Table 6.

The contribution of cues based on envelope and fine structure to N₀S₀ model decision variables. Results for human subjects from Study 3 are presented along with results from each computational model. The entries $R_E^2$ and $R_F^2$ denote the proportion of variance in the baseline detection patterns explained by chimeric detection patterns measured using stimuli sharing the same envelopes or fine structures as the baseline stimuli, respectively. If the addition of envelope as a predictor to fine structure as a predictor significantly increased the proportion of variance explained (i.e., $R_{EF}^2$ was significantly higher than $R_F^2$), then $R_E^2$ is underlined. If the addition of fine structure as a predictor to envelope as a predictor significantly increased the proportion of variance explained (i.e., $R_{EF}^2$ was significantly higher than $R_E^2$), then $R_F^2$ is underlined. Note that the $R_{EF}^2$ values are the proportion of variance explained by a multiple regression including both envelope and fine structure as predictors; all $R_{EF}^2$ values were statistically significant (p<0.05). Model abbreviations are the same as in Fig. 1.

N0S0		ES∕N0	P(Y∣T+N)			P(Y∣N)
			$R_E^2$	$R_F^2$	$R_{EF}^2$	$R_E^2$	$R_F^2$	$R_{EF}^2$
Subjects	S1	10	0.17	0.18	0.41	0.17	0.59	0.69
	S2	10	0.26	0.29	0.37	0.18	0.48	0.54
	S3	10	0.05	0.28	0.32	0.00	0.71	0.72
	S4	11	0.69	0.11	0.73	0.45	0.30	0.68
	S5	11	0.35	0.07	0.38	0.20	0.39	0.49
	S6	11.5	0.66	0.25	0.66	0.35	0.36	0.58
Models	DA	10	0.97	0.16	0.98	0.91	0.30	0.94
	BR	10	0.88	0.12	0.93	0.71	0.20	0.90
	ES	10	0.94	0.03	0.95	0.88	0.02	0.92
	PO	10	0.82	0.21	0.85	0.40	0.43	0.71