Varieties of Auditory Attention

Alain, C., Arnott, S.A., & Dyson, B. OUP (2013). Varieties of– auditory attention. InNEWGEN “The UNCORRECTED PROOF FIRSTPROOFS, Sat Jun 15 2013, Oxford Handbook of Cognitive Neuroscience: Volume I Core Topics”, K.N. Ochsner & S.M. Kosslin (Vol. Eds.), Oxford University Press, pp. 215-236. CH A PT E R

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Varieties of Auditory Attention

Claude Alain, Stephen R. Arnott, and Benjamin J. Dyson

Abstract Research on attention is one of the major areas of investigation within psychology, neurology, and cognitive neuroscience. There are many areas of active investigations that aim to understand the brain networks and mechanisms that support attention, in addition to the relationship between attention and other cognitive processes like working memory, vigilance, and learning. This chapter focuses on auditory attention, with a particular emphasis on studies that have examined the neural underpinnings of sustained, selective, and divided attention. The chapter begins with a brief discussion regarding the possible role of attention in the formation and perception of sound objects as the underlying units of selection. The similarities and differences in neural networks supporting various aspects of auditory attention, including selective attention, sustained attention, and divided attention are then discussed. The chapter concludes with a description of the neural networks involved in the control of attention and a discussion of future directions. Key Words: attention, perception, cognitive neuroscience, working memory

Varieties of Auditory Attention Modern research in psychology and neuroscience has shown that attention is not a unitary phenomenon and that attentional processes may vary as a function of the sensory modality and the task at hand. In audition, one can think of attention in terms of mode or types of processes that might be engaged during everyday listening situations, namely sustained attention, selective attention, and divided attention. Each plays an important role in solving complex listening situations that are often illustrated colloquially using the cocktail party example, although in most everyday situations, all three modes of attention can be called on, depending on the context or situation. Imagine, for example, waiting to be seated at a restaurant. While waiting, it is sustained attention that enables us to monitor the auditory environment for a particular event (i.e., the maître d to call out your name).

During the wait, we may also start to selectively attend to an interesting conversation occurring within the general dining cacophony, thereby leading to a division of our attentional resources between the conversation and the maître d. Common to all three aspects of attention (i.e., sustained, selective, divided) are processes that allow us to switch from one auditory source to another as well as to switch from one attentional mode to another. Auditory attention is both flexible and dynamic in that it can be driven by external events such as loud sounds (e.g., a plate smashing on the floor) as well as by internal goal-directed actions that enable listeners to prioritize and selectively process task-relevant sounds at a deeper level (e.g., what the maître d is saying to the restaurant manager about the reservation), often at the expense of other, less relevant stimuli. This brings up another important issue in research related to auditory attention, and that is 215

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the role of bottom-up, data-driven attentional capture (e.g., one’s initial response to a fire alarm) in relation to top-down, controlled attention (e.g., the voluntary control of attention often associated with goal-directed behavior). This distinction between exogenous and endogenous factors appears inherent to all three modes of auditory attention. Owing to the fact that current theories of attention have been developed primarily to account for visual scene analysis, as well as the fact that early work on attention demonstrated greater equity between vision and audition, there has been a tendency to assume that general principles derived from research on visual attention can also be applied to situations that involve auditory attention. Despite these links, analogies between auditory and visual attention may be misleading. For instance, in vision, it appears that attention can be allocated to a particular region of retinal space. However, the same does not necessarily apply to audition, in the sense that as far as it is known, there is no topographic representation of auditory space in the human brain. Although evidence suggests that we can allocate auditory attention to various locations of retinal space, we can also attend to sounds that are outside of our sight (e.g., Tark & Curtis, 2009), which makes auditory attention particularly important for monitoring changes that occur outside our visual field. However, it is important to point out that we do not necessarily have to actively monitor our auditory environment in order to notice occasional or peculiar changes in sounds occurring outside our visual field. Indeed, there is ample evidence from scalp recording of event-related potentials (ERPs) showing that infrequent changes in the ongoing auditory scene are automatically detected and can trigger attention to them (Näätänen et al., 1978; Picton et al., 2000; Winkler et al., 2009). These changes in the auditory environment may convey important information that could require an immediate response, such as a car horn that is increasing in intensity. In that respect, audition might be thought of as being at the service of vision, especially in situations that require the localization of sound sources in the environment (Arnott & Alain, 2011; Kubovy & Van Valkenburg, 2001). Such considerations set the scene for the following discussion, in which a number of central questions pertaining to attention and auditory cognitive neuroscience will be tackled. What are the similarities and differences in the brain areas associated with exogenous and endogenous auditory attention? What is the neural network that enables us to switch attention between modality 216

and tasks? Are there different networks for switching attention between auditory spatial locations and objects? If so, are these the same as those used in the visual modality? What are the neural networks supporting the engagement and disengagement of auditory attention? This chapter focuses on the psychological and neural mechanisms supporting auditory attention. We review studies on auditory attention, with a particular emphasis on human studies, although we also consider animal research when relevant. Our review is by no means exhaustive but rather aims to provide a theoretical framework upon which future research can be generated. We begin with a brief discussion regarding the remit of attention and explore the possible mechanisms involved in the formation of auditory objects and the role of attention on sound object formation. We then discuss the similarities and differences in neural networks supporting various aspect of auditory attention, including selective attention, sustained attention, and divided attention. We conclude by describing the neural networks involved in the control of attention and discuss future directions.

What Are We Attending to? Although the auditory environment usually comprises a myriad of sounds from various physical sources, only a subset of those sounds “enter” our awareness. Continuing with the earlier analogy, for instance, we may start to listen to a nearby conversation while the maître d speaks with other patrons. The sounds from both conversations may overlap in time, yet we can follow and switch from one conversation to the other with seeming effortlessness: each conversation appears as a separate auditory stream, with our attention alternating between them at will. During the past 30 years, researchers have identified numerous cues that help listeners sort the incoming acoustic data into distinct sound sources, hereafter referred to as auditory objects or streams. For instance, sounds with common spatial locations, onsets, and spectral profiles usually originate from the same physical source and therefore are usually assigned to the same perceptual object (Alain & Bernstein, 2008; Bregman, 1990; Carlyon, 2004). The sounds that surround us often change in a predictive manner such that a certain sound may lead us to expect the next one. While listening to the maître d, we anticipate the upcoming sounds that indicate that our table is ready. Knowledge and experience with the auditory environment are particularly helpful in solving complex listening situations in which sounds

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from one source may partially overlap and mask those from another source. In everyday listening situations, our auditory scene changes constantly, and observers must be able to keep track of multiple sound objects. Once an auditory scene has been parsed into its component objects, selectively attending to one stream (e.g., shouts from the kitchen) while ignoring all the other talkers (e.g., maître d) and background noise becomes crucial for effective communication, especially in acoustically adverse or “cocktail party” environments (Cherry, 1953; Moray & O’Brien, 1967). Some visual models have characterized attention in spatial or frequency terms, likening attention to a “spotlight” or “filter” that moves around, applying processing resources to whatever falls within a selected spatial region (e.g., Brefczynski & DeYoe, 1999; LaBerge, 1983; McMains & Somers, 2004). Other models discuss resource allocation on the basis of perceptual objects, in which attending to a particular object enhances processing of all features of that object (e.g., Chen & Cave, 2008). Recent models of auditory attention have been consistent with the latter conception of attention in that the underlying units of selection are discrete objects or streams, and that attending to one component of an auditory object facilitates the processing of other properties of the same object (Alain & Arnott, 2000; Shinn-Cunningham, 2008). In the visual domain, the object-based account of attention was proposed to explain why observers are better at processing visual features that belong to the same visual object than when those visual features are distributed between different objects (Duncan, 1984; Egly et al., 1994). For instance, Duncan (1984) showed that performance declined when individuals were required to make a single judgment about each of two visually overlapping objects (e.g., the size of one object and the texture of the other object), compared with when those two judgments had to be made about a single object. The robustness of the findings was demonstrated not only in behavioral experiments (Baylis & Driver, 1993) but also in neuroimaging studies employing functional magnetic resonance imaging (fMRI; e.g., O’Craven et al., 1999; Yantis & Serences, 2003) and ERPs (e.g., Valdes-Sosa et al., 1998). Such findings indicated that attention is preferentially allocated to a visual object such that the processing of all features belonging to an attended object is facilitated. Alain and Arnott (2000) proposed an analogous object-based account for audition in which listeners’ attention is allocated to auditory objects derived from the ongoing auditory scene according

to perceptual grouping principles (Bregman, 1990). More recently, Shinn-Cunningham (2008) has drawn a parallel between object-based auditory and visual attention, proposing that perceptual objects form the basic units of auditory attention. Central to the object-based account of auditory attention is the notion that several perceptual objects may be simultaneously accessible for selection and that interactions between object formation and selective attention determine how competing sources interfere with perception (Alain & Arnott, 2000; Shinn-Cunningham, 2008). Although there is some behavioral evidence supporting the notion that objects can serve as an organizational principle in auditory memory (Dyson & Ishfaq, 2008; Hecht et al., 2008), further work is required to understand how sound objects are represented in memory when these objects occur simultaneously. Moreover, the object-based account of auditory attention posits that perceptual objects form the basic unit for selection rather than individual acoustic features of the stimuli such as pitch and location (Mondor & Terrio, 1998). However, in many studies, it is often difficult to distinguish feature- and object-based attention effects because sound objects are usually segregated from other concurrent sources using simple acoustic features such as pitch and location (e.g., the voice and location of the maître d in the restaurant). Therefore, how do we know that attention is allocated to a sound object rather than its defining acoustic features? One way to distinguish between feature- and object-based attentional accounts is to pit the grouping of stimuli into perceptual objects against the physical proximity of features (e.g., Alain et al., 1993; Alain & Woods, 1993, 1994; Arnott & Alain, 2002a; Bregman & Rudnicky, 1975; Driver & Baylis, 1989). Figure 11.1A shows an example in the auditory modality where the physical similarity between three different streams of sounds was manipulated to promote perceptual grouping while at the same time decreasing the physical distance between taskrelevant and task-irrelevant stimuli. In that study, Alain and Woods (1993) presented participants with rapid transient pure tones that varied along three different frequencies in random order. In the example shown in Figure 11.1A, participants were asked to focus their attention to the lowest pitch sound in order to detect slightly longer (Experiment 1) or louder (Experiment 2) target stimuli while ignoring the other sounds. In one condition, the tones composing the sequence were evenly spaced along the frequency domain, whereas in another condition, the two task-irrelevant frequencies were al ain, arnot t, dyson


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TIME (MS) Figure 11.1 A, Schemata of the stimuli presented in three different conditions. In the evenly spaced (ES) condition, tones were equally spaced along the frequency axis (tone 1 = 1,048, tone 2 = 1,482, and tone 3 = 2,096 Hz). In the clustering easy (CE) condition, the two task-irrelevant tones (i.e., tone 2 = 1,482 and tone 3 = 1,570 Hz) were clustered based on frequency to promote the segregation of the task-relevant frequency (i.e., tone 1). In the clustering hard (CH) condition, the task-relevant frequency was clustered with the middle distracters (1 = 1,400 Hz). Arrows indicate the frequency to be attended in each condition. Targets (defined as longer or louder than other stimuli) are shown by asterisks. B, Response time to infrequent duration targets embedded in the attended stream. C, Group mean difference wave between event-related brain potentials elicited by the same stimuli when they were attended and unattended in all three conditions from the midline frontal scalp region. The negativity is plotted upward. (Adapted from Alain & Woods, 1994.)

grouped together by making the extreme sound (lowest or highest, depending on the condition) more similar to the middle pitch tone. Performance improved when the two task-irrelevant sounds were grouped together even though this meant having more distractors closer in pitch to the task-relevant stimuli (see Figure 11.1B). Figure 11.1C shows the effects of perceptual grouping on the selective attention effects on ERPs, which was isolated in the difference wave between the ERPs elicited by the same sounds when they were task relevant and when they were task irrelevant. Examining the neural activity that occurred in the brain during these types of tasks suggested that perceptual grouping enhances selective attention effects in auditory cortices, primarily along the Sylvian fissure (Alain et al., 1993; Alain & Woods, 1994; Arnott & Alain, 2002a). Taken together, these studies show that perceptual grouping 218

can override physical similarity effects during selective listening, and suggest that sound objects form the basic unit for attentional selection. One important assumption of the object-based account of auditory attention is that sound objects are created and segregated independently of attention and that selection for further processing takes place after this initial partition of the auditory scene into its constituent objects. Although there is evidence to suggest that sound segregation may occur independently of listeners’ attention, there are also some findings that suggest otherwise (Shamma et al., 2011). The role of attention on perceptual organization has been investigated for sounds that occur concurrently as well as for sounds that are sequentially grouped into distinct perceptual streams. In the case of concurrent sound segregation (Figure 11.2), the proposal that concurrent sound



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Figure 11.2 A, Schematic representation of harmonic complex tones (each horizontal line represents a pure tone) used in studies that have examined the role of attention on concurrent sound segregation. Participants were presented with a harmonic complex that had all tonal elements in tune (fusion) or included a mistuned harmonic. In the active listening task, participants indicated whether they heard one sound or two sounds (i.e., a buzz plus another sound with a pure tone quality). In the passive listening condition, participants watched a muted movie of their choice with subtitles. B, Auditory event-related potentials (ERPs) to complex harmonic tones were measured over the right frontal-central scalp region (FC2). The difference wave reveals the object-related negativity (ORN), an ERP component that indexes the perception of concurrent sound objects. Note the similarity in ORN amplitude during active listening (participants indicated whether they heard one sound or two concurrent sound objects) and passive listening (participants watched a muted subtitle movie of their choice). During active listening, the ORN is followed by a positive wave (P400) thought to be related to the perceptual decision. (Adapted from Alain, Arnott, & Picton, 2001.)

segregation is not under volitional control was confirmed in ERP studies using passive listening (Alain et al., 2001a, 2002; Dyson & Alain, 2004) as well as active listening paradigms that varied auditory (Alain & Izenberg, 2003) or visual attentional demands (Dyson et al., 2005). For sequential sound segregation, the results are more equivocal. In most studies, the effect of attention on sequential sound organization has been investigated by mixing two sets of sound sequences that differ in terms of some acoustic feature (e.g., in the frequency range of two sets of interleaved pure tones). In a typical frequency paradigm, sounds are presented in patterns

of “ABA—ABA—”, in which “A” and “B” are tones of different frequencies and “—” is a silent interval (Figure 11.3A). The greater the stimulation rate and the feature separation, the more likely and rapidly listeners are to report hearing two separate streams of sounds (i.e., one of A’s and another of B’s), with this type of perceptual organization or stream segregation taking several seconds to build up. Using similar sequences as those shown in Figure 11.3A, Carlyon and colleagues found that the buildup of stream segregation was affected by auditory (Carlyon et al., 2001) and visual (Cusack et al., 2004) attention, and they proposed that attention may be al ain, arnot t, dyson


