Implicit Learning, Bilingualism, and Dyslexia: Insights From a Study Assessing AGL With a Modified Simon Task

On the interaction between implicit statistical learning and the alternation advantage: Evidence from manual and oculomotor serial reaction time tasks

In this study, we examine how implicit statistical learning (ISL) interacts with the cognitive bias of the alternation advantage in serial reaction time (SRT) tasks. Our aim was to disentangle perceptual from motor aspects of learning, as well as to shed light on the cognitive sources of this alternation effect. We developed a manual (Study 1) and an oculomotor (Study 2) two-choice SRT task, with visual stimuli following the regularities of two binary artificial grammars (Fibonacci and its modification Skip). While these grammars share some deterministic transitional regularities, they differ in their probabilistic transitional regularities and distributional properties. The pattern of manual RTs in Study 1 provide evidence for ISL, showing that subjects learned the deterministic and probabilistic transitions in the two grammars. We also found a bias toward alternation (vs. repetition) in correspondence to non-deterministic points, regardless of their statistical properties in the grammars. Study 2 provides further evidence for both ISL and the alternation advantage, in terms of shorter manual RTs and higher accuracy rates of anticipatory eye movements. Saccadic responses preceding stimulus onset allow us to argue for the perceptual nature of ISL: participants detected regularities in the string by forming S-S associations based on the sequence of the perceived stimuli. Moreover, we propose that shifts in visuospatial attention preceding oculomotor programming play a role in the occurrence of the alternation advantage, and that such an effect is driven by the spatial location of the stimulus. These findings are also discussed with respect to the presence of two (possibly interacting) parsing strategies: statistical generalizations on the string vs. local hierarchical reconstruction.

Syncopation as structure bootstrapping: the role of asymmetry in rhythm and language

Syncopation - the occurrence of a musical event on a metrically weak position preceding a rest on a metrically strong position - represents an important challenge in the study of the mapping between rhythm and meter. In this contribution, we present the hypothesis that syncopation is an effective strategy to elicit the bootstrapping of a multi-layered, hierarchically organized metric structure from a linear rhythmic surface. The hypothesis is inspired by a parallel with the problem of linearization in natural language syntax, which is the problem of how hierarchically organized phrase-structure markers are mapped onto linear sequences of words. The hypothesis has important consequences for the role of meter in music perception and cognition and, more particularly, for its role in the relationship between rhythm and bodily entrainment.

Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants' pattern of anticipation could not be accounted for by "flat" statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions.

Dyslexia: A Bibliometric and Visualization Analysis

Dyslexia is a disorder characterized by an impaired ability to understand written and printed words or phrases. Epidemiological longitudinal data show that dyslexia is highly prevalent, affecting 10-20% of the population regardless of gender. This study aims to provide a detailed overview of research status and development characteristics of dyslexia from types of articles, years, countries, institutions, journals, authors, author keywords, and highly cited papers. A total of 9,166 publications have been retrieved from the Social Sciences Citation Index (SSCI) and Science Citation Index Expanded (SCI-E) from 2000 to 2021. The United States of America, United Kingdom, and Germany were the top three most productive countries in terms of the number of publications. China, Israel, and Japan led the Asia research on dyslexia. University of Oxford had the most publications and won first place in terms of h-index. Dyslexia was the most productive journal in this field and Psychology was the most used subject category. Keywords analysis indicated that "developmental dyslexia," "phonological awareness," children and fMRI were still the main research topics. "Literacy," "rapid automatized naming (RAN)," "assessment," "intervention," "meta-analysis," "Chinese," "executive function," "morphological awareness," "decoding," "dyscalculia," "EEG," "Eye tracking," "rhythm," "bilingualism," and "functional connectivity" might become the new research hotspots.

Disentangling sequential from hierarchical learning in Artificial Grammar Learning: Evidence from a modified Simon Task

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.