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. 2013 Apr 24;4:203. doi: 10.3389/fpsyg.2013.00203

Table 3.

Summary of regression models fitted to lexical decision latencies to compounds’ left constituents presented as isolated words (column B), compound’s right constituents presented as isolated words (column C) and compound words (column D).

A. Variable B. RT to left C. RT to right D. RT to compound
Valence of left β^=-0.012, SE = 0.004, p = 0.003 β^=-0.010, SE = 0.004, p = 0.009
Valence of right β^=-0.017, SE = 0.005, p = 0.001 β^=-0.014, SE = 0.005, p = 0.002
Valence of compound β^=-0.018, SE = 0.005, p < 0.001
Arousal of left ns ns
Arousal of right β^=-0.012, SE = 0.006, p = 0.040 ns
Arousal of compound ns
Imageability of left β^=-0.018, SE = 0.002, p < 0.001 ns
Imageability of right β^=-0.018, SE = 0.003, p < 0.001 ns
Imageability of compound β^=-0.009, SE = 0.003, p = 0.002
Concreteness of left β^=-0.014, SE = 0.004, p = 0.002 ns
Concreteness of right β^=-0.008, SE = 0.004, p = 0.044 ns
Concreteness of compound β^=-0.023, SE = 0.008, p = 0.008
SER of left β^=-0.021, SE = 0.002, p < 0.001 ns
SER of right β^=-0.021, SE = 0.003, p < 0.001 ns
SER of compound β^=-0.024, SE = 0.005, p < 0.001
BOI of left β^=-0.007, SE = 0.003, p = 0.012 ns
BOI of right β^=-0.008, SE = 0.003, p = 0.008 ns
BOI of compound ns

Column A lists critical predictors in the models. Estimated regression coefficients, standard errors and p-values are reported for all models in which critical predictors reached significance at the 0.05 level.