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Cognitive Measures and Performance on the Antisaccade Eye Movement Task

机译:反扫视眼运动任务的认知措施和表现

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摘要

The antisaccade (AS) task is considered a prominent measure of inhibitory control, but it is still unclear which cognitive processes are used for successful performance of the task. Previous results have provided evidence for the involvement of several processes, including working memory (WM), inhibition and attention. Thus, the aim of this study was to explore, using a range of neuropsychological tests, which cognitive factors predict individual differences in AS performance. To do so, 143 healthy participants underwent a battery including tests measuring inhibition, working memory, cognitive flexibility, sustained attention, IQ and fluency. Hierarchical stepwise regression analyses were conducted to assess the association with AS performance. Performance on the Trail-Making-Test, version B (TMT-B), a test measuring flexibility, divided attention and WM, was found to significantly predict AS latency. Rapid Visual Information Processing (RVIP), used to assess sustained attention and WM, significantly predicted AS error rate. Other cognitive measures, however, did not significantly predict AS performance. Bayesian Model Averaging supported these conclusions and showed that non-significant predictors are unlikely to be associated with AS outcomes. Several explanations are provided for the associations of TMT-B and RVIP with AS performance; as the tests measure a range of different cognitive processes, interpretation of these results remains less clear. For a better understanding of the cognitive mechanisms underlying AS performance, future research should make use of a wider range of attention and WM tests.
机译:防扫视(AS)任务被认为是抑制控制的重要措施,但目前尚不清楚哪些认知过程用于成功执行任务。先前的结果为涉及多个过程的证据提供了证据,包括工作记忆(WM),抑制和注意力。因此,本研究的目的是使用一系列神经心理学测试来探索哪些认知因素可以预测AS表现的个体差异。为此,对143名健康参与者进行了一系列测试,包括测量抑制,工作记忆,认知柔韧性,持续注意力,智商和流利度的测试。进行了分层逐步回归分析,以评估与AS表现的关联。发现在B试行测试(TMT-B)上的性能,该测试可衡量灵活性,注意力分散和WM,可显着预测AS延迟。快速视觉信息处理(RVIP)用于评估持续关注和WM,可显着预测AS错误率。然而,其他认知指标并不能显着预测AS表现。贝叶斯模型平均支持这些结论,并表明非重要的预测因素不太可能与AS结果相关。针对TMT-B和RVIP与AS性能的关联提供了几种解释;由于测试测量了一系列不同的认知过程,因此对这些结果的解释仍然不清楚。为了更好地理解AS性能的认知机制,未来的研究应利用更广泛的注意力和WM测试。

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