Imagine yourself sitting at a table in a crowded restaurant, chatting with a friend of yours. Can you follow the conversation with your friend easily, or does the high noise level in general or more specifically the conversations heard from other tables interfere with speech intelligibility? While the human auditory system has impressive abilities in structuring the mixture of sound waves arriving at the ears into different auditory objects or streams (auditory scene analysis; e.g., Bregman, 1990; Carlyon, 2004), listeners show considerable variation when it comes to speech understanding in adverse acoustic conditions, such as the almost proverbial 'cocktail-party' situation described above (Bronkhorst, 2000; Cherry, 1953). Surprisingly, pronounced individual differences in speech identification in background noise are observed even in listeners with normal hearing, that is, with audiometric thresholds better than 20 dB HL within the frequency range most important for speech (100 Hz–4 kHz; Byrne et al., 1994), usually taken as an indication of approximately intact processing in the inner ear. For example, Ruggles and Shinn-Cunningham (2011) tested normal-hearing subjects in a simulated cocktail-party listening task with two competing speakers presented 15° to the left and right of the target speaker. Across listeners, the percentage of correct responses in reporting a sequence of digits produced by the target speaker varied between 40% and 85% in an anechoic condition. Compatible with these experimental results, in clinical settings a relevant number of patients with normal audiometric findings complain about hearing difficulties in daily life (Zhao and Stephens, 2007).

Understanding speech in a cocktail-party situation with interfering speakers and other background noise requires selective attention (Bronkhorst, 2015; Cherry, 1953; Shinn-Cunningham, 2008; Xiang et al., 2010), and was even proposed to be the 'best-known real life example of selective attention' (Pashler, 1998). The information from the target speaker needs to be processed, while information from other sound sources should be ignored. The main hypothesis tested in the present study was that individual differences in the capability to direct auditory selective attention to the relevant stimulus in the presence of distractors explain a significant proportion of the inter-individual variance in cocktail-party listening performance. Although several aspects of speech appear to be processed outside the focus of attention (Pulvermüller and Shtyrov, 2006), attention enhances the representation of speech (and other sounds) at relatively early stages (e.g., Choi et al., 2014; Srinivasan et al., 2012; Xiang et al., 2010; Zion Golumbic et al., 2013). On the behavioral level, the importance of attention is illustrated by studies that manipulated the a-priori information concerning the target speaker. For instance, in an experiment by Kidd et al. (2005); the speech identification performance was better when the listener knew in advance which of three talkers (presented at different spatial positions) would be the target speaker, compared to conditions where the target location was uncertain. Thus, for exactly identical acoustic signals, being able to direct attention to the correct location results in a large improvement in speech recognition (e.g., Best et al., 2007; Kitterick et al., 2010). The direction of selective attention to the target speaker can be impaired due to limitations imposed by the acoustic signal or by perceptual or cognitive characteristics of the listener (cf. Mattys et al., 2012). If a listener has problems in using acoustic cues for the formation of auditory objects or streams, or on a more cognitive level is incapable of ignoring irrelevant information, then speech identification performance will be low even when fundamental frequency, timbre, and spatial location of the speakers differ.

With respect to listeners' attentional capabilities, it is important to take into consideration that attention is a multifaceted phenomenon (cf. Styles, 2006). In a cocktail party situation, it is required to attend to a certain speaker (target) and to ignore the other sound sources (distractors). We were interested in whether speech understanding in a cocktail-party situation could be related to a more general ability to focus attention on a target in the presence of distractors. Surprisingly, this particular aspect of attention has not been studied very systematically in previous experiments that investigated the role of attention for speech understanding in noise (Füllgrabe et al., 2014; Gatehouse and Akeroyd, 2008; Heinrich et al., 2015; Neher et al., 2009, 2011, 2012; Schoof and Rosen, 2014; van Rooij et al., 1989). Most of these studies used tests developed for neuropsychological settings, like the Test of Everyday Attention (TEA; Robertson et al., 1996) and the Trail Making Tests (TMT; Bowie and Harvey, 2006), indexing visual search (TEA Map Search, Telephone Search), task/attentional switching (TEA Visual Elevator and Auditory Elevator with Reversal, TMT-B), sustained attention (TMT-A, TEA Lottery Test and Elevator Counting), or divided attention (TEA Telephone Search While Counting). Only one TEA subtest ("Elevator counting with distraction") directly addresses the capability to ignore distractors. Here, participants have to count low pitch tones ('targets') while ignoring interspersed high pitch tones ('distractors'). We are aware of only two studies that included this subtest (Gatehouse and Akeroyd, 2008; Neher et al., 2009), in hearing-impaired listeners. Concerning the other tasks, while switching attention is relevant for situations where the target speaker changes dynamically, for example in a conversation involving more than two persons, there are many situations where the target speaker does not change. Visual search and sustained attention seem even less relevant for cocktail-party listening. For this reason, our study included tasks in which subjects had to identify an auditory or visual target element in the presence of distractors. In our view, this is the most important aspect of attention in cocktail-party listening.

As a measure of visual selective attention, we used a flanker task as established by Eriksen and Eriksen (1974); where a target stimulus is surrounded by task-irrelevant distractors (flankers). In the critical incompatible condition, the flankers and the target call for opposite responses. If the incompatible flankers produce only small response time (RT) costs, then the participant has a high ability to focus visual selective attention on the target stimulus. This flanker interference is defined as the difference between the average response time (RT) in the incompatible condition and in a neutral condition where the flankers are not associated with one of the responses relevant for the target.

To measure the individual ability to direct auditory selective attention to a target stimulus while ignoring distractors, we used an intensity discrimination task under backward masking. If – as in the present study – a target sound is followed by a backward masker after a silent inter-stimulus interval (ISI) of 50 ms or more, it is virtually impossible that the masker affects the representation of the target in the auditory nerve (Kiang et al., 1965; Plack and Viemeister, 1992). Instead, strong effects of the backward masker on intensity discrimination can be explained by a failure to selectively attend to the target sounds while ignoring the maskers (Oberfeld and Stahn, 2012; Oberfeld et al., 2012; Schlauch et al., 1997). For example, in a study from our lab that quantified the amount of attention directed to the maskers using a behavioral reverse-correlation approach (Oberfeld et al., 2014), the effect of non-simultaneous masking on the intensity difference limen (DL) was well accounted for by the attention to the maskers, explaining 72% of the variance.

Listeners with normal audiometric thresholds may differ in their sensitivity to the temporal fine structure (TFS) of sounds (e.g., Füllgrabe, 2013; Ruggles et al., 2011), which is necessary for using interaural time difference (ITD) cues to sound localization. These differences were proposed to be due to cochlear neuropathy, which could for instance be caused by moderate noise exposure (Bharadwaj et al., 2015; Kujawa and Liberman, 2009) and is sometimes described as 'hidden hearing loss' (Plack et al., 2014) because it cannot be detected using standard measures of audiometric threshold. Several studies showed a correlation between TFS sensitivity and the recognition of speech in noise, for normal-hearing as well as for hearing-impaired listeners (Bharadwaj et al., 2015; Füllgrabe et al., 2014; Neher et al., 2011, 2012; Ruggles et al., 2011; Schoof and Rosen, 2014). For this reason, our study included binaural sensitivity for the temporal fine structure as a potential predictor of speech identification in a cocktail-party situation, using a task proposed by Hopkins and Moore (2010) that measures the smallest detectable interaural phase difference (IPD) of a sinusoidal carrier relative to an IPD of 0°.

As additional cognitive measures, working memory capacity (e.g., Akeroyd, 2008; Füllgrabe and Rosen, 2016) measured in a sentence span test (Daneman and Carpenter, 1980), and processing speed (e.g., Salthouse, 1996; Tun and Wingfield, 1999) measured by the RT in the neutral condition of the visual flanker task, were included as potential predictors of speech-in-noise identification. The latter was measured in a simulated cocktail-party listening situation with two competing speakers that were presented 25° to the left and right of the target speaker, who was positioned in front of the listener (azimuthal angle 0°). In addition, self-reported hearing-related problems in daily life were assessed via the Speech, Spatial and Qualities of Hearing Scale (SSQ) by Gatehouse and Noble (2004), using the German version (Kießling et al., 2011).

To which extent did speech understanding in a cocktail-party situation depend on the capability of directing selective attention to a target in the presence of distractors, binaural sensitivity for the temporal fine structure (TFS), and other factors? To answer this question, a multiple linear regression analysis was conducted (the statistical details are described in Materials and methods). The criterion variable was the speech recognition score (SRS) defined as the proportion correct in the simulated cocktail-party listening task with two interfering speakers. We used a sentence identification task based on a German matrix test (Wagener et al., 1999a) and presented binaural simulations of an anechoic environment (see Materials and methods). The nine predictors were (1) the elevation of the intensity difference limen caused by the backward masker (DL_elev), defined as the difference between the DL under masking and the DL in quiet, which measures the capability of directing auditory selective attention to a target (Oberfeld et al., 2014), (2) the amount of flanker interference in the flanker task (Int_Flanker), which indexes visual selective attention, (3) the IPD threshold in the TFS-LF (Hopkins and Moore, 2010) test (TFS_th), which measures binaural sensitivity for the temporal fine structure, (4) the pure-tone average threshold on the better ear (PTA_BE) at octave frequencies between 125 Hz and 4 kHz, (5) the average asymmetry in the hearing thresholds between left and right ear in the same frequency range (HL_diff), (6) the intensity-DL in quiet (DL_quiet), which represents a suprathreshold measure of hearing ability that is not related to selective attention, (7) the response time in the neutral condition of the flanker task (RT_neutral), which was included as a measure of processing speed (Salthouse, 2000), and (8) the proportion of correctly recalled consonants in the sentence span task (SS_Pcorr) that indexes working memory capacity. Finally, (9) the age of the participant was added as a predictor, as in previous studies (e.g., Neher et al., 2012), to investigate whether the observed inter-individual differences in cocktail-party listening are determined by other factors related to age. Note that due to the relatively large sample size it was not necessary to summarize the different predictors into a small number of factors as in some previous studies (Füllgrabe et al., 2014; Heinrich et al., 2015; Schoof and Rosen, 2014; van Rooij et al., 1989).

The regression model showed a good fit, R² = 0.57, p<0.001, N = 45. As can be seen in Table 1, the performance in the cocktail-party listening task was significantly negatively related to the intensity-DL elevation under backward masking. Thus, compatible with our hypotheses, participants who showed a better capability of focusing attention on the target sounds in the intensity discrimination task were less affected by the interfering speakers in the cocktail-party listening task. In the same line of reasoning, the significant negative regression coefficient for flanker interference shows that a high capability of directing visual selective attention corresponded to good performance on the cocktail-party listening task. The IPD threshold measured in the TFS-LF task was also significantly negatively related to the SRS. Thus, compatible with previous studies (e.g., Füllgrabe et al., 2014; Neher et al., 2011, 2012; Ruggles et al., 2012), listeners who showed high sensitivity for the TFS performed better in the spatial listening task. None of the remaining predictors showed a significant association with the performance in the spatial listening task. Notably, neither for age nor for working memory capacity did the regression coefficient differ significantly from 0.

What can be concluded about the relative importance of the different psychoacoustic and cognitive predictors for explaining individual differences in cocktail-party listening? In our data, the nine predictors were partly correlated (see Table 2). In such a case, it can be misleading to gauge the relative importance of the predictors by considering the squared standardized regression coefficients (cf. Tonidandel and LeBreton, 2011). For this reason, we used the 'dominance analysis' approach proposed by Budescu (1993); which was shown to be a useful measure of the relative importance of predictors in a regression model, both on theoretical grounds and in simulation studies (LeBreton et al., 2004; Thomas et al., 2014; Tonidandel and LeBreton, 2011). Dominance analysis provides a quantitative measure of relative importance by examining the change in the variance-accounted-for (ΔR²) resulting from adding a predictor to all possible regression models containing subsets of the predictors. For example, if there are three predictors (A, B, and C), then there are four possible subset models to which predictor C can be added (that is, models containing only the intercept term, intercept and predictor A, intercept and predictor B, and intercept and predictors A and B, respectively). A predictor’s general dominance weight (GDW; Azen and Budescu, 2003) is found by averaging the squared semipartial correlations across all of the possible subset models. This measure indexes a variable's contribution to the prediction of the dependent variable, by itself and in combination with the other predictors. The sum of the GDWs is the total proportion of variance explained by the regression model, R².

As shown in Table 1, the general dominance weight was highest for the IPD threshold in the TFS-LF task, followed by the DL-elevation in the intensity discrimination task. According to these results, sensitivity for the binaural TFS and auditory selective attention are the most important predictors of cocktail-party listening. The contribution of flanker interference was much lower, and the GDW for the non-significant predictor sentence span was even slightly higher than the GDW for flanker interference.

To validate the conclusions based on dominance analysis, we used a second approach to variable selection. In the Lasso method proposed by Tibshirani (1996); regression coefficients for predictors with only a small explanatory value are set to 0 (shrinkage), so that the Lasso method effectively performs subset selection, that is, selects the most important predictors (James et al., 2013). The Lasso approach is widely used in the field of statistical learning (Hastie et al., 2009). It involves a tuning parameter λ to impose a so-called l₁-penalty (Tibshirani, 2011) on the regression model. We used 4-fold cross-validation for selecting the best model, that is, the optimal value of λ (James et al., 2013). In cross-validation, the set of observations is randomly divided into k groups of approximately equal size. The first group is treated as a validation set, and the model is fit on the remaining k − 1 groups. The mean squared error is computed on the observations in the validation set, and this procedure is repeated k times, each time selecting a different group of observations as the validation set. In this approach, first the λ value corresponding to the smallest cross-validation error is selected, and then the regression model is fit to all of the available observations using the selected value of λ. Compatible with the dominance analysis presented above, the model selected by the Lasso method contained the predictors DL_elev, TFS_th, and Int_Flanker (see Table 1), and the highest regression coefficients were estimated for DL_elev and TFS_th. In addition, the Lasso procedure selected the predictor SS_Pcorr (see Table 1). Thus, unlike the ordinary least-squares (OLS) multiple regression, the Lasso indicated a contribution of working memory span to speech identification in noise.

Finally, it is interesting to compare these multiple regression results to the pairwise partial correlation coefficients with the SRS, controlling for age (see Table 2). The predictors DL_elev and TFS_th showed a significantly negative partial correlation with the SRS, compatible with the results from ordinary least-squares (OLS) regression and the Lasso. As for the Lasso, the partial correlation coefficient for SS_Pcorr was also significant. It appears possible that the (moderate) correlations between SS_Pcorr and DL_elev and age (see Table 2) increased the standard error of the regression coefficient for SS_Pcorr in the multiple regression analysis shown in Table 1. The opposite pattern occurred for Int_Flanker. Here, the partial correlation coefficient for Int_Flanker was not significant, unlike in the multiple regressions.

The relation between the scores on the SSQ questionnaire (Gatehouse and Noble, 2004), representing self-reported hearing abilities in daily life, and the performance in the cocktail-party listening task was analyzed via linear multiple regression. The SRS was the criterion variable, and age and the three SSQ subscales (speech hearing: SSQ_speech; spatial hearing: SSQ_spatial; other qualities: SSQ_qualities) were entered as predictors. Using the same criteria as for the regression analysis presented in Table 1 (see Materials and methods), three participants were excluded as outliers. The model explained only a small, non-significant portion of the variance, R² = 0.137, p=0.17, N = 47. Only the regression coefficient for the 'Other qualities' scale was significant, showing a positive relation between this SSQ subscore and the SRS (see Table 3). Thus, persons reporting better hearing abilities on the SSQ 'Other qualities' scale tended to perform better in the spatial listening task. As seen in Table 2, the partial correlations controlling for age indicated a significant negative relation between the DL-elevation and SSQ_speech, and between the IPD threshold in the TFS-LF test and SSQ_spatial and SSQ_qualities.

