Table 2 Distribution of model predictor variables in the datasets

ObjLen

Raw:

Raw:

[1, 39], mean = 4.21, SD = 3.89

[1, 33], mean = 5.22, SD = 4.04

Log-transformed:

Log-transformed:

[0, 3.66], mean = 1.15, SD = 0.72

[0, 3.50], mean = 1.40, SD = 0.72

AdvP

False = 733; true = 214

False = 712; true= 276

BA_after

False = 707; true = 240

False = 778; true = 210

BA_before

False = 708; true = 239

False = 816; true = 172

ObjAnimacy

False = 837; true = 110

False = 984; true = 4

ObjHasPronDem

False = 805; true = 142

False = 835; true = 153

ObjIsPron

False = 891; true= 56

False = 963; true = 25

ObjMention_after

False = 672; true = 275

False = 805; true = 183

ObjMention_before

False = 656; true = 291

False = 811; true = 177

TextMode

Spoken = 32; spoken-to-be-written = 10; written = 886; written-to-be-read = 7; written-to-be-spoken = 12

Spoken = 70; spoken-to-be-written = 2; written = 880; written-to-be-read = 16; written-to-be-spoken = 20

VerbComp

14 different complement/aspect after 放 fàng (上shàng “up”; 下xià “down”; 入rù “into”; 到dào “into”; 回huí “back”; 在zài “at”; 好hǎo “properly”; 妥tuǒ “properly”; 平píng “flat”; 進jìn “into”; 錯cuò “wrong”; 了le ASP-PERF; 低dī “low”; 給gěi “to”)

16 different complement/aspect after 拿 ná (上shàng “up”; 下xià “down”; 出 chū “out”; 去qù “go”; 光 guāng “empty”; 回 huí “back”; 住 zhù “stay”; 走 zǒu “away”; 來lái “come”; 掉 diào “remove”; 給gěi “to”; 進jìn “into”; 開 kāi “away”; 過 guò “over”; 到₁ dào “reach” (e.g., 拿到兩本書nádàoliǎngběnshū “take (and reach) two books”; 到₂ dào “to” (e.g., 拿兩本書到那兒 náliǎngběnshūdàonàer “take two books (to) there”)

VP_after

False = 821; true = 126

False = 619; true = 369

VP_before

False = 877; true = 70

False = 874; true = 114