Unraveling the Relation between EEG Correlates
of Attentional Orienting and Sound Localization
Performance: A Diffusion Model Approach

Laura-Isabelle Klatt1, Daniel Schneider1, Anna-Lena Schubert2, Christina Hanenberg1,
Jörg Lewald1,3, Edmund Wascher1, and Stephan Getzmann1

Abstract

■ Understanding the contribution of cognitive processes and
their underlying neurophysiological signals to behavioral phenom-
ena has been a key objective in recent neuroscience research.
Using a diffusion model framework, we investigated to what ex-
tent well-established correlates of spatial attention in the elec-
troencephalogram contribute to behavioral performance in an
auditory free-field sound localization task. Younger and older par-
ticipants were instructed to indicate the horizontal position of a
predefined target among three simultaneously presented distrac-
tors. The central question of interest was whether posterior alpha
lateralization and amplitudes of the anterior contralateral N2 sub-
component (N2ac) predict sound localization performance (accu-
racy, mean RT) and/or diffusion model parameters (drift rate,
boundary separation, non-decision time). Two age groups were
compared to explore whether, in older adults (who struggle with
multispeaker environments), the brain–behavior relationship

would differ from younger adults. Regression analyses revealed
that N2ac amplitudes predicted drift rate and accuracy, whereas
alpha lateralization was not related to behavioral or diffusion
modeling parameters. This was true irrespective of age. The re-
sults indicate that a more efficient attentional filtering and se-
lection of information within an auditory scene, reflected by
increased N2ac amplitudes, was associated with a higher speed
of information uptake (drift rate) and better localization per-
formance (accuracy), while the underlying response criteria
(threshold separation), mean RTs, and non-decisional pro-
cesses remained unaffected. The lack of a behavioral correlate
of poststimulus alpha power lateralization constrasts with the
well-established notion that prestimulus alpha power reflects
a functionally relevant attentional mechanism. This highlights
the importance of distinguishing anticipatory from poststimulus
alpha power modulations. ■

INTRODUCTION

When multiple sources of acoustic information are simul-
taneously present, selective filtering of the available in-
formation is necessary to, for instance, focus on a talker
of interest while ignoring traffic noise, music playing in
the background, or other peoples’ conversations. This
capacity of the human auditory system is especially as-
tonishing, given that the incoming auditory signals often
overlap in time, space, or spectral content. The behav-
ioral effects of such selective orienting of attention in
noisy, multispeaker environments, usually referred to as
“cocktail party scenarios” (Cherry, 1953), have been stud-
ied for decades (for a review, see Bronkhorst, 2015).
However, the contribution of neural signals to observable
behavioral performance and its underlying cognitive pro-
cesses is still poorly understood. Here, we investigated
the relationship between well-established correlates of
spatial attention in the electroencephalogram (EEG)
and behavioral performance in an auditory sound local-

1Leibniz Research Centre for Working Environment and Human
Factors, 2Heidelberg University, 3Ruhr-University Bochum

© 2020 Massachusetts Institute of Technology

ization task. In particular, we specified the role of modu-
lations in the alpha frequency band as well as an anterior
contralateral N2 subcomponent (N2ac; Gamble & Luck,
2011) with respect to sound localization performance.

Lateralized modulations of alpha power amplitude
have been shown to reflect the orienting of spatial atten-
tion in visual (Foster, Sutterer, Serences, Vogel, & Awh,
2017; Ikkai, Dandekar, & Curtis, 2016; Rihs, Michel, &
Thut, 2007; Worden, Foxe, Wang, & Simpson, 2000), tac-
tile (Haegens, Luther, & Jensen, 2012; Haegens, Händel,
& Jensen, 2011), and auditory space (Klatt, Getzmann,
Wascher, & Schneider, 2018b; Wöstmann, Vosskuhl,
Obleser, & Herrmann, 2018; Wöstmann, Herrmann, Maess,
& Obleser, 2016). Typically, alpha power is shown to
decrease contralaterally to the attended location (Kelly,
Gomez-Ramirez, & Foxe, 2009; Sauseng et al., 2005) or to in-
crease contralaterally to the unattended or ignored location
(Kelly, Lalor, Reilly, & Foxe, 2006; Worden et al., 2000).
Consistently across different modalities, this lateralized
pattern of alpha-band activity has been shown to be linked
to visual detection performance (Händel, Haarmeier, &
Jensen, 2011; van Dijk, Schoffelen, Oostenveld, & Jensen,
2008; Thut, Nietzel, Brandt, & Pascual-Leone, 2006), tactile

Journal of Cognitive Neuroscience 32:5, pp. 945–962
https://doi.org/10.1162/jocn_a_01525

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discrimination acuity (Craddock, Poliakoff, El-deredy,
Klepousniotou, & Lloyd, 2017; Haegens et al., 2011), and
listening performance (Tune, Wöstmann, & Obleser, 2018;
Wöstmann et al., 2016). Going beyond a mere correlational
approach, recent studies applying stimulation techniques,
such as TMS or continuous transcranial alternating current
stimulation, suggest a causal role of alpha oscillations in the
processing of incoming information (Wöstmann et al., 2018;
Romei, Gross, & Thut, 2010). Two major (not necessarily
mutually exclusive) mechanisms have been proposed to
underlie those asymmetric modulations of alpha power
oscillations: target enhancement (Noonan et al., 2016;
Yamagishi, Goda, Callan, Anderson, & Kawato, 2005)
and distractor inhibition (Schneider, Göddertz, Haase,
Hickey, & Wascher, 2019; Rihs et al., 2007; Kelly et al.,
2006; Worden et al., 2000). Although the majority of pre-
vious studies investigated prestimulus alpha oscillations
as an index of anticipatory allocation of spatial attention
in young adults, we focused on poststimulus alpha later-
alization in a sound localization task, simulating a “cocktail
party scenario.” Such an experimental setup more closely
resembles frequent real-life situations, in which a person
searches for a sound of interest (e.g., a voice or a ringing
phone) without knowing in advance where to look for it. In
fact, there is first evidence that distinct attentional mecha-
nisms contribute to the preparation for as opposed to the
ongoing processing of a stimulus (van Ede, Szebényi, &
Maris, 2014). In addition, we explore whether the pro-
posed mechanistic function of alpha oscillations extends
to samples of older participants, which remains an ongoing
matter of debate (Tune et al., 2018; Mok, Myers, Wallis, &
Nobre, 2016; Hong, Sun, Bengson, Mangun, & Tong, 2015;
Vaden, Hutcheson, McCollum, Kentros, & Visscher, 2012).
A second neural measure of interest, indicating the alloca-
tion of attention within an auditory scene, is the N2ac. The
N2ac has been shown to be evoked in the N2 latency range
(starting at around 200 msec) when detecting or localizing a
target sound in the presence of one or multiple distractor
stimuli, using artificial sounds (Gamble & Luck, 2011), ani-
mal vocalizations (Klatt, Getzmann, Wascher, & Schneider,
2018a; Lewald & Getzmann, 2015), or spoken numerals
(Lewald, Hanenberg, & Getzmann, 2016). Although the
N2ac was originally suggested to reflect the allocation of
selective attention to the target (Gamble & Luck, 2011),
analogously to the visual posterior contralateral N2 sub-
component (N2pc; Eimer, 1996; Luck & Hillyard, 1994),
its functional significance remains ambiguous. Here, we
aimed to provide further evidence on the functional signif-
icance of the N2ac by investigating its relationship to sound
localization performance.

