Recent findings suggest that a salient, irrelevant sound attracts attention to its location involuntarily and facilitates processing of a colocalized visual event [McDonald, J. J., Störmer, V. S., Martinez, A., Feng, W. F., & Hillyard, S. A. Salient sounds activate human visual cortex automatically. Journal of Neuroscience, 33, 9194–9201, 2013]. Associated with this cross-modal facilitation is a sound-evoked slow potential over the contralateral visual cortex termed the auditory-evoked contralateral occipital positivity (ACOP). Here, we further tested the hypothesis that a salient sound captures visual attention involuntarily by examining sound-evoked modulations of the occipital alpha rhythm, which has been strongly associated with visual attention. In two purely auditory experiments, lateralized irrelevant sounds triggered a bilateral desynchronization of occipital alpha-band activity (10–14 Hz) that was more pronounced in the hemisphere contralateral to the sound's location. The timing of the contralateral alpha-band desynchronization overlapped with that of the ACOP (∼240–400 msec), and both measures of neural activity were estimated to arise from neural generators in the ventral-occipital cortex. The magnitude of the lateralized alpha desynchronization was correlated with ACOP amplitude on a trial-by-trial basis and between participants, suggesting that they arise from or are dependent on a common neural mechanism. These results support the hypothesis that the sound-induced alpha desynchronization and ACOP both reflect the involuntary cross-modal orienting of spatial attention to the sound's location.
Sudden sounds can influence perceptual processing of subsequent visual stimuli that occur in the same region of space. For example, a salient noise burst improves the detection of a faint visual stimulus that is presented in rapid succession at the same location (McDonald, Teder-Sälejärvi, & Hillyard, 2000; Dufour, 1999). Salient sounds can also improve the discrimination of subsequent visual stimuli at the sound's location (Feng, Störmer, Martinez, McDonald, & Hillyard, 2014; Frassinetti, Bolognini, & Ladavas, 2002), increase their apparent luminance contrast (Störmer et al., 2009), and accelerate their perceived timing (McDonald, Teder-Sälejärvi, Di Russo, & Hillyard, 2005). These behavioral effects are accompanied by increases in the amplitude of the neural response elicited in the occipital cortex by the visual stimulus (for reviews, see McDonald, Whitman, Störmer, & Hillyard, 2013; McDonald, Green, Störmer, & Hillyard, 2012). Interestingly, these cross-modal cueing effects are elicited by sounds that are not predictive of the location of the subsequent visual target and thus may be considered involuntary or exogenous.
To investigate the neural basis of this cross-modal cueing effect, McDonald, Störmer, Martinez, Feng, and Hillyard (2013) recently examined the ERPs that were elicited by lateralized nonpredictive peripheral noise bursts. These sounds were found to elicit an enlarged positive deflection over contralateral visual cortex beginning ∼200 msec after sound onset and lasting for about 250 msec. This positivity was termed the auditory-evoked contralateral occipital positivity (ACOP). Surprisingly, the ACOP appeared not only in cross-modal tasks in which the sounds were followed by visual targets on most trials (Experiment 1) but also across a series of purely auditory tasks that involved no visual stimuli (Experiments 2–4). These findings indicate that peripheral, salient sounds activated contralateral visual cortex involuntarily, regardless of their task relevance and regardless of the target's modality (visual or auditory). The magnitude of the ACOP was found to correlate with participants' contrast judgments of subsequent visual stimuli (McDonald, Störmer, et al., 2013) as well as with participants' visual discrimination performance (Feng et al., 2014) in the cross-modal tasks. On the basis of these results, McDonald and colleagues (McDonald, Whitman, et al., 2013; McDonald et al., 2012) proposed that the peripheral, salient sound acts as an exogenous cue that attracts visual attention to its location involuntarily and that the ACOP reflects a facilitatory process that enables the subsequent enhancement of visual processing in the hemisphere contralateral to the location of a salient sound.
