3 resultados para distributional congruence

em Universidade do Minho


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[Excerpt] Synchronization of periodic movements like side-by-side walking [7] is frequently modeled by coupled oscillators [5] and the coupling strength is defined quantitatively [3]. In contrast, in most studies on sensorimotor synchronization (SMS), simple movements like finger taps are synchronized with simple stimuli like metronomes [4]. While the latter paradigm simplifies matters and allows for the assessment of the relative weights of sensory modalities through systematic variation of the stimuli [1], it might lack ecological validity. Conversely, using more complex movements and stimuli might complicate the specification of mechanisms underlying coupling. We merged the positive aspects of both approaches to study the contribution of auditory and visual information on synchronization during side-by-side walking. As stimuli, we used Point Light Walkers (PLWs) and auralized steps sound; both were constructed from previously captured walking individuals [2][6]. PLWs were retro-projected on a screen and matched according to gender, hip height, and velocity. The participant walked for 7.20m side by side with 1) a PLW, 2) steps sound, or 3) both displayed in temporal congruence. Instruction to participants was to synchronize with the available stimuli. [...]

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Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed.