Table 2.

Logistic regression models predicting choices in beauty judgment and approach-avoidance tasks.

	Estimate [95% CI]	Z value	P value	OR [95% CI]
Approach-avoidance
Perceived curvature	− 0.05 [− 0.11, 0.01]	− 1.78	0.076	1 [0.95, 1.05]
Computational curvature	− 0.09 [− 0.21, 0.04]	− 1.35	0.176	0.94 [0.83, 1.06]
Perceived angularity	− 0.10 [− 0.17, 0.04]	− 3.07	0.002**	0.93 [0.88, 0.99]
Beauty
Perceived curvature	− 0.17 [− 0.25, − 0.08]	− 3.88	0.0009**	0.89 [0.83, 0.95]
Computational curvature	0.11 [− 0.07, 0.29]	1.23	0.22	1.14 [0.96, 1.36]
Perceived angularity	− 0.10 [− 0.20, − 0.01]	− 2.08	0.037*	1 [0.92, 1.08]

OR odds ratio; logistic regression predicting choices: “not beautiful” vs. “beautiful” (beauty judgments), “exit” vs. “enter” (approach-avoidance decisions). **p < 0.01; *p < 0.05. The Nagelkerke pseudo R-squared value was computed for each model as a global effect size measure (approach avoidance = 0.002, beauty = 0.011).