We then fitted data from the phase-aligned and phase-scrambled configuration separately and again tried to predict the performance of Q or Qp masks according to the phase-aligned or phase-scrambled parameters, respectively. The model has 6 parameters for each fit (
Se S was fixed at 100, and 5 parameters were free to vary—see
Table A2). The fitted results for the phase-aligned configuration are shown in
Figure A3 and the parameters and the
x 2 goodness-of-fit test are shown in
Table A2. The divisive inhibition model fit the S and M mask data quite well. However, the derived parameters underestimated the masking effect found with the Q mask even though the model was fitted just to the phase-aligned data. The fitted results for the phase-scrambled configuration are shown in
Figure A4 and the parameters and the
x 2 goodness-of-fit test are shown in
Table A2. The divisive inhibition model fit the S/Mp mask data quite well and the estimated parameters predict the performance of Qp mask better than in the phase-aligned case. We also tested a non-linear summation model, where the inhibitory denominator involves non-linear summation, namely, the output of the
I S and
I M were summed together after each of them were raised by power
q. However, the goodness of fit was worse (
x Q 2 are 26.788, 52.834, and 22.904 for PCH, TD, and GM, respectively, for phase-aligned configuration;
x Qp 2 are 15.194 and 2.233 for PCH and TD, respectively, for phase-scrambled configuration). The modeling results suggest that phase alignment of mask components must play an important role in pattern masking.