Adaptive sensory processing entails the prioritization of task-relevant features with respect to competing information. Top-down modulation of activity in neural ensembles encoding task-relevant or distracting information is crucial in achieving this goal. In particular, regionally specific power changes around the alpha frequency range have been linked to such a putative top-down-mediated gain modulation, with enhanced power reflecting relatively inhibited states (Jensen and Mazaheri, 2010; Klimesch et al., 2007). For the visual modality especially, a vast amount of empirical evidence supports this notion. For example, increased alpha power in parieto-occipital cortical regions contralateral to the unattended hemifield is a very robust finding (e.g. Busch and VanRullen, 2010; Thut et al., 2006). The general inhibitory gating function of localized alpha increases has also been reported with respect to more specific visual features, leading to remarkable spatially circumscribed alpha modulations (Jokisch and Jensen, 2007; Zumer et al., 2014) even at a retinotopic level (Popov et al., 2019). Also for the domain of working memory, alpha increases have been reported during the retention period in the visual (e.g. Jensen et al., 2002; Klimesch et al., 1999), somatosensory (e.g. Haegens et al., 2009) and auditory modalities (e.g. Obleser et al., 2012), putatively protecting the to-be-remembered information against interference. This load-dependent top-down amplification of alpha and its concomitant inhibition account are widely accepted, but circumscribed decreases in alpha to beta power (often labeled as desynchronization) have also been deemed functionally important in the context of working-memory tasks. In a prioritization account, they reflect an enhanced activation of performance-relevant neural ensembles (e.g. Noh et al., 2014; Sauseng et al., 2009). A recent framework by Hanslmayr et al., 2016 explicitly links the extent of alpha/beta desynchronization to the representational strength of the information content in episodic memory (for supportive evidence see Griffiths et al., 2019). This is in line with a framework by van Ede, 2018 stressing the importance of regionally specific alpha and beta decreases when item-specific information needs to be prioritized in the retention period of working-memory tasks.

Distracting sounds are ever-present in natural listening environments and necessitate flexible exertion of inhibition or prioritization processes. Besides stimulus-feature information, which can influence the precise location of alpha modulations in the visual system (Popov et al., 2019), temporal cues can also be exploited (Rohenkohl and Nobre, 2011; van Ede and Chekroud, 2018): that is, when distracting sound input can be temporally predicted, inhibition or prioritization processes should be regulated in an anticipatory manner in relevant auditory regions. As in other sensory modalities (Frey et al., 2015; Weisz and Obleser, 2014), an increasing amount of evidence points to a functional role of alpha oscillations in listening tasks. Increased alpha oscillations have been observed in putatively visual brain regions when focusing attention on auditory input in cue-target periods (Frey et al., 2014; Fu et al., 2001; Snyder and Foxe, 2010). A similar posterior pattern is also observed in challenging listening situations, for example, with increased cognitive load or when faced with background noise (for reviews see Johnsrude and Rodd, 2016; Rönnberg et al., 2011). However, increases in alpha oscillations as a mechanism for selective inhibition (Strauß et al., 2014) have rarely been shown for auditory cortex, in which feature-specific processing of target and distractor sounds takes place. With regards to alpha desynchronization in auditory cortex, different lines of evidence showing an association between (also illusory) sound perception and low auditory cortical alpha power (e.g. Lange et al., 2014; Weisz et al., 2007; Weisz and Obleser, 2014; for invasive recordings illustrating sound-sensitive alpha desynchronization in anterolateral Heschl’s Gyrus, see Billig et al., 2019), suggest a link to representational content as described above.

The goal of the present study was to test whether power modulations in the alpha/beta range (Hanslmayr et al., 2016; van Ede, 2018) in task-relevant auditory cortical areas prior to a temporally predictable distractor, which was presented in the same (i.e. auditory) modality as the target, would better fit with an inhibition or prioritization account. On a general level, power increases would be predicted by an inhibition account, whereas decreases would be expected according to a prioritization account. Furthermore, both alternative accounts make opposing predictions regarding the relationship between pre-distractor alpha modulations and the strength of memorized information in the retention period (see Figure 1).

Figure 1

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We adapted a Sternberg task variant introduced by Bonnefond and Jensen, 2012 to the auditory modality. These researchers illustrated pronounced alpha and beta increases, as well as phase effects, in parieto-occipital regions prior to the presentation of a more potent but temporally predictable visual distractor in the retention period. Using magnetoencephalography (MEG) and decoding, we first identified regions that were informative as to whether a speech item was part of a memory set or not, and focused subsequent spectral analysis on the left superior temporal gyrus (lSTG). This region, which is crucially involved in phonological short-term memory (Jacquemot and Scott, 2006), expressed marked alpha/beta desynchronization prior to a strong distractor. Importantly, by time-generalizing the aforementioned classifier (King and Dehaene, 2014), we implemented a proxy for the strength of memorized information that could be compared between trials with high or low power. Specifically, we show that lower pre-distractor beta power in lSTG goes along with relatively enhanced memory representation in the same period. For alpha power, however, a negative correlation was observed between the strength of memorized information and the extent to which power was modulated at an individual level. Overall, our study draws a nuanced picture that points to differential alpha and beta processes in the auditory cortex that altogether support the prioritization of relevant information in working memory (van Ede, 2018).

Thirty-three healthy participants performed a modified Sternberg task (Bonnefond and Jensen, 2012) adapted to the auditory modality. In each trial they listened to a sequence of four consonants spoken by a female voice (see Figure 1A). These items had to be memorized across a 2-s retention period, after which a probe item was presented. Participants were requested to report whether the probe item was part of the memory set or not. Critically, precisely 2 s after onset of the last memory item, a distractor was presented that was either strong (a consonant spoken by a male voice) or weak (an acoustically scrambled version of a consonant). The different distractor types were presented blockwise in a manner counterbalanced across participants.