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needed for stream segregation to occur. Also consistent with this hypothesis are findings from neuropsychological studies in which patients with unilateral neglect following brain lesion show impaired buildup in streaming relative to age-matched controls when stimuli are presented to the neglected side (Carlyon et al., 2001). In addition to the pro-attention studies reviewed above, there is also evidence to suggest that attention may not be required for sequential perceptual organization to occur. For instance, patients with unilateral neglect who are unaware of sounds presented to their neglected side experience the “scale illusion” (Deutsch, 1975), which can occur only if the sounds from the left and right ears are grouped together (Deouell et al., 2008). Such findings are difficult to reconcile with a model invoking a required role of attention in stream segregation and suggest that some organization must be taking place outside the focus of attention, as others have suggested previously (e.g., Macken et al., 2003; McAdams & Bertoncini, 1997; Snyder et al., 2006). This apparent discrepancy could be reconciled by assuming that sequential stream segregation relies on multiple 220

levels of representations (Snyder et al., 2009), some of which may be more sensitive to volitional control (Gutschalk et al., 2005; Snyder et al., 2006). In summary, evidence from ERP studies suggests that perceptual organization of acoustic features into sound objects can occur independently of attention (e.g., Alain & Izenberg, 2003; Alain & Woods, 1994; Snyder et al., 2006; Sussman et al., 2007). However, it is very likely that attention facilitates perceptual organization and that selective attention may determine which stream of sounds is in the foreground and which is in the background (i.e., figure–ground segmentation) (Sussman et al., 2005). These views are compatible with the object-based account of auditory attention in which primitive perceptual processes sort the incoming acoustic data into its constituent sources, allowing selective processes to work on the basis of meaningful objects (Alain & Arnott, 2000).

Mechanisms of Auditory Attention As mentioned earlier, selective attention enables us to prioritize information processing such that only a subset of the vast sensory world (and internal



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thought) receives more in-depth analysis. In the last decade, there has been a growing interest in three important mechanisms that could serve to optimize the contrast between sounds of interest and those that are “task irrelevant.” These are enhancement (also referred to as gain), the sharpening of receptive fields for task-relevant information, and the possible suppression of task-irrelevant information (for a discussion of these ideas related to stimulus repetition, see Grill-Spector et al., 2006). The enhancement and suppression mechanisms were originally proposed to account for visual attention (e.g., Hillyard et al., 1998), and such models posit feature-specific enhancements in regions that are sensitive to the attended features as well as suppression in regions that are sensitive to the unattended (task-irrelevant) features.

Attention as an Enhancement and Suppression Mechanism to Enhance Figure–Ground Segregation In the auditory attention literature, the notion of a gain mechanism was introduced early on (e.g., Hillyard et al., 1973), although it was not originally ascribed as a feature-specific process. For instance, early studies in nonhuman primates showed that the firing rate of auditory neurons increased when sounds occurred at the attended location (Benson & Hienz, 1978) or when attention was directed toward auditory rather than visual stimuli (Hocherman et al., 1976), consistent with a mechanism that “amplifies” or enhances the representation of taskrelevant stimuli. Electroencephalography (EEG; Hillyard et al., 1973; Woods et al., 1994) and magnetoencephalography (MEG; Ross et al., 2010; Woldorff et al., 1993) studies provide further evidence for neural enhancement during auditory selective attention. For instance, the amplitude of the N1 wave (negative wave at ∼100 ms after sound onset) from scalp-recorded auditory evoked potentials is larger when sounds are task relevant and fall within an “attentional spotlight” compared with when the same sounds are task irrelevant (Alho et al., 1987; Giard et al., 2000; Hansen & Hillyard, 1980; Hillyard et al., 1973; Woldorff, 1995; Woods et al., 1994; Woods & Alain, 2001). The notion of feature-specific enhancement and suppression as the principle mechanisms to enhance figure–ground segregation implies that selective attention would “amplify” neural activity in specific cortical areas that respond preferably to particular stimulus attributes. Because the task-relevant stream of sounds in most neuroimaging studies of

auditory selective attention is usually defined by the pitch or the location of the stimuli, the featurespecific enhancement and suppression hypothesis can be evaluated by comparing neural activity for identical stimuli when they are task relevant and task irrelevant. Such comparisons have revealed different ERP-selective attention effects for pitch and location attributes (Degerman et al., 2008; Woods & Alain, 2001). Figure 11.4 shows a schematic diagram of a typical experiment in which participants are presented with multidimensional stimuli. The deployment of stimuli constructed by the orthogonal combination of two frequencies (i.e., 250 and 4000 Hz) and two locations (left and right ear) have proved helpful for investigating featurespecific attention effects (e.g., attend to high tones) as well as object-based attention effects that rely on the conjunction of sound features (e.g., attend to high tones in the left ear). Using such paradigms under feature-specific conditions, Woods and colleagues have shown that auditory selective attention modulates activity in frequency-specific regions of auditory cortex (Woods et al., 1994; Woods & Alain, 2001). The fact that attention enhances the amplitude of the auditory evoked response from a tonotopically organized generator provides strong support for models that posit the attentional gain of sensory processing (Hillyard et al., 1987; Woldorff et al., 1993). In addition to enhanced responses to features deemed task relevant by virtue of the task instructions, object-based (or conjunctive) attentional effects have also been observed, including neural facilitation for objects expressed both during and after featural processing (e.g., Woods et al., 1994; Woods & Alain, 1993, 2001). For example, in complex listening situations in which target sounds are defined by a combination of features,