This study further tested the hypothesis of cross-modal attention capture by examining oscillatory brain activity elicited by task-irrelevant sounds, in particular, the alpha rhythm (∼8–14 Hz). The occipital alpha rhythm has been tightly linked with visual processing and is strongly modulated by voluntary shifts of visual selective attention (Payne & Sekuler, 2014; Klimesch, 1999, 2012; Foxe & Snyder, 2011; Jensen & Mazaheri, 2010; Klimesch, Sauseng, & Hanslmayr, 2007). This linkage between the alpha rhythm and voluntary shifts of attention is illustrated by numerous studies that examined oscillatory brain activity in tasks that used symbolic cues to direct participants' attention to the expected location of a subsequent visual target. When the cue directed participants' attention to one side of the visual field, alpha-band activity was found to decrease in the contralateral hemisphere and/or to increase in the ipsilateral hemisphere over occipital cortex (for a review, see Marshall, O'Shea, Jensen, & Bergmann, 2015; Capilla, Schoffelen, Paterson, Thut, & Gross, 2014; Händel, Haarmeier, & Jensen, 2011; Hanslmayr, Gross, Klimesch, & Shapiro, 2011; Green & McDonald, 2010; Kelly, Gomez-Ramirez, & Foxe, 2009; van Gerven & Jensen, 2009; Siegel, Donner, Oostenveld, Fries, & Engel, 2008; Rihs, Michel, & Thut, 2007; Thut, Nietzel, Brandt, & Pascual-Leone, 2006; Yamagishi et al., 2003; Worden, Foxe, Wang, & Simpson, 2000). These lateralized changes in alpha-band activity led to the proposal that alpha-band event-related synchronization reflects the active inhibition of task-irrelevant regions of visual space, whereas alpha-band event-related desynchronization reflects the release of inhibition and enhanced processing of stimuli at the attended location (Payne & Sekuler, 2014; Klimesch, 2012; Foxe & Snyder, 2011; Jensen & Mazaheri, 2010; Klimesch et al., 2007). These findings have shown that modulations of the alpha rhythm provide a reliable index of the spatial allocation of visual attention and, as such, can be used to evaluate the hypothesis that visual attention is captured involuntarily by salient but irrelevant sounds.
This study investigated whether salient, irrelevant sounds trigger changes in the alpha-band rhythm over occipital cortex in parallel with the ACOP. If such sounds indeed modulated alpha-band oscillatory activity in a spatially selective manner, this would support the hypothesis that salient sounds capture attention involuntarily and consequently bias visual processing. Of particular interest was whether the sound would produce a lateralized increase in alpha-band amplitude (synchronization), a lateralized decrease in alpha amplitude (desynchronization), or both. These respective patterns of alpha-band modulation would provide evidence as to whether a salient sound biases sensory processing by means of suppressing or enhancing cortical processing. We analyzed EEG and ERP data from two experiments initially reported by McDonald, Störmer, et al. (2013), in which only auditory stimuli were presented. Participants made judgments about target tones and were asked to ignore task-irrelevant noise bursts that appeared at random intervals on the left or right side within the stimulus sequence (Figure 1). While participants performed these tasks, electrophysiological responses triggered by the task-irrelevant noise bursts were examined in both the time domain and in the time frequency domain. The noise bursts elicited a contralateral positive deflection in the ERP waveform over occipital cortex (the ACOP), as previously reported (McDonald, Störmer, et al., 2013). Critically, the ACOP was accompanied by alpha-band desynchronization that was largest over the occipital scalp contralateral to the sound's location. To investigate the relationship between the ACOP and modulations in alpha rhythm, correlations between ACOP magnitude and alpha-band desynchronization were examined both within participants on a trial-by-trial basis and between participants.
Thirteen participants completed Experiments 1 and 2. Data from one of the participants had to be excluded because of excessive artifacts in the EEG signal (>30% trials affected). Of the remaining 12 participants (seven women, 18–28 years), all participants reported normal or corrected-to-normal vision and normal hearing. The study was approved by the Human Research Protections Program of the University of California San Diego.
Stimuli and Procedure
Stimuli and procedure have previously been described in detail (McDonald, Störmer, et al., 2013; Experiments 3 and 4). The experiments were conducted in a dimly lit, electrically shielded, soundproof booth. All sounds were delivered from two loudspeakers mounted to the left and right sides of a computer monitor at an eccentricity of 25°. A small black cross (0.5° × 0.5°) was presented on a gray background (10 cd/m2) in the center of the screen. Participants were instructed to maintain fixation on the central cross throughout each experimental block.
In both experiments (here labeled Experiments 1 and 2), task-irrelevant bursts of pink noise (83 msec; 0.5–15 kHz, 78 dB SPL) and target tones were presented in random order with variable ISIs (2000–2500 msec). As illustrated in Figure 1, irrelevant noise bursts were presented randomly on the left or right side, and target tones were presented either bilaterally at the left and right locations (Experiment 1) or at a central location (Experiment 2).
