Binary logistic GEE results for the Path ROI (per time window of interest).

Table shows GEE modelling results for occurrences of looks to the Path ROI as a function of Verb Speed (fast vs. slow) and Time (continuous predictor referring to the 50 ms time-bins per window). Shown are the by-participant/by-item GEE parameter estimates and corresponding SEs (both in logit units) along with generalised score chi square statistics and related p-values (at df = 1). As for interactions (S×T), a positive parameter estimate indicates a more positive slope for the Time predictor in the fast than in the slow Verb Speed condition. ΔQIC refers to the goodness of fit of the Verb Speed×Time model in relation to a competitor Agent-Verb Suitability×Lexical Frequency×Time model (negative ΔQICs indicate superior fit of the Verb Speed×Time model).本表格展示了以动词速度（快与慢）和时间（连续预测变量，对应每个窗口的50毫秒时间分箱）为自变量时，被试注视路径感兴趣区（Path ROI）事件的广义估计方程（Generalized Estimating Equations，GEE）建模结果。表格呈现了被试/项目水平的GEE参数估计值及其对应的标准误（均以对数单位表示），同时包含广义得分卡方统计量及相关p值（自由度df=1）。针对交互效应（S×T），参数估计值为正则表明：在快动词速度条件下，时间预测变量的回归斜率较慢动词速度条件下更为正向。ΔQIC（差异准信息准则，ΔQuasi Information Criterion）用于衡量动词速度×时间模型相较于竞争模型——即施事-动词适配性×词汇频率×时间模型——的拟合优度：负向ΔQIC值表明动词速度×时间模型的拟合效果更优。