Decoding probe-related information

Our main goal was to investigate the extent to which different distractor levels influence the strength of memorized information during the retention period, in particular prior to the predictable onset of the distractor. Also, we wanted to relate these effects to potential alpha power modulations in the pre-distractor period. To this end, we first applied temporal decoding, using linear discriminant analysis (LDA, 5-fold cross validation, repeated five times, AUC as a metric; see Figure 1A and 'Materials and methods' for details), on the post-probe MEG sensor-level activity, to classify whether a probe was part of the memory set or not. The results are depicted in Figure 2A and show robust and sustained above-chance classification performance rapidly commencing ~334 ms after probe onset (p_cluster = 4e-4) and lasting until the onset of the response prompt at 700 ms post-probe (Cohen’s d in this period was generally >0.8, that is ‘strong and very strong effects’). The time course of this effect is very much in line with those seen in evoked response studies on old vs new effects in short-term memory (Danker et al., 2008; Kayser et al., 2003), indicating that early sensory activation is not informative on whether a probe was a memorized item or not. In subsequent analyses, a 0.4–0.7-s post-probe decoding period was used as a training set, and the derived classifiers were applied in a time-generalized manner to the preceding retention period (yielding a quantitative proxy for the strength of memorized information; see below and also Figure 1).

Although the results so far show that whether a probe was part of a memory set or not can be differentiated based on the MEG data, the resulting temporal decoding pattern uses all sensors and is therefore spatially agnostic. In order to obtain insights into which brain regions may be contributing to the effect and aiding the identification of task-relevant region(s) in a data-driven manner (Jacquemot and Scott, 2006), we adapted an approach to derive Informative Activity in source space (Marti and Dehaene, 2017). In brief, this approach projects the sensor level classifier weights to source space using beamformer filters. To make the effects more interpretable, we implemented a within-subject permutation analysis and z-scored the classifier-weights in a first-level analysis. These data were subsequently tested at group-level against zero within a nonparametric cluster permutation test using a t-test, yielding a positive cluster (p=4e^-4) collapsed over the relevant post-probe time period. As shown in Figure 2A, Informative Activity can be detected in widespread cortical regions encompassing temporal, parietal and frontal areas. Although the pattern was bilateral, there was a clear left-hemispheric dominance, with the most pronounced effect localizable to lSTG. Given the particular involvement of this latter area in processing speech sounds (e.g. Mesgarani et al., 2014; see also Billig et al., 2019) and its central role in phonological short-term memory (Jacquemot and Scott, 2006), it was used as a task-relevant region of interest for the spectral analysis (see below).

Figure 2

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After illustrating the above-chance decoding performance for the probe, we tested the extent to which informative patterns are present in the retention period, and how they are modulated by the distractor especially prior to its anticipated onset. The classifier described above was employed for this purpose using time generalization (King and Dehaene, 2014). The rationale for this approach (see also Figure 1) was that if a probe was part of the memory set, it should share neural patterns with those elicited by the actual memory set. This should not be the case for probes that were not part of the memory set, so that time-generalizing these post-probe patterns to the retention period should give a quantitative proxy for the strength of memorized information. The full time generalization result is shown separately for the strong and weak distractor conditions in Figure 2B. Trivially, the strongest classification results are obtained approximately at the onset of the target (at 2 s), which corresponds to the area close to the diagonal of the time generalization matrix. We were mainly interested in the decoding performance in the period preceding the anticipated distractor at 1 s, that is, in the off-diagonal pattern. It is evident that for the late 0.4–0.7-s post-probe training time period (probe onset at 2 s shown on the y-axis), decoding accuracy gradually ramps up over the retention interval.

Statistical analysis was done for a 500-ms window preceding the onset of the distractor (Figure 2B, right panel), focusing on the data-driven 0.4–0.7-s post-probe training time window (see above). This analysis yielded two peak effects at ~400 ms and ~200 ms (Cohen’s d in these periods ~0.6 and ~0.55, respectively, that is, medium effect sizes) preceding the distractor onset, during which critical t-values (±2.0369) were exceeded. However, only the latter difference was significant following a nonparametric permutation test (p_cluster = 0.0156). These results suggest that memorized information is differentially activated prior to distractor onset, with relatively reduced activation prior to the strong distractor. In fact, when post-hoc testing the decoding accuracy over the entire test- and training-time window, average decoding accuracy prior to the strong distractor was significantly below chance (t₃₂ = −3.98, p=3.63e-04), whereas it did not differ for weak distractors (t₃₂ = −1.73, p=0.09). Below-chance decoding may appear surprising but is not uncommon in time- and condition-generalization decoding approaches (King and Dehaene, 2014).

Although the precise neural processes leading to these patterns are challenging to pinpoint, in functional terms, this result translates into a systematic relative absence of memory-item specific patterns akin to those elicited during relevant periods following probe onset. As this activation gradually ramps up toward the anticipated onset of the probe in both conditions, albeit somewhat delayed in the strong distractor condition, it is clear that some representation with regards to the memorized item would also need to be present during the periods of below chance decoding. Recently, Stokes, 2015 and Wolff et al., 2017 described network-level ‘activity silent’ processes encoding working memory content, and similar processes could be present in the early part of the retention period in the present task. Overall, the described decoding effect is in line with the behavioral results described above, implying an overall detrimental impact associated with anticipation of a strong distractor.

Pre-distractor modulations of induced oscillatory activity

In a next step, we focused on modulations of oscillatory activity in the lSTG, a region dominating our source level analysis of informative activity with regards to the probe decoding (i.e. whether memorized or not) and strongly suggested to be task-relevant for phonological short-term memory by previous research (see, for example, Jacquemot and Scott, 2006). We focused our (statistical) analysis on the period immediately preceding the predictable occurrence of the distractor. According to an inhibition account, alpha/beta enhancements would be expected to precede the strong distractor in particular, putatively reflecting an anticipatory suppression of its processing. A prioritization account, on the other hand, would predict a power reduction in the same frequency range, putatively reflecting an anticipatory activation of the memorized information.