In this study, the diffusion modeling approach (Ratcliff,
1978) was applied, allowing for a more detailed under-
standing of behavioral patterns in discrimination tasks (for
recent reviews, see Voss, Nagler, & Lerche, 2013; Ratcliff &
McKoon, 2008). Although diffusion models are still only
rarely used in cognitive neuroscience research (see, e.g.,
Schubert, Nunez, Hagemann, & Vandekerckhove, 2019;

Nunez, Vandekerckhove, & Srinivasan, 2017; Schubert,
Hagemann, Voss, Schankin, & Bergmann, 2015; Ratcliff,
Philiastides, & Sajda, 2009; Philiastides, Ratcliff, & Sajda,
2006), the interest in and the application of this methodo-
logical approach has increased considerably during the past
decade. The general purpose of diffusion models is to
decompose the cognitive processes underlying a binary
decision. As one of the major advantages of the diffusion
model, the estimation procession is not limited to single
mean or median values but takes the whole RT distribution
into account. Specifically, the resulting separation of pro-
cessing components offers an enormous potential to pro-
vide more detailed descriptions of cognitive processes
and to generate more accurate predictions for behavioral
and neurophysiological data (Turner, Rodriguez, Norcia,
McClure, & Steyvers, 2016; Ratcliff & McKoon, 2008).

The diffusion model assumes that, in order for a deci-
sion to be made and a reaction to be executed, evidence
for either response is accumulated in the course of a noisy
process until it reaches either the decision boundary of re-
sponse A or response B (see Figure 2 in Voss et al., 2013
for an illustration of this evidence accumulation process).
The basic diffusion model includes the following pa-
rameters: The drift rate v describes the speed at which
evidence is accumulated (or “the rate of accumulation of
information”; Ratcliff & McKoon, 2008, p. 3), with higher
drift rates resulting in shorter RTs and fewer errors.
Threshold separation a indicates the amount of infor-
mation considered until a decision is made. That is, con-
servative response criteria that are associated with slower
but more accurate responses result in large estimates of a,
whereas more liberal response criteria result in smaller es-
timates of a. Threshold separation and drift rate have been
shown to be negatively correlated due to the fact that in-
dividuals with higher drift rates tend to allow more liberal
response criteria (i.e., smaller threshold separation values;
Schmiedek, Oberauer, Wilhelm, Süß, & Wittmann, 2007).
A priori biases toward one of the decision thresholds are
reflected by starting point z. Beyond that, the model also
includes non-decisional processing, such as response exe-
cution, working memory access, or stimulus encoding.
The latter is indicated by the non-decision time constant
t0. Typically, older adults show a slowing in this decision-
unrelated domain (Ratcliff, Thapar, Gomez, & McKoon,
2004; Ratcliff, Thapar, & McKoon, 2001). Finally, trial-to-
trial variability in drift rate (sv), non-decision time (st0),
starting point (sz), and the proportion of contaminated tri-
als ( pdiff; e.g., underlying non-diffusion-like processes) can
be accounted for.

In summary, here we aimed at characterizing the rela-
tion between electrophysiological correlates of atten-
tional orienting within a complex auditory scene (i.e.,
alpha lateralization and N2ac) and sound localization
performance, which was assessed by classical RT and accu-
racy measures as well as by diffusion modeling parameters.
We hypothesized that, if the cognitive processes reflected by
alpha power modulations and N2ac amplitudes contribute

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to the successful selection of the target from a sound array
containing simultaneously present distractors, they should
in turn contribute to the information accumulation process
that results in the localization of the target. Hence, alpha
power modulations and N2ac amplitudes should predict
drift rate (i.e., the speed of information accumulation)
and, in turn, RT and accuracy.

The data analyzed here were taken from a separate study
on effects of auditory training on cocktail party listening
performance in younger and older adults (Hanenberg,
Getzmann, & Lewald, unpublished). Exclusively pretraining
data of this study were used. The sample analyzed here in-
cluded both age groups. Although we did not primarily aim
at the investigation of age effects, age differences with re-
spect to sound localization performance, alpha lateralization
and N2ac, as well as the relation between these electro-
physiological correlates of attentional orienting and sound
localization performance were considered. Irrespective of
the expected age-related decline, we proposed the latter
brain–behavior relationship to be true for both age groups.

METHODS

Participants

The original sample included 28 older adults and 24
younger adults. Data for three younger participants were
discarded because of technical problems with the EEG
recording. In addition, two older participants were ex-
cluded from analysis because their performance was be-
low (14% correct) or very close to (30%) chance level
(25%). Consequently, the final sample included 26 older
adults (mean age = 69 years, range = 56–76 years, 13
women) and 21 younger adults (mean age = 24 years,
range = 19–29 years, 11 women). All participants were
right-handed as assessed by the Edinburgh Handedness
Inventory (Oldfield, 1971).

An audiometry, including 11 pure-tone frequencies (0.125–
8 kHz; Oscilla USB100, Inmedico) was conducted. Hearing
thresholds in the speech frequency range (<4 kHz) in- dicated normal hearing (≤25 dB) for all younger par- ticipants and mild impairments older participants (≤40 dB). The study was conducted in accordance with the Declaration of Helsinki approved by the Ethical Committee the Leibniz Research Centre for Working Environment Human Factors. All participants gave their written informed consent participation. Experimental Setup, Procedure, Stimuli The original study, which data were collected (Hanenberg et al., unpublished), comprised three training sessions on 3 days, with experimental blocks per session (15 min pretraining, 15 min posttraining, 1 hr posttraining) and with intervals 1–3 weeks between sessions. For pres- ent reanalysis, exclusively obtained pre- training blocks, pooled across sessions, were used. experiment a dimly lit, echo- reduced, sound-proof room. Participants seated a comfortable chair that positioned equal distances to left, right, front wall Participants’ head position stabilized chin rest. A semicircular array nine broadband loudspeakers (SC5.9; Visaton; housing volume 340 cm3) mounted par- ticipant at distance 1.5 m from participant’s head. Only four loudspeakers, located azimuthal positions of −60°, −20°, 20°, 60°, used experimental setup this study. red light-emitting diode (diameter =3 mm, luminous intensity =0.025 mcd) attached right below central loudspeaker median plane the participant’s head eye level. was continuously on served as fixation point. The sound localization task applied a modification multiple-sources approach has been several previous studies auditory selec- tive spatial attention “cocktail party scenarios” (Lewald, 2016, 2019; Lewald & Getzmann, 2015; Zündorf, Karnath, & Lewald, 2011, 2014; Karnath, 2013). Details present version have been previously described (Lewald et 2016). Briefly, participants indi- cated predefined target numeral was presented simultaneously distractor numerals. The kept constant each participant was counterbalanced age groups such that an number of times within overall experiment. Four 1-syllable nu- merals (“eins,” 1; “vier,” 4; “acht,” 8; “zehn,” 10), spoken by two male (mean pitch =141 Hz) female (mean pitch =189 native German speakers, sound stimuli numerals presented equally often possible posi- tions (located −60°, 60° azimuth). Numerals presented trial spoken different speakers. pressure level sound arrays 66 dB(A), measured par- ticipant’s using sound-level meter 0.5-in. free- field measuring microphone (Types 2226 4175, Brüel & Kjær). The trial, posi- tion, positions, speakers varying between trials following fixed pseudorandom order. stimu- lus duration 600 msec, followed response period of 2 sec intertrial interval 525 resulting in a total 3.125 sec. given by pressing one out buttons the index finger right hand. buttons were arranged semicircular array, related to four possible locations (i.e., far inner inner right, right). Each block consisted 288 trials, re- sulting block. As already mentioned above, assessed on different pooled. Thus, there of 864 trials participant. On 3 partici- pants completed short sequence 10 trials Klatt al. 947 D o w n l o a d e d l l >3000 msec) RTs were discarded. Subsequently, data
were log-transformed and z-standardized to exclude all
trials with RTs exceeding ± 3 SDs of the mean for each
individual participant.

