Table 3.

Statistical analyses of the data from the cross-oddball and recognition tasks taking into account the type of task (digit parity categorization versus semantic categorization)

	Effect	df1	df2	F (df1, df2)	MSE	p	${\eta }_p^2$	BF10
Oddball task (RTs)
S		2	236	48.975	426.803	< 0.001	0.293	3.851 × 1015
T		1	118	93.740	7567.068	< 0.001	0.443	2.732 × 1013
S × T		2	236	0.065	426.803	0.937	0.001	0.059
Oddball task (proportion correct)
S		2	236	0.670	0.003	0.512	0.006	0.056
T		1	118	14.739	.029	< 0.001	0.111	113.336
S × T		2	236	0.443	0.003	0.643	0.004	0.094
Recognition task (d)
S		1	118	0.550	0.594	0.460	0.005	0.179
T		1	118	0.246	1.147	0.621	0.002	0.213
S × T		1	118	0.015	0.594	0.902	< 0.001	0.205
Recognition task (C)
S		1	118	141.111	0.141	< 0.001	.545	3.984 × 1020
T		1	118	5.361	0.258	0.022	0.043	1.102
S × T		1	118	0.207	0.141	0.650	0.002	0.199
Recognition task (RTs)
S		1	118	1.168	20,530.536	0.282	0.010	0.155
T		1	118	1.606	139,233.895	0.208	0.013	0.451
P		1	118	0.107	29,385.656	0.744	0.001	0.107
S × T		1	118	0.980	20,530.536	0.324	0.008	0.203
S × P		1	118	13.267	29,385.656	< 0.001	0.101	118.706
T × P		1	118	0.234	29,385.656	0.629	0.002	0.146
S × T × P		1	118	0.446	24,398.730	0.506	0.004	0.267

S sound condition (standard, disgusting deviant, neutral deviant), T task condition (digit parity categorization, semantic categorization), P probe type (negative, positive)