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

Abstract

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.

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

“…The use of the Fibonacci grammar aims precisely at avoiding this problem because it allows us to evaluate the learning during the processing without having to compare the performance to an alternative sequence. Our conceptual framework critically diverges from Vender et al (2020) in that rather than hypothesizing that the parser extracts some formal properties of the Fibonacci grammar (k-points), we hypothesize that it proceeds through recursively merging points that span across deterministic transitions, and then using the output of this process to merge new deterministic transitions between groups of points, resulting in the progressive building of a hierarchical structure. Participants may also develop knowledge of formal properties of the Fibonacci grammar; however, this question is beyond the scope of the present study.…”

Language-developmental trajectory in autism

“…Preliminary findings in a recent study in our lab show that adults who report an autism diagnosis, or individuals in the Broader Autism Phenotype (as indicated by their poor scores on the Social Responsiveness Scale, SRS, Constantino & Gruber, 2012) in fact show superior performance in a visual-motor statistical learning task (where the sequence was a Fibonacci grammar, see Vender et al 2019Vender et al , 2020 -namely, they show robust detection of both simple and more complex sequential patterns, where the TD group shows learning only of the simpler pattern (see Zwart et al 2018 for a similar finding). Given that these individuals showed ceiling performance on our language tasks, we suggest that their strong language may have been developmentally supported by strong statistical learning abilities, acting as a protective factor against the negative effect of the social deficit.…”

Finding hierarchical structure in binary sequences : evidence from Lindenmayer grammar learning

“…In the present study, we implemented Fibonacci sequences in an SRT task, thus avoiding the need to create non-grammatical Fib-strings (like in Geambaşu et al, 2016Geambaşu et al, , 2020. In contrast to Vender et al (2019Vender et al ( , 2020, dots were presented in the center of the screen, to avoid the interfering congruency factor introduced by the Simon task. Importantly, we developed new analyses, substantially different from those conducted in these 4 papers, which allowed us to more finely tease apart hierarchical learning from the tracking of surface regularities.…”