The free software fast-dm ( Voss & Voss, 2007) was
used to fit a diffusion model to the RT distributions of
the present data. The model parameters were estimated
based on an iterative permutation process using the
Kolmogrov-Smirnov test statistic. The starting point z
was set to 0.5, presuming that participants were not bi-
ased toward one of the two response categories (correct
target location vs. distractor locations). The parameters
a, v, and t0 were allowed to vary freely. In addition, pa-
rameters sv and st0 were estimated because they led to a
notable improvement of model fit. Trial-to-trial variability
of starting point (sz), the difference in speed of response
execution (d ), as well as the measure for the percentage
of contaminants ( pdiff) were set to 0. To graphically eval-
uate model fit, we plotted observed versus predicted ac-
curacy as well as observed versus predicted values of the
RT distribution for the first (.25), second (.50), and third
(.75) quartile. Predicted parameter values were derived
using the construct-samples tool of fast-dm ( Voss &

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Voss, 2007). That is, 500 data sets were generated for
each participant based on each individual’s empirical pa-
rameter values and number of trials. Finally, the mean
quartile values and mean response accuracy were cal-
culated for each participant. Pearson correlations were
calculated to quantify the relationship between empirical
data and model predictions for both age groups. If the
majority of data points lie close to the line of perfect cor-
relation, good model fit can be assumed.

ERP Analysis

To investigate the N2ac component (Gamble & Luck, 2011),
we computed the mean contralateral and ipsilateral ERP
amplitude at frontocentral electrodes FC3/4 for older
adults and FC5/6 for younger adults. The contralateral
portion comprised the average signal at left hemispheric
electrodes in right target trials and right hemispheric elec-
trodes in left target trials, whereas the ipsilateral portion
included the average signal at left hemispheric electrodes
in left target trials and right hemispheric electrodes in
right target trials. Mean amplitude was measured from
477 to 577 msec relative to sound array onset. The mea-
surement window was based on a 100-msec time window
set around the 50% fractional area latency (FAL; Luck,
2014; Hansen & Hillyard, 1980) in the grand-averaged
contralateral minus ipsilateral difference curve averaged
across age groups and electrodes (50% FAL = 527 msec).
To determine the FAL, the area under the difference
curve was measured in a broad time window ranging from
200 to 800 msec relative to sound array onset. The latency
at which this area is divided in two equal halves denotes
the 50% FAL. We determined a common analysis time
window for both age groups because a prior control anal-
ysis did not reveal any significant differences between the
50% FAL for younger (M = 525.86 msec) and older adults
(M = 517.50 msec), Z = 0.26, p = .80, U3 = 0.48. The
respective electrodes of interest (i.e., FC3/4 and FC5/6)
were chosen to include the scalp sites with the most
pronounced asymmetry (i.e., peak asymmetry in the age-
specific grand-averaged waveform) for each age group.
This age-specific mean amplitude was measured in the
time window specified above.

Time–Frequency Data
To obtain time–frequency representations of the single-trial
oscillatory power, we convolved the epoched, stimulus-
locked EEG data with three-cycle complex Morlet wavelets.
The number of cycles increased with frequency by a factor
of 0.5, that is, half as fast as the number of cycles in the
respective fast Fourier transformation. This resulted in
three-cycle wavelets at the lowest frequency (4 Hz) and
11.25-cycle wavelets at the highest frequency (30 Hz). To
quantify asymmetries in the attentional modulation of total
oscillatory power (induced + evoked activity), the alpha lat-
eralization index (ALI) was calculated (Wildegger, van Ede,

Woolrich, Gillebert, & Nobre, 2017; Wöstmann et al., 2016;
Haegens et al., 2011). The latter quantifies the strength of
the ipsilateral minus contralateral difference in alpha power
relative to the total power across both hemispheres:

Þ
ð
ALI ¼ ipsilateral alpha power−contralateral alpha power
Þ
ð
ipsilateral alpha power þ contralateral alpha power
(1)

This normalization controls for potential confounds through
differences in overall power level when comparing the
two age groups. Mean ipsilateral and contralateral power
was extracted in the alpha frequency band (8–12 Hz) at
electrodes PO7/PO8 in a time window ranging from 705
to 902 msec relative to the onset of the sound array. The
measurement window was based on a 200-msec time win-
dow set around the 50% FAL in the ALI difference curve av-
eraged across age groups (50% FAL = 804 msec). The 50%
FAL was calculated based on a broad time window ranging
from 300 to 1400 msec relative to sound array onset. We
determined a common analysis time window for both age
groups, because a control analysis did not reveal any signif-
icant differences between the 50% FAL for younger (M =
796.00 msec) and older adults (M = 860.57 msec), Z =
−1.27, p = .20, U3 = 0.31. The electrodes sites were se-
lected based on a range of previous studies (e.g., Klatt
et al., 2018b; van Driel, Gunseli, Meeter, & Olivers, 2017;
van Ede, Niklaus, & Nobre, 2017; Myers, Walther, Wallis,
Stokes, & Nobre, 2015; Van der Lubbe, Bundt, & Abrahamse,
2014; Gould, Rushworth, & Nobre, 2011; Thut et al., 2006),
revealing a parieto-occipital scalp distribution and show-
ing PO7/8 to be a representative choice of electrodes
when measuring alpha lateralization. To minimize the
family wise error rate, we chose to limit the analysis to
one pair of electrodes. The ALI is positive when alpha
power is higher over the ipsilateral hemisphere (relative
to the target sound) and/or lower over the contralateral
hemisphere. In contrast, negative values indicate higher
alpha power contralateral to the target and/or lower alpha
power over ipsilateral electrode sites. The lateralization in-
dex was calculated using the raw, baseline-uncorrected
power values. ALI values for younger and older adults
were submitted to parametric two-sample t tests, using
Satterthwaite’s approximation to assess degrees of free-
dom. Subsequently, one-sample t tests were conducted
to test for significance of alpha lateralization within or
across age groups.

Multiple Regression

To investigate to what extent alpha lateralization and
N2ac amplitudes predict behavior in the given auditory
localization task, we applied regression analyses. Sep-
arate multiple linear regression models were evaluated
for mean RT, drift rate v, threshold separation a, and
non-decision time t0 as response variables, using the fitlm

Klatt et al.