The time-frequency representations in Figure 3A, which display the induced power in the 5–25 Hz range, show strong ongoing alpha/beta activity with a peak ~10 Hz in the lSTG. Analogous to the study by Bonnefond and Jensen, 2012, a 500-ms period preceding the occurrence of the distractor is marked, suggesting an alpha power decrease in the strong as compared with the weak distractor condition. This impression is supported by a nonparametric permutation test (p_cluster = 0.0104), which yields a significant difference in this period with peak differences of ~12–13 Hz and ~21–22 Hz (Figure 3A, right panel), comparable to those recorded in the study in the visual domain (Bonnefond and Jensen, 2012). Given the perfect temporal predictability of the distractor occurrence, stronger prestimulus phase alignment of alpha oscillations could be expected (as reported in the visual modality by Bonnefond and Jensen, 2012; but see van Diepen et al., 2015). This process putatively exploits the fact that excitability varies over an alpha cycle, to align its inhibitory phase optimally to suppress processing of the irrelevant sound maximally. However, even though clear post-distractor evoked alpha enhancements could be observed (see Figure 3B), no prominent evoked alpha could be observed preceding the distractor (an analysis using ITC leads to an identical conclusion; data not shown). For the sake of completeness, we ran an analogous statistical test as for the induced power, showing no difference at the cluster corrected level (Figure 3B, right panel). Since no pronounced evoked alpha activity was identified in the pre-distractor period, we refrained from further analysis (such as phase opposition effects). This result extends a previous report (van Diepen et al., 2015) in finding no evidence that auditory cortical alpha phase is adjusted in a top-down manner.

Figure 3

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Overall, the spectral analysis suggests a pronounced decrease of alpha to beta power prior to the expected occurrence of the strong distractor. Although this pattern appears to support a prioritization account, it does not fit well with the decoding result at a first sight. One interpretation, along the lines of the inhibition account, could be that the expectation of a more salient auditory distractor may involuntarily draw more selective attention towards it (reflected in an anticipatory alpha decrease), making it more difficult to suppress. On the basis of the results presented so far, however, these alternatives cannot be differentiated. In the next part, by exploiting the possibility of time-generalizing the classifiers trained above (for rationale see also Figure 1A), we will attempt to address this important issue by linking pre-distractor alpha power modulations to the strength of memorized information in the retention period.

Strength of memorized information and pre-distractor alpha power

To address the functional relevance of pre-distractor alpha and beta power modulations in the lSTG in greater detail, trials were sorted according to alpha (13 ± 3 Hz) or beta (21 ± 3 Hz) power in this region in a 400–1000-ms time period following the onset of the retention period (i.e. a 600-ms pre-distractor window centered on peak latency effect at 700 ms). Subsequently, these trials were median split into high and low alpha or beta power bins. Analogous to the analysis described above (see also Figure 2), we trained a classifier on all trials to discriminate whether a probe was part of a memory set or not, and applied the classifier to a 0.5-s time-window prior to distractor presentation separately for the high- and low-power trials (analogous to the analysis shown in Figures 2 and 3) . On the basis of the previous analysis, we again focused on a 400–700-ms training time period and calculated the average strength of memorized information for each bin as well as the difference between bins for each individual participant. A functional relationship between pre-distractor alpha/beta power and strength of memorized information should be reflected in two ways (with different directions predicted according to a prioritization or inhibition account; see Figure 1B). First, strength of memorized information should differ overall between low- and high-power bins. Second, stronger (relative) differences between the bins should be reflected in stronger concomitant differences in the strength of memorized information.

To test for overall differences in the strength of memorized information between low- and high-power bins we first ran a repeated measures ANOVA using frequency band (alpha and beta) and bin (high and low) as factors. This analysis showed that the strength of memorized information did not differ overall between the frequency bands (F_1,32 = 1.19, p=0.28). A trend was observed for bins (F_1,32 = 3.29, p=0.08) with low-power bins showing relatively increased strength of memorized information (see Figure 4A). However, the interaction effect (F_1,32 = 4.06, p=0.05) indicated that this difference was not uniform for the alpha and beta bands. Comparing strength of memorized information within each frequency band showed that a difference was only significant for beta (t₃₂ = 2.27, p=0.03) and not for alpha (t₃₂ = 0.06, p=0.95). Thus, in line with a prioritization account (see Figure 1B), overall pre-distractor power in the beta frequency range in lSTG goes along with relatively increased strength of memorized information.

Figure 4

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The previous approach treats low- and high-power bins equally among participants. If there is a functional relationship between the oscillatory processes in lSTG and strength of memorized information, however, more extreme power differences between the bins should accompany more extreme differences in terms of strength of memorized information (see Figure 1B). In this respect, an interesting first observation is that modulations between low- and high-power trials (computed in each participant as the log₁₀ ratio between high and low power trials in the relevant band) were significantly stronger for alpha oscillations as compared to beta oscillations (t₃₂ = 3.11, p=0.004; compare also Figure 4B and C). Correlating this power modulation measure with the differences in strength of memorized information between low- and high-power trials yielded a significant effect only for alpha oscillations (alpha — r = −0.36, p=0.04; beta — r = 0.06, p=0.72), a pattern that better fits a prioritization account (see Figure 1B). A follow-up correlation analysis on the separate bins shows that for alpha this effect is driven by the high-power trials (Figure 4B). Altogether, relatively desynchronized states in the alpha and beta bands appear to go along with relatively increased strength of memorized information, which is in accordance with a prioritization process. Interestingly, our analysis of these trials points to a more nuanced and differential picture for alpha and beta oscillations, which have been frequently treated in a homogenous manner. Beta desynchronization appears to prioritize memorized information in general, whereas the alpha processes seem to be dependent on individually varying modulations.

Memory-related information is mainly carried by low-frequency activity

So far we have established that overall alpha and beta power in lSTG is relatively reduced prior to the onset of an anticipated strong distractor, and that this process could be involved in prioritizing auditory information in working memory. A valid concern could be that if it were mainly alpha/beta power reductions in lSTG that were carrying the decodable information (trained post-probe onset and time-generalized to retention period) then this would make the analysis somewhat circular. Although this reasoning conflicts with the condition comparison effects showing that lower alpha/beta powers prior to strong distractors are associated with on average lower decoding accuracy for memorized information, we explicitly followed up this issue. For this purpose, the basic decoding shown in Figure 1A, that is whether a probe was part of a memory set or not, was repeated following filtering of the data in different frequency bands (broadband — 1–30 Hz; theta — 1–7 Hz; alpha — 9–17 Hz; beta — 18–24 Hz) and by either applying the Hilbert transform or not. Importantly for the purpose of addressing the potential circularity issue, decoding was poor for the alpha and beta bands in general (see Figure 5A), and did not differ from chance for the relevant time period upon which our time-generalized effects are based (see Figure 5B).