In Experiment 1, the target tones consisted of a bilateral pair of tones that were presented in rapid succession (8-msec ISI) to the left and right speakers. One of the tones was high pitched (2100 Hz), and the other was low pitched (315 Hz). Each tone lasted 53 msec, including 5-msec rise and fall times. The 2100-Hz tone was fixed at 68 dB SPL, and the 315-Hz tone was varied in loudness from trial to trial in five steps (61, 66, 68, 70, or 73 dB SPL). To adjust for apparent loudness differences between high- and low-frequency tones, each participant adjusted the loudness of the 68-dB 315-Hz tones to appear equally loud as the 2100-Hz tones before the experiment using a matching procedure. During the experiment, participants were instructed to report the pitch (high vs. low) of the peripherally presented tone that appeared louder in volume by pressing one of two buttons. Response buttons were counterbalanced between participants. In Experiment 2, a single 1000-Hz target tone was delivered with equal intensity from both speakers so that it was perceived to come from the center of the screen. Here, participants were instructed to simply press a button each time they heard the central target tone. In both experiments, the lateralized noise bursts were always task irrelevant, and participants never responded to them. Each experiment consisted of five blocks of 120 stimuli each; overall, each experiment presented 360 irrelevant noise bursts (180 left, 180 right) and 280 target tones in random order (∼56% lateralized sounds and 44% target tones). Thus, the peripheral noise bursts were not predictive of target occurrence. The order of experiments was counterbalanced across the 12 participants.
Electrophysiological Recordings and Analysis
EEG was recorded continuously from 62 tin electrodes using a modified 10–10 montage that included five nonstandard sites inferior to the standard occipital locations (McDonald, Teder-Sälejärvi, Di Russo, & Hillyard, 2003). Electrode impedances were kept below 5 kΩ. Scalp signals were amplified with a gain of 10,000 and band-pass filtered online from 0.1 to 80 Hz. Signals were digitized at a rate of 500 Hz, and the right mastoid served as the reference during the recording. The horizontal EOG was recorded bipolarly from two electrodes positioned lateral to the external canthi. Artifact rejection was performed to remove epochs that contained horizontal eye movements, blinks, and amplifier blocking. Horizontal eye movements were detected on the horizontal EOG channel, and blinks were detected by electrodes below the left eye and at FP1, located above the left eye. All EEG analyses examined the electrical activity elicited by the task-irrelevant noise bursts that were presented at the left or right of the video monitor. The artifact-free data were analyzed both in the time domain (ERPs) and in the time frequency domain (using wavelets).
ERPs elicited by the left and right noise bursts were averaged separately and were then collapsed across sound position (left, right) and lateral position of the electrode (left, right) to obtain waveforms recorded contralaterally and ipsilaterally with respect to the sound. The averaged waveforms were digitally low-pass filtered (3-dB point at 25 Hz) and digitally rereferenced to the average of left and right mastoids. The ACOP was measured for each participant as the contralateral minus ipsilateral amplitude with respect to a 100-msec prestimulus baseline at two posterior electrode sites (PO7/PO8) with a measurement window of 260–360 msec after the onset of the sound (see McDonald, Störmer, et al., 2013). ERP analysis was carried out using in-house software (ERPSS, University of California San Diego).
For the time frequency analysis, scalp channels were rereferenced to the average of left and right mastoids, segmented into −400- to +900-msec epochs with respect to onset of the irrelevant noise bursts and analyzed on a single-trial basis via complex Morlet wavelets before averaging, following the methods of Lakatos et al. (2004) and Torrence and Compo (1998). Single-trial spectral amplitudes were calculated via six-cycle wavelet at 76 different frequencies increasing logarithmically from 1 to 64 Hz separately for each electrode, time point (every 2 msec), sound location (left, right), and participant. Single-trial spectral amplitudes were then averaged across trials separately for each condition and participant, and a mean baseline (−100 to 0 msec) was subtracted from each time point for each frequency separately (Pitts, Padwal, Fennelly, Martínez, & Hillyard, 2014). To ensure that the results did not depend on the particular choice of number of cycles or the baseline period of the wavelet analysis, all analyses were repeated using four-cycle wavelet and a baseline period of −300 to −200 msec. The pattern of results was the same, as can be seen in Supplementary Figures S1 and S2. Mean spectral amplitudes elicited by the left and right noise bursts were then collapsed across sound position (left, right) and lateral position of the electrode (left, right) to reveal sound-induced modulations ipsilateral and contralateral to the noise bursts. The main analysis was focused on alpha-band amplitude modulations over the range of 10–14 Hz at the same electrode sites (PO7/PO8) and during the same time interval (260–360 msec) as the ACOP. Alpha-band amplitude modulations were also measured between 8 and 10 Hz, but no reliable differences were found between contralateral and ipsilateral sites in this lower band (see Results). Data processing was carried out using EEGLAB (Delorme & Makeig, 2004) and ERPLAB (Lopez-Calderon & Luck, 2014) tool boxes and custom-written scripts in MATLAB (The MathWorks, Natick, MA).
