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194AppendicesTable S20. Model table for the Bayesian zero-one inflated beta regressionpredicting Looking time proportion to the left picture from Date outcomeand Gender.Left biasPredictors Estimates CI (95%)Intercept 0.87 0.80 – 0.96phi_Intercept 4.18 3.38 – 5.21zoi_Intercept 0.02 0.00 – 0.05coi_Intercept 0.58 0.19 – 1.50DateAgainLeft[Yes] 0.79 0.73 – 0.84Gender[Female] 1.03 0.93 – 1.13DateAgainRight[Yes] 1.32 1.24 – 1.41DateAgainLeft[Yes]:Gender[Female] 1.03 0.96 – 1.11Gender[Female]:DateAgainRight[Yes] 0.98 0.91 – 1.04phi_DateAgainLeft[Yes] 1.16 1.05 – 1.29phi_Gender[Female] 1.03 0.84 – 1.27phi_DateAgainRight[Yes] 0.92 0.83 – 1.03phi_DateAgainLeft[Yes]:Gender[Female] 0.98 0.88 – 1.08phi_Gender[Female]:DateAgainRight[Yes] 0.88 0.79 – 0.97zoi_DateAgainLeft[Yes] 0.73 0.52 – 1.03zoi_Gender[Female] 1.03 0.49 – 2.15zoi_DateAgainRight[Yes] 0.49 0.34 – 0.69zoi_DateAgainLeft[Yes]:Gender[Female] 0.81 0.57 – 1.13zoi_Gender[Female]:DateAgainRight[Yes] 1.27 0.89 – 1.79coi_DateAgainLeft[Yes] 0.43 0.23 – 0.83coi_Gender[Female] 0.85 0.42 – 1.89coi_DateAgainRight[Yes] 3.18 1.67 – 6.18coi_DateAgainLeft[Yes]:Gender[Female] 1.62 0.86 – 3.08coi_Gender[Female]:DateAgainRight[Yes] 1.38 0.73 – 2.75Random Effectsσ2 0.01τ00Subject 0.06NSubject 35Observations 1009Notes: All predictors were sum-coded. Estimates for the predictors wereexponentiated, so that they represent Odds Ratios for the beta, coi and zoiparameters.Table S21. Model table for the Bayesian mixed model that predicts RTfrom Pre-date attractiveness ratings and Gender. This analysis was performed on the complete cases-dataset.RTPredictors Estimates CI (95%)Estimates CI (95%)1.190.74PredictorsInterceptGender[Female]AttractivenessDistractor 2.06-0.93 – 3.34-1.49 – 2.920.61 – 3.48-2.83 – 0.65-1.52 – 1.280.28 – 3.79AttractivenessProbe -1.11Gender[Female]:AttractivenessDistractor -0.10Gender[Female]:AttractivenessProbe 2.06Random Effects2580.382.295.2419.83σ2τ00Subjectτ11Subject:AttractivenessDistractorτ11Subject:AttractivenessP robeNSubject 55Observations 3198Table S22. Model table for the Bayesian mixed model that predicts RTfrom Post-date attractiveness ratings and Gender. This analysis was performed on the complete cases-dataset.RTPredictors Estimates CI (95%)Predictors Estimates CI (95%)Intercept 0.38Gender[Female] -0.27AttractivenessDistractor 1.78AttractivenessProbe -0.91-1.38 – 2.19-2.19 – 1.630.55 – 3.01-2.48 – 0.64-0.77 – 1.68-0.38 – 2.75Gender[Female]: AttractivenessDistractor 0.46Gender[Female]: AttractivenessProbe 1.19Random Effects2581.061.301.7313.89σ2τ00Subjectτ11Subject:AttractivenessDistractorτ11Subject:AttractivenessP robeNSubject 55Observations 3198Notes: Gender was sum-coded, while Post-date attractiveness ratings werecentered around 4 (the middle option).Table s23. Model table for the Bayesian mixed model that predicts RTfrom Date outcome and Gender. This analysis was performed on the complete cases-dataset.RTPredictors Estimates CI (95%)Predictors Estimates CI (95%)Intercept 0.80 -1.19 – 2.77Gender[Female] 1.01 -1.04 – 3.05DateAgainProbe[yes] -0.93 -3.31 – 1.35DateAgainDistractor[yes] 1.97 0.01 – 3.97Gender[Female]:DateAgainProbe[yes] 2.39 0.04 – 4.70Gender[Female]:DateAgainDistractor[yes] 0.87 -1.14 – 2.86Random Effectsσ2 2590.00τ00Subject 1.62τ11Subject:DateAgainP robe[yes] 26.86τ11Subject:DateAgainDistractor[yes] 4.46NSubject 55Observations 3198Notes: All predictors were sum-coded.Iliana Samara 17x24.indd 194 08-04-2024 16:37