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首页> 外文期刊>Experimental Brain Research >Dual LATER-unit model predicts saccadic reaction time distributions in gap, step and appearance tasks.
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Dual LATER-unit model predicts saccadic reaction time distributions in gap, step and appearance tasks.

机译:双LATER单位模型可预测间隙,步距和外观任务中的扫视反应时间分布。

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Saccadic latencies have long been known to depend on the relative timing of the appearance of the new target, and offset of the original fixation target. Previous studies have tended to conclude that two separate effects are at work, one equivalent to competitive inhibition from the fixation target, and the other due to its offset providing a warning that shortens latency. In this study, we propose a simpler explanation, based on a well-established model of reaction time, LATER (linear approach to threshold with ergodic rate), that in addition to predicting mean latencies also--more challengingly--predicts latency distributions. We show that observed distributions, using gap, step and appearance tasks under three conditions of prior probability, can be accurately predicted by using a pair of LATER units, one corresponding to fixation target offset and the other to peripheral target onset. Because fixation offset is probabilistically associated with target appearance, when the fixation unit is activatedit increases the target's decision signal (that represents probability) in a fixed proportion, speeding responses. In contrast, when the fixation target remains present, the fixation unit is not activated, and responses are slower. Both these effects generate characteristic changes in the shapes of the latency distributions that can be accurately predicted by the model.
机译:人们早已知道眼跳潜伏期取决于新靶标出现的相对时间以及原始注视靶标的偏移。先前的研究趋于得出以下结论:两种单独的作用在起作用,一种等效于对固定目标的竞争性抑制,另一种由于其抵消提供了缩短潜伏期的警告。在这项研究中,我们基于完善的反应时间模型LATER(遍历率阈值的线性方法)提出了一个更简单的解释,即除了预测平均潜伏期(更具挑战性)之外,还可以预测潜伏期分布。我们显示,通过使用一对LATER单元(一个对应于固视目标偏移,另一个对应于周边目标发作),可以准确地预测在先验概率的三个条件下使用间隙,台阶和外观任务所观察到的分布。因为注视偏移可能与目标外观相关,所以当启动注视单元时,它会以固定比例增加目标的决策信号(代表概率),从而加快响​​应速度。相反,当固视目标仍然存在时,固视单元不被激活,并且响应较慢。这两种影响都会在潜伏期分布的形状中产生特征性变化,可以由模型准确预测。

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