Figure 5

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For the broadband analysis, decoding was also significantly above chance when using only the analytic amplitude part following the Hilbert transform (t₃₂ = 4.19, p=0.0002), although this is significantly lower than when using the original broadband signal (t₃₂ = −6.22, p=5.68e^–07). A similar pattern can be observed for the theta band, with significant above-chance decoding for the real (t₃₂ = 9.62, p=5.78e-11) and analytic amplitude (t₃₂ = 6.26, p=5.11e-07) versions of the signal (for real vs analytic amplitude, t₃₂ = −4.80, p=3.25e^–05). Decoding when using only the theta band was overall superior as compared to that using the broadband signal (real signal — t₃₂ = 10.71, p=4.10e^–12; analytic amplitude — t₃₂ = 7.11, p=4.53e^–08). To summarize: alpha/beta band modulations, which are different on average prior to strong and weak distractors, do not appear to carry actual (decodable) information about the memorized item (see also Griffiths et al., 2019). The most important signal component contributing to our analysis (Figure 1A) is in the theta frequency range. Interestingly, the temporal fine structure, as compared to simple amplitude modulations, seems to contain relevant representational information.

In the current study, we investigated the neural dynamics prior to an anticipated distractor in the auditory modality. We were particularly interested in potential modulations of alpha power in auditory cortical regions, which have shown patterns similar to those described in various cognitive tasks in the visual system (Frey et al., 2015). Also alpha-power modulations, although mainly in non-auditory regions, have been previously linked to listening effort or attentional control (McGarrigle et al., 2014; Pichora-Fuller et al., 2016; Wöstmann et al., 2017). In order to understand the functional relevance of potential alpha-power modulations, it was important to link them to the informational content carried by the neural patterns in the same time period. For this purpose, we adapted a modified Sternberg paradigm first proposed by Bonnefond and Jensen, 2012 to the auditory system: that is, we introduced (in a block wise manner) putatively weak and strong auditory distractor items in the retention period with predictable timing. Although perfect temporal predictability of a distractor is rare in natural environments (e.g. ticking of clock, dripping faucet), this was maintained in the present study to assure maximum comparability. In particular, strict temporal predictability should boost some potential effects, especially those pertaining to increasing pre-distractor phase consistency.

The behavioral effects are weaker than in the original visual experiment by Bonnefond and Jensen, 2012. Overall, the behavioral finding of lower accuracy for strong distractors suggests an adverse effect of strong acoustic distractors on the representation of memorized information. This notion was supported on a neural level using a time-generalization approach (King and Dehaene, 2014). This encompassed first training of classifiers to decode whether a probe item was part of the memory set or not, with these classifiers being used subsequently as a quantitative proxy for the strength of memorized information. These classifiers were then time-generalized to the period around the distractor presentation in the retention interval. Interestingly, classifier accuracy, especially prior to the strong distractor, was significantly below chance level. Such patterns are not uncommon in M/EEG studies using time- and condition-generalized decoding (see King and Dehaene, 2014) and are also seen in fMRI studies (e.g. van Loon et al., 2018). Descriptively, in electrophysiology, below-chance decoding can arise when the neural patterns underlying representations are opposing and/or temporally shifted. Thus below-chance decoding cannot be interpreted as the absence of condition- or feature-relevant information. However, a functional interpretation is challenging (King and Dehaene, 2014). On the basis that our approach of training classifiers on the post-probe period and time-generalizing them to the retention period only provides a limited access (hence a proxy) to the strength of memorized information, we would hesitate to interpret the results in absolute terms. When contrasting the conditions in relative terms, we find that anticipation of a strong distractor went along with relatively weak memorized information prior to distractor onset.

Interestingly, alpha/beta power prior to the presentation of the strong distractor decreased, whereas it was relatively sustained in the weak distractor condition. This effect is at odds with findings reported in the visual modality using an analogous paradigm (Bonnefond and Jensen, 2012), where — in line with idea of an inhibitory role for alpha oscillations (Jensen and Mazaheri, 2010; Klimesch et al., 2007) — induced alpha/beta enhancements were seen prior to a strong distractor. As in our study, the induced power effect in the Bonnefond and Jensen study was broadband and thus included the beta frequency range (see Figure 2 in Bonnefond and Jensen, 2012), although these authors focused only on the alpha (8–12 Hz) parts in their follow-up analysis. This, in general, is in line with our induced power effect shown in Figure 3, which shows peak effects around 13 Hz and 22 Hz. Our follow-up analysis, however, shows that both alpha and beta powers are relevant in a seemingly differential manner with respect to prioritization of memorized information. In the Bonnefond and Jensen study, pronounced pre-distractor evoked alpha power effects were observed that could not be identified in our auditory task. This negative finding could be modality specific but does also fit well with recent reported problems in identifying attentional pre-stimulus alpha-phase adjustment (van Diepen et al., 2015).

An integration of our alpha/beta findings with these lines of evidence appears challenging on the basis of power effects alone, as they could be interpreted either as the involuntary direction of attentional resources to the anticipated strong distractor (a sort of ‘failed inhibition’ within a gating account) or as a top-down driven prioritization of memorized information in anticipation of a strong distractor (see, for example, Hanslmayr et al., 2016 and van Ede, 2018). The group-level effect of reduced decoding accuracy prior to the strong distractor could be seen to support the first interpretation, but this analysis does not relate the pre-distractor alpha/beta power to estimated strength of memorized information. A desirable analysis would be to perform a single-trial analysis within each participant, but obtaining a reliable quantification of strength of memorized information at a single-trial level is challenging (because of low signal-to-noise, for example). As an alternative approach, we sorted trials according to the pre-distractor power in the respective bands and performed a median split. A prioritization account would entail two predictions: 1) that the low-power bin should be associated with relatively enhanced strength of memorized information prior to the distractor onset; and 2) that participants with more extreme power differences between the low- and high-power bins should also show more extreme differences in the strength of memorized information. We observed that for beta, the first prediction was met, whereas for alpha, it was the second prediction. Overall, while desynchronization in both frequency ranges appears to prioritize representations as suggested by other frameworks (Hanslmayr et al., 2016; van Ede, 2018), our results suggest functionally distinct contributions by auditory cortical alpha and beta oscillations. Interestingly, the extent to which alpha power was modulated within participants was significantly stronger than that for beta. Further studies will be needed to elaborate whether these distinct neural response patterns are linked to different cognitive processes that may subserve prioritization, such as temporal anticipation or selective attention. Altogether, our study underlines the value of combining conventional spectral analysis approaches with multi-voxel pattern analysis (MVPA) in advancing our understanding of the functional role of brain oscillations in the auditory system.