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nontarget sounds that share either frequency or location features with the targets have also shown attention-related effects that differ in amplitude distribution (Woods et al., 1994; Woods and Alain, 1993, 2001). Differences in amplitude distribution are indicative that attention modulates neural activity arising from different cortical fields related to the processing of different sound features. Such findings are also consistent with the dual-pathway model of auditory scene analysis (Rauschecker & Scott, 2009; Rauschecker & Tian, 2000) in which sound identity and sound location are preferably processed along ventral (what) and dorsal (where) pathway streams. There is also evidence that such gain mechanisms play an important role during sustained selective attention to a single speech stream embedded in a multiple-talker environment (Kerlin et al., 2010). Although the evidence for feature-based and object-based enhancement is compelling, that related to suppression of auditory stimuli occurring outside the attentional spotlight are more equivocal. There are some findings consistent with an active suppression mechanism. For example, the amplitude of the P2 wave (positive deflection at ∼180 ms after sound onset) from the auditory ERPs elicited by task-irrelevant sounds is larger during intramodal attention than during intermodal (auditoryvisual) attention (Degerman et al., 2008; Michie et al., 1993). Although the enhanced P2 amplitude during intramodal attention may reflect an active suppression mechanism, there is no evidence that the suppression is feature specific. Moreover, one cannot rule out the possibility that during intermodal attention, participants’ attention may have wandered to the auditory stimuli, thereby modulating the amplitude of ERPs (i.e., increased negativity) to the so-called ‘unattended’ stimuli. Therefore, the higher P2 amplitude observed during selective attention tasks may not reflect suppression, but instead may simply indicate attention effects during the control baseline condition. In a more recent study, Munte et al. (2010) measured auditory evoked responses from two locations, each containing a spoken story and bandpass-filtered noise. Participants were told to focus their attention on a designated story/location. Consistent with prior research, the N1 elicited by the speech probe was found to be larger at the attended than at the unattended location. More important, the noise probes from the taskrelevant story’s location showed a more positive frontal ERP response at about 300 ms than did the 222

probes at the task-irrelevant location. Although the enhanced positivity may be indicative of a suppression mechanism, there is a possibility that it reflects novelty or target-related activity, which may comprise a positive wave that peaks about the same time. Moreover, using a similar design, but measuring the effect of sustained selective attention in the EEG power spectrum, Kerlin et al. (2010) found no evidence of suppression for the task-irrelevant stream of speech. If suppression of task-irrelevant sounds does occur, what underlying mechanisms support it? It is known that the auditory system is composed of descending efferent pathways that are particularly important for modulating neural activity at the peripheral level. Nevertheless, it remains unclear whether such suppression would be an appropriate strategy in everyday listening situations. Although one could argue that a feature-specific suppression mechanism would help to prevent information overload, such a suppression mechanism could also have a negative impact in the sense that important information could be missed or undetected. Perhaps a more parsimonious account would be a facilitatory process that enhances representation of task-relevant information in sensory and shortterm memory, while representations of task-irrelevant information would simply decay progressively without necessarily postulating active suppression. Indeed, increasing attentional demands reduces the amplitude of the mismatch negativity, an ERP component that reflects sensory memory, but has little impact on the amplitude of the N1 and P2 waves, which reflect sensory registration (Alain & Izenberg, 2003). Thus, attention appears to play an important role in maintaining and enhancing sensory representations such that conditions that prevent listeners from attending to task-irrelevant sounds cause a processing deficit in detecting changes in the unattended stream of sounds (depending both on sensory memory and a comparison process between recent and incoming stimuli) (e.g., Alain & Woods, 1997; Arnott & Alain, 2002b; Trejo et al., 1995;Woldorff et al., 1991). This is akin to the model proposed by Cowan (1988, 1993), in which attention plays an important role in keeping “alive” sensory representations for further and more in-depth processing.

