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Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics.

ABSTRACT: Why does Fitts' law fit various human behavioural data well even though it is not a model based on human physical dynamics? To clarify this, we derived the relationships among the factors applied in Fitts' law-movement duration and spatial endpoint error-based on a multi-joint forward- and inverse-dynamics models in the presence of signal-dependent noise. As a result, the relationship between them was modelled as an inverse proportion. To validate whether the endpoint error calculated by the model can represent the endpoint error of actual movements, we conducted a behavioural experiment in which centre-out reaching movements were performed under temporal constraints in four directions using the shoulder and elbow joints. The result showed that the distributions of model endpoint error closely expressed the observed endpoint error distributions. Furthermore, the model was found to be nearly consistent with Fitts' law. Further analysis revealed that the coefficients of Fitts' law could be expressed by arm dynamics and signal-dependent noise parameters. Consequently, our answer to the question above is: Fitts' law for reaching movements can be expressed based on human arm dynamics; thus, Fitts' law closely fits human's behavioural data under various conditions.

SUBMITTER: Takeda M

PROVIDER: S-EPMC6930222 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics.

Takeda Misaki M Sato Takanori T Saito Hisashi H Iwasaki Hiroshi H Nambu Isao I Wada Yasuhiro Y

Scientific reports 20191224 1

Why does Fitts' law fit various human behavioural data well even though it is not a model based on human physical dynamics? To clarify this, we derived the relationships among the factors applied in Fitts' law-movement duration and spatial endpoint error-based on a multi-joint forward- and inverse-dynamics models in the presence of signal-dependent noise. As a result, the relationship between them was modelled as an inverse proportion. To validate whether the endpoint error calculated by the mod ...[more]

PMID: 31874974

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Project description:OBJECTIVE:Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]). APPROACH:Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law. MAIN RESULTS:We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder. SIGNIFICANCE:The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

Dataset Information

Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics.

Publications

Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics.

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