In a relatively large sample of young, normal-hearing participants (N = 50; age range 18–30 years), we studied the role of perceptual and cognitive factors for speech understanding in a cocktail-party situation with spatially separated interfering speakers. Our main hypothesis was that individual differences in the ability to direct auditory selective attention to the relevant stimulus, while ignoring distractors, explain a significant proportion of the inter-individual variance in cocktail-party listening performance. To test this hypothesis, we included tasks that assessed auditory and visual selective attention in the presence of distractors, using non-speech stimuli. Among the many different aspects of attention (cf. Styles, 2006), this ability seems particularly relevant in a cocktail-party situation where it is necessary to selectively attend to the target speaker and to ignore the interfering speakers and other background noise (Bronkhorst, 2015; Shinn-Cunningham, 2008).

In our experiment, the individual ability to selectively attend to an auditory target stimulus in the presence of distractors (measured in an intensity discrimination task under backward masking; Oberfeld et al., 2014) as well as the ability to attend to a visual target stimulus (measured in a Flanker task; Kramer and Jacobson, 1991) explained a significant portion of the variance in sentence identification performance with two interfering talkers. Together, the two measures of selective attention explained approximately the same proportion of variance as the binaural TFS sensitivity, which was the predictor with the highest relative importance (see Table 1). These results are compatible with our hypothesis that not only rather basic auditory factors like spatial hearing abilities contribute to individual differences in cocktail-party listening, but that in the cognitive domain a general ability of focusing attention on a relevant target stimulus represents an additional important predictor. One of the few previous studies that measured selective attention in the presence of distractors (Gatehouse and Akeroyd, 2008) found that in unaided hearing-impaired listeners (mean age about 66 years) the word recognition performance in static background noise was positively related to performance in the 'Elevator counting with distraction' subtest of the Tests of Everyday Attention. However, the analysis did not control for effects of age. In a recent study (Cahana-Amitay et al., 2016), a composite score for 'inhibition' that included the error rate in an incongruent condition of the Stroop color-word test (Stroop, 1935) was negatively correlated with sentence-final word recognition (controlling for age) in a group of subjects ranging in age between 55 and 84 years.

Our data also illustrate that it is only of limited value to use a broad, unspecific concept of 'attention' and to study associations between speech-in-noise understanding and aspects of attention that are not necessarily related to the requirements of cocktail-party listening. Thus, it seems more appropriate to recognize that attention has many facets (Styles, 2006), to not aggregate across the performance on tasks measuring very different aspects of attention in order to define a general 'attention' factor, and to formulate hypotheses concerning the potential importance of a particular aspect of attention for speech identification in noise. In this line of thinking, we note that in the simulated cocktail-party listening task as well as in the two tasks measuring auditory and visual selective attention, the participants had a-priori knowledge of the spatial or temporal position of the target. For this reason, the direction of attention to the target could be viewed as being endogenous (top-down) rather than exogenous (bottom up) (Posner, 1980). Limitations in the ability to focus attention while performing these tasks should thus be related to the dorsal fronto-parietal attentional system in the cortex rather than to the ventral network (cf. Buschman and Miller, 2007; Corbetta and Shulman, 2002). To test this hypothesis, future experiments on attentional factors influencing cocktail-party listening could include tasks measuring both the endogenous and exogenous orienting of attention, using for instance temporal or spatial cueing (e.g., Coull and Nobre, 1998; Posner, 1980). Alternatively, one could argue that in the intensity discrimination task, the onset of the backward masker elicits a capture of attention away from the target tones (e.g., Desimone and Duncan, 1995; Jonides and Yantis, 1988; Yantis and Jonides, 1990). Thus, it might even be necessary to further qualify the description of the particular aspect of attention that is indexed by the intensity discrimination task under backward masking and say that is measures the ability to suppress salient, but task-irrelevant auditory events.