Attention Enhances Perception by Sharpening of Tuning Curve In addition to enhancement and suppression mechanisms, the effects of attention may be



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mediated by a mechanism that selectively sharpens the receptive fields of neurons representing task-relevant sounds, a mechanism that may also enhance figure–ground separation. Kauramaki et al. (2007) used the notched noise technique in which a pure tone is embedded within noise that has a segment whose width around the pure tone is parametrically varied to ease target detection (Figure 11.5). Kauramaki et al. measured the N1 wave elicited by the pure tone during selective attention and found a decrease in attention effects when the width of the notched noise was decreased. However, the shape of the function was significantly different from a multiplicative one expected on the basis of simple gain model of selective attention (see also Okamoto et al., 2007). According to Kauramaki et al. (2007), auditory selective attention in humans cannot be explained by a gain model, whereby only the neural activity level is increased. Rather, selective attention additionally enhances auditory cortex frequency selectivity. This effect of selective attention on frequency tuning evolves rapidly, within a few seconds after attention switching, and appears to A

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occur for neurons in nonprimary auditory cortices (Ahveninen et al., 2011). In summary, attentional effects on sensory response functions include an increase in gain and sharpening of tuning curves that appears to be specific to the task-relevant feature. This feature-specific attention effect is also accompanied by a more general suppression response, although the evidence for such suppression in the auditory domain remains equivocal. Both gain and suppression mechanisms, as well as sharper receptive field tuning, may enhance figure–ground segregation, thereby easing the monitoring and selection of task-relevant information.

Neural Network of Auditory Attention The development of positron emission tomography (PET) and fMRI has allowed researchers to make major strides in identifying the brain areas that play an important role in auditory attention. In the next sections, we briefly review the brain areas involved in sustained, selective (intramodal and intermodal), and divided attention in an effort to draw commonalities as well as important differences

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in the neural networks supporting the various types of auditory attention.

Sustained Attention The process of monitoring the auditory environment for a particular event (e.g., perhaps you are still waiting for the maître d to call out your name) has been studied in a number of ways in order to reveal the neural architecture supporting sustained attention. One example is the oddball paradigm in which participants are asked to respond to infrequent targets sounds in a train of nontarget stimuli. In oddball tasks, the target stimuli differ from the standard sounds along a particular dimension (e.g., pitch, duration, intensity, location). The detection of the task-relevant stimuli is typically accompanied by a parietal-central positive wave of the ERP, the P300 or P3b. Moreover, fMRI studies have identified several brain areas that show increased hemodynamic activity during the detection of these oddball targets relative to the nontarget stimuli, including the auditory cortex bilaterally, parietal cortex and prefrontal cortex, supramarginal gyrus, frontal operculum, and insular cortex bilaterally (Linden et al., 1999; Yoshiura et al., 1999). The increased fMRI signals for target versus nontarget conditions are consistent over various stimuli (i.e., auditory versus visual stimuli) and response modalities (i.e., button pressing for targets versus silently counting the targets) and can be regarded as specific for target detection in both the auditory modality and the visual modality (Linden et al., 1999). Interestingly, the amount of activity in the anterior cingulate and bilateral lateral prefrontal cortex, temporal-parietal junction, postcentral gyri, thalamus, and cerebellum was positively correlated with an increase in time between targets (Stevens et al., 2005). The fact that these effects were only observed for target and not novel stimuli suggests that the activity in these areas indexes the updating of a working memory template for the target stimuli or strategic resource allocation processes (Stevens et al., 2005). Another task that requires sustained attention is the n-back working memory task in which participants indicate whether the incoming stimulus matches the one occurring one, two, or three positions earlier. Alain and colleagues (2008) used a variant of this paradigm and asked their participants to press a button only when the incoming stimulus sound matched the previous one (1-back) in terms of identity (i.e., same sound category such as animal sounds, human sounds, or musical instrument sounds) or location. Distinct patterns of neural 224

activity were observed for sustained attention and transient target-related activity. Figure 11.6 shows sustained task-related activity during sound identity and sound location processing after taking into account transient target-related activity. The monitoring of sound attributes recruited many of the areas previously mentioned for the oddball task, including auditory, parietal, and prefrontal cortices (see also, Martinkauppi et al., 2000; Ortuno et al., 2002). Interestingly, changes in task instruction modulated activity in this attentional network, with greater activity in ventral areas, including the anterior temporal lobe and inferior frontal gyrus, when participants’ attention was directed toward sound identity and greater activity in dorsal areas, including the inferior parietal lobule, superior parietal cortex, and superior frontal gyrus, when participants’ attention was directed toward sound location. Although such data again suggest the relationship between specific regions or pathways within the brain and specific perceptual and cognitive function, it is important to consider the extent to which clear delineations can be made. For example, although the inferior parietal lobule (IPL) is clearly involved in spatial analysis and may play an important role in monitoring or updating sound source location in working memory, there is also plenty of evidence demonstrating its involvement in nonspatial processing (see Arnott et al., 2004). In fact, some of this activity may be accounted for in terms of an action–perception dissociation (Goodale & Milner, 1992) in which the dorsal auditory pathway brain regions are important for acting on objects and sounds in the environment (Arnott & Alain, 2011). For example, in a task requiring listeners to categorize various sounds as being “material” (i.e., malleable sheets of paper, Styrofoam, aluminium foil, or plastic being crumpled in a person’s hands), “human” (i.e., nonverbal vocalizations including coughing, yawning, snoring, and throat clearing) or “noise” (i.e., randomized material sounds), Arnott et al. (2008) found increased blood-oxygenlevel- dependent (BOLD) effect along a dorsal region, the left intraparietal sulcus (IPS), in response to the material sounds. A very similar type of activation was also reported by Lewis and colleagues when listeners attended to hand-related (i.e., tool) sounds compared with animal vocalizations (Lewis, 2006; Lewis et al., 2005). Both groups proposed that such sounds triggered a “mental mimicry” of the motor production sequences that most likely would have produced the sounds, with the left hemispheric activation reflecting the right-handed dominance of the participants. This explanation finds support in the fact that area



