## 4 Discussion  
In line with the literature (e.g., [PERSON], [PERSON] [5], [PERSON] and [PERSON] [55]), our findings corroborated no improvement in speech intelligibility in noise with NR processing, while showing considerable variability in NR-driven changes in performance among individuals with similar hearing sensitivity. Given that most people who experienced an increase in performance with NR 2 also showed enhanced performance with NR 1, the same underlying individual characteristics, such as noise tolerance, may be responsible for such performance changes due to NR. The present study utilized both cortical and subjective measures to comprehensively capture the multifaceted nature of individual noise tolerance and to ensure a holistic assessment of variability in NR outcomes. Our findings indicated that the neural SNR, the amplitude ratio of cortical auditory responses to target speech relative to background noise, can predict NR outcomes. These findings were consistent with [PERSON], [PERSON] [31], who reported a significant negative correlation between neural SNR and hearing outcomes with mild NR processing (i.e., same as NR 1 in the present study); individuals with higher neural SNR tended to get lower NR benefits in perceptual outcomes. Further, the neural SNR exhibited a stronger correlation with the hearing outcome associated with stronger NR processing (i.e., NR 2) than that observed between the neural SNR and the NR 1 outcome. This differential correlation suggests that the neural SNR is an efficient measure that reflects the underlying neurophysiological dynamic of individual noise tolerance and offers acute responsiveness to the nuances \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{6}{c}{**Model Fit Measures**} \\ \hline Model & \(R\) & \(R^{2}\) & Adjusted \(R^{2}\) & \(F\) & df1 & df2 & \(p\) \\ \hline
1 & 0.522 & 0.273 & 0.247 & 10.51 & 1 & 28 & 0.003 \\ \hline
2 & 0.576 & 0.331 & 0.282 & 6.69 & 2 & 27 & 0.004 \\ \hline \multirow{3}{*}{Comparisons} & Model & Model & \(\Delta R^{2}\) & \(F\) & df1 & df2 & \(p\) \\ \cline{2-8}  & 1 & 2 & 0.059 & 2.37 & 1 & 27 & 0.136 \\ \hline \multicolumn{8}{c}{**Model Coefficients**} \\ \cline{2-8}  & \multirow{2}{*}{Model} & Unstandardized & Standardized & \multirow{2}{*}{\(t\)} & \multirow{2}{*}{\(p\)} & Collinearity \\ \cline{3-3} \cline{5-8}  & & B & SE & Beta & & Tolerance & VIF \\ \hline
1 & (Constant) & \(-\)0.109 & 0.025 & & \(-\)4.32 & \(<\)0.001 & \\ \cline{2-8}  & Neural SNR & \(-\)0.008 & 0.003 & \(-\)0.522 & \(-\)3.24 & 0.003 & 1 & 1 \\ \hline
2 & (Constant) & \(-\)0.027 & 0.059 & & \(-\)0.45 & 0.656 & & \\ \cline{2-8}  & Neural SNR & \(-\)0.007 & 0.003 & \(-\)0.454 & \(-\)2.77 & 0.01 & 0.926 & 1.08 \\ \hline Tolerance Rating & \(-\)0.021 & 0.014 & \(-\)0.252 & \(-\)1.54 & 0.136 & 0.926 & 1.08 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of stepwise regression results for the dependent variable (behavioral accuracy changes by noise-reduction 2 algorithm) with the neural signal-to-noise ratio (SNR) and noise-tolerance rating as predictors. of NR's mixed effects between noise attenuation and speech distortions. The neural SNR uniquely incorporates an individual's efficiency in inhibiting background noise, a critical aspect of noise tolerance previously underexplored in auditory evoked potential studies [22; 23; 24] and studies of neural tracking of the speech envelope [25; 26; 27]. By computing the amplitude ratio between the respective responses to target speech onset and noise onset, the index of neural SNR offers a better understanding of varying perceptual benefits of NR processing among individuals with different noise-tolerance characteristics. Our recent findings indicated that within normal-hearing individuals, the variability in neural SNR was mainly attributed to individual differences in noise-onset evoked responses [30].