Table 13. Multiple regression, French-German, word-like pseudowords.

	Model 1		Model 2
(Intercept)	0.01(0.16)		−0.11(0.47)
Language	−0.01(0.23)		−0.08(0.28)
Length			−0.04(0.15)
Syllables count			0.09(0.29)
Baseword Frequency			−0.01(0.13)
Orthographic N			−0.07(0.12)
Phonological N			−0.09(0.13)
Body N			−0.24(0.14)
Bigram Frequency			−0.04(0.14)
R2	0.00		0.07
Adj. R2	−0.01		−0.03
Num. obs.	80		80
	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-0.1128	0.4660	-0.24	0.8093
Language	-0.0764	0.2780	-0.28	0.7841
Length	-0.0421	0.1541	-0.27	0.7856
Syllables count	0.0916	0.2885	0.32	0.7519
Baseword Frequency	-0.0069	0.1280	-0.05	0.9571
Orthographic N	-0.0665	0.1183	-0.56	0.5758
Phonological N	-0.0949	0.1306	-0.73	0.4698
Body N	-0.2436	0.1444	-1.69	0.0960
Bigram Frequency	-0.0442	0.1386	-0.32	0.7507

***p < 0.001;

**p < 0.01;

*p < 0.05