Mini Pinyin: A modified miniature language for studying language learning and incremental sentence processing

Abstract

Artificial grammar learning (AGL) paradigms are used extensively to characterise (neuro-)cognitive bases of language learning. However, despite their effectiveness in characterising the capacity to learn complex structured sequences, AGL paradigms lack ecological validity and typically do not account for cross-linguistic differences in sentence comprehension. Here, we describe a new modified miniature language paradigm-Mini Pinyinthat mimics natural language as it is based on an existing language (Mandarin Chinese) and includes both structure and meaning. Mini Pinyin contains a number of cross-linguistic elements, including varying word orders and classifier-noun rules. To evaluate the effectiveness of Mini Pinyin, 76 (mean age = 24.9; 26 female) monolingual native English speakers completed a learning phase followed by a sentence acceptability judgement task. Generalised mixed effects modelling revealed that participants attained a moderate degree of accuracy on the judgement task, with performance scores ranging from 25%-100% accuracy depending on the word order of the sentence. Further, sentences compatible with the canonical English word order were learned more efficiently than non-canonical word orders. We controlled for inter-individual differences in statistical learning ability, which accounted for ~20% of the variance in performance on the sentence judgement task. We provide stimuli and statistical analysis scripts as open source resources and discuss how future research can utilise this paradigm to study the neurobiological basis of language learning. Mini Pinyin affords a convenient tool for improving the future of language learning research by building on the parameters of traditional AGL or existing miniature language paradigms.

Slow oscillation-spindle coupling predicts sequence-based language learning

“…As such, a logit mixed model is particularly suited to the current data due to: (a) inter-individual differences in language learning and performance on the sentence judgement tasks; (2) the use of categorical response variables (i.e., binomial correct/incorrect responses) as a measure of language learning, and; (3) the ability to account for item variability, given that items may vary in familiarity across participants and thus influence learning outcomes (Baayen et al, 2008;Quené, Huub, & Bergh, 2008). Further, the use of a trial-based outcome variable in our main statistical models allows for more fine-grained analyses of by-item and by-participant variability (e.g., Cross et al, 2020a) which are lost in aggregated variables, such as proportion correct or d'. The behavioural model took the following form:…”

“…To better characterise the sleep-based and task-related neural mechanisms underlying complex rule learning in language, we analysed EEG during an 8-hour nocturnal sleep period and sentence judgement tasks. Participants learned the artificial miniature language Mini Pinyin (Cross et al, 2020a), which contains two main sentence constructions, namely fixed and flexible word orders.…”

“…Stimuli consisted of sentences from a modified miniature language modelled on Mandarin Chinese, named Mini Pinyin (Cross et al, 2020a). Mini Pinyin contains 32 transitive verbs, 25 nouns, 2 coverbs, and 4 classifiers.…”

“…Using a novel complex modified miniature language paradigm (Cross et al, 2020a), we demonstrated that a period of sleep facilitates the extraction of fixed relative to flexible word order rules. Importantly, the key distinction between these word order permutations is that successful interpretation of fixed word order sentences relates to the sequential position of the noun phrases and verb (i.e., the first noun phrase is invariably the actor and the sentence is verb-final).…”

“…However, it remains unclear if sleep-related memory processing extends to rules containing both complex structure and meaning, as in language at the sentence level (Cross et al, 2018). Here, we investigated whether sleep predicts the generalisation of an artificial modified miniature language (MML; Cross et al, 2020a). Using a nocturnal sleep paradigm with full-scalp electroencephalography, non-rapid eye movement (NREM) sleep spindleslow oscillation coupling events were used to predict sensitivity to various word order rules and taskrelated oscillatory activity.…”