Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.
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DOI：
10.1371/journal.pcbi.1002771
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发表时间：
2012
影响因子：
4.3
通讯作者：
Vijayakumar S
中科院分区：
文献类型：
--
作者：
Acerbi L;Wolpert DM;Vijayakumar S
人类已经被证明能够适应定时任务的时间统计，以优化其响应的准确性，与贝叶斯集成的预测一致。这表明，他们建立了一个内部表示的时间间隔（先验）和误差（损失函数）的实验强加的分布。贝叶斯理想观察者的响应在很大程度上取决于这些内部表示，这些内部表示之前仅针对简单分布进行了研究。为了研究这些表征的性质，我们要求受试者重现从不同复杂性的潜在时间分布中得出的时间间隔，从均匀到高度偏斜或双峰，同时还改变了确定性能反馈的错误映射。间隔再现时间的分布和反馈的影响，在良好的协议与性能优化贝叶斯观察员和演员模型。贝叶斯模型比较强调，受试者整合提供的反馈，并表示与平滑近似的实验分布。从数据的主观先验的非参数重建表明，他们一般是在协议的真实分布到三阶矩，但系统较重的尾巴。特别是，高阶统计特征（峰度，多模态）似乎更难获得。我们的研究结果表明，人类只有很小的限制学习低阶统计特性的单峰（包括峰值和偏斜）分布的时间间隔的指导下，纠正反馈，他们的行为是很好地解释贝叶斯决策理论。人类在计时任务中的表现取决于最近经历的时间间隔的背景。事实上，人们可以利用先前的经验来提高他们的时间性能。考虑到时间对于感知和行动的相关性，人类如何学习和表征时间信息是神经科学中的一个基本问题。在这里，我们要求受试者重现从不同分布（不同的时间背景）中得出的时间间隔的持续时间。我们建立了一套模型，人们可能会在这样的时间任务的行为，这取决于他们是如何代表的时间背景。模型和数据之间的比较使我们能够建立，在一般情况下，科目整合任务相关的时间信息与提供的错误反馈，以提高他们的计时性能。对受试者的反应进行分析，我们可以重建他们对时间背景的内部表征，并将其与真实分布进行比较。我们发现，在纠正反馈的帮助下，人类可以学习实验中使用的时间间隔的单峰分布的良好近似，即使是持续时间的偏斜分布;另一方面，在类似的条件下，我们发现时间间隔的多峰分布更难获得。
Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory. Human performance in a timing task depends on the context of recently experienced time intervals. In fact, people may use prior experience to improve their timing performance. Given the relevance of time for both sensing and acting in the world, how humans learn and represent temporal information is a fundamental question in neuroscience. Here, we ask subjects to reproduce the duration of time intervals drawn from different distributions (different temporal contexts). We build a set of models of how people might behave in such a timing task, depending on how they are representing the temporal context. Comparison between models and data allows us to establish that in general subjects are integrating task-relevant temporal information with the provided error feedback to enhance their timing performance. Analysis of the subjects' responses allows us to reconstruct their internal representation of the temporal context, and we compare it with the true distribution. We find that with the help of corrective feedback humans can learn good approximations of unimodal distributions of time intervals used in the experiment, even for skewed distributions of durations; on the other hand, under similar conditions, we find that multimodal distributions of timing intervals are much harder to acquire.
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