The role of feedback in implicit and explicit artificial grammar learning: a comparison between dyslexic and non-dyslexic adults

ENIGMA: A Web Application for Running Online Artificial Grammar Learning Experiments

Artificial grammar learning (AGL) is an experimental paradigm frequently adopted to investigate the unconscious and conscious learning and application of linguistic knowledge. This paper will introduce ENIGMA ( https://enigma-lang.org ) as a free, flexible, and lightweight Web-based tool for running online AGL experiments. The application is optimized for desktop and mobile devices with a user-friendly interface, which can present visual and aural stimuli and elicit judgment responses with RT measures. Without limits in time and space, ENIGMA could help collect more data from participants with diverse personal and language backgrounds and variable cognitive skills. Such data are essential to explain complex factors influencing learners' performance in AGL experiments and answer various research questions regarding L1/L2 acquisition. The introduction of the core features in ENIGMA is followed by an example study that partially replicated Chen (Lang Acquis 27(3):331-361, 2020) to illustrate possible experimental designs and examine the quality of the collected data.

The Role of In-Group and Out-Group Facial Feedback in Implicit Rule Learning

Implicit learning refers to the fact that people acquire new knowledge (structures or rules) without conscious awareness. Previous studies have shown that implicit learning is affected by feedback. However, few studies have investigated the role of social feedback in implicit learning concretely. Here, we conducted two experiments to explore how in-group and out-group facial feedback impact different difficulty levels of implicit rule learning. In Experiment 1, the Chinese participants in each group could only see one type of facial feedback, i.e., either in-group (East Asian) or out-group (Western) faces, and learned the implicit rule through happy and sad facial expressions. The only difference between Experiment 2 and Experiment 1 was that the participants saw both the in-group and out-group faces before group assignment to strengthen the contrast between the two group identities. The results showed that only in Experiment 2 but not Experiment 1 was there a significant interaction effect in the accuracy of tasks between the difficulty levels and groups. For the lowest difficulty level, the learning accuracy of the in-group facial feedback group was significantly higher than that of the out-group facial feedback group, whereas this did not happen at the two highest levels of difficulty. In conclusion, when the contrast of group identities was highlighted, out-group feedback reduced the accuracy of the least difficult task; on the contrary, there was no accuracy difference between out-group and in-group feedback conditions. These findings have extensively important implications for our understanding of implicit learning and improving teaching achievement in the context of educational internationalization.

Can adults with developmental dyslexia apply statistical knowledge to a new context?

We investigated transfer of artificial grammar learning in adults with and without dyslexia in 3 experiments. In Experiment 1, participants implicitly learned an artificial grammar system and were tested on new items that included the same symbols. In Experiment 2, participants were given practice with letter strings and then tested on strings created with a different letter set. In Experiment 3, participants were given practice with shapes and then tested on strings created with different shapes. Results show that in Experiment 1, both groups demonstrated utilization of pre-trained instances in the subsequent grammaticality judgement task, while in Experiments 2 (orthographic) and 3 (nonorthographic), only typically developed participants demonstrated application of knowledge from training to test. A post hoc analysis comparing between the experiments suggests that being trained and tested on an orthographic task leads to better performance than a nonorthographic task among typically developed adults but not among adults with dyslexia. Taken together, it appears that following extensive training, individuals with dyslexia are able to form stable representations from sequential stimuli and use them in a subsequent task that utilizes strings of similar symbols. However, the manipulation of the symbols challenges this ability.

Cognitive mechanisms of statistical learning and segmentation of continuous sensory input

Two classes of cognitive mechanisms have been proposed to explain segmentation of continuous sensory input into discrete recurrent constituents: clustering and boundary-finding mechanisms. Clustering mechanisms are based on identifying frequently co-occurring elements and merging them together as parts that form a single constituent. Bracketing (or boundary-finding) mechanisms work by identifying rarely co-occurring elements that correspond to the boundaries between discrete constituents. In a series of behavioral experiments, I tested which mechanisms are at play in the visual modality both during segmentation of a continuous syllabic sequence into discrete word-like constituents and during recognition of segmented constituents. Additionally, I explored conscious awareness of the products of statistical learning—whole constituents versus merged clusters of smaller subunits. My results suggest that both online segmentation and offline recognition of extracted constituents rely on detecting frequently co-occurring elements, a process likely based on associative memory. However, people are more aware of having learnt whole tokens than of recurrent composite clusters.

Unraveling the Interconnections Between Statistical Learning and Dyslexia: A Review of Recent Empirical Studies

One important aspect of human cognition involves the learning of structured information encountered in our environment, a phenomenon known as statistical learning. A growing body of research suggests that learning to read print is partially guided by learning the statistical contingencies existing between the letters within a word, and also between the letters and sounds to which the letters refer. Research also suggests that impairments to statistical learning ability may at least partially explain the difficulties experienced by individuals diagnosed with dyslexia. However, the findings regarding impaired learning are not consistent, perhaps partly due to the varied use of methodologies across studies - such as differences in the learning paradigms, stimuli used, and the way that learning is assessed - as well as differences in participant samples such as age and extent of the learning disorder. In this review, we attempt to examine the purported link between statistical learning and dyslexia by assessing a set of the most recent and relevant studies in both adults and children. Based on this review, we conclude that although there is some evidence for a statistical learning impairment in adults with dyslexia, the evidence for an impairment in children is much weaker. We discuss several suggestive trends that emerge from our examination of the research, such as issues related to task heterogeneity, possible age effects, the role of publication bias, and other suggestions for future research such as the use of neural measures and a need to better understand how statistical learning changes across typical development. We conclude that no current theoretical framework of dyslexia fully captures the extant research findings on statistical learning.