Our results may seem at odds with findings that frequently point to alpha power enhancements as an adaptive process within challenging listening tasks (for review see Strauß et al., 2014). In line with dominant views regarding the functional relevance of alpha oscillations (Jensen and Mazaheri, 2010; Klimesch et al., 2007), alpha enhancements in such circumstances have been linked to the selective inhibition of irrelevant ‘channels’ of auditory information (Strauß et al., 2014). In terms of tasks, a large proportion of studies illustrating enhanced task-related alpha power are not fully comparable to the present study because they are based on manipulation of selective attention (Fu et al., 2001; Worden et al., 2000) or on the processing of degraded (and thus more challenging) speech (but see alternative studies showing alpha decreases to track increased speech degradation, for example McMahon et al., 2016 and Miles et al., 2017; see also Hauswald et al., 2019). Some studies also reported the functional relevance of alpha enhancements during retention periods of auditory working memory tasks (e.g. Obleser et al., 2012; Wilsch et al., 2015), but these studies did not introduce strong/weak distractors at predictable time points. Importantly, however, the sources of these alpha enhancement effects have most frequently been identified in non-auditory brain regions (e.g. Obleser and Weisz, 2012; Wilsch et al., 2015) and rarely in the auditory cortex (e.g. Müller and Weisz, 2012). Indeed, reduction of auditory cortical alpha activity has been commonly linked to attended (Frey et al., 2014) as well as perceived (including illusory) auditory input (e.g. Leske et al., 2014; Müller et al., 2013; Weisz et al., 2007; Billig et al., 2019; for a more general perspective see Lange et al., 2014). The association of alpha modulations to attended/ignored or perceived auditory information has to date been very indirect.

Our study significantly advances this state by showing a relationship between alpha/beta power in the left auditory cortex and strength of memorized information in the retention period. This result supports the interpretations of studies showing alpha power reductions in the auditory modality and a more general assertion that cortical alpha/beta desynchronization during memory tasks represents the content of memorized information (Hanslmayr et al., 2016). Similar to a recent fMRI study using a representational similarity approach (Griffiths et al., 2019), we show that suppression of alpha/beta power itself does not carry the information content but is likely to be a process that enables this to occur. In our study, which used broadband signals in a first step of the decoding analysis, this content-specific information appears to be largely driven by slow (delta/theta) activity, with the temporal fine structure containing relevant information on top of the slower amplitude changes. Overall, our results can be reconciled with those of previous studies focusing on alpha enhancements in auditory tasks, as alpha reductions or enhancements may show engagements of different neural systems in processing relevant or blocking irrelevant information, respectively (van Ede, 2018). The functional versatility of alpha power modulations in listening tasks also serves as a precaution not to equate, for example, alpha power enhancements simplistically to concepts such as listening effort (McGarrigle et al., 2014; Pichora-Fuller et al., 2016).

In summary, precise predictability of the occurrence of an auditory distractor leads to an anticipatory prioritization of memorized information. We show that modulations of alpha/beta oscillations in task-relevant auditory cortical regions could be a relevant process mediating the ‘protection’ of relevant auditory information against interference. In doing so, our study significantly adds to our understanding of the functional role of alpha and beta oscillations in the auditory system. Interestingly, our results suggest a somewhat differential and to date unreported pattern for these frequency ranges in the auditory system: desynchronized beta states in task-relevant auditory regions appear to accompany the prioritization of information, but the extent of prioritization seems more level dependent for alpha. Illustrating a link to prioritization processes is a crucial first step in understanding the functional role of auditory cortical alpha/beta modulations during retention periods. Prospective studies using, for example, experimental neuromodulation techniques are needed to go beyond the correlational level. Future studies will need to test the extent to which the main direction of our findings generalizes to more natural listening situations such as speech, in which the temporal features of the distractor are not predictable with absolute precision.

All the preprocessed, downsampled raw data and processing MATLAB and R scripts could be found at https://doi.org/10.17605/OSF.IO/PW9RD.

1. 1. Weisz N
   2. Kraft N
   3. Demarchi G

   (2020) Open Science Framework

   Auditory cortical alpha / beta desynchronization prioritizes the representation of memory items during a retention period.

   https://doi.org/10.17605/OSF.IO/PW9RD

1. 1. Billig AJ
   2. Herrmann B
   3. Rhone AE
   4. Gander PE
   5. Nourski KV
   6. Snoad BF
   7. Kovach CK
   8. Kawasaki H
   9. Howard MA
   10. Johnsrude IS

   (2019) A Sound-Sensitive source of alpha oscillations in human Non-Primary auditory cortex

   The Journal of Neuroscience 39:8679–8689.

   https://doi.org/10.1523/JNEUROSCI.0696-19.2019

   • PubMed
   • Google Scholar
2. 1. Bonnefond M
   2. Jensen O

   (2012) Alpha oscillations serve to protect working memory maintenance against anticipated distracters

   Current Biology 22:1969–1974.

   https://doi.org/10.1016/j.cub.2012.08.029

   • PubMed
   • Google Scholar
3. 1. Busch NA
   2. VanRullen R

   (2010) Spontaneous EEG oscillations reveal periodic sampling of visual attention

   PNAS 107:16048–16053.

   https://doi.org/10.1073/pnas.1004801107

   • PubMed
   • Google Scholar
4. 1. Danker JF
   2. Hwang GM
   3. Gauthier L
   4. Geller A
   5. Kahana MJ
   6. Sekuler R

   (2008) Characterizing the ERP Old-New effect in a short-term memory task

   Psychophysiology 45:784–793.

   https://doi.org/10.1111/j.1469-8986.2008.00672.x

   • Google Scholar
5. 1. Delorme A
   2. Makeig S

   (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

   Journal of Neuroscience Methods 134:9–21.

   https://doi.org/10.1016/j.jneumeth.2003.10.009

   • PubMed
   • Google Scholar
6. 1. Demarchi G
   2. Sanchez G
   3. Weisz N

   (2019) Automatic and feature-specific prediction-related neural activity in the human auditory system

