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

Abstract

Artificial grammar learning (AGL) paradigms are used extensively in psycholinguistics and the cognitive neuroscience of language to characterise the (neuro-)cognitive basis of language learning. However, despite their effectiveness in characterising the (non-)human 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 – termed Mini Pinyin – that mimics natural language in that it is (a) based on an existing language (Mandarin Chinese) and (b) includes both structure and meaning. Mini Pinyin further contains a number of cross-linguistic elements, including varying word order arrangements and classifier-noun rules. In order to evaluate the effectiveness of Mini Pinyin, we had 76 (mean age = 24.9; 26 female) monolingual native English speakers complete an implicit learning phase followed by a sentence acceptability judgement task. Generalised linear mixed effects modelling revealed that participants were able to attain a moderate degree of accuracy on the judgement task, with performance scores ranging from 25% - 100% accuracy depending on the specific word order of the sentence. Further, sentences that were compatible with the canonical English word order were learned more efficiently than non-canonical word orders. We also controlled for inter-individual differences in statistical learning ability (SLA), demonstrating that SLA accounted for approximately 20% of the variance in performance on the sentence judgement task. We provide all stimulus and experimental task files 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. As such, Mini Pinyin affords a convenient tool for improving the future of language learning research by going beyond the parameters of traditional AGL or existing miniature language paradigms.

Oscillatory and aperiodic neural activity jointly predict language learning

“…Stimuli were based on the modified miniature language Mini Pinyin (for a detailed description of the language, see Cross et al, 2020a; see also Cross et al, 2020b), which contains grammatical rules present in a number of natural languages (see Figure 1A and 1B for example sentence constructions and vocabulary items). Briefly, each sentence in Mini Pinyin contains two noun phrases and a verb phrase, while each noun is associated with a different classifier: human nouns are preceded by ge, while animals, and small and large objects are preceded by zhi, xi and da, respectively.…”

Slow oscillation-spindle coupling predicts sequence-based language learning

“…Stimuli consisted of sentences from an MML modelled on Mandarin Chinese, named Mini Pinyin (Cross, Zou-Williams, Wilkinson, Schlesewsky, Bornkessel-Schlesewsky, 2020). Mini Pinyin contains 32 transitive verbs, 25 nouns, 2 coverbs, and 4 classifiers.…”

“…As described in Table 1, ge specifies a human noun, zhi for animals, and xi and da for small and large objects, respectively. Overall, 576 unique sentences (288 grammatical, 288 ungrammatical) were created and divided into two equivalent sets (see Cross et al, 2020 for a complete description of the stimuli).…”

Neural and cognitive correlates of performance in dynamic multi-modal settings

“…VSL ability refers to an individual’s proficiency in extracting underlying regularities and patterns in the environment (Siegelman et al, 2017). As the ability to grasp language relies on understanding grammatical regularities, VSL ability most notably exhibits strong relationships with a range of linguistic functions including sentence comprehension (Misyak & Christiansen, 2012), speech perception abilities (Conway et al, 2010) and second language acquisition (Cross, Zou-Williams et al, 2020; Frost et al, 2013). Performance on the TMA task relies upon being able to flexibly adjust to novel incoming information (e.g., regarding contact movement) that may be erratic, and unlikely to follow statistical regularities.…”

“…The IAF refers to the peak frequency within the alpha band (Klimesch, 1999) and is a property of the human EEG that exhibits trait-like characteristics (Grandy, Werkle-Bergner, Chicherio, Schmiedek, et al, 2013). IAF has been found to positively correlate with IQ (Grandy, Werkle-Bergner, Chicherio, Lövdén, et al, 2013), memory performance (Klimesch, 1996, 1999), visuo-perceptual ability (Cecere et al, 2015; Samaha & Postle, 2015) and memory outcomes following sleep (Chatburn et al, 2021; Cross, Zou-Williams, Wilkinson, Schlesewsky & Bornkessel-Schlesewsky, 2020). However, the relationship between alpha and cognitive performance is not always reliably exhibited (Ouyang et al, 2020).…”