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首页> 外文期刊>Journal of vision >Predictions as a window into learning: Anticipatory fixation offsets carry more information about environmental statistics than reactive stimulus-responses
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Predictions as a window into learning: Anticipatory fixation offsets carry more information about environmental statistics than reactive stimulus-responses

机译：预测是学习的窗口：与反应性刺激响应相比，预期的注视偏移能提供有关环境统计的更多信息

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A core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them to make predictions. Most investigations of this issue have relied on reactive paradigms, in which inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel anticipatory oculomotor metric to determine how input statistics impact anticipatory behavior that is decoupled from target-driven-response. We implemented transition constraints between target locations, so that the probability of a target being presented on the same side as the previous trial was 70% in one condition (pret70) and 30% in the other (pret30). Rather than focus on responses to targets, we studied subtle endogenous anticipatory fixation offsets (AFOs) measured while participants fixated the screen center, awaiting a target. These AFOs were small (0.4° from center on average), but strongly tracked global-level statistics. Speaking to learning dynamics, trial-by-trial fluctuations in AFO were well-described by a learning model, which identified a lower learning rate in pret70 than pret30, corroborating prior suggestions that pret70 is subjectively treated as more regular. Most importantly, direct comparisons with saccade latencies revealed that AFOs: (a) reflected similar temporal integration windows, (b) carried more information about the statistical context than did saccade latencies, and (c) accounted for most of the information that saccade latencies also contained about inputs statistics. Our work demonstrates how strictly predictive processes reflect learning dynamics, and presents a new direction for studying learning and prediction.
机译：行为的神经生物学和计算模型的基本核心问题是个人如何学习环境统计数据并使用它们进行预测。关于这个问题的大多数研究都依赖于反应性范式，其中关于预测过程的推论是通过对可能发生变化的刺激的响应建模来得出的。在这里，我们部署了一种新颖的预期动眼指标，以确定输入统计数据如何影响与目标驱动响应分离的预期行为。我们在目标位置之间实施了过渡约束，因此在一种情况下（pret70），目标出现在与先前试验相同的一侧的概率是70％，在另一种情况下（pret30），则是30％。我们没有专注于对目标的反应，而是研究了细微的内源性预期注视偏移量（AFO），这些偏移是在参与者固定屏幕中心并等待目标时测得的。这些AFO很小（平均距中心<0.4°），但是在全球范围内追踪很强。说到学习动态，AFO的逐次试验波动由一个学习模型很好地描述，该模型确定了pret70中的学习率低于pret30，从而证实了先前的建议，即pret70在主观上被认为是更规律的。最重要的是，与扫视潜伏期的直接比较表明，AFO：（a）反映了类似的时间积分窗口;（b）携带了比扫视潜伏期更多的统计背景信息;（c）占了扫视潜伏期的大多数信息包含有关投入的统计信息。我们的工作证明了预测过程如何严格反映学习动态，并为学习和预测提供了新的方向。

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