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ð

function implemented in the MATLAB Statistics and
Machine Learning toolbox (R2018a). To account for the
fact that accuracy proportions range inbetween 0 and 1,
a beta regression was calculated for accuracy as a re-
sponse variable using the R betareg package by Cribari-
Neto and Zeileis (2010). For all five regression analyses,
N2ac amplitudes, ALI, and age group served as pre-
dictors. In addition, to assess whether the relationship
between electrophysiological correlates and behavioral
outcomes differed between age groups, two interaction
terms were also included (i.e., age:N2ac, age:ALI). Effects
coding was used as a contrast scheme for the age group
variable to enable a proper interpretation of lower and
higher order effects. Model assumptions were verified by
examination of residuals plots: Pearson residuals were
plotted against fitted values and against predictor variables
to assess nonconstant error variance (heteroscedasticity)
and deviations from linearity, respectively. In addition,
normal probability plots were examined to evaluate nor-
mality of residuals. In case of a nonsignificant Durbin–
Watson test, returning a test statistic close to 2, residuals
were assumed to be uncorrelated. Variation inflation fac-
tors were inspected for signs of multicollinearity. Finally,
to check for influential cases, leverage and cook’s distance
were examined. Values exceeding 1 for cook’s distance
(Cook & Weisberg, 1982) or 3 × kþ1
Þ
(with k indicating
n
the number of predictors and n indicating the sample
size) for leverage (Pituch & Stevens, 2016) were set as cut-
offs for further inspection. The inspection of residuals
plots indicated deviations from normality for the drift rate
regression model. Refitting the model with a log trans-
formation (to base 10) of the drift rate values (v + 1; a
constant was added to avoid negative values) resulted in
approximately normally distributed residuals. Thus, ordi-
nary least square regression was applied. For the models
regarding threshold separation, non-decision time, and
RT, the residual probability plots indicated some outliers.
To reduce outlier effects, we fitted a robust regression
model, using an iterative reweighted least squares proce-
dure and a bisquare weight function. Adjusted R-squared
(denoted as R2) is reported as a goodness-of-fit statistic. To
correct for the fact that we conducted separate multiple
regression analyses for each of the five dependent vari-
ables, p values for regression coefficients were corrected
using a Bonferoni–Holm procedure (Holm, 1979). Note
that in each case the five p values belonging to the same
type of estimate (i.e., intercept, N2ac fixed effect, ALI fixed
effect, age fixed effect, N2ac:age interaction term, or ALI:
age interaction term) were corrected for multiple testing.
To visualize the relationship between single predictors and
outcomes, marginal effects plots (ggeffect function from
ggeffects package; Lüdecke, 2018) and adjusted response
functions (plotInteraction and plotAdjustedResponse
functions) were used for the beta regression model (in R)
and linear regression models (in MATLAB), respectively.
Adjusted response functions describe the relationship be-

tween the fitted response and a specific predictor, whereas
the other predictors are averaged out by averaging the fitted
values over the data used in the fit. Adjusted response values
are computed by adding the residual to the adjusted fitted
value for each observation (The MathWorks, 2019). When
plotting marginal effects using “ggeffect,” the other factors are
held constant at an average value (Lüdecke, 2018).

Statistical Tests and Effect Sizes

Data were considered normally distributed if the Lilliefors
test (Lilliefors, 1967) yielded insignificant results ( p >
.05). For normally distributed data, parametric two-sample
Welch’s t tests were applied. Degrees of freedom were
estimated using Satterthwaite’s approximation, assuming
unequal variances. Wilcoxon rank-sum test served as the
nonparametric counterpart in case of nonnormality. To
test for significance within an age group, a parametric
one-sample t test or the nonparametric Wilcoxon signed-
rank test was applied. Measures of effect sizes were calcu-
lated using the MES toolbox provided by Hentschke and
Stüttgen (2011). For parametric one- and two-sample t
tests, g1 and Hedge’s g (in the following referred to as g)
are reported, respectively. For both measures, effect sizes
of ±0.2 are typically referred to as small, values of ±0.5 as
medium, and values of ±0.8 as large. For nonparametric t
tests, Cohen’s U3 is reported. Cohen’s U3 is a measure of
overlap of two distributions, with 0.5 indicating minimal
overlap and 0 or 1 indicating maximal overlap. The sig-
nificance of effects was assessed at a significance level of
α = .05. The Bonferroni–Holm correction procedure
was applied to correct for multiple comparisons when
appropriate (Holm, 1979). Adjusted p values are denoted
as padj.

Given that p values from standard inferential statistics
do not allow any conclusions on whether or not the null
hypothesis is true, we additionally report the Bayes factor
(BF) to strengthen the interpretability of effects in this
study. In essence, the BF provides a “continuous” mea-
sure, which indicates how much more likely the ob-
served results are under a given hypothesis, compared
with an alternative hypothesis (for an introduction to
Bayesian statistics, see Quintana & Williams, 2018;
Wagenmakers et al., 2018). A BF of 1 indicates that the
results are equally likely under both hypotheses (i.e.,
the null and the alternative hypothesis). A BF < 1 pro- vides increasing evidence in favor of the null hypothesis relative to the alternative hypothesis, whereas a BF > 1
provides increasing evidence favoring the alternative
hypothesis over the null hypothesis (Dienes, 2014). To
facilitate the interpretation of BFs, the classification
scheme originally proposed by Jeffreys (1961) is applied:
The latter suggests that a BF > 3 and > 10 provide mod-
erate and strong evidence for the alternative hypothesis,
respectively, whereas a BF < 0.33 or < 0.1 suggests mod- erate and strong evidence in favor of the null hypothesis, 950 Journal of Cognitive Neuroscience Volume 32, Number 5 D o w n l o a d e d l l / / / / j t t f / i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j t / . f o n 0 5 M a y 2 0 2 1 respectively. Finally, BFs between 0.33 and 3 are inter- preted in terms of anecdotal evidence. However, it should be noted that those cutoffs have no absolute meaning (Dienes, 2014) in that evidence is continuous and it is di- rectly interpretable in terms of an odds ratio (Quintana & Williams, 2018). The notation BF10 indicates the Bayes factor for the alternative hypothesis (i.e., that the means of the samples are different). BF functions implemented in MATLAB by Krekelberg (2019) and the BayesFactor package implemented in R (function: linearReg.R2stat) by Morey and Rouder (2018) were used to calculate BFs for t tests and regression, respectively. To obtain a BF for a specific coefficient in our regression model (BFcoef), the BF for the full model and the restricted model were com- pared according to the following formula: BFfull/BFrestr. BFfull indicates the BF for the full model, including all pre- dictors, whereas BFrestr indicates the BF for the restricted model, omitting the coefficient of interest. Default priors, that is, the Jeffrey–Zellner–Siow Prior for t tests and a mixture of g-priors according to Liang, Paulo, Molina, Clyde, and Berger (2008) for regression, were applied. Because those packages do not support the calcula- tion of BFs for beta regression, no Bayesian statistics are provided for the regression analysis of accuracy data. RESULTS Behavioral Results Figure 1 shows the proportion of correct responses (Figure 1A) as well as mean RTs (Figure 1B) separately for both age groups. Diffusion parameters are depicted in Figure 2. On average, younger adults showed higher accuracy (t(43.05) = −3.36, p = .002, padj = .01, g = 0.92, BF10 = 14.21) and faster responses than older adults (t(38.56) = 2.80, p = .008, padj = .038, g = −0.83, BF10 = 6.93). The BFs indicated that the alterna- tive model was around 14 times and six times more likely than the null model, respectively, thus providing strong and moderate support for a difference between age groups. Although mean RTs do not offer any insights into the underlying causes of prolonged RTs, diffusion parame- ters allow for a closer look at different possible explana- tions for the observed difference between age groups, including a slowdown of information update (i.e., higher drift rate v), a more conservative response criterion (i.e., higher threshold separation a), or delayed response exe- cution (i.e., higher response constant t0). In our sample, older adults showed a significantly reduced drift rate (t(44.89) = −2.51, p = .016, padj = .047, g = 0.70, BF10 = 3.01), higher non-decision time (t(41.31) = 2.81, p = .008, padj = .038, g = −0.82, BF10 = 6.59), as well as higher variability of non-decision time (t(40.26) = 5.25, p < .001, padj < .001, g = −1.43, BF10 = 153.9). Threshold separation values (t(44.24) = −0.66, p = .513, padj = .513, g = 0.19, BF10 = 0.35) and trial- to-trial variability of drift rate (Z = −1.21, p = .226, padj = .453, U3 = 0.29, BF10 = 0.35) did not differ significantly between age groups. Although the BFs supported clas- sical inferential statistics for significant results (BFs > 3),
for insignificant results they fell short of the criterion for
moderate evidence for equivalence (BFs > 0.33). To graph-
ically assess the fit of the estimated diffusion models, ob-
served RT quartiles (.25, .5, .75) and observed accuracy
were plotted against the corresponding value of the pre-
dicted distributions. As can be seen in Figure 3, the major-
ity of data points lie close to the line of perfect correlation,
indicating adequate model fit.