The significance levels of the mean ERP amplitudes and mean alpha-band amplitude modulations were evaluated separately for each experiment using paired t tests with the factor Electrode lateralization (contralateral vs. ipsilateral relative to the sound location).
Topographical Mapping and Source Analysis
To examine the topographical scalp distribution of the ACOP and sound-induced decreases in alpha-band amplitude, isopotential contour maps were created for the contralateral and ipsilateral ERP activity as well as contralateral and ipsilateral alpha-band amplitude (10–14 Hz). Maps were plotted for the time interval 260–360 msec after sound onset with respect to the prestimulus baseline. In addition, maps were created from the contralateral-minus-ipsilateral amplitude differences. The resulting amplitude differences were assigned to electrodes on the right side of the head, and voltages at midline electrode were artificially set to zero (McDonald et al., 2013; Störmer et al., 2009). Scalp topographies of the ACOP and contralateral-minus-ipsilateral alpha-band amplitude modulations were compared by ANOVA following the normalization (i.e., scaling) procedure of McCarthy and Wood (1985).
Source locations of the neural generators of the ACOP and the contralateral-minus-ipsilateral alpha decrease (measured with respect to the prestimulus baseline) were estimated by distributed linear inverse solutions based on a local autoregressive average (LAURA; Grave de Peralta Menendez, Murray, Michel, Martuzzi, & Gonzalez Andino, 2004), using the CARTOOL software (brainmapping.unige.ch/cartool.php). LAURA estimates 3-D current density distributions using a realistic head model with a solution space of 4024 nodes equally distributed within the gray matter of the average template brain of the Montreal Neurological Institute. LAURA makes no a priori assumptions regarding the number of sources or their locations and can deal with multiple, simultaneously active sources. The estimated current source distributions were transformed into a standardized coordinate system and projected onto a structural brain image supplied by MRIcro (Rorden & Brett, 2000) using AFNI software (Cox, 1996) to visualize the anatomical brain regions giving rise to the ACOP and sound-induced alpha desynchronization.
Relation between ACOP and Sound-induced Alpha-band Desynchronization
To examine the relation between the lateralized ACOP in the ERP waveform and the lateralized alpha-band decrease induced by the sounds, several additional analyses were performed. First, the time course of the ACOP (contralateral-minus-ipsilateral waveform) was compared with the time course of the alpha-band amplitude decrease (contralateral-minus-ipsilateral alpha-band amplitude). Mean amplitudes of the ACOP and alpha-band decrease (with respect to the prestimulus baseline) were measured for each participant in successive 20-msec intervals starting at sound onset (0 msec). The significance of each averaged time bin was tested with a one-tailed t test to identify the time interval at which the ACOP and decrease in alpha-band activity emerged. The onset time for each measure was taken as the first significant 20-msec interval that was followed by at least five more successive significant intervals. Second, to test whether ACOP amplitudes were correlated with the magnitude of the contralateral alpha-band decrease on a trial-by-trial basis, all trials (i.e., lateralized sound presentations) were sorted from having low to high ACOP amplitude values, separately for each participant and each experiment. The sorted trials were divided in half (median split), and the alpha-band decreases at contralateral and ipsilateral scalp sites were then calculated for each half, resulting in mean alpha-band decrease values for trials with high and low ACOP amplitude. The magnitudes of the contralateral-minus-ipsilateral alpha-band decrease for low versus high ACOP trials were compared by means of paired t tests. Third, to examine whether those individuals with larger ACOP amplitudes also elicited greater alpha-band decreases, ACOP amplitudes and contralateral-minus-ipsilateral alpha-band decrease values were determined for each individual, and a between-subject correlation was calculated for each experiment and over both experiments.