   Nature Communications 10:3440.

   https://doi.org/10.1038/s41467-019-11440-1

   • PubMed
   • Google Scholar
7. Software

   1. Elekta Neuromag MaxFilter User’s Guide

   (2012)

   Elekta Neuromag MaxFilter User’s Guide, version Software Version 2.2 (NM24057A-A)

   Elektra Neuromag, Helsinki, Finland.
8. Software

   1. Ellis D

   (2011) Time-domain scrambling of audio signals in Matlab

   demo_scramble.m.

   http://www.ee.columbia.edu/~dpwe/resources/matlab/scramble/
9. 1. Frey JN
   2. Mainy N
   3. Lachaux JP
   4. Müller N
   5. Bertrand O
   6. Weisz N

   (2014) Selective modulation of auditory cortical alpha activity in an audiovisual spatial attention task

   Journal of Neuroscience 34:6634–6639.

   https://doi.org/10.1523/JNEUROSCI.4813-13.2014

   • PubMed
   • Google Scholar
10. 1. Frey JN
    2. Ruhnau P
    3. Weisz N

    (2015) Not so different after all: the same oscillatory processes support different types of attention

    Brain Research 1626:183–197.

    https://doi.org/10.1016/j.brainres.2015.02.017

    • PubMed
    • Google Scholar
11. 1. Fu KM
    2. Foxe JJ
    3. Murray MM
    4. Higgins BA
    5. Javitt DC
    6. Schroeder CE

    (2001) Attention-dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parieto–occipital alpha-band oscillations

    Brain Research. Cognitive Brain Research 12:145–152.

    https://doi.org/10.1016/s0926-6410(01)00034-9

    • PubMed
    • Google Scholar
12. 1. Griffiths BJ
    2. Mayhew SD
    3. Mullinger KJ
    4. Jorge J
    5. Charest I
    6. Wimber M
    7. Hanslmayr S

    (2019) Alpha/beta power decreases track the fidelity of stimulus-specific information

    eLife 8:e49562.

    https://doi.org/10.7554/eLife.49562

    • PubMed
    • Google Scholar
13. 1. Haegens S
    2. Osipova D
    3. Oostenveld R
    4. Jensen O

    (2009) Somatosensory working memory performance in humans depends on both engagement and disengagement of regions in a distributed network

    Human Brain Mapping 31:26–35.

    https://doi.org/10.1002/hbm.20842

    • Google Scholar
14. 1. Hanslmayr S
    2. Staresina BP
    3. Bowman H

    (2016) Oscillations and episodic memory: addressing the synchronization/Desynchronization conundrum

    Trends in Neurosciences 39:16–25.

    https://doi.org/10.1016/j.tins.2015.11.004

    • PubMed
    • Google Scholar
15. 1. Haufe S
    2. Meinecke F
    3. Görgen K
    4. Dähne S
    5. Haynes JD
    6. Blankertz B
    7. Bießmann F

    (2014) On the interpretation of weight vectors of linear models in multivariate neuroimaging

    NeuroImage 87:96–110.

    https://doi.org/10.1016/j.neuroimage.2013.10.067

    • PubMed
    • Google Scholar
16. Preprint

    1. Hauswald A
    2. Keitel A
    3. Rösch S
    4. Weisz N

    (2019) Degraded auditory and visual speech affects theta synchronization and alpha power differently

    bioRxiv.

    https://doi.org/10.1101/615302

    • Google Scholar
17. 1. Jacquemot C
    2. Scott SK

    (2006) What is the relationship between phonological short-term memory and speech processing?

    Trends in Cognitive Sciences 10:480–486.

    https://doi.org/10.1016/j.tics.2006.09.002

    • PubMed
    • Google Scholar
18. 1. Jensen O
    2. Gelfand J
    3. Kounios J
    4. Lisman JE

    (2002) Oscillations in the alpha band (9-12 hz) increase with memory load during retention in a short-term memory task

    Cerebral Cortex 12:877–882.

    https://doi.org/10.1093/cercor/12.8.877

    • PubMed
    • Google Scholar
19. 1. Jensen O
    2. Mazaheri A

    (2010) Shaping functional architecture by oscillatory alpha activity: gating by inhibition

    Frontiers in Human Neuroscience 4:186.

    https://doi.org/10.3389/fnhum.2010.00186

    • PubMed
    • Google Scholar
20. Book

    1. Johnsrude IS
    2. Rodd JM

    (2016) Factors That Increase Processing Demands When Listening to Speech

    In: Hickok G, Small S. L, editors. Neurobiology of Language. Elsevier. pp. 491–502.

    https://doi.org/10.1016/B978-0-12-407794-2.00040-7

    • Google Scholar
21. 1. Jokisch D
    2. Jensen O

    (2007) Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream

    Journal of Neuroscience 27:3244–3251.

    https://doi.org/10.1523/JNEUROSCI.5399-06.2007

    • PubMed
    • Google Scholar
22. 1. Kayser J
    2. Fong R
    3. Tenke CE
    4. Bruder GE

    (2003) Event-related brain potentials during auditory and visual word recognition memory tasks

    Cognitive Brain Research 16:11–25.

    https://doi.org/10.1016/S0926-6410(02)00205-7

    • PubMed
    • Google Scholar
23. 1. King JR
    2. Dehaene S

    (2014) Characterizing the dynamics of mental representations: the temporal generalization method

    Trends in Cognitive Sciences 18:203–210.

    https://doi.org/10.1016/j.tics.2014.01.002

    • PubMed
    • Google Scholar
24. 1. Kleiner M
    2. Brainard D
    3. Pelli D
    4. Ingling A
    5. Murray R
    6. Broussard C

    (2007)

    What's new in psychtoolbox-3

    Perception 36:1–16.