The working memory span did not show a strong relation to cocktail-party listening. Thus, although the sentence identification task we presented requires working memory for storing the sequence of five words, the performance on this task appeared to be more strongly limited by failures of selective attention than by memory aspects. This result is compatible with previous data indicating that working memory capacity and speech identification in noise are associated in older, hearing-impaired participants (see Akeroyd, 2008), while in normal hearing subjects this correlation is weaker or even absent (Füllgrabe and Rosen, 2016). Thus, measures of selective attention should be included in future studies instead of focusing only on working memory capacity. In general, working memory and attention are not independent (Cowan et al., 2005). For instance, Conway et al. (2001) studied the probability that in a dichotic listening task participants recognize their own name on the ignored channel (Moray, 1959), and found that this probability was higher in participants with a low WM span. Also, the working memory load affects speech understanding in a cocktail-party setting (Francis, 2010). In line with these results, in our data the working memory span showed a marginally significant negative correlation (controlling for age) with the DL-elevation (auditory selective attention), see Table 2.

Our results confirm the association between binaural TFS sensitivity for speech identification in a spatial listening task, compatible with earlier studies (Füllgrabe et al., 2014; Neher et al., 2011; Neher et al., 2012; Ruggles et al., 2011; Schoof and Rosen, 2014). The significant negative association between the IPD threshold and the SRS could be attributed to a reduced benefit from ITD cues in listeners with impaired binaural TFS sensitivity, although a reduction in TFS sensitivity could be also associated with other perceptual impairments beyond sound source localization (Moore, 2008). Spatial cues facilitate auditory streaming/grouping (e.g., Darwin and Hukin, 1999; David et al., 2015), selective attention (e.g., Ihlefeld and Shinn-Cunningham, 2008), and speech recognition (e.g., Culling et al., 2004). The importance of spatial cues was reported to increase with the number of interfering sound sources (Yost et al., 1996). Thus, one should expect a smaller influence of TFS sensitivity in situations with only one interfering speaker (compared to two as in the present study). Also, the simulated anechoic environment might have caused an overestimation of the importance of ITD cues. In a typical reverberant environment, spatial cues to sound source segregation are reduced (e.g., Lavandier and Culling, 2010). It remains to be shown whether the ability to attend to a target in the presence of distractors plays a stronger role than binaural TFS sensitivity in a reverberant setting. On the other hand, the spatial separation of 25° between target and distractor speakers was larger than in some previous studies that used a separation of only 15° (Ruggles et al., 2011, 2012, Ruggles and Shinn-Cunningham, 2011). The corresponding stronger ITD cues in our study might have reduced the importance of the sensory representation of the acoustic stimulus for task performance, which would emphasize the relative importance of central factors. Note that in cocktail-party situations in daily life, the spatial separation between the target speakers and competing speakers will often be even larger than 25°.

The self-report measures of hearing abilities (SSQ scores) showed only weak associations with performance in the cocktail-party listening task (controlling for age). In hearing-impaired listeners, some earlier studies (Gatehouse and Akeroyd, 2008; Heinrich et al., 2015) also reported rather small correlations between SSQ scores and speech identification in static or amplitude-modulated background noise, even without controlling for age. Füllgrabe et al. (2014) found no correlation between SSQ scores and speech-in-noise perception in audiometrically normal-hearing listeners.

Compared to previous studies on predictors of cocktail-party listening, our experiments introduced several methodological improvements. The sample size in our study was larger than in some previous experiments, so that it was possible to measure the influence of several variables on sentence identification in noise, without first combining predictors into a small number of factors. We also used multiple linear regression analyses, rather than pairwise correlations as in some previous experiments. Predictors like hearing thresholds, age, working memory span and TFS sensitivity are partly correlated (see Table 2). Multiple linear regression accounts for correlations among the predictors (Gauss, 1821), while interpreting pairwise correlations is extremely difficult for a large set of intercorrelated predictors. Unlike most previous studies, we also report the reliability of the measured variables, which in most cases was acceptable to high. Finally, we used two established approaches for assessing the relative importance of predictors, dominance analysis (Budescu, 1993) and Lasso (Tibshirani, 1996). The Lasso method was proposed to avoid some of the problems of forward or backward selection in stepwise regression (Harrell, 2015; James et al., 2013).

