Materials

Learning materials comprised a list of 60 Pseudo-English-Chinese word pairs (see Table 1 in Supplementary Material). The number of word pairs to be learned was mainly determined by previous literature (e.g., Dahlström et al., 2011; Nakata, 2016; Batterink et al., 2017) and our pilot data suggested that 60 pseudo-English-Chinese word pairs were able to detect individual differences in word learning ability and avoid the ceiling or floor effects.

The pseudowords were all pronounceable and orthographically legal in English, varied in length from 6 to 8 letters with 2 syllables, and were controlled in terms of the orthographic neighborhood size measured by the Orthographic Levenshtein Distance (OLD20) (Yarkoni et al., 2008). OLD20 is computed as the mean Levenshtein Distance from a word to its 20 closest orthographic neighbors. The smaller the value of OLD20, the fewer steps (additions, deletions, or substitutions) required to convert a pseudoword into a real word. Given this, the OLD20 of all pseudowords in this study was kept at 3 or higher, with an aim to reduce the associative memory effect while retaining the English word-formation rules of these pseudowords. All pseudo-words used in this study were generated in accordance with the lexical features of real English words (e.g., phonology, orthography, word length, number of syllables) to improve the ecological validity.

Each English pseudoword (e.g., “plulfot”) was paired with a Chinese meaning (e.g., “草原”), and these Chinese equivalents were all two-character words selected from the Modern Chinese Frequency Dictionary (1986). Additionally, in order to control the difficulty and familiarity of these Chinese words, we calculated the number of strokes (M = 14.32, SD = 3.13) and word frequency (M = 0.0034, SD = 0.0027), and excluded the words with extreme values (≥ ± 2 SD). Similar to the literature (Lu et al., 2017), all Chinese words were concrete nouns that belonged to one of the following semantic categories: fruits, vegetables, animals, body parts, work, sports, clothing, furniture, electrical appliances, and natural scene.