As previously reported (McDonald, Störmer, et al., 2013), the task-irrelevant noise bursts elicited an ACOP in both experiments (Figures 2A and 3A). ERP waveforms recorded over posterior scalp sites contralateral to the sound became more positive relative to the waveforms ipsilateral to the sound starting at about 240 msec and lasting for about 200 msec. On the basis of our previous study (McDonald, Störmer, et al., 2013), the ACOP was measured as the contralateral-minus-ipsilateral difference amplitude measured over lateral posterior scalp sites (PO7/PO8) over the time interval of 260–360 msec. This measure showed that the ACOP was reliable across both experiments (Experiment 1: t(11) = 3.88, p = .003, η2 = 0.60; Experiment 2: t(11) = 4.70, p = .001, η2 = 0.66). The earlier negative peak at around 100 msec (also larger contralaterally with respect to the sound) was shown by McDonald et al. (2013) to originate from auditory cortex; this peak represents the far-field (posterior) recording of the well-known N1 component of the auditory evoked potential.
As shown in Figures 2B and 3B, the noise bursts also triggered changes in the oscillatory alpha band. Alpha-band amplitude decreased bilaterally after the irrelevant sounds, with a more pronounced decrease in the hemisphere contralateral to the sound's location. Mean alpha-band (10–14 Hz) amplitude modulations were measured at the same posterior electrode sites (PO7/PO8) and during the same time interval (260–360 msec) as the ACOP (see also Figure S1A). Statistical analysis confirmed that the decrease in alpha-band amplitude was greater contralaterally to the sound's location than ipsilaterally (see Figures 2B and 3B; Experiment 1: t(11) = −2.54, p = .03, η2 = 0.36; Experiment 2: t(11) = −2.60, p = .02, η2 = 0.37). When tested against the prestimulus baseline, both ipsilateral and contralateral alpha-band amplitudes showed decreases (Experiment 1, ipsilateral: t(11) = −1.89, p = .08, η2 = 0.21; contralateral: t(11) = −3.80, p = .003, η2 = 0.56; Experiment 2, ipsilateral: t(11) = −2.64, p = .02, η2 = 0.38; contralateral: t(11) = −3.12, p = .009, η2 = 0.47). For the lower alpha-band range (8–10 Hz), the difference between contralateral and ipsilateral alpha-band amplitude was not reliable (Experiment 1: p = .15; Experiment 2: p = .26). Thus, all subsequent analyses focused on the higher alpha band of 10–14 Hz.
As can be seen in Figures 2 and 3, the contralateral-minus-ipsilateral alpha-band decrease and the ACOP showed similar scalp distributions with a focus over contralateral occipital scalp. Although the ACOP distribution appeared to be centered over more ventral regions than the alpha decrease, statistical comparisons of the scalp topographies showed no significant difference between the two (Electrodes × Measure interaction in Experiment 1: F(1, 24) = 1.06, p = .39; Experiment 2: F(1, 24) = 1.39, p = .12). These sharply focused scalp distributions suggest that both the lateralized alpha decrease and the ACOP arise from posterior visual cortex.
To gain a better understanding of the neural sources underlying ACOP and contralateral alpha-band decrease, the neural sources of the contralateral-minus-ipsilateral waveforms and the contralateral-minus-ipsilateral alpha-band activity were estimated using distributed source analysis approach (LAURA; Grave de Peralta Menendez et al., 2004). As depicted in Figures 2 and 3, the LAURA solutions showed strong, overlapping sources of ACOP and alpha-band decrease in ventral-occipital cortex that were consistent across both experiments (Talairach coordinates of the peak activity: Experiment 1, ACOP: x = 22, y = −84, z = −9; alpha decrease: x = 33, y = −73, z = −8; Experiment 2, ACOP: x = 28, y = −78, z = −9; alpha decrease: x = 34, y = −70, z = −7). In addition to these well-defined sources in occipital cortex, LAURA estimated weaker sources in the STS (x = 52, y = −46, z = 20) and the middle temporal sulcus (x = 50, y = −41, z = −8) in both experiments. Given these additional source estimates, we cannot eliminate the possibility that some alpha decrease was also taking place in auditory regions. However, it is evident that the principal sources lie in visual cortex.
