Dataset Information

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome.

ABSTRACT: Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For example, in baseball, batters must judge whether the opposing pitcher will throw a fastball or a breaking ball; tennis players must decide whether an opposing player will serve out wide or down the middle. However, few methods have focused on quantifying categorical outcome; thus, how to decompose motion data into task-relevant and task-irrelevant components when the outcome is categorical rather than continuous remains unclear. Here, we propose a data-driven method to decompose motion data into task-relevant and task-irrelevant components when the outcome takes categorical values. We applied our method to experimental data where subjects were required to throw fastballs or breaking balls with a similar form. Our data-driven approach can be applied to the unclear relation between motion and outcome, and the relation can be estimated in a data-driven manner. Furthermore, our method can successfully evaluate how the task-relevant components are modulated depending on the task requirements.

SUBMITTER: Furuki D

PROVIDER: S-EPMC7015904 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome.

Furuki Daisuke D Takiyama Ken K

Scientific reports 20200212 1

Decomposition of motion data into task-relevant and task-irrelevant components is an effective way to clarify the diverse features involved in motor control and learning. Several previous methods have succeeded in this type of decomposition while focusing on the clear relation of motion to both a specific goal and a continuous outcome, such as a 10 mm deviation from a target or 1 m/s hand velocity. In daily life, it is vital to quantify not only continuous but also categorical outcomes. For exam ...[more]

PMID: 32051444

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Project description:Access to WM can be restricted on the basis of goal-relevant properties such as spatial location. However, the extent of voluntary control over which features of an attended multi-feature object are encoded and maintained in WM is debated. Some evidence suggests that attending to an object leads to obligatory storage of all of its features, whereas other evidence suggests that access to WM can be restricted to only goal-relevant features. Another possibility is that all features are initially encoded, but irrelevant features are removed from WM over time. To address these various possibilities, we used pattern classification of EEG signals to track the temporal evolution of representations reflecting the encoding and storage of task-relevant and irrelevant features in WM. In different blocks, participants remembered the orientation, color or both orientation and color of a colored, oriented grating. The color and orientation of the grating was randomly drawn from two distinct feature bins on each trial. To examine trial-specific activity reflecting storage of the object's features, a support vector machine (SVM) classifier was trained to classify what bin the stimulus features came from. Importantly, for orientation, the classifier produced reliably above-chance classification across the delay when orientation was task-relevant but not when it was task-irrelevant. Interestingly, orientation could be accurately classified on trials for which both orientation and color were remembered. Moreover, a separate measure corresponding to the probability of a feature belonging to the correct bin was significantly higher when orientation was task-relevant compared to task-irrelevant during encoding. Above-chance classification for color was only present during the initial 500 msec across all conditions. Our results suggest that although information about all of an object's features is present in the initial stimulus-evoked neural response, information about the task-irrelevant features is attenuated during stimulus encoding and is largely absent throughout the delay.

Dataset Information

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome.

Publications

A data-driven approach to decompose motion data into task-relevant and task-irrelevant components in categorical outcome.

Similar Datasets

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