Table 2 Logistic regression models predicting choices in beauty judgment and approach-avoidance tasks.

From: Neural dissociation between computational and perceived measures of curvature

 

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]

  1. 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).
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