Publications
CONFERENCE (INTERNATIONAL) Varying Subjective Speed-accuracy Biases to Evaluate the Generalizability of Experimental Conclusions on Pointing-facilitation Techniques
Shota Yamanaka, Taiki Kinoshita (Meiji University), Yosuke Oba (Meiji University), Ryuto Tomihari (Meiji University), Homei Miyashita (Meiji University)
The ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
April 23, 2023
In typical experiments to evaluate novel pointing-facilitation techniques, participants are asked to perform a task as rapidly and accurately as possible. However, the balance can differ among participants, and the techniques' effectiveness would change if the majority of participants give weight to either speed or accuracy. We investigated the effects of three subjective biases (emphasizing speed, neutral, and emphasizing accuracy) on the evaluation results of pointing-facilitation techniques, namely Bubble Cursor and Bayesian Touch Criterion (BTC). The results indicate that Bubble Cursor outperformed the baseline in terms of movement time and error rate under all bias conditions, while BTC underperformed a simpler target-prediction technique, which was an inconsistent outcome to the original study. Examining multiple biases enables researchers to discuss the (dis)advantages of novel or existing techniques more precisely, which can be beneficial to reach a more reliable conclusion.
Paper :
Varying Subjective Speed-accuracy Biases to Evaluate the Generalizability of Experimental Conclusions on Pointing-facilitation Techniques
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