Table 2.

Multiple linear regressions predicting positive and negative emotion recognition accuracy from positive (Unex), negative (Intan), and disorganised (Cogdis) schizotypy.

	Pos Accuracy								95% Conf Int
Predictor	B	SE	t	p	BF10	$R_{partial}^2$	VIF	β	LC	HC
Intercept	69.480	2.337	29.727	<0.001
Pos Scz	−0.106	0.187	−0.565	=0.572	0.191	1.156	1.326	−0.045	−0.202	0.112
Neg Scz	−0.290	0.170	−1.706	=0.090	0.477	1.399	1.105	−0.124	−0.268	0.019
Dis Scz	0.213	0.175	1.217	=0.225	0.256	0.718	1.447	0.101	−0.063	0.266
	Neg Accuracy								95% Conf Int
Predictor	B	SE	t	p	BF 10	$R_{partial}^2$	VIF	β	LC	HC
Intercept	59.202	2.400	24.666	<0.001
Pos Scz	−0.323	0.192	−1.683	=0.094	0.671	1.362	1.326	−0.131	−0.285	0.023
Neg Scz	−0.473	0.175	−2.706	=0.007	3.238	3.449	1.105	−0.192	−0.333	−0.052
Dis Scz	0.567	0.180	3.152	=0.002	4.387	4.622	1.447	0.256	0.096	0.417

Positive: F_{(3, 205)} = 1.152, p = 0.329, R² = 1.7%, $R_{adjusted}^2$ = 0.2%. Negative: F_{(3, 205)} = 4.469, p =0.005, R² = 6.1%, $R_{adjusted}^2$ = 4.8%. VIF, Variance Inflation Factor. Bayesian priors are full Cauchy (location = 0, scale = 0.354).