results of experiment 2 indicate that the superimposed viewing condition also facilitates motor planning, shortening the decision-making process.…”

“…The benefits of placing a superimposed image over the work plane on task performance have been documented in both virtual and endoscopic task environments [7,8,16]. Experiment 2 extended these benefits to motor planning, which takes place even before the actual movement is initiated.…”

“…Methods to overcome the problem, such as displaying the surgical image above the patient using a superimposition technique [7,8,15], were also investigated.…”

Targeting across displayless space

“…Fitts's Law [20,15] predicts movement time based on a user's movements in motor space, not on the distance traveled by the cursor. Although we can assume that switching attention between displays during targeting has a time cost, is this reflected in a slower ballistic movement or a lengthening of the homing-in phase?…”

On the generality of sensorimotor control strategies

“…Esta relação foi inicialmente modelada por Fitts (1954), que demonstrou que o tempo de movimento em tais tarefas é uma função linear do logaritmo do dobro da amplitude de movimento dividida pela largura do alvo. Esta proposição, a qual veio a ser conhecida como Lei de Fitts, tem recebido amplo suporte empírico em diferentes tipos de tarefa envolvendo velocidade e precisão de movimento, tais como contatar um alvo físico com a mão ou estilete (Fitts, 1954;Fitts & Peterson, 1964;MacKenzie, Marteniuk, Dugas, Liske & Eickmeier, 1987;Marteniuk, MacKenzie, Jeannerod, Athenes & Dugas, 1987), movimentos de contatação feitos com a cabeça (Andres & Hartung, 1989), atingir um alvo em monitor de computador através de movimentos com um mouse (Graham, 1996), e em tarefas de agarrar objetos (Bootsma, Marteniuk, MacKenzie & Zaal, 1994). A explicação que tem sido dada para esse fenômeno é que o aumento do índice de dificuldade, particularmente pela diminuição da largura do alvo, gera uma maior demanda de processamento de "feedback" em função da maior restrição espacial colocada pela tarefa, fazendo com que o tempo de movimento seja aumentado como conseqüência do maior número de ajustes necessários para obtenção de sucesso.…”