N2 Anterior Contralateral Component

Figure 4 presents the ERPs at frontocentral electrodes
FC3/4 for older adults and electrodes FC5/6 for younger
adults. In addition, the corresponding topographies
based on the contralateral minus ipsilateral difference
wave in the analysis time window are depicted. N2ac am-
plitudes (i.e., contralateral minus ipsilateral differences)

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Figure 1. Proportion of (A)
correct responses and (B) mean
RTs for younger and older
adults. Colored horizontal lines
indicate the respective group
mean. Dots indicate individual
participants’ mean values.
*padj < .05. o n 0 5 M a y 2 0 2 1 Klatt et al. 951 Figure 2. Diffusion model parameter estimates for younger and older participants. Dots represent single subject data. Colored horizontal lines show the mean model parameters within age groups. *padj < .05, ***padj < .001. D o w n l o a d e d l l / / / / j t t f / i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j t / f . o n 0 5 M a y 2 0 2 1 did not differ significantly between younger (M = −0.37, SD = 0.47) and older adults (M = −0.38, SD = 0.40, t (39.37) = −0.09, p = .926, g = 0.03, BF10 = 0.29). The BF of 0.29 can be interpreted as insufficient evi- dence, supporting neither the null nor the alternative hy- pothesis. A one-sample t test confirmed that across both age groups, N2ac amplitudes were significantly different from zero (t(46) = −6.02, p < .001, padj < .001, g1 = −0.88, BF10 > 1000). However, it should be noted that
the original analysis time window was based on the 50%
FAL in the grand-averaged difference waveform across
both age groups; thus, this procedure favors a significant
result when testing overall N2ac amplitudes against zero.
To avoid this problem of “double dipping,” we performed
a second one-sample t test, using a broader analysis time
window of 400–600 msec post sound array onset. The lat-
ter yielded comparable results (t(46) = −4.41, p < .001, padj < .001, g1 = −0.64, BF10 > 1000). Consistently, the

BF provided strong evidence in favor of the presence of
an N2ac component across both age groups.

Alpha Lateralization
The time–frequency plots in Figure 5 illustrate the asym-
metric modulation of alpha power (8–12 Hz) at electrodes
PO7/8 time-locked to sound array onset for younger
(Figure 5A) and older adults (Figure 5B), respectively. In
addition, the corresponding topographies based on the
normalized ipsilateral minus contralateral difference in al-
pha power are depicted. Although younger adults appeared
to show larger alpha power lateralization than older adults,
the analysis revealed no significant difference in alpha
power lateralization between age groups (t(41.23) =
−1.43, p = .161, g = 0.42, BF10 = 1.13). The BF suggested
that the data were insensitive to distinguish the null (no
amplitude difference between groups) from the alternative

952

Journal of Cognitive Neuroscience

Volume 32, Number 5

Figure 3. Graphical analysis of
model fit. Scatter plots show the
observed proportion of correct
responses as well as the first
three quartiles (.25, .5, .75) of
the observed RT distribution as
a function of the corresponding
value from the predicted
distribution. Dots and
diamonds represent single
subject data for younger and
older participants. r denotes the
corresponding Pearson
correlation coefficients,
separately for younger (ry) and
older (ro) adults.

hypothesis (difference in amplitudes between age groups).
Yet, a one-sample t test confirmed that alpha lateralization
across both age groups was significantly different from zero
(t(46) = 6.07, p < .001, padj < .001, g1 = 0.89, BF10 >
1000), and the BF consistently suggested strong evidence
for the alternative hypothesis. As mentioned above (cf.
N2 Anterior Contralateral Component section), the analysis
time window (determined based on the 50% FAL in the
grand-averaged waveform) favors a significant result when
testing across age groups, against zero. Thus, a second

one-sample t test was performed, based on a broader anal-
ysis time window of 600–900 msec post sound array onset,
yielding comparable results (t(46) = 5.91, p < .001, padj < .001, g1 = 0.86, BF10 > 1000).

Regression Analyses

We examined the relationship between mean alpha
power lateralization, N2ac amplitudes, and behavioral
performance (including diffusion model parameters)

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Figure 4. N2ac component at frontocentral electrodes FC5/6 for (A) younger and at FC3/4 for (B) older participants. Contralateral and ipsilateral
portions of the signal as well as the resulting difference wave (contralateral minus ipsilateral) are depicted. Scalp topographies show the
distribution of voltage differences based on the contralateral minus ipsilateral difference wave in the time window used for statistical analysis
(highlighted in gray in ERP figures).

Klatt et al.

953

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Figure 5. Grand-average time–frequency plots of lateralization indices at electrodes PO7/8 for (A) younger and (B) older adults. The scalp
topographies are based on normalized differences of ipsilateral minus contralateral alpha power in the time window used for statistical analysis.
Bar graphs show the mean difference (left minus right) for the left (i.e., PO7) and right (i.e., PO8) hemisphere. Error bars indicate the SEM. Line
plots (right) illustrate the contralateral and ipsilateral portion of the raw ERSPs as well as the resulting ALI.

using multiple linear regression. The estimated param-
eters are provided in Table 1. Participants with greater
N2ac amplitudes showed higher accuracy (Z = −3.93,
p < .001, padj < .001) and higher drift rate (t(41) = −2.79, p = .008, padj = .032, BFcoef = 7.75), whereas there was no significant effect of alpha lateralization on those performance outcomes (accuracy: Z = −1.54, p = .124, padj = .499; drift rate: t(41) = −0.37, p = .712, padj = 1.067, BFcoef = 0.43). For both models, there was no significant interaction with age (all padj > .160). The cor-
responding BFs (only available for the drift rate model; cf.
Statistical Tests and Effect Sizes section) were below 3
(BFcoef ≤ 0.65) but above 0.33, thus lending insufficient
evidence for the null or the alternative hypotheses. The
full models, including all predictors, explained 26% and
36% of variance in drift rate (R2
adj = .26, F(5, 41) =
4.15, p = .004) and accuracy (pseudo-R2 = .36, precision
parameter phi = 9.73, SE = 1.96, z = 4.97, Pr(>|z|) < .001), respectively. For all other models tested, nei- ther N2ac amplitudes nor alpha power lateralization or their interaction with age groups served as statistically significant predictors (all padj > .095; cf. Table 1). For all
but one parameter, the corresponding BFs were in-
conclusive (3 < BFcoef > 0.33), providing no substantial
support for the alternative hypothesis, but neither for
the null hypothesis. However, for the regression model
predicting non-decision time, the BF for the interaction
term N2ac*Age ( p = .095) lend moderate evidence in fa-
vor of the alternative hypothesis (BFcoef = 5.92), suggest-
ing that, in older adults, less pronounced N2ac amplitudes
were associated with higher non-decision times. In con-
trast, the latter relationship appeared absent in younger
adults. Age group, not surprisingly, significantly predicted
non-decision time (t(41) = 3.00, p = .005, padj = .018,
BFcoef = 15.27), accuracy (Z = 3.03, p = .002, padj =
.012), and drift rate (t(41) = −2.86, p = .007, padj =
.020, BFcoef = 8.78). Although age group failed to serve
as a significant predictor for RT in the regression model
framework (t(41) = 1.82, p = .075, padj = .151, BFcoef =
2.38), the results largely confirm the behavioral age dif-
ferences reported in the Behavioral Results section. The BF
of 2.38 suggests that the data may simply be underpowered

