HUMAN PERFORMANCE IN MOTION PLANNING in .NET

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HUMAN PERFORMANCE IN MOTION PLANNING
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Another surprise is that the statistics undermines the predominant belief among subjects and among robotics and cognitive science experts that humans should be doing signi cantly better when moving a physical as opposed to a virtual arm. Isn t the physical arm quite similar to our own arm, which we use so ef ciently To be sure, the subjects did better with the physical arm but only a little better, not by as much as one would expect, and only for the (easier) left-to-right direction of motion. Once the task became a bit harder, the difference disappeared: When moving the physical arm in the right-to-left direction, more often than not the subjects performance was signi cantly worse than when moving the virtual arm in the left-to-right direction, and more or less comparable to moving the virtual arm in the same right-to-left direction (see Table 7.1). In other words, letting a subject move the physical arm does not guarantee more con dence than when moving a virtual arm: Some other factors seem to play a more decisive role in the subjects performance. In an attempt to extract the (possibly hidden) effects of our experimental factors on one s performance, two types of analysis have been undertaken for the Experiment One data:
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The rst one, Principal Components Analysis (PCA), has been carried out as a preliminary study, to understand the general nature of obtained observation data and to see if such factors as subjects gender, specialization, and
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TABLE 7.1. Descriptive Statistics for the Data in Experiment One Descriptive Statistics Variable/Task Length of path Virtual visible LtoR Virtual visible RtoL Virtual invisible LtoR Virtual invisible RtoL Physical visible LtoR Physical visible RtoL Physical invisible LtoR Physical invisible RtoL Time to completion: Virtual visible LtoR Virtual visible RtoL Virtual invisible LtoR Virtual invisible RtoL Physical visible LtoR Physical visible RtoL Physical invisible LtoR Physical invisible RtoL Valid N 24 24 24 24 23 24 24 23 24 24 24 24 24 24 24 24 Mean 58.77 176.92 85.82 156.08 27.70 142.97 60.57 160.19 265.54 692.79 376.02 675.75 46.21 218.50 122.88 299.88 Minimum 18.26 29.54 21.88 17.59 13.92 15.78 15.17 14.26 82 186 72 66 14 15 19 22 Maximum 147.23 391.41 340.15 392.41 51.69 396.45 306.13 501.59 595 912 920 941 102 902 612 900 Std. Dev. 31.74 91.28 71.65 96.89 11.64 109.38 75.78 145.10 163.19 252.00 282.82 329.91 26.49 228.44 155.37 244.63
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RESULTS EXPERIMENT ONE
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age group have a noticeable effect on the subjects performance in motion planning tasks. The second, more pointed analysis addresses separate effects of individual factors on subjects performance the effect of interface (virtual versus physical), scene visibility, and the direction of motion. This study makes use of tools of nonparametric analysis and univariate analysis of variance. Only brief summaries of the techniques used are presented below. For more details on the techniques the reader should refer to the sources cited in the text below; for details related to this speci c study see Ref. 121.
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7.4.1 Principal Components Analysis We attempt to answer the following questions: 1. To what extent are the factors used interface (virtual or physical), scene visibility, and direction of motion indicative of human performance 2. Can these factors be replaced by some hidden factors that describe the same data in a clearer and more compact way 3. Which factor or which part of the factor s variance is most indicative of one s performance in a motion planning task 4. Do the patterns of subjects performance differ as a function of their gender, college specialization, and age group 5. Can we predict one s performance in one task based on their performance in another task Principal Components Analysis (PCA) addresses these questions based on analysis of the covariance matrix of the original set of independent variables [122, 123]. In our case this would be the covariance matrix of a set of twolevel tasks. The analysis seeks to identify hidden factors called the principal components which turn out to be eigenvectors of the sample covariance matrix. The matrix s eigenvalues represent variation of the principal components; the sum of eigenvalues is the total variance of the original sample data and is equal to the sum of variances of the original variables. With the principal components (eigenvectors) conveniently ordered from the largest to the smallest, the rst component accounts for most of the total variance in the sample data, the second component accounts for the next biggest part of the total variance, and so on. The rst component can thus be called the most important hidden factor, and so on. This ordering sometimes allows the researcher to (a) drop the last few components if they account for too small a part of the total variance and (b) claim that the data can be adequately described via a smaller set of variables. Often attempts are made to interpret the hidden factors in physical terms, arguing that if the hidden factors could be measured directly they would allow a signi cantly better description of the phenomenon under discussion.
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