Trajectory-based interactions, such as navigating through nested-menus, drawing curves, and moving in 3D worlds, are becoming common tasks in modern computer interfaces. Users' performances in these tasks cannot be successfully modeled with Fitts' law as it has been applied to pointing tasks. Therefore we explore the possible existence of robust regularities in trajectory-based tasks. We used "steering through tunnels" as our experimental paradigm to represent such tasks, and found that a simple "steering law" indeed exists. The paper presents the motivation, analysis, a series of four experiments, and the applications of the steering law.

This article reports the results from three experimental studies of reaching behavior in a head-coupled stereo display system with a hand-tracking subsystem for object selection. It is found that lag in the head-tracking system is relatively unimportant in predicting performance, whereas lag in the hand-tracking system is critical. The effect of hand lag can be modeled by means of a variation on Fitts ’ Law with the measured system lag introduced as a multiplicative variable to the Fitts ’ Law index of difilculty. This means that relatively small lags can cause considerable degradation in performance if the targets are small. Another finding is that errors are higher for movement in and out of the screen, as compared to movements in the plane of the screen, and there is a small (10’%) time penalty for movement in the Z direction in all three experiments. Low frame rates cause a degradation in performance; however, this can be attributed to the lag which is caused by low frame rates, particularly if double buffering is used combined with early sampling of the hand-tracking device.

A stochastic optimized-submovement model is proposed for Pitts ' law, the classic logarithmic tradeoff between the duration and spatial precision of rapid aimed movements. According to the model, an aimed movement toward a specified target region involves a primary submovement and an optional secondary corrective submovement. The submovements are assumed to be programmed such that they minimize average total movement time while maintaining a high frequency of target hits. The programming process achieves this minimization by optimally adjusting the average magnitudes and durations of noisy neuromotor force pulses used to generate the submovements. Numerous results from the literature on human motor performance may be explained in these terms. Two new experiments on rapid wrist rotations yield additional support for the stochastic optimizedsubmovement model. Experiment 1 revealed that the mean durations of primary submovements and of secondary submovements, not just average total movement times, conform to a square-root approximation of Pitts ' law derived from the model. Also, the spatial endpoints of primary submovements have standard deviations that increase linearly with average primary-submovement velocity, and the average primary-submovement velocity influences the relative frequencies of secondary submovements, as predicted by the model. During Experiment 2, these results were replicated and

KSPC is the number of keystrokes, on average, to generate each character of text in a given language using a given text entry technique. We systematically describe the calculation of KSPC and provide examples across a variety of text entry techniques. Values for English range from about 10 for methods using only cursor keys and a SELECT key to about 0.5 for word prediction techniques. It is demonstrated that KSPC is useful for a priori analyses, thereby supporting the characterisation and comparison of text entry methods before labour-intensive implementations and evaluations.

A new technique to enter text using a mobile phone keypad is described. For text input, the traditional touchtone phone keypad is ambiguous because each key encodes three or four letters. Instead of using a stored dictionary to guess the intended word, our technique uses probabilities of letter sequences --- "prefixes" --- to guess the intended letter. Compared to dictionary-based methods, this technique, called LetterWise, takes significantly less memory and allows entry of nondictionary words without switching to a special input mode. We conducted a longitudinal study to compare LetterWise to Multitap, the conventional text entry method for mobile phones. The experiment included 20 participants (10 LetterWise, 10 Multitap), and each entered phrases of text for 20 sessions of about 30 minutes each. Error rates were similar between the techniques; however, by the end of the experiment the mean entry speed was 36% faster with LetterWise than with Multitap.

This paper reviews four conceptions of the nature of sensory feedback mechanisms mediating voluntary performance, including serial chaining, This report was supported in part by National Science Foundation Grant GS-1601 to the Ohio State University, administered by the author. The author is grateful to D. E. Berlyne, A. E. Goss, and A.M. Liberman for commenting on portions of an earlier draft. 2 Requests for reprints should be sent to Anthony G. Greenwald, Department of Psychology, Ohio State University, 404C West 17th Avenue, Columbus, Ohio 43210. closed-loop, and fractional anticipatory goal response mechanisms, and gives particular attention to a reformulation of the principle of ideo-motor action. At the outset, certain boundaries of the present treatment should be marked off. First, verbal mediating mechanisms and the related topics of meaning and meaningfulhess will not be given detailed coverage since it would expand this paper greatly, and perhaps unnecessarily, to attempt to do justice to the literature on verbal mediation. A number of influential writers (Goss, 1961; Luria, 1961; Miller & Dollard, 1941; Osgood, 1957; Paw lov, 1955) have assumed, as is assumed here, that verbal mediators of skilled performance differ from nonverbal mediators primarily in that the former operate at higher levels of performance organization. Accordingly, a 73 74 ANTI{ONY G. GREENWALD later section of this paper briefly treats the application to verbal behavior of principles developed herein regarding nonverbal sensory feedback mediating mechanisms. Second, since the present focus will be on mediation processes in performance of learned skills, data and theorization concerning me- diation in classical conditioning and concerning innately organized skills will not be considered...

1 This chapter provides a basic introduction to various of the computational issues that arise in the study of motor control and motor learning. A broad set of topics is discussed, including feedback control, feedforward control, the problem of delay, observers, learning algorithms, motor learning, and reference models. The goal of the chapter is to provide a unified discussion of these topics, emphasizing the complementary roles that they play in complex control systems. The choice of topics is motivated by their relevance to problems in motor control and motor learning; however, the chapter is not intended to be a review of specific models. Rather we emphasize basic theoretical issues with broad applicability. Many of the ideas described here are developed more fully in standard textbooks in modern systems theory, particularly textbooks on discrete-time systems (˚Aström & Wittenmark, 1984), adaptive signal processing (Widrow & Stearns, 1985), and adaptive control systems (Goodwin & Sin, 1984; ˚Aström & Wittenmark, 1989). These texts assume a substantial background in control

Speed/accuracy decisions in task performance: Built-in trade-off

Ten 5s were tested on a pursuit tracking test under display conditions that caused them to make false moves. The mean latency of the false moves was longer than the mean latency of correct moves. The mean time needed to arrest false moves was less than the mean time needed to initiate any motor response, whether correct or incorrect. Forty-one percent of the false moves were arrested in less than the estimated minimum time needed to process visual feedback. The data confirm previous reports that ^"s are able to correct errors more quickly than they can respond to external stimuli. Several experiments have shown that when two stimuli are separated by an interval of.5 sec. or less, the latency of the second response is longer than that of the first. In order to explain this finding, Welford (1952) has suggested that sensory feedback from the first response may "capture " the central mechanisms for a brief period and thereby