Figure 4 displays the superimposed time courses of the ACOP (contralateral-minus-ipsilateral difference waveform) and the alpha-band decrease (contralateral-minus-ipsilateral alpha-band amplitude), separately for each experiment. In Experiment 1 (Figure 4A), the ACOP became significant starting at 220 msec and remained so until 420 msec (all ps < .05). In contrast, the contralateral alpha-band decrease was significant over the interval of 160–340 msec but showed a more sustained time course than the ACOP and became significant again at 520–600 msec (all ps < .05). In Experiment 2 (Figure 4B), the ACOP was significant between 240 and 500 msec after sound onset, whereas the decrease in alpha band became significant somewhat later, between 280 and 520 msec after sound onset (all ps < .05). Overall, the ACOP and the lateralized decrease in alpha-band activity were substantially overlapped in time, although their time courses took different forms in Experiment 1. The timing of this auditory-induced alpha decrease was similar when evaluated with a four-cycle wavelet using an earlier baseline period of −300 to −200 msec relative to the sound onset (see Figure S1B). In this analysis, however, the time ranges of significant alpha decrease were more restricted in both experiments in relation to those obtained with the six-cycle wavelet.
To further examine the relationship between ACOP and the lateralized alpha decrease, trials were sorted based on their ACOP amplitude, and the magnitude of the lateralized decrease in alpha-band amplitude was calculated for trials with high and low ACOP, respectively. Following this median split, trials in which the sounds elicited a larger ACOP showed a larger sound-induced decrease in lateralized (contralateral minus ipsilateral) alpha-band amplitude (Figure 5A and B). As shown in Figure 5C, this relation between ACOP and alpha-band decrease was reliable across both experiments (Experiment 1: t(11) = 2.23, p = .03, η2 = 0.31; Experiment 2: t(11) = 2.70, p = .02, η2 = 0.39). This relation was also reliable in the analysis with a four-cycle wavelet and a −300- to −200-msec baseline (Figure S2A and B).
Figure 5D depicts the between-subject correlations, revealing that participants who showed larger ACOPs in response to the irrelevant sounds also elicited stronger contralateral alpha-band decreases. This relation was evident across both experiments (Experiment 1: r(10) = −.56, p = .05; Experiment 2: r(10) = −.69, p = .01; both experiments collapsed: r(22) = −.59, p = .002). We found similar correlations when the alpha decrease was quantified with the four-cycle wavelet and baseline of −300 to −200 msec (see Figure S2C; Experiment 1: r(10) = −.47, p = .10; Experiment 2: r(10) = −.60, p = .03; both experiments collapsed: r(22) = −.49, p = .01).
Together, these results suggest a strong connection between the ACOP and the lateralized decrease in alpha-band activity after the salient but irrelevant peripheral sound.
This study investigated whether a salient, peripheral sound influences visual processing by modulating the oscillatory alpha rhythm, an important neural marker of visual selective attention (see Introduction). In two separate experiments, we found that peripheral sounds triggered a bilateral desynchronization of alpha-band activity over occipital scalp, which was more pronounced over the hemisphere contralateral to the sound's location. This contralaterally dominant alpha-band desynchronization was paralleled by the previously described ACOP, a positive deflection in the ERP waveform that is also larger over the hemisphere contralateral to the sound's location. Interestingly, these sound-induced modulations of visual cortex activity occurred although no visual stimuli were presented and the lateralized sounds were not relevant to the ongoing task. The finding that the ACOP was paralleled by a contralaterally dominant desynchronization of the occipital alpha rhythm provides further support for our hypothesis (McDonald, Störmer, et al., 2013; McDonald, Whitman, et al., 2013) that salient sounds provoke an involuntary orienting of spatial attention to the sound's location and that such orienting has a multimodal influence on sensory processing.
Occipital Alpha-band Desynchronization Reflects Involuntary Attention
Effects of attention on sensory processing have been associated with changes in the oscillatory alpha rhythm (∼8–14 Hz), both in the visual and auditory modalities. These changes in alpha-band activity have been interpreted as the consequence of top–down attentional control operations that selectively bias processing in early sensory cortices in favor of task-relevant information. In studies of voluntary attention, changes in alpha-band activity have been observed in anticipation of both visual (Payne & Sekuler, 2014; Klimesch, 2012; Foxe & Snyder, 2011; Jensen & Mazaheri, 2010) and auditory (Mazaheri et al., 2014; Müller & Weisz, 2012; Banerjee, Snyder, Molholm, & Foxe, 2011; Bastiaansen & Brunia, 2001) target stimuli. In many of these studies, the degree of alpha reduction (i.e., “blocking” or “desynchronization” of the alpha rhythm) after a spatial cue was correlated with improved perceptual processing of visual events at the attended location (e.g., Mazaheri et al., 2014; van Dijk, Schoffelen, Oostenveld, & Jensen, 2008; Yamagishi, Callan, Anderson, & Kawato, 2008; Hanslmayr et al., 2007; Thut et al., 2006; Ergenoglu et al., 2004). These alpha-band modulations were generally larger over sensory cortex, and their specific scalp distributions depended on the sensory modality of the task. For example, in a recent study, participants were cued to either attend to visual or auditory stimuli, and alpha power was found to decrease over occipital cortex during the visual discrimination task and over temporal regions during the auditory discrimination task (Mazaheri et al., 2014).
