Abstract
In this study we asked whether Fitts’ Law, a well-established relationship that predicts movement times (MTs) for direct movements between two positions, could be extended to predict MTs for curved, obstacle avoiding, movements. We had participants make movements in the presence of an obstacle. Using these data, we tested an extensions of Fitts’ Law that predicted MTs based on the movement’s index of difficulty and the distance that the obstacle intruded into the direct movement path. Including both factors led to more accurate predictions of MTs for obstacle-avoiding movements than was possible with the index of difficulty alone. In addition, the simple extension of Fitts’ Law did as well as a model which relied on the obtained movement paths between targets. This is an encouraging outcome because it suggests that the physical layout of the workspace can be used to predict MTs for obstacle avoiding movements, an accomplishment that fits with the spirit of Fitts’ Law.
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Notes
The end-of-block score also encouraged participants to initiate their movements as quickly as possible. These initiation times did not differ between obstacle present and obstacle absent blocks (means of 461 and 449 ms, respectively; P = 0.62 for a t test of the difference). We have observed such equivalence in another recent study of obstacle avoidance (Jax and Rosenbaum 2007).
In addition to the formulation of Fitts’ Law presented in Eq. 1, we also tried to predict MTs using the Shannon formulation of index of difficulty (see MacKenzie 1992), but found that the original formulation was able to account for a slightly higher proportion of variance in our data set (R 2 of 0.847 vs. 0.832 in the obstacle-absent condition, and 0.225 vs. 0.209 in the obstacle-present condition).
OI, and not log2(OI), was used in both regressions because in both cases OI was able to account for a slightly higher proportion of variance in our data set. For the OI alone model, R 2 values were 0.420 and 0.381 using OI and log2(OI), respectively. For the ID and OI model, the adjusted R 2 values were 0.886 and 0.856 using OI and log2(OI), respectively.
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Acknowledgments
This work was supported by grant F31 NS 047784-01 from the National Institutes of Health to SAJ, and by grant SBR-94-96290 from the National Science Foundation, grants KO2-MH0097701A1 and R15 NS41887-01 from the National Institute of Mental Health, and grants from the Research and Graduate Studies Office of The College of Liberal Arts, Pennsylvania State University, to DAR. We thank Jeremy Graham for help with programming and data collection and to an anonymous reviewer for his or her helpful suggestions.
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Jax, S.A., Rosenbaum, D.A. & Vaughan, J. Extending Fitts’ Law to manual obstacle avoidance. Exp Brain Res 180, 775–779 (2007). https://doi.org/10.1007/s00221-007-0996-y
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DOI: https://doi.org/10.1007/s00221-007-0996-y