## 4 Discussion
This suggests that how well an individual can utilize selective attentional control to suppress background noise significantly influences the degree of their noise tolerance and leads to different needs for external signal processing (e.g., NR strength) to aid the perception of target speech embedded in noise. Although both measures in the current study served to index individual noise tolerance, the neural SNR and the subjective noise-tolerance ratings did not correlate. The lack of correlation between these measures could be advantageous, allowing each measure to capture different perspectives on individual noise tolerance and independently explain variability in hearing outcomes. In fact, noise-tolerance rating correlated with outcomes in the NR 2 condition and accounted for extra variance in NR 2 outcomes in the stepwise regression model. Even a relatively modest increase in the variance explained by subjective ratings can provide valuable insights into understanding the complexity of individual noise tolerance and its relationship to NR outcomes. According to the literature, when making judgments on noise tolerance in a given listening situation, listeners employ multiple criteria: for instance, noise annoyance, speech interference, and cognitive effort [18; 19]. One of the reasons that subjective ratings in the current study, taken from the Weinstein Questionnaire, were not sensitive enough to capture individual noise tolerance characteristics was that those ratings solely reflected _noise annoyance_ criteria, and there was a lack of a systematic approach using paired comparison with other criteria to determine the dominant criteria for noise-tolerance judgment for each individual [18]. Developing more sensitive measures to capture subjective correlates of individual noise tolerance and integrating them with a physiological index, such as the neural SNR, would offer a more comprehensive and accurate assessment of noise tolerance in a way neither measure could accomplish alone. One of the limitations of this study was that the measure of neural SNR fell short of disentangling the source of variability between peripheral hearing deficits and top-down attentional efficacy. An individual's high neural SNR may result from successful auditory scene analysis involving a chain of neurophysiological processes [32; 33; 56], including sensory encoding of acoustic features [57; 58; 59; 60], auditory object formation by grouping those features [61; 62], and selectively attending to target objects embedded in background noise [63; 64; 65; 66; 67]. It is evident that these sensory and central processes exhibit significant variability among individuals with similar audiograms, influencing their hearing outcomes [68; 69; 70; 71; 72]. Future studies could benefit from employing both sensory and selective-attention measures in the same individual participants to assess the relative contributions of peripheral and top-down processes to the cortical index of individual noise tolerance (i.e., neural SNR). Emerging evidence shows that selective attentional efficacy varies even in people with normal hearing and that it can modulate cortical representation of speech sounds [73; 74; 75; 76]. Exploring variability in peripheral hearing damage would provide deeper insights into the influence of cochlear frequency tuning [77; 78; 79], cochlear tonotopy [80; 81; 82], and auditory nerve integrity [83; 84] on the neural SNR. To build on the groundwork laid by our findings, future research should incorporate people with mild-to-moderate hearing loss and employ peripheral and downstream measures, which are believed to influence the neural SNR, to comprehensively assess the neurophysiological mechanisms underlying individual variability in NR outcomes. Future studies could also benefit from adopting alternative methodologies or advanced technologies.