Under What Conditions Can Recursion Be Learned? Effects of Starting Small in Artificial Grammar Learning of Center-Embedded Structure

The "Starting-Small" Effect in Phonology: Evidence From Biased Learning of Opaque and Transparent Vowel Harmony

The starting-small effect is a cognitive advantage in language acquisition when learners begin by generalizing on regularities from structurally simple and shorter tokens in a skewed input distribution. Our study explored this effect as a potential explanation for the biased learning of opaque and transparent vowel harmony. In opaque vowel harmony, feature agreement occurs strictly between adjacent vowels, and an intervening "neutral vowel" blocks long-distance vowel harmony. Thus, opaque vowel harmony could be acquired even if learners start with structurally simpler and more frequent disyllabic tokens. Alternatively, transparent vowel harmony can only be observed in longer tokens demonstrating long-distance agreement by skipping a neutral vowel. Opaque vowel harmony is predicted to be learned more efficiently due to its compatibility with local dependency acquired via starting-small learning. In two artificial grammar learning experiments, learners were exposed to both vowel harmony patterns embedded in an equal number of disyllabic and trisyllabic tokens or a skewed distribution with twice as many disyllabic tokens. In Exp I, learners' test performance suggests the consistently biased learning of local and opaque vowel harmony with starting-small learning. Furthermore, in Exp II, the acquired vowel harmony patterns varied significantly by working memory capacity with a balanced but not skewed input distribution, presumably because of the ease of cognitive demand with starting-small learning.

Rules generalization in children with dyslexia

BACKGROUND

Rule learning (RL) is the ability to extract and generalize higher-order repetition-based structures. Children with Developmental Dyslexia (DD) often report difficulties in learning complex regularities in sequential stimuli, which might be due to the complexity of the rule to be learned. Learning high-order repetition-based rules represents a building block for the development of language skills.

AIMS

This study investigates the ability to extract and generalize simple, repetition-based visual rules (e.g., ABA) in 8-11-year-old children without (TD) and with a diagnosis of Development Dyslexia (DD) and its relationship with language and reading skills.

METHOD

Using a forced-choice paradigm, children were first exposed to a visual sequence containing a repetition-based rule (e.g., ABA) and were then asked to recognize familiar and novel rules generated by new visual elements. Standardized language and reading tests were also administered to both groups.

RESULTS

The accuracy in recognizing rules was above chance for both groups, even though DD children were less accurate than TD children, suggesting a less efficient RL mechanism in the DD group. Moreover, visual RL was positively correlated with both language and reading skills.

CONCLUSION

These results further confirm the crucial role of RL in the acquisition of linguistic skills and mastering reading abilities.

Modality, presentation, domain and training effects in statistical learning

While several studies suggest that the nature and properties of the input have significant effects on statistical learning, they have rarely been investigated systematically. In order to understand how input characteristics and their interactions impact statistical learning, we explored the effects of modality (auditory vs. visual), presentation type (serial vs. simultaneous), domain (linguistic vs. non-linguistic), and training type (random, starting small, starting big) on artificial grammar learning in young adults (N = 360). With serial presentation of stimuli, learning was more effective in the auditory than in the visual modality. However, with simultaneous presentation of visual and serial presentation of auditory stimuli, the modality effect was not present. We found a significant domain effect as well: a linguistic advantage over nonlinguistic material, which was driven by the domain effect in the auditory modality. Overall, the auditory linguistic condition had an advantage over other modality-domain types. Training types did not have any overall effect on learning; starting big enhanced performance only in the case of serial visual presentation. These results show that input characteristics such as modality, presentation type, domain and training type influence statistical learning, and suggest that their effects are also dependent on the specific stimuli and structure to be learned.

ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings

Recordings captured by wearable microphones are a standard method for investigating young children's language environments. A key measure to quantify from such data is the amount of speech present in children's home environments. To this end, the LENA recorder and software-a popular system for measuring linguistic input-estimates the number of adult words that children may hear over the course of a recording. However, word count estimation is challenging to do in a language- independent manner; the relationship between observable acoustic patterns and language-specific lexical entities is far from uniform across human languages. In this paper, we ask whether some alternative linguistic units, namely phone(me)s or syllables, could be measured instead of, or in parallel with, words in order to achieve improved cross-linguistic applicability and comparability of an automated system for measuring child language input. We discuss the advantages and disadvantages of measuring different units from theoretical and technical points of view. We also investigate the practical applicability of measuring such units using a novel system called Automatic LInguistic unit Count Estimator (ALICE) together with audio from seven child-centered daylong audio corpora from diverse cultural and linguistic environments. We show that language-independent measurement of phoneme counts is somewhat more accurate than syllables or words, but all three are highly correlated with human annotations on the same data. We share an open-source implementation of ALICE for use by the language research community, enabling automatic phoneme, syllable, and word count estimation from child-centered audio recordings.

Statistical Learning and the Effect of Starting Small in Developmental Dyslexia

Purpose Impairments in statistical learning abilities of individuals with developmental dyslexia (DD) have been demonstrated in word segmentation and in visual artificial grammar learning (AGL) tasks, but so far, little attention has been devoted to the AGL abilities of this population in the acoustic verbal domain. This study aimed to test whether adolescents with dyslexia have difficulties in extracting abstract patterns from auditory sequences of nonsense syllables based on a finite state grammar relative to typically developing (TD) peers. We also tested whether incremental presentation of stimuli of different lengths (starting small) has a facilitating effect on learning complex structures in dyslexia (and in TD) as opposed to presenting strings in random order. Method Thirty-one adolescents with DD and 31 age-matched control participants completed an AGL task. Participants passively listened to acoustic sequences of nonsense syllables generated by an artificial grammar in the training phase. In the test phase, they were presented with pairs of novel grammatical and nongrammatical sequences and were required to decide which member of a sequence pair was more similar to the material heard during training. Results Performance levels and the proportion of learners were smaller in participants with DD than in the control group. While the starting small effect was nominally present both in performance levels and in the number of learners in participants with DD, but not in the group with TD, the presentation of strings in incremental order did not statistically improve learning performance in either group. Conclusion Our results suggest that (a) statistical learning of abstract sequences in the acoustic domain is less efficient in people with dyslexia than in TD controls and (b) while incremental presentation of stimuli of different length did not improve learning in our study, the observed pattern of results suggests that the effects of different training designs should be explored further in developmental disorders.

Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study

The ability to learn and process sequential dependencies is essential for language acquisition and other cognitive domains. Recent studies suggest that the learning of adjacent (e.g., "A-B") versus nonadjacent (e.g., "A-X-B") dependencies have different cognitive demands, but the neural correlates accompanying such processing are currently underspecified. We developed a sequential learning task in which sequences of printed nonsense syllables containing both adjacent and nonadjacent dependencies were presented. After incidentally learning these grammatical sequences, twenty-one healthy adults (age M = 22.1, 12 females) made familiarity judgments about novel grammatical sequences and ungrammatical sequences containing violations of the adjacent or nonadjacent structure while in a 3T MRI scanner. Violations of adjacent dependencies were associated with increased BOLD activation in both posterior (lateral occipital and angular gyrus) as well as frontal regions (e.g., medial frontal gyrus, inferior frontal gyrus). Initial results indicated no regions showing significant BOLD activations for the violations of nonadjacent dependencies. However, when using a less stringent cluster threshold, exploratory analyses revealed that violations of nonadjacent dependencies were associated with increased activation in subcallosal cortex, paracingulate cortex, and anterior cingulate cortex (ACC). Finally, when directly comparing the adjacent condition to the nonadjacent condition, we found significantly greater levels of activation for the right superior lateral occipital cortex (BA 19) for the adjacent relative to nonadjacent condition. In sum, the detection of violations of adjacent and nonadjacent dependencies appear to involve distinct neural networks, with perceptual brain regions mediating the processing of adjacent but not nonadjacent dependencies. These results are consistent with recent proposals that statistical-sequential learning is not a unified construct but depends on the interaction of multiple neurocognitive mechanisms acting together.

Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants' endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item's chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.