    • Google Scholar
25. 1. Klimesch W
    2. Doppelmayr M
    3. Schwaiger J
    4. Auinger P
    5. Winkler T

    (1999) 'Paradoxical' alpha synchronization in a memory task

    Cognitive Brain Research 7:493–501.

    https://doi.org/10.1016/S0926-6410(98)00056-1

    • PubMed
    • Google Scholar
26. 1. Klimesch W
    2. Sauseng P
    3. Hanslmayr S

    (2007) EEG alpha oscillations: the inhibition-timing hypothesis

    Brain Research Reviews 53:63–88.

    https://doi.org/10.1016/j.brainresrev.2006.06.003

    • PubMed
    • Google Scholar
27. 1. Lange J
    2. Keil J
    3. Schnitzler A
    4. van Dijk H
    5. Weisz N

    (2014) The role of alpha oscillations for illusory perception

    Behavioural Brain Research 271:294–301.

    https://doi.org/10.1016/j.bbr.2014.06.015

    • PubMed
    • Google Scholar
28. 1. Leske S
    2. Tse A
    3. Oosterhof NN
    4. Hartmann T
    5. Müller N
    6. Keil J
    7. Weisz N

    (2014) The strength of alpha and beta oscillations parametrically scale with the strength of an illusory auditory percept

    NeuroImage 88:69–78.

    https://doi.org/10.1016/j.neuroimage.2013.11.014

    • PubMed
    • Google Scholar
29. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
30. 1. Marti S
    2. Dehaene S

    (2017) Discrete and continuous mechanisms of temporal selection in rapid visual streams

    Nature Communications 8:1955.

    https://doi.org/10.1038/s41467-017-02079-x

    • PubMed
    • Google Scholar
31. 1. McGarrigle R
    2. Munro KJ
    3. Dawes P
    4. Stewart AJ
    5. Moore DR
    6. Barry JG
    7. Amitay S

    (2014) Listening effort and fatigue: what exactly are we measuring? A british society of audiology cognition in hearing special interest group 'white paper'

    International Journal of Audiology 53:433–445.

    https://doi.org/10.3109/14992027.2014.890296

    • PubMed
    • Google Scholar
32. 1. McMahon CM
    2. Boisvert I
    3. de Lissa P
    4. Granger L
    5. Ibrahim R
    6. Lo CY
    7. Miles K
    8. Graham PL

    (2016) Monitoring alpha oscillations and pupil dilation across a Performance-Intensity function

    Frontiers in Psychology 7:745.

    https://doi.org/10.3389/fpsyg.2016.00745

    • PubMed
    • Google Scholar
33. 1. Mesgarani N
    2. Cheung C
    3. Johnson K
    4. Chang EF

    (2014) Phonetic feature encoding in human superior temporal gyrus

    Science 343:1006–1010.

    https://doi.org/10.1126/science.1245994

    • PubMed
    • Google Scholar
34. 1. Miles K
    2. McMahon C
    3. Boisvert I
    4. Ibrahim R
    5. de Lissa P
    6. Graham P
    7. Lyxell B

    (2017) Objective assessment of listening effort: coregistration of pupillometry and EEG

    Trends in Hearing 21:233121651770639.

    https://doi.org/10.1177/2331216517706396

    • PubMed
    • Google Scholar
35. 1. Müller N
    2. Keil J
    3. Obleser J
    4. Schulz H
    5. Grunwald T
    6. Bernays RL
    7. Huppertz HJ
    8. Weisz N

    (2013) You can't stop the music: reduced auditory alpha power and coupling between auditory and memory regions facilitate the illusory perception of music during noise

    NeuroImage 79:383–393.

    https://doi.org/10.1016/j.neuroimage.2013.05.001

    • PubMed
    • Google Scholar
36. 1. Müller N
    2. Weisz N

    (2012) Lateralized auditory cortical alpha band activity and interregional connectivity pattern reflect anticipation of target sounds

    Cerebral Cortex 22:1604–1613.

    https://doi.org/10.1093/cercor/bhr232

    • PubMed
    • Google Scholar
37. 1. Noh E
    2. Herzmann G
    3. Curran T
    4. de Sa VR

    (2014) Using single-trial EEG to predict and analyze subsequent memory

    NeuroImage 84:712–723.

    https://doi.org/10.1016/j.neuroimage.2013.09.028

    • PubMed
    • Google Scholar
38. 1. Nolte G

    (2003) The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors

    Physics in Medicine and Biology 48:3637–3652.

    https://doi.org/10.1088/0031-9155/48/22/002

    • PubMed
    • Google Scholar
39. 1. Obleser J
    2. Wöstmann M
    3. Hellbernd N
    4. Wilsch A
    5. Maess B

    (2012) Adverse listening conditions and memory load drive a common α oscillatory network

    Journal of Neuroscience 32:12376–12383.

    https://doi.org/10.1523/JNEUROSCI.4908-11.2012

    • PubMed
    • Google Scholar
40. 1. Obleser J
    2. Weisz N

    (2012) Suppressed alpha oscillations predict intelligibility of speech and its acoustic details

    Cerebral Cortex 22:2466–2477.

    https://doi.org/10.1093/cercor/bhr325

    • Google Scholar
41. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:1–9.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
42. 1. Pichora-Fuller MK
    2. Kramer SE
    3. Eckert MA
    4. Edwards B
    5. Hornsby BW
    6. Humes LE
    7. Lemke U
    8. Lunner T
    9. Matthen M
    10. Mackersie CL
    11. Naylor G
    12. Phillips NA
    13. Richter M
    14. Rudner M
    15. Sommers MS
    16. Tremblay KL
    17. Wingfield A

    (2016) Hearing impairment and cognitive energy: the framework for understanding effortful listening (FUEL)

    Ear and Hearing 37:5S–27.

    https://doi.org/10.1097/AUD.0000000000000312

    • PubMed
    • Google Scholar
43. 1. Popov T
    2. Gips B
    3. Kastner S
    4. Jensen O

    (2019) Spatial specificity of alpha oscillations in the human visual system

    Human Brain Mapping 40:4432–4440.

    https://doi.org/10.1002/hbm.24712

    • PubMed
    • Google Scholar
44. 1. Rohenkohl G
    2. Nobre AC

    (2011) α oscillations related to anticipatory attention follow temporal expectations

    Journal of Neuroscience 31:14076–14084.

    https://doi.org/10.1523/JNEUROSCI.3387-11.2011

    • PubMed
    • Google Scholar
45. 1. Rönnberg J
    2. Rudner M
    3. Lunner T

    (2011) Cognitive hearing science: the legacy of stuart gatehouse

    Trends in Amplification 15:140–148.