954

Journal of Cognitive Neuroscience

Volume 32, Number 5

Table 1. Estimated Parameters, Standard Errors, Confidence Intervals, and t Test (or z Test) Statistics for Each Predictor in the Linear (or Beta) Regression Model

Outcome

v

a

t0

Accuracy

RT

Predictors

b (SE) [95% CI]

t

b (SE) [95% CI]

t

b (SE) [95% CI]

t

b (SE)

z

b (SE) [95% CI]

t

Intercept

0.22*** (0.05)
[0.12 0.31]

−0.22* (0.08)
[−0.38 −0.06]

−2.52 (6.77)
[−16.20 11.16]

−0.14* (0.05)
[−0.24 −0.04]

−0.16 (0.08)
[−0.32 0.00]

−4.67 (6.78)
[−18.35 9.02]

N2ac

ALI

Age

N2ac*Age

ALI*Age

Adjusted/

pseudo-R2

4.43, p < .001 padj < .001 −2.79, p = .008 padj = .032 1.55*** (0.09) [1.36 1.74] 16.47, p < .001 padj < .001 0.77*** (0.04) [0.70 0.86] 19.77, p < .001 padj < .001 0.89*** (0.16) 5.63, p < .001 padj < .001 1.24*** (0.04) [1.15 1.33] 28.95, p < .001 padj < .001 −0.32 (0.15) [−0.62 −0.01] −2.10, p = .042 padj = .126 0.08 (0.06) [−0.05 0.21] 1.29, p = 0.203 padj = .407 −1.02*** (0.26) −3.93, p < .001 padj < .001 −0.37, p = .712 padj = 1.067 −10.87 (13.05) [−37.22 15.49] −0.83, p = .410 padj = 1.230 10.18 (5.45) [−0.83 21.19] 1.87, p = .069 padj = .345 −33.48 (21.81) −1.54, p = .124 padj = .499 0.08 (0.07) [−0.06 0.22] 3.73 (5.94) [−8.27 15.74] −2.86, p = .007 padj = .020 −2.02, p = .050 padj = .160 −0.69, p = .495 padj = .965 −0.07 (0.09) [−0.26 0.12] −0.11 (0.15) [−0.41 0.20] −0.76, p = .452 padj = .452 −0.72, p = .474 padj = .474 0.12* (0.04) [0.03 0.20] 0.15 (0.06) [0.03 0.28] 3.00, p = .005 padj = .018 2.44, p = .018 padj = .095 −0.48* (0.16) −0.53 (0.26) 3.03, p = .002 padj = .012 0.08 (0.04) [−0.01 0.16] −2.05, p = .039 padj = .160 0.09 (0.04) [−0.04 0.24] −10.06 (13.05) [−36.41 16.30] −0.77, p = .445 padj = 1.336 11.23 (5.45) [0.22 22.24] 2.06, p = .046 padj = .229 −15.30 (21.78) −0.70, p = .483 padj = 1.336 9.19 (5.94) [−2.81 21.19] 1.14, p = .261 padj = .407 0.63, p = .533 padj = 1.230 1.82, p = .075 padj = .151 1.43, p = .160 padj = .319 1.55, p = .130 padj = .519 .26 .03 .32 .36 − .14 F(5, 41) = 2.46, p = .048 F-statistic F(5, 41) = 4.15, p = .004 F(5, 41) = 1.25, p = .302 F(5, 41) = 5.29, p = .001 v, a, t0, and RT denote drift rate, threshold separation, non-decision time, and mean RTs, respectively. SE = standard error, CI = confidence interval. Adjusted R2 is given for linear regression models (v, a, t0, and RT); pseudo-R2 is given for beta-regression (accuracy). p denotes uncorrected p values; padj denotes p values corrected for multiple comparison using a Bonferroni–Holm correction procedure (Holm, 1979). Asterisks denote significant estimates with adjusted p values as *padj < .05, ***padj < .001. D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j f t . / l K a t t e t a l . 9 5 5 o n 0 5 M a y 2 0 2 1 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j f t . / o n 0 5 M a y 2 0 2 1 Figure 6. Participants’ drift rate and mean accuracy as a function of mean N2ac amplitude. Triangles represent younger participants (n = 21); squares represent older participants (n = 26). For the linear drift rate regression model, an adjusted response function describes the relationship between the fitted response and N2ac amplitudes, whereas the other predictors are averaged out by averaging the fitted values over the data used in the fit. Adjusted response data points are computed by adding the residual to the adjusted fitted value for each observation. For the accuracy beta regression model, the marginal effect of the interaction N2ac amplitude by age group is displayed, holding the other factors constant at an average value. to reveal a relation between RT and age group in the present regression model. Figure 6 visualizes the reported results for those outcomes that were significantly predicted by N2ac amplitudes. that the data is inconclusive with respect to age effects in the electrophysiological data. Age differences in behav- ioral performance are briefly reviewed below. DISCUSSION In this study, we investigated the contribution of post- stimulus alpha power lateralization and N2ac amplitudes to sound localization performance in a sample of younger and older adults. Both measures have been associated with the deployment of attention in auditory space. We hypothesized that if the cortical processes reflected by alpha lateralization and N2ac amplitudes contribute to successful target selection, their magnitudes should be related to the information accumulation process (i.e., drift rate; cf. diffusion model framework, as outlined in the Introduction) and in turn to localization accuracy and RTs. In fact, what we found only partially confirmed this hypothesis: N2ac amplitudes significantly predicted both drift rate and accuracy, whereas alpha lateralization was not associated with any of the behavioral outcomes. We thus proposed that N2ac and alpha lateralization re- flect distinct aspects of attentional orienting in auditory scenes. Classical frequentist inferential statistics suggested that the observed relationship did not depend on age and that both age groups showed comparable neural signa- tures. However, Bayesian alternatives to classical hypothe- ses testing raised doubts about these claims, suggesting Cocktail Party Sound Localization in Older and Younger Adults As expected, older adults showed fewer correct responses and slower RTs than younger adults. This is in line with the often-described difficulties of older people to follow a con- versation in noisy (“cocktail party”) environments, which depends on the integrity of both sensory and cognitive functions (Shinn-Cunningham, 2017). Declined perfor- mance in older adults in the present task is likely to be related to age-related deficits in concurrent sound segre- gation (Hanenberg, Getzmann, & Lewald, 2019; Alain & McDonald, 2007; Snyder & Alain, 2005). Traditionally, such deficits have been interpreted as a result of a gen- eral sensory-cognitive decline (e.g., Pichora-Fuller, Alain, & Schneider, 2017), assuming all aspects of processing in an experimental task to be globally slowed in aging adults (Myerson, Hale, Wagstaff, Poon, & Smith, 1990). The diffu- sion model allows to differentiate between different aspects of processing that might be affected by age (Ratcliff, Spieler, & McKoon, 2000): Consistent with previous results (Ratcliff, Thapar, & McKoon, 2003, 2011; Ratcliff et al., 2001), we found an increase in non-decision time for older adults. In addition, older participants varied more strongly in their non-decision time from trial to trial, indicating that this 956 Journal of Cognitive Neuroscience Volume 32, Number 5 process was noisier in older adults (Spaniol, Madden, & Voss, 2006). However, rather untypically, the two age groups did not differ in their threshold separation values. This contradicts the wide-spread assumption that older adults usually aim to minimize errors (leading to more con- servative response criteria) whereas younger adults focus on balancing speed and accuracy (Starns & Ratcliff, 2010). The observed lack of differences in response criteria between older and younger adults could be due to the relatively long response period in this study, potentially inducing a change in task goals in younger adults. Alternatively, as the corresponding BFs were rather inconclusive, we cannot exclude that the data are simply underpowered and therefore fail to reveal significant differences in our sample. Furthermore, supporting a line of evidence that showed differences in the rate of information accumulation in some contexts (Ratcliff et al., 2004, 2011; Spaniol et al., 2006), older adults had significantly decreased drift rates. Given the current state of research, the conditions under which drift rate decreases with age are still hard to grasp. Here, drift rate was significantly predicted by N2ac ampli- tudes. In participants