Stimulus variation-based training enhances artificial grammar learning

The current study was designed to explore whether statistical learning ability is affected by the diversity of the stimulus set used in the training phase. The effect of stimulus diversity was assessed by controlling and manipulating the number of exposures to a given set and the number of unique strings presented to the learner during the training phase. 147 students participated in two studies. In the unvaried stimulus study, 71 participants learned the same basic set of 15 exemplars, once(15 × 1 exposure), twice (15 × 2 exposures = 30 total strings) and 3 times (15 × 3 exposures = 45 total strings). In the varied stimulus study, 75 participants learned 15, 30 and 45, all of which were unique, unrepeated exemplars. All groups were asked to classify test strings for their grammaticality following training. Results of the d' measures in the unvaried stimulus study indicate similar performance across the groups. Conversely, the results of the varied stimulus study show that the group presented with 45 unique strings performed significantly better than the baseline group (15 strings). Analysis of the differences across the equivalent groups in the two studies (15 × 2 exposures vs. 30 unique strings and 15 × 3 exposures vs. 45 unique strings) indicates differences in performance only between the group who was presented with the same 15 strings three times and the group presented with 45 unrepeated strings. Taken together, our results shed additional light on the central role of stimulus variation in Artificial Grammar Learning.

An evolutionary account of intermodality differences in statistical learning

The cognitive mechanisms underlying statistical learning are engaged for the purposes of speech processing and language acquisition. However, these mechanisms are shared by a wide variety of species that do not possess the language faculty. Moreover, statistical learning operates across domains, including nonlinguistic material. Ancient mechanisms for segmenting continuous sensory input into discrete constituents have evolved for general-purpose segmentation of the environment and been readopted for processing linguistic input. Linguistic input provides a rich set of cues for the boundaries between sequential constituents. Such input engages a wider variety of more specialized mechanisms operating on these language-specific cues, thus potentially reducing the role of conditional statistics in tokenizing a continuous linguistic stream. We provide an explicit within-subject comparison of the utility of statistical learning in language versus nonlanguage domains across the visual and auditory modalities. The results showed that in the auditory modality statistical learning is more efficient with speech-like input, while in the visual modality efficiency is higher with nonlanguage input. We suggest that the speech faculty has been important for individual fitness for an extended period, leading to the adaptation of statistical learning mechanisms for speech processing. This is not the case in the visual modality, in which linguistic material presents a less ecological type of sensory input.

Deficits in statistical leaning of auditory sequences among adults with dyslexia

Recently, it has been suggested that developmental dyslexia (DD) is related to deficits in general mechanisms of statistical learning (SL). The aim of the current study was to explore these relations using a nonlinguistic auditory artificial grammar learning (A-AGL) task. Most studies using AGL to explore the role of SL among readers with dyslexia used visual stimuli. The current study explored SL abilities among adults with DD using a nonlinguistic auditory task, because evidence suggests that SL is affected by the modality of stimuli. Forty-eight (21 DD and 27 typically developed [TD]) adults participated in two A-AGL tasks: implicit and explicit. The results showed a significant difference between the groups, as TD readers outperformed adults with DD. This difference in performance supports the SL deficit hypothesis among adults with dyslexia, although the causal relations between auditory SL and reading still require further examination. In addition, no difference was found between the implicit and explicit tasks, suggesting that unlike the visual AGL, participants with DD do not benefit from elevating attentional resources during A-AGL.

Reading as Statistical Learning

Purpose

The purpose of this tutorial is to explain how learning to read can be thought of as learning statistical regularities and to demonstrate why this is relevant for theory, modeling, and practice. This tutorial also shows how triangulation of methods and cross-linguistic research can be used to gain insight.

Method

The impossibility of conveying explicitly all of the regularities that children need to acquire in a deep orthography, such as English, can be demonstrated by examining lesser-known probabilistic orthographic cues to lexical stress. Detection of these kinds of cues likely occurs via a type of implicit learning known as statistical learning (SL). The first part of the tutorial focuses on these points. Next, studies exploring how individual differences in the capacity for SL relate to variability in word reading accuracy in the general population are discussed. A brief overview of research linking impaired SL and dyslexia is also provided. The final part of the tutorial focuses on how we might supplement explicit literacy instruction with implicit learning methods and emphasizes the value of testing the efficacy of new techniques in the classroom. The basic and applied research reviewed here includes corpus analyses, behavioral testing, computational modeling, and classroom-based research. Although some of these methods are not commonly used in clinical research, the depth and breadth of this body of work provide a compelling case for why reading can be thought of as SL and how this view can inform practice.

Conclusion

Implicit methods that draw on the principles of SL can supplement the much-needed explicit instruction that helps children learn to read. This synergy of methods has the potential to spark innovative practices in literacy instruction and remediation provided by educators and clinicians to support typical learners and those with developmental disabilities.

Atypical predictive processing during visual statistical learning in children with developmental dyslexia: an event-related potential study

Previous research suggests that individuals with developmental dyslexia perform below typical readers on non-linguistic cognitive tasks involving the learning and encoding of statistical-sequential patterns. However, the neural mechanisms underlying such a deficit have not been well examined. The aim of the present study was to investigate the event-related potential (ERP) correlates of sequence processing in a sample of children diagnosed with dyslexia using a non-linguistic visual statistical learning paradigm. Whereas the response time data suggested that both typical and atypical readers learned the statistical patterns embedded in the task, the ERP data suggested otherwise. Specifically, ERPs of the typically developing children (n = 12) showed a P300-like response indicative of learning, whereas the children diagnosed with a reading disorder (n = 8) showed no such ERP effects. These results may be due to intact implicit motor learning in the children with dyslexia but delayed attention-dependent predictive processing. These findings are consistent with other evidence suggesting that differences in statistical learning ability might underlie some of the reading deficits observed in developmental dyslexia.