    https://doi.org/10.1177/1084713811409762

    • PubMed
    • Google Scholar
46. 1. Sauseng P
    2. Klimesch W
    3. Heise KF
    4. Gruber WR
    5. Holz E
    6. Karim AA
    7. Glennon M
    8. Gerloff C
    9. Birbaumer N
    10. Hummel FC

    (2009) Brain oscillatory substrates of visual short-term memory capacity

    Current Biology 19:1846–1852.

    https://doi.org/10.1016/j.cub.2009.08.062

    • PubMed
    • Google Scholar
47. 1. Snyder AC
    2. Foxe JJ

    (2010) Anticipatory attentional suppression of visual features indexed by oscillatory alpha-band power increases: a high-density electrical mapping study

    Journal of Neuroscience 30:4024–4032.

    https://doi.org/10.1523/JNEUROSCI.5684-09.2010

    • PubMed
    • Google Scholar
48. 1. Stokes MG

    (2015) 'Activity-silent' working memory in prefrontal cortex: a dynamic coding framework

    Trends in Cognitive Sciences 19:394–405.

    https://doi.org/10.1016/j.tics.2015.05.004

    • PubMed
    • Google Scholar
49. 1. Strauß A
    2. Wöstmann M
    3. Obleser J

    (2014) Cortical alpha oscillations as a tool for auditory selective inhibition

    Frontiers in Human Neuroscience 8:350.

    https://doi.org/10.3389/fnhum.2014.00350

    • PubMed
    • Google Scholar
50. Conference

    1. Taulu S
    2. Simola J
    3. Kajola M

    (2005) Applications of the signal space separation method

    IEEE Transactions on Signal Processing.

    https://doi.org/10.1109/TSP.2005.853302

    • Google Scholar
51. 1. Thut G
    2. Nietzel A
    3. Brandt SA
    4. Pascual-Leone A

    (2006) Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention Bias and predicts visual target detection

    Journal of Neuroscience 26:9494–9502.

    https://doi.org/10.1523/JNEUROSCI.0875-06.2006

    • PubMed
    • Google Scholar
52. Preprint

    1. Treder MS

    (2018) Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA

    arXiv.

    https://arxiv.org/abs/1803.10016

    • Google Scholar
53. Software

    1. Treder M

    (2019) MVPA-Light

    GitHub.

    https://github.com/treder/MVPA-Light
54. 1. van Diepen RM
    2. Cohen MX
    3. Denys D
    4. Mazaheri A

    (2015) Attention and temporal expectations modulate power, not phase, of ongoing alpha oscillations

    Journal of Cognitive Neuroscience 27:1573–1586.

    https://doi.org/10.1162/jocn_a_00803

    • PubMed
    • Google Scholar
55. 1. van Ede F

    (2018) Mnemonic and attentional roles for states of attenuated alpha oscillations in perceptual working memory: a review

    European Journal of Neuroscience 48:2509–2515.

    https://doi.org/10.1111/ejn.13759

    • PubMed
    • Google Scholar
56. 1. van Ede F
    2. Chekroud SR

    (2018) Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors

    Nature Communications 8:1449.

    https://doi.org/10.1038/s41467-018-03960-z

    • Google Scholar
57. 1. van Loon AM
    2. Olmos-Solis K
    3. Fahrenfort JJ
    4. Olivers CN

    (2018) Current and future goals are represented in opposite patterns in object-selective cortex

    eLife 7:e38677.

    https://doi.org/10.7554/eLife.38677

    • PubMed
    • Google Scholar
58. 1. Van Veen BD
    2. van Drongelen W
    3. Yuchtman M
    4. Suzuki A

    (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

    IEEE Transactions on Biomedical Engineering 44:867–880.

    https://doi.org/10.1109/10.623056

    • PubMed
    • Google Scholar
59. 1. Weisz N
    2. Dohrmann K
    3. Elbert T

    (2007) The relevance of spontaneous activity for the coding of the tinnitus sensation

    Progress in Brain Research 166:61–70.

    https://doi.org/10.1016/S0079-6123(07)66006-3

    • PubMed
    • Google Scholar
60. 1. Weisz N
    2. Obleser J

    (2014) Synchronisation signatures in the listening brain: a perspective from non-invasive neuroelectrophysiology

    Hearing Research 307:16–28.

    https://doi.org/10.1016/j.heares.2013.07.009

    • PubMed
    • Google Scholar
61. 1. Wilsch A
    2. Henry MJ
    3. Herrmann B
    4. Maess B
    5. Obleser J

    (2015) Alpha oscillatory dynamics index temporal expectation benefits in working memory

    Cerebral Cortex 25:1938–1946.

    https://doi.org/10.1093/cercor/bhu004

    • PubMed
    • Google Scholar
62. 1. Wolff MJ
    2. Jochim J
    3. Akyürek EG
    4. Stokes MG

    (2017) Dynamic hidden states underlying working-memory-guided behavior

    Nature Neuroscience 20:864–871.

    https://doi.org/10.1038/nn.4546

    • PubMed
    • Google Scholar
63. 1. Worden MS
    2. Foxe JJ
    3. Wang N
    4. Simpson GV

    (2000) Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex

    The Journal of Neuroscience 20:RC63.

    https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

    • PubMed
    • Google Scholar
64. 1. Wöstmann M
    2. Lim SJ
    3. Obleser J

    (2017) The human neural alpha response to speech is a proxy of attentional control

    Cerebral Cortex 27:3307–3317.

    https://doi.org/10.1093/cercor/bhx074

    • PubMed
    • Google Scholar
65. 1. Zumer JM
    2. Scheeringa R
    3. Schoffelen JM
    4. Norris DG
    5. Jensen O

    (2014) Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex

    PLOS Biology 12:e1001965.

    https://doi.org/10.1371/journal.pbio.1001965

    • PubMed
    • Google Scholar

Author details

1. Nathan Weisz

   Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron Universität Salzburg, Salzburg, Austria

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   nathan.weisz@sbg.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7816-0037

2. Nadine Gabriele Kraft

   Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron Universität Salzburg, Salzburg, Austria

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2818-2283

3. Gianpaolo Demarchi

   Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron Universität Salzburg, Salzburg, Austria

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   gianpaolo.demarchi@sbg.ac.at

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7597-9298

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