with higher N2ac amplitudes (i.e., more negative difference waves) drift rates were higher, whereas participants with lower N2ac amplitudes tended to have lower drift rates. Hence, differences in drift rate may reflect the differences in the ability to extract rele- vant information from a perceptual scene (in this case, an array of concurrently presented sounds). In the fol- lowing section, we will discuss this relationship in more detail. N2ac Amplitudes Predict Drift Rate and Accuracy To date, little is known about the functional relevance of the N2ac component. The regression analysis conducted here revealed that N2ac amplitudes significantly pre- dicted variations in accuracy as well as drift rate, while they were unrelated to mean RTs, threshold separation, or non-decision time. These findings add to the sparse literature that has so far investigated the N2ac com- ponent in different contexts (Klatt et al., 2018b; Lewald et al., 2016; Gamble & Woldorff, 2015a, 2015b; Lewald & Getzmann, 2015; Gamble & Luck, 2011). In addition, to our best knowledge, this is the first study to show an N2ac component in a sample of older adults. Gamble and Luck (2011) originally proposed that the N2ac arises to resolve the competition between simultaneously pres- ent stimuli and reflects the attentional orienting toward a target. They further elucidated that this may be based on the biasing of neural coding toward the attended stimulus, as observed in the visual modality. In fact, the observed relationship of N2ac amplitudes and drift rate may support this line of reasoning: Drift rate conceptually reflects the quality of relevant information derived from sensory input that eventually drives the decision process (Ratcliff et al., 2000). Hence, the better participants may be able to re- solve competition between concurrent sounds by focusing on the target (i.e., N2ac amplitude), the better the quality of information that prompts participants to make a deci- sion (i.e., drift rate; or in other words, the higher the rate of evidence accumulation in favor of a given response). In turn, it logically follows that the better or more consis- tently participants are able to focus their attention onto a relevant target sound (i.e., N2ac amplitude), the higher their overall accuracy. Interestingly, in addition to the similar N2ac ampli- tudes for younger and older adults, we found no signifi- cant interactions between N2ac amplitudes and age, neither for accuracy nor for drift rate (cf. Table 1). This may suggest that the variances within age groups contrib- ute more strongly to the observed relationship than the variance between age groups. However, the difficulties of interpreting a null effect, such as a missing interaction with age, need to be considered as a caveat here. Al- though regression lines in Figure 6 show a trend toward an interaction of N2ac amplitude and age group, the cal- culated BFs (cf. Regression Analyses section) suggest the data to be insensitive to age group differences, providing no substantial evidence in favor of the null or alternative hypothesis. Nevertheless, one may raise the question, if lower N2ac amplitudes result in lower drift rates and decreased performance, why did older adults not show reduced N2ac amplitudes, given that they performed sig- nificantly worse than the younger adults? On the one hand, the well-pronounced N2ac component in older adults may, at least in part, have resulted from the re- cruitment of additional top–down resources to allow for more efficient target selection. This interpretation would be in line with the decline-compensation hypothesis (Cabeza, Anderson, Locantore, & McIntosh, 2002; for a re- view, see Schneider, Pichora-Fuller, & Daneman, 2010), proposing that age-related declines in peripheral and central auditory processing are compensated for by in- creased allocation of cognitive resources. Increases in at- tentional focusing, however, might not be sufficient to completely compensate for the reduced performance of the older group. On the other hand, we cannot exclude that we simply failed to find a significant difference in N2ac amplitudes due to a lack of power, as the calcula- tion of BFs provided no substantial evidence in favor of the null hypothesis. Is Poststimulus Alpha Power Lateralization Functionally Relevant? This study also investigated alpha lateralization as a mea- sure of attentional orienting within an auditory scene. Typically, alpha lateralization manifests in a bilateral de- crease of alpha power, which is more pronounced over the contralateral hemisphere (relative to a target or a cue). This spatially specific modulation of oscillatory activ- ity has been repeatedly associated with the top–down con- trolled voluntary allocation of attention (Ikkai et al., 2016; Haegens et al., 2011; Thut et al., 2006; Foxe, Simpson, Klatt et al. 957 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j f / t . o n 0 5 M a y 2 0 2 1 & Ahlfors, 1998). Here, we replicated this consistently observed response in the alpha frequency band in a sam- ple of younger and older participants who performed an auditory localization task, requiring them to indicate the location of a predefined target stimulus among three con- cordantly presented distractors. Our results suggested that older adults may, in principle, be able to recruit the same oscillatory mechanisms as younger adults when searching for a target among simultaneously present distractors (Klatt et al., 2018b). Although Bayesian statistics were in- decisive in whether the nonsignificant difference in alpha lateralization between age groups presents a true null effect, the preserved poststimulus alpha lateralization cor- roborated a number of studies, showing intact alpha later- alization in older adults when anticipating an upcoming (lateralized) stimulus (Heideman et al., 2018; Leenders, Lozano-Soldevilla, Roberts, Jensen, & De Weerd, 2018; Tune et al., 2018). However, recent studies did not find alpha lateralization in older adults, although they were still able to perform the task as well as their younger counter- parts (van der Waal, Farquhar, Fasotti, & Desain, 2017; Hong et al., 2015). This poses the question to what extent lateralized alpha dynamics are functionally relevant for behavior. It is relatively undisputed that alpha power lateraliza- tion tracks the locus and timing of spatial attention (Bae & Luck, 2018; Foster et al., 2017; Samaha, Iemi, & Postle, 2017). In addition, a growing body of evidence supports the notion that the alpha rhythm as a correlate of spatial attention, so far predominantly investigated in the visual attention literature, analogously operates in dif- ferent modalities (Klatt et al., 2018a, 2018b; Wöstmann et al., 2016, 2018; Thorpe, D’Zmura, & Srinivasan, 2012; Haegens et al., 2011). Yet, what remains a matter of de- bate is (1) how alpha power lateralization aids selective spatial attention and (2) whether it reflects a necessary prerequisite for successful behavioral performance. Regarding the how, two prevailing views exist: The gating by inhibition theory, proposed by Jensen and Mazaheri (2010), suggested that the relative increase of alpha power over the ipsilateral hemisphere inhibits re- gions processing irrelevant information. Alternatively, it has been suggested that the relative decrease of alpha power over the contralateral hemisphere results in in- creased cortical excitability, allowing for enhanced pro- cessing of the targets (Noonan et al., 2016; Yamagishi et al., 2005). Both mechanisms are not necessarily mutu- ally exclusive. In fact, Foster and Awh (2019) just recently pointed out that a lot of the empirical evidence is com- patible with either the target enhancement or the distrac- tor suppression account. Recent evidence suggested that both mechanisms might independently contribute to at- tentional orienting (Schneider et al., 2019). In line with those latter findings, Capilla, Schoffelen, Paterson, Thut, and Gross (2014) proposed distinct sources and behav- ioral correlates for the ipsilateral and contralateral por- tion of the alpha power signal. Adressing the second question—Does alpha lateraliza- tion reflect a necessary prerequisite for successful behav- ioral performance?