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Figure 11.6 A, Schematic of n-back task used to investigate sustained working memory to sound identity and sound location. B, Task differences in sustained activity during a working memory task. Warm colors indicate greater sustained activity during working memory for sound identity, and cool colors indicate greater sustained activity during working memory for sound location. The activation maps are displayed on the cortical surface using surface mapping (SUMA). IFG, inferior frontal gyrus; IPL, inferior parietal lobule; STG, superior temporal gyrus. (Adapted from Alain et al., 2008.)

hAIP, a region shown to be active not only during real hand-grasping movements but also during imagined movements, as well as passive observation of people grasping three-dimensional objects, is located proximally at the junction of the anterior IPS and inferior postcentral sulcus (Culham et al., 2006). Additionally, it is noteworthy that the IPS is an area of the IPL known to make use of visual input, and that it is particularly important for integrating auditory and visual information (Calvert, 2001; Macaluso et al., 2004; Meienbrock et al., 2007), especially with respect to guiding and controlling action in space (e.g., Andersen et al., 1997; Sestieri et al., 2006). As noted earlier, knowledge about auditory material properties is largely dependent on prior visual experiences, at least for normally sighted individuals. Thus, it is plausible that the IPS auditory

material–property activity reflects the integration of auditory input with its associated visual knowledge. The above data remind us that successful interaction with the complex and dynamic acoustic environment that we ultimately face involves the coordination and integration of attentional demands from other modalities such as vision, the timely initiation of task-appropriate action, and the maintenance of attentional processes over long periods of time. Nevertheless, there are cases in which less important signals must be sacrificed for more important signals, and the brain regions associated with selective attention are those to which we now turn.

Selective Attention Auditory selective attention was originally studied using dichotic listening situations in which two al ain, arnot t, dyson


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different streams of speech sounds were presented simultaneously in both ears (Broadbent, 1962; Cherry, 1953; for a review, see Driver, 2001). In such situations, participants were asked to shadow (repeat) the message presented one ear while ignoring the speech sounds presented at the irrelevant location (i.e., the other ear). This intramodal attention (i.e., between streams of sounds) involves sustained attention to a particular stream of sounds in the midst of others, usually defined by its most salient features, such as pitch and location (e.g., Hansen & Hillyard, 1983). This form of selective attention differs from intermodal attention, whereby participants are presented with streams of auditory and visual stimuli and alternatively focus on either auditory or visual stimuli in order to detect infrequent target stimuli. The neural networks supporting intramodal and intermodal selective attention are examined next in turn.

Intramodal Selective Attention Intramodal auditory selective attention tasks engage a network of frontal, temporal, and parietal regions (Hill & Miller, 2009; Lipschutz et al., 2002), and activity in these areas appears to be sensitive to task demands. For instance, using identical stimuli, Hill and Miller (2009) found greater activity in dorsal brain regions when listeners were told to attend to the location of a particular talker in a multiple-talker situation, whereas more ventral activity was observed when participants attended to the pitch (voice) of the talker. Once again, this dissociation between spatial and nonspatial auditory attention is consistent with the general trend of showing greater activity in ventral (what) and dorsal (where) brain regions in auditory tasks that require sound identification or sound localization, respectively (Alain et al., 2001b, 2008; Arnott et al., 2004, 2005; Degerman et al., 2006; Leung & Alain, 2011; Maeder et al., 2001; Rama et al., 2004). Recently, there has been increased interest in examining the effects of attention on auditory cortical activity. This interest is motivated in part by the notion that the auditory cortex is not a single entity but rather comprises many cortical fields that appear to be differentially sensitive to sound frequency and sound location, and by the analogous discovery of feature-specific enhancement and suppression of visual neurons during visual selective attention. Although the effects of attention on neural activity in auditory cortical areas are not disputed, the effects of attention on the primary auditory cortex remain equivocal. For instance, some 226

