Table 3.

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

	Est/Beta	SE	95% CI	t	p
FIXED EFFECTS
Intercept	1,360.4	77.06	1,210.43–1,510.32	17.65	0.0000***
Time_Segment1	−113.89	29.14	−170.58–−57.19	−3.91	0.0001***
Time_Segment2	−29.54	16.07	−60.81–1.73	−1.84	0.0664
Time_Segment3	−12.64	14.81	−41.47–16.18	−0.85	0.3936
Time_Segment4	−11.44	16.98	−44.49–21.60	−0.67	0.5006
2-back_blocks	162.18	46.76	71.19–253.16	3.47	0.0006***
3-back_blocks	656.84	45.23	568.82–744.86	14.52	0.0000***
Time_Segment1 X 2-back_blocks	2.57	33.85	−63.29–68.43	0.08	0.9396
Time_Segment1 X 3-back_blocks	−100.48	32.69	−164.10–−36.87	−3.07	0.0022**
Time_Segment2 X 2-back_blocks	−21.9	21.29	−63.33–19.54	−1.03	0.3041
Time_Segment2 X 3-back_blocks	−28.31	20.89	−68.96–12.34	−1.36	0.1757
Time_Segment3 X 2-back_blocks	0.55	21.64	−41.56–42.66	0.03	0.9798
Time_Segment3 X 3-back_blocks	−43.40	21.22	−84.69–−2.11	−2.05	0.0411*
Time_Segment4 X 2-back_blocks	−15.86	24.86	−64.24–32.52	−0.64	0.5238
Time_Segment4 X 3-back_blocks	−7.12	24.19	−54.20–39.96	0.29	0.7685
			Variance	S.D.	Correlation
RANDOM EFFECTS
Participant			145,815.51	381.86
Time_Segment1			9,306.06	96.47	−0.76
Time_Segment2			1,394.46	37.34	−0.87
Time_Segment3			50.86	7.13	−0.74
Time_Segment4			122.9	11.09	−0.9
				Marginal	Conditional
MODEL FIT R2
				0.38	0.77

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.