Despite the new findings concerning the role of the ability to direct selective attention to a target stimulus for speech identification in noise and the methodological features of our study, there are of course several limitations. First, we included only a single measure each for auditory and selective attention in the presence of distractors. From a psychometric point of view, it would be desirable to include different paradigms, in order to test whether the results generalize to different tasks indexing selective attention, and to increase the reliability of the measures of selective attention. In the auditory modality, we used intensity discrimination under backward masking as an index of selective attention. In this task, the target sounds have to be selected on the basis of their temporal position within a trial. We had decided against a task measuring the spatial direction of attention (e.g., Sach et al., 2000; Spence and Driver, 1994) because the performance on such a task depends on abilities of spatial hearing, which we assessed separately in terms of the binaural TFS sensitivity. Also, the precise perception of the temporal structure of speech is important for intelligibility (Zion Golumbic et al., 2012), and non-simultaneous masking can negatively affect speech identification (Dirks and Bower, 1970). Still, it would be interesting to study whether the spatial direction of attention shows a similar relation to cocktail-party listening as the temporal direction of attention. Spatial and temporal attention were reported to involve different brain areas (e.g., Michalka et al., 2015).

Second, we studied a relatively young group of listeners. It remains to be shown whether attentional abilities play a similar role in normal-hearing older subjects, or in hearing-impaired listeners. Age-related changes in selective attention have been reported (Zanto and Gazzaley, 2014), just as for other cognitive skills (e.g., Salthouse, 1996; Sander et al., 2012). At the same time, the probability of audiometrically relevant hearing losses as well as of 'hidden hearing losses' (Plack et al., 2014) increases with age, partly due to noise exposure across the life span. In fact, the TFS sensitivity shows a gradual deterioration with age in normal-hearing listeners (Füllgrabe, 2013; Grose and Mamo, 2010; King et al., 2014; Ross et al., 2007). Thus, the relative importance of psychoacoustic and cognitive predictors of speech understanding in a cocktail-party situation might differ between young, middle-aged and older groups, and future research should address this aspect.

Third, our participants were rather homogeneous in terms of education and (being university students) very likely also in terms of socioeconomic status and cognitive aptitude. Thus, it would be desirable to study potential psychoacoustic and cognitive predictors in a less homogeneous and more representative sample.

Fourth, our simulated cocktail-party listening task presented two spatially separated interfering speakers with the same voice as the target speaker, producing sentences with a fixed syntactical structure, and presented with a spatial separation of only 25° from the target speaker. These characteristics likely rendered the task more difficult than a realistic cocktail-party listening situation. On the other hand, communication situations in daily life often include some relatively static background noise in addition to competing speakers, while in our experiment no background noise was presented. Taken together, we assume realistic communication situations to be somewhat, but not dramatically, less difficult than the simulated cocktail-party listening task we presented. For this reason, although the average speech identification performance should be somewhat better in many realistic situations, we do not assume the latter situations to be so easy that individual differences are strongly reduced. Thus, the association between for selective attention in the presence of distractors or binaural TFS-sensitivity and speech-in-noise identification should apply to other cocktail-party situations, although ultimately this is an empirical question that should be tested in future experiments.

In conclusion, the individual ability to focus attention on a target stimulus in the presence of distractors explained a significant portion of the inter-individual variance in cocktail-party listening performance in a relatively young sample of normal-hearing listeners. Previous studies had reported that speech identification in multitalker situations is associated not only to auditory abilities such as binaural TFS sensitivity, but also to cognitive factors, predominantly in older and often hearing-impaired listeners (e.g., Akeroyd, 2008) but also in young normal-hearing listeners (Zekveld et al., 2013). Our results highlight the importance of studying aspects of attention directly relevant for speech identification in noise, rather than measuring associations with less relevant facets of attention such as visual search or to aggregate across very different aspects of attention in order to define a general 'attention' factor.

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Author details

1. Daniel Oberfeld

   Department of Psychology, Section Experimental Psychology, Johannes Gutenberg-Universität, Mainz, Germany

   Contribution

   DO, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   oberfeld@uni-mainz.de

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6710-3309

2. Felicitas Klöckner-Nowotny

   Department of Psychology, Section Experimental Psychology, Johannes Gutenberg-Universität, Mainz, Germany

   Contribution

   FK-N, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

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