fMRI studies do not find evidence for attention effects on primary auditory cortex in Heschl’s gyrus (Hill & Miller, 2009; Petkov et al., 2004), whereas others report enhancements in frequency-sensitive regions, although the attention effects are not necessarily restricted to them (Paltoglou et al., 2009). The lack of attention effects on the BOLD signal from the primary cortex does not mean that neural activity in primary auditory cortex is not modulated by attention; it may be too weakly or differentially modulated such that the BOLD effect cannot capture it. As we have already seen, studies that have used another imaging technique such as EEG or MEG provide evidence suggesting that selective attention amplifies neural activity in frequency-sensitive regions (Woods et al., 1994; Woods & Alain, 2001) as well as in or near primary areas (Woldorff & Hillyard, 1991; Woldorff et al., 1993). In addition, single-unit research in mammals has shown that attention can modulate the neural firing rate of neurons in primary auditory cortex (Benson and Hienz, 1978; Hocherman et al., 1976). Moreover, intramodal selective attention to location (i.e., left or right ear) has been shown to increase BOLD activity in the right middle frontal gyrus regardless of the location of attentional focus (Lipschutz et al., 2002). In contrast, brain regions including the middle and inferior frontal cortex, frontal eye fields (FEFs), and the superior temporal cortex in the contralateral hemisphere did show attention-related activity according to which ear was being attended to (Lipschutz et al., 2002). Activation in the superior temporal cortex extended through the temporal-parietal junction to the inferior parietal cortex, including the IPS (Lipschutz et al., 2002). The activation in the human homologue of FEFs during auditory spatial attention has been reported in several studies (e.g., Lipschutz et al., 2002; Tzourio et al., 1997; Zatorre et al., 1999), but the results should be interpreted with caution because these studies did not control for eye movements, which could partly account for the activation in the FEFs. In a more recent study, Tark and Curtis (2009) found FEF activity during audiospatial working memory task even for sounds that were presented behind the head to which it was impossible to make saccades. Their findings are consistent with the proposal that FEF plays an important role in processing and maintaining sound location (Arnott & Alain, 2011). In addition to enhanced activity in cortical areas during intramodal auditory selective attention, there is also evidence from fMRI that selective



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attention modulates activity in the human inferior colliculus (Rinne et al., 2008). The inferior colliculus is a midbrain nucleus of the ascending auditory pathway with diverse internal and external connections. The inferior colliculus also receives descending projections from the auditory cortex, suggesting that cortical processes affect inferior colliculus operations. Enhanced fMRI in basal ganglia has also been observed during auditory selective attention to speech sounds (Hill & Miller, 2009). There is also some evidence that selective attention may modulate neural activity at the peripheral level via descending projections. However, the effects of attention on the peripheral and efferent auditory pathways remain equivocal. For example, although some studies report attention effects on the peripheral auditory systems as measured with evoked otoacoustic emissions (Giard et al., 1994; Maison et al., 2001), other studies do not (Avan & Bonfils, 1992; Michie et al., 1996; Timpe-Syverson & Decker, 1999).

Intermodal Auditory Attention In intermodal attention studies, the effects of attention on auditory processing are assessed by comparing neural activity to auditory stimuli when participants perform a demanding task in another modality (usually visual), with activity elicited by the same stimuli when attention is directed toward the auditory stimuli. Rinne et al. (2007) found enhanced activity in auditory areas during a crossmodal attention task, with the intermodal attention effects extending to both the posterior and superior temporal gyrus. However, there was no difference in parietal cortex or prefrontal cortex when attention was directed to auditory versus visual (i.e., picture) stimuli. Similarly, Kawashima et al. (1999) reported comparable activation in the right prefrontal cortex during visual and auditory attention to speech sounds. However, they did find a difference in parietal cortex activation between attention to auditory and visual stimuli (Kawashima et al., 1999), suggesting that the parietal cortex may play a different role during auditory and visual attention. Interestingly, electrophysiological investigations of the chinchilla cochlea demonstrate that as the attentional demands to the visual system increase (as in the case of an increasingly difficult visual discrimination task), there is a corresponding decrease in the sensitivity of the cochlea that appears mediated by efferent projections to the outer hair cells (Delano et al., 2007). Accordingly, it seems plausible that intermodal attention could theoretically

alter auditory processing at the very earliest stages of auditory processing (i.e., at sensory transduction). Further underscoring the need to consider the interaction of attentional demands between modalities, there is evidence to suggest that during intermodal selective attention tasks, attention to auditory stimuli may alter visual cortical activity. The attentional repulsion effect is one example of this. Traditionally understood as a purely visual phenomenon, attentional repulsion refers to the perceived displacement of a vernier stimulus in a direction that is opposite to that of a brief peripheral visual cue (Suzuki & Cavanagh, 1997). Observers in these behavioral tasks typically judge two vertical lines placed above and below one another to be offset in a direction that is opposite to the location of a briefly presented irrelevant visual stimulus. Under the assumption that the repulsion effect exerts its effect in early retinotopic areas (i.e., visual cortex; Pratt & Turk-Browne, 2003; Suzuki & Cavanagh, 1997), Arnott and Goodale (2006) sought to determine whether peripheral auditory events could also elicit the repulsion effect. In keeping with the notion that sounds can alter occipital activity, peripheral sounds were also found to elicit the repulsion effect. More direct evidence for enhancement of activity in occipital cortex comes from an fMRI study in which selective attention to sounds was found to activate visual cortex (Cate et al., 2009). In that particular study, the occipital activations appeared to be specific to attended auditory stimuli given that the same sounds