開始時刻
前のセッションの直後
200. Input, Interaction and Time
発表数
4
時間
2分30秒
発表担当
目次
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User Performance in Consecutive Temporal Pointing: An Exploratory Study
説明
A significant amount of research has recently been conducted on user performance in so-called temporal pointing tasks, in which a user is required to perform a button input at the timing required by the system. Consecutive temporal pointing (CTP), in which two consecutive button inputs must be performed while satisfying temporal constraints, is common in modern interactions, yet little is understood about user performance on the task. Through a user study involving 100 participants, we broadly explore user performance in a variety of CTP scenarios. The key finding is that CTP is a unique task that cannot be considered as two ordinary temporal pointing processes. Significant effects of button input method, motor limitations, and different hand use were also observed.
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Waiting Time Perceptions for Faster Count-downs/ups Are More Sensitive Than Slower Ones: Experimental Investigation and Its Application
説明
Countdowns and count-ups are very useful displays that explicitly show how long users should wait and also show the current processing states of a given task. Most countdowns or count-ups decrease or increase their digit every one second exactly, and most users have an implicit assumption that the digit changes every one second exactly. However, there are no studies that investigate how users perceive wait times with these countdowns and count-ups and that consider changing users' perception of time passing as shorter than the actual passage of time by means of countdowns and count-ups while taking into account such user assumptions. To clarify these issues, we first investigated how users perceive countdowns "from 3/5/10 to 0" and count-ups "from 0 to 3/5/10" that have different lengths of intervals from 800 to 1200 msec (Experiment 1). Next, on the basis of the results of Experiment 1, we explored a novel method for presenting countdowns to make users perceive the wait time as being shorter than the actual wait time (Experiment 2) and investigated whether such countdowns can be used in realistic applications or not (Experiment 3). As a result, we found that countdowns and count-ups that were "from 250 msec shorter to 10% longer" than 3, 5, or 10 sec were perceived as 3, 5, or 10 sec, respectively, and those "from 5 to 0" (their lengths were 5 sec) that first displayed extremely shorter intervals were perceived as being shorter than their actual length (5 sec). Finally, we confirmed the applicability and effectiveness of such displays in a realistic application. Thus, we strongly argue that these findings could become indispensable knowledge for researchers in this research field to reduce users' cognitive load during wait times.
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Mouse2Vec: Learning Reusable Semantic Representations of Mouse Behaviour
説明
The mouse is a pervasive input device used for a wide range of interactive applications. However, computational modelling of mouse behaviour typically requires time-consuming design and extraction of handcrafted features, or approaches that are application-specific. We instead propose Mouse2Vec – a novel self-supervised method designed to learn semantic representations of mouse behaviour that are reusable across users and applications. Mouse2Vec uses a Transformer-based encoder-decoder architecture, which is specifically geared for mouse data: During pretraining, the encoder learns an embedding of input mouse trajectories while the decoder reconstructs the input and simultaneously detects mouse click events. We show that the representations learned by our method can identify interpretable mouse behaviour clusters and retrieve similar mouse trajectories. We also demonstrate on three sample downstream tasks that the representations can be practically used to augment mouse data for training supervised methods and serve as an effective feature extractor.
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The Effect of Latency on Movement Time in Path-steering
説明
In current graphical user interfaces, there exists a (typically unavoidable) end-to-end latency from each pointing-device movement to its corresponding cursor response on the screen, which is known to affect user performance in target selection, e.g., in terms of movement time (MT). Previous work also reported that a long latency increases MTs in path-steering tasks, but the quantitative relationship between latency and MT had not been previously investigated for path-steering. In this work, we derive models to predict MTs for path-steering and evaluate them with five tasks: goal crossing as a preliminary task for model derivation, linear-path steering, circular-path steering, narrowing-path steering, and steering with target pointing. The results show that the proposed models yielded an adjusted R^2 > 0.94, with lower AICs and smaller cross-validation RMSEs than the baseline models, enabling more accurate prediction of MTs.
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