—a range of spatial-cueing studies has provided compelling evidence showing behavioral per- formance to be predicted by the degree of alpha lateral- ization (Haegens et al., 2011; Kelly et al., 2009; Thut et al., 2006). On the contrary, our findings question the notion that alpha power lateralization reflects a behaviorally rel- evant attentional mechanism: Surprisingly, we did not find any association between alpha lateralization and dif- fusion model parameters, mean RTs, or accuracy. This could be explained by the fact that this study differed from the majority of previous studies in that it investi- gated alpha power modulations following stimulus presen- tation. That is, although alpha lateralization may in fact be necessary to successfully shift one’s attention in anticipa- tion of an upcoming stimulus, it does not appear to be a required neural response in the attentional processing following the presentation of a multisound array. This is in line with the proposal previously made by van Ede et al. (2014), who similarly concluded that the relevance of attentional modulations might be “restricted to situa- tions in which attention influences perception through an- ticipatory processes” (p. 139). However, in contrast to our results, these authors found that alpha lateralization was completely abolished during the processing of an ongoing tactile stimulus. Alternatively, the lack of a relationship with behavioral performance may be due to the fact that we calculated a relative measure of alpha amplitudes, that is, the differ- ence between ipsilateral and contralateral alpha power. In a cued somatosensory detection task, van Ede et al. (2014) found only contralateral alpha power amplitudes to be related to tactile detection performance, whereas fluctuations in the contralateral minus ipsilateral differ- ence failed to predict performance. Similarly, other stud- ies using a relative index of alpha power modulations did not find a strong relationship with behavioral performance (Tune et al., 2018; Limbach & Corballis, 2017). These find- ings or rather null findings might strengthen the emerging view that both target enhancement (i.e., contralateral alpha power decrease) and distractor suppression (i.e., ipsilateral alpha power increase) differentially contribute to task performance (Schneider et al., 2019) and that this should be taken into account when analyzing the contribu- tion of alpha power oscillations to behavior. Yet, it should be noted that there are studies that successfully demon- strated an effect of the relative strength of alpha lateraliza- tion on task performance (Haegens et al., 2011; Kelly et al., 2009), suggesting the reasons for those diverging results are likely to be more complex than just a methodological artifact. Also, it has to be noted that the respective BFs (below 1, but above 0.33) were rather indecisive; thus, although our data do not seem to support a significant relationship between alpha lateralization and behavioral performance, they cannot provide compelling evidence for a true null effect either. 958 Journal of Cognitive Neuroscience Volume 32, Number 5 D o w n l o a d e d l l / / / / j f / t t i t . : / / f r o m D o h w t n t p o : a / d / e d m i f r t o p m r c h . p s i l d v i r e e r c t c . m h a i e r d . u c o o m c n / j a o r t c i c n e / - a p r d t i 3 2 c l 5 e 9 - 4 p 5 d f 2 0 / 1 3 3 2 3 / 6 5 2 / 9 o 4 c 5 n / _ a 1 _ 8 0 6 1 1 5 7 2 7 5 1 p / d j o b c y n g _ u a e _ s 0 t 1 o 5 n 2 0 5 8 . p S d e f p e b m y b e g r u 2 e 0 s 2 t 3 / j t . f / o n 0 5 M a y 2 0 2 1 Critically, one question remains unanswered: If alpha lateralization is not a necessary component of poststimu- lus attentional processing in an auditory scene, what does it reflect? It might be that poststimulus alpha lateraliza- tion is an “optional response” that may result in more ef- fective target enhancement or distractor inhibition, when a specific strategy is applied. Hence, because of different strategies used by different participants, there might be no overall relationship between alpha lateralization and be- havior when analyzed across all participants (Limbach & Corballis, 2017; Rihs, Michel, & Thut, 2009). Alterna- tively, as shown in a previous study using a very similar task design, auditory poststimulus alpha lateralization might be more closely related to the spatial specificity of the task (Klatt et al., 2018b). In the latter study, a lateralization of alpha power was only evident when par- ticipants were instructed to localize (instead of to simply detect) a target sound within a multisound array. Hence, we proposed that, in poststimulus attentional processing, the lateralization of alpha power indexes the access to a spatiotopic template that is used to generate a spatially specific response (Klatt et al., 2018b). If alpha lateraliza- tion reflects such a process, one may argue that there should be no or a substantially reduced alpha lateraliza- tion in incorrect trials, and thus, alpha lateralization should in fact be associated with behavioral performance. Such differences in ALI amplitudes for correct versus incorrect trials have in fact been reported (Tune et al., 2018; Wöstmann et al., 2016, 2018). The fact that we cal- culated ALIs based on each participant’s mean alpha power in correctly answered trials may explain why we fail to capture such differences for a rather coarse, dichotic measure of behavioral performance such as accuracy. Conclusion In summary, fluctuations in N2ac amplitude predicted the rate of information accumulation (i.e., drift rate) as well as overall accuracy. We conclude that the N2ac component reflected the participants’ ability to resolve competition be- tween co-occurring sounds by focusing on the target. This, in turn, determined the quality of the information accu- mulated during the decision-making process and thereby affected overall accuracy levels. In contrast, alpha lateraliza- tion was unrelated to behavioral performance, suggesting that successful attentional orienting within an auditory scene (as opposed to in anticipation of an upcoming target sound), does not rely on alpha lateralization. Our findings strengthen the proposal that alpha lateralization is not spe- cific to the visual domain but may reflect a supramodal at- tentional mechanism that generalizes to the auditory domain (Thorpe et al., 2012; Kerlin, Shahin, & Miller, 2010). Yet, we highlight that it is important to distinguish between cue-related, anticipatory modulations of alpha power and poststimulus alpha power lateralization. Acknowledgments This work was supported by the German Federal Ministry of Education and Research in the framework of the TRAIN-STIM project (Grant Number 01GQ1424E). The authors are grateful to David Schmude, Jonas Heyermann, Stefan Weber, and Michael- Christian Schlüter for their help in running the experiments; to Peter Dillmann and Tobias Blanke for preparing software and parts of the electronic equipment; and to two anonymous reviewers for valuable comments on a previous version of this manuscript. Reprint requests should be sent to Laura-Isabelle Klatt, Leibniz Research Centre for Working Environment and Human Factors, Ardeystraße 67, 44139 Dortmund, Germany, or via e-mail: klatt@ ifado.de. REFERENCES Alain, C., & McDonald, K. L. (2007). 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