In contrast to these studies of voluntary attention, here, we found that an auditory stimulus triggered a decrease in alpha-band oscillatory activity over occipital cortex, even in tasks that solely presented auditory stimuli. This points to a clear distinction between the effects of voluntary and involuntary orienting of attention. Voluntary orienting of attention influences cortical processing in a task- and modality-specific manner, whereas the involuntary orienting of attention triggered by a salient sound influences cortical processing in the visual processing pathways. Furthermore, the present findings indicate that alpha-band modulations do not represent a mechanism uniquely associated with the top–down, voluntary allocation of attention but reflect a more general sensory biasing response that can be triggered reflexively (see also Mlynarski, Freigang, Bennemann, Stohr, & Rubsamen, 2014).
Enhancement versus Suppression of Visual Processing
Interestingly, alpha-band activity was reduced in both hemispheres after the irrelevant, nonpredictive sounds, with a greater reduction in the hemisphere contralateral to the sound. Relative decreases and increases of local alpha-band activity have been linked to the enhancement and active suppression of sensory information, respectively (reviewed in Klimesch, 2012). Several studies investigating the link between alpha-band activity and anticipatory attention have used spatially informative symbolic cues to direct attention voluntarily to a specific location before a task-relevant target. In these studies, alpha-band power was typically found to decrease in the hemisphere contralateral to the cued location and to increase in the hemisphere ipsilateral to the cued location in anticipation of an incoming visual target (Payne, Guillory, & Sekuler, 2013; Klimesch, 2012; Foxe & Snyder, 2011; Gomez-Ramirez et al., 2011; Händel et al., 2011; Hanslmayr et al., 2007, 2011; Green & McDonald, 2010; Jensen & Mazaheri, 2010; Kelly, Lalor, Reilly, & Foxe, 2006; Thut et al., 2006; Worden et al., 2000). Thus, the contralateral decrease of alpha-band activity has been linked to preparatory activity to facilitate the processing of the expected stimulus, whereas the ipsilateral increase of alpha-band activity has been associated with the active inhibition of sensory inputs at the to-be-ignored location (Klimesch, 2012; Gomez-Ramirez et al., 2011; Händel et al., 2011; Hanslmayr et al., 2007, 2011; Jensen & Mazaheri, 2010; Klimesch et al., 2007; Kelly et al., 2006; Thut et al., 2006; Worden et al., 2000). If such an ipsilateral alpha increase is indeed a sign of suppressed processing of the unattended visual field, as the above-cited articles have proposed, this would imply that no such suppression occurred in this study.
Evidence for such a selection-without-suppression hypothesis was recently obtained by Feng et al. (2014) in an ERP study where a nonpredictive, lateralized auditory cue was followed by a visual target on the same side (valid trial) or the opposite side (invalid trial). On all trials, participants were required to discriminate the masked target letter (T vs. L). It was found that the ACOP after the sound was larger preceding correctly discriminated targets on validly cued trials but not on invalidly cued trials. This finding is consistent with the hypothesis that the attentional process associated with the ACOP facilitates processing at the validly cued location but does not suppress the processing at the invalidly cued location. This finding together with the present observation of no ipsilateral alpha amplitude increase (synchronization) after the peripheral sound points to a fundamental difference between mechanisms of involuntary, exogenous orienting of attention, and the voluntary, endogenous orienting of attention. Whereas voluntary attention reportedly engages both excitatory and suppressive processes, as indexed by decreases and increases in the alpha band, respectively, the involuntary orienting of attention appears to exclusively rely on the enhancement of sensory processing, as indexed by alpha-band desynchronization.
