Fitts’ Law: On Calculating Throughput and Non-ISO Tasks

• Autores: Maria Francesca Roig Maimó, Scott MaCkenzie, Cristina Suemay Manresa Yee , Javier Varona Gómez
• Localización: Revista Colombiana de Computación, ISSN 1657-2831, ISSN-e 2539-2115, Vol. 19, Nº. 1, 2018 (Ejemplar dedicado a: Colombian Journal of Computing), págs. 7-28
• Idioma: inglés
• DOI: 10.29375/25392115.3226
• Títulos paralelos:
  • Ley de Fitts: Sobre el Cálculo del Rendimiento y Tareas No ISO
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    En este trabajo se presenta el uso de una tarea de selección de objetivos en la evaluación de un head-tracker para dispositivos móviles. Primero, se describe el método de cálculo del rendimiento mediante un ejemplo detallado. A continuación, se discute el método de cálculo para tareas que no cumplen el estándar ISO. Debido a la amplitud no constante de la tarea dentro de cada secuencia, se calcula el rendimiento utilizando dos métodos de agregación de datos: por secuencia, calculando la amplitud media, y por condiciones comunes A-W. Se recomienda calcular el rendimiento utilizando la división de medias y el ajuste de precisión. La media general de rendimiento ha sido de 0,74 bps (un 45 % menor que con una tarea ISO). Se presentan dos recomendaciones de diseño para tareas que no cumplen el estándar ISO: mantener constantes A-W dentro de cada secuencia y utilizar estrategias para evitar el tiempo de reacción.

  • English

    We used a target-selection task to evaluate head-tracking as an input method for mobile devices. First, the method of calculating Fitts’ throughput is described by means of a raw data detailed example. Then, the method of calculating throughput is discussed for non-ISO tasks, since the procedure targets were randomly positioned from trial to trial. Due to a non-constant amplitude within each sequence of trials, throughput was calculated using two methods of data aggregation: the first one, by sequence of trials using the mean amplitude, and the second one, by common A-W conditions. For each data set, we used four methods for calculating throughput. The grand mean for throughput (calculated through the division of means and the adjustment for accuracy) was of 0.74 bps, which is 45 % lower than the value obtained using an ISO task. We recommend to calculate throughput using the division of means plus the adjustment for accuracy, and to avoid using the reciprocal slope of the regression model. We present various design recommendations for non-ISO tasks, such as: i) to keep amplitude and constant target within each sequence of trials, and ii) to use strategies to avoid or remove reaction time.

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