Table A3.

Results of fitting the weighted CoG model to the data obtained in the dual-task conditions (Experiment 3).

Leftward								Rightward
Conditio n	α	Slope	Int	SD	R2	L	p	α	Slope	Int	SD	R2	L	p
M L								M L
Saccadic Task A lone	0.87	0.95	−3	31	0.42	686.9		0.81	1.27	−129	34	0.52	688.8
Dual Tasks:
Same Target Set	0.78	1.06	48	30	0.46	1342.7	<.05*	0.81	1.23	−112	33	0.51	1520.1	0.96
Different Target Sets: ES	0.66	1.21	123	30	0.45	1378.2	<.001*	0.79	1.07	−35	31	0.43	1442.0	0.56
Different Target Sets: EP	0.49	0.98	24	29	0.32	946.3	<.001*	0.60	0.84	66	30	0.25	928.6	<.001*
GT								GT
Saccadic Task A lone	1.00	0.87	−51	36	0.40	1723.3		0.95	1.04	−28	36	0.46	1604.8
Dual Tasks:
Same Target Set	0.78	1.07	43	33	0.42	2382.4	<.001*	0.79	1.13	−69	36	0.40	2352.4	<.001*
Different Target Sets: ES	0.81	1.04	26	36	0.35	2423.8	<.001*	0.76	1.11	−63	36	0.38	2350.5	<.001*
Different Target Sets: EP	0.86	1.05	27	38	0.37	2402.6	<.01*	0.91	0.98	12	35	0.42	2232.8	0.43
SDK								SDK
Saccadic Task A lone	0.67	1.20	108	27	0.50	633.0		0.72	1.15	−77	31	0.38	699.7
Dual Tasks:
Same Target Set	0.74	0.99	8	20	0.61	1710.5	<.001*	0.77	1.04	−31	23	0.57	1829.5	<.05*
Different Target Sets: ES	0.62	1.10	64	23	0.51	1851.6	<.05*	0.70	0.96	5	25	0.42	1769.9	0.57
Different Target Sets: EP	0.53	0.99	17	22	0.47	1773.5	<.001*	0.61	1.02	−28	24	0.44	1715.8	<.001*

ES: Emphasize Saccade

EP: Emphasize Percept

Slope and Int are the values of the slope and intercept of the given model (m and b in eq. A1).

SD is the root mean square error (RMSE) about the best fit line given by the model.

L the negative log of the maximum likelihood of the given model (eq. A2).

p values were obtained fro m testing the fit of the weighted COG Model W relative to a model F where ^α was set to the value obtained when the saccadic task was done alone (df=1, see equation A4).

^*indicates Model W is a significantly better fit than Model F.