The Time Course of Sensory Modulations during Involuntary Attention
In both of the present experiments, the decrease in alpha-band activity over the hemisphere contralateral to the sound's location began around 200–300 msec after the sound onset (Figure 4). This time course differs substantially from the time course observed in voluntary attention studies, which usually observed changes in alpha-band activity starting at around 500–900 msec after the presentation of a symbolic cue (Gomez-Ramirez et al., 2011; Green & McDonald, 2010; Snyder & Foxe, 2010; Foxe, Simpson, & Ahlfors, 1998). This suggests that the time course of the contralateral alpha desynchronization parallels that of the cue's consequence on behavioral performance. In particular, salient but irrelevant stimuli facilitate behavioral performance very rapidly after cue onset, with the largest cue effect evident when the cue-target onset asynchrony is 100–200 msec, whereas centrally presented symbolic cues facilitate performance only after a longer delay, starting at about 400–600 msec after onset (Wright & Ward, 2008; Mackeben & Nakayama, 1993; Nakayama & Mackeben, 1989; Jonides, 1980). This suggests that the behavioral effects closely coupled to the modulations of occipital alpha-band activity and that the temporal onset of these modulations and their behavioral consequences depends on the time required to process the cue and to identify the cued location.
Alpha Desynchronization Is Paralleled by the ACOP
In this study, contralateral alpha desynchronization and ACOP overlapped substantially in time. The ACOP peaked at about 300–400 msec in both experiments, whereas the contralateral alpha desynchronization was maximal between ∼200 and 400 msec in Experiment 1 and between ∼350 and 500 msec in Experiment 2. Furthermore, ACOP and alpha desynchronization showed similar scalp topographies with a clear focus over contralateral visual cortex. In line with the similarity in scalp topographies, ACOP and alpha desynchronization were estimated to arise from overlapping neural sources in contralateral visual cortex. Finally, amplitude variations of the ACOP were correlated with the magnitude of the alpha desynchronization, both across participants and across trials. These correlations, together with the temporal and topographical similarities between the ACOP and the contralateral alpha-band desynchronization, suggest that the two measures depend on or arise at least in part from a common physiological process in the visual cortex. One possibility is that these lateralized changes in visual cortex activity are initiated by a common neural event, perhaps in another brain region such as a higher level multisensory region that has been implicated in the control of attention (e.g., STS, posterior parietal sulcus; Hopfinger, Buonocore, & Mangun, 2000). In such a case, both the ACOP and the alpha desynchronization could be activated in concert and would thus co-occur in time as well as covary in strength but would nonetheless reflect distinct mechanisms that could independently bias sensory processing. In this connection, it should be noted that the alpha desynchronization outlasted the ACOP in Experiment 1. Another possibility is that portions of the ACOP and alpha-band modulations may actually reflect the same neural process, which manifests itself in both the averaged ERP and in time frequency modulations. Mazaheri and Jensen (2008) proposed that low-frequency components of the averaged ERP waveforms may be generated by asymmetric modulations of the amplitudes of higher frequency brain oscillations. Specifically, if peaks and troughs of the ongoing oscillations were affected differentially by sensory and/or cognitive events, this could lead to lower frequency ERPs in the averaged waveform that are not canceled out during the averaging process (see also van Dijk, van der Werf, Mazaheri, Medendorp, & Jensen, 2010). According to this account, the ACOP might be a lower-frequency by-product of the lateralized alpha desynchronization, and the ACOP and the contralateral alpha desynchronization would thus represent the same biasing mechanism in visual cortex. At this point, it is not possible to distinguish between the alpha asymmetry account and the separate process account because, in both cases, the scalp recordings would show a change in alpha-band activity and a lower frequency component in the ERP (e.g., the ACOP) occurring in parallel. Thus, it remains an open question whether alpha desynchronization and ACOP represent the same sensory biasing process or whether they reflect two dissociable mechanisms that could modulate visual processing individually.
This study found that peripheral, irrelevant sounds elicit a bilateral reduction in alpha-band amplitude that was more pronounced over contralateral visual cortex. These changes in alpha rhythm were paralleled by the ACOP, a contralateral positivity apparent in the ERP waveform. The contralateral alpha-band desynchronization and the ACOP overlapped in their time courses and scalp topographies and were both estimated to arise from contralateral visual cortex. Furthermore, the magnitude of the alpha desynchronization and the ACOP amplitude correlated across trials in both experiments as well as between participants. This close connection points to a common underlying mechanism that is initiated by salient sounds and, as shown in our previous studies (Feng et al., 2014; McDonald, Störmer, et al., 2013), effectively biases sensory processing in early visual cortex. We hypothesize that the alpha-band desynchronization and ACOP are both manifestations of the involuntary orienting of spatial attention to the sound's location that facilitates the neural processing of visual stimuli at the location of the sound.
We thank Matthew M. Marlow for technical assistance. This work was supported by a Marie Curie fellowship (EU grant PIOF-GA-2012-329920 to V. S. S.) and National Science Foundation (grant BCS-1029084 to S. A. H.).
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