Department of Cultures and Civilizations, University of Verona, Verona, Italy.
Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, United Kingdom.
PLoS One. 2020 May 14;15(5):e0232687. doi: 10.1371/journal.pone.0232687. eCollection 2020.
In this paper we probe the interaction between sequential and hierarchical learning by investigating implicit learning in a group of school-aged children. We administered a serial reaction time task, in the form of a modified Simon Task in which the stimuli were organised following the rules of two distinct artificial grammars, specifically Lindenmayer systems: the Fibonacci grammar (Fib) and the Skip grammar (a modification of the former). The choice of grammars is determined by the goal of this study, which is to investigate how sensitivity to structure emerges in the course of exposure to an input whose surface transitional properties (by hypothesis) bootstrap structure. The studies conducted to date have been mainly designed to investigate low-level superficial regularities, learnable in purely statistical terms, whereas hierarchical learning has not been effectively investigated yet. The possibility to directly pinpoint the interplay between sequential and hierarchical learning is instead at the core of our study: we presented children with two grammars, Fib and Skip, which share the same transitional regularities, thus providing identical opportunities for sequential learning, while crucially differing in their hierarchical structure. More particularly, there are specific points in the sequence (k-points), which, despite giving rise to the same transitional regularities in the two grammars, support hierarchical reconstruction in Fib but not in Skip. In our protocol, children were simply asked to perform a traditional Simon Task, and they were completely unaware of the real purposes of the task. Results indicate that sequential learning occurred in both grammars, as shown by the decrease in reaction times throughout the task, while differences were found in the sensitivity to k-points: these, we contend, play a role in hierarchical reconstruction in Fib, whereas they are devoid of structural significance in Skip. More particularly, we found that children were faster in correspondence to k-points in sequences produced by Fib, thus providing an entirely new kind of evidence for the hypothesis that implicit learning involves an early activation of strategies of hierarchical reconstruction, based on a straightforward interplay with the statistically-based computation of transitional regularities on the sequences of symbols.
在本文中，我们通过研究一组学龄儿童的内隐学习来探究序列和层次学习之间的相互作用。我们采用了一种序列反应时任务，其形式为一种修改后的西蒙任务，其中刺激按照两种不同的人工语法规则组织，具体来说是林登迈尔系统：斐波那契语法（Fib）和跳过语法（前者的修改版）。选择语法是由本研究的目标决定的，该目标是研究在接触输入的过程中，结构敏感性是如何出现的，而输入的表面过渡属性（根据假设）可以引导结构。迄今为止进行的研究主要旨在调查可通过纯粹统计术语学习的低级表面规则，而尚未有效地调查层次学习。我们的研究核心是能够直接确定序列和层次学习之间的相互作用：我们向儿童呈现了两种语法，Fib 和 Skip，它们具有相同的过渡规则，因此为序列学习提供了相同的机会，而在层次结构上则有很大的不同。更具体地说，序列中有一些特定的点（k 点），尽管在这两种语法中都会产生相同的过渡规则，但在 Fib 中支持层次结构的重建，而在 Skip 中则不支持。在我们的方案中，儿童只需执行传统的西蒙任务，他们完全不知道任务的真正目的。结果表明，两种语法都发生了序列学习，这表现在整个任务中反应时间的减少，而在 k 点的敏感性上存在差异：我们认为，这些点在 Fib 中的层次结构重建中发挥作用，而在 Skip 中则没有结构意义。更具体地说，我们发现儿童在 Fib 产生的序列中与 k 点对应的反应更快，从而为内隐学习涉及基于与符号序列的统计过渡规则计算的直接相互作用的层次结构重建策略的早期激活这一假设提供了全新的证据。
