Table 4.

Time segmented conditional piecewise growth model on IES in inhibition training.

	Est/Beta	SE	95% CI	t	p
FIXED EFFECTS
Intercept	1, 545.28	52.36	1,443.30–1,647.26	29.51	0.0000***
Time_Segment1	−156.84	16.19	−188.37–−125.31	−9.69	0.0000***
Time_Segment2	−15.70	6.67	−28.70–−2.70	−2.35	0.0190*
Time_Segment3	−17.55	5.40	−28.08–−7.03	−3.25	0.0012**
Time_Segment4	−14.76	5.75	−25.97–−3.55	−2.57	0.0106*
Incongruent_blocks	170.37	17.37	136.53–204.20	9.81	0.0000***
Time_Segment1 X Incongruent_blocks	−9.86	12.10	−33.43–13.72	−0.81	0.4158
Time_Segment2 X Incongruent_blocks	−12.70	7.78	−27.85–2.46	−1.63	0.1034
Time_Segment3 X Incongruent_blocks	−0.23	7.56	−14.95–14.50	−0.03	0.9759
Time_Segment4 X Incongruent_blocks	−0.09	7.99	−15.64–15.47	−0.01	0.9915
			Variance	S.D.	Correlation
RANDOM EFFECTS
Participant			63,693.25	252.38
Time_Segment1			4,642.83	68.14	−0.80
Time_Segment2			350.21	18.71	−0.48
Time_Segment3			15.21	3.90	0.48
Time_Segment4			28.76	5.36	−0.60
				Marginal	Conditional
MODEL FIT R2
				0.42	0.92

Model equation: IES ~ (Time_Segment1 + Time_Segment2 + Time_Segment3 + Time_Segment4) ^*Difficulty_of_blocks, random = ~ Time_Segment1 + Time_Segment2 + Time_Segment3 + Time_Segment4 | Participants, corAR1(0, form = ~ 1 | Participants)

^*p < 0.05;

^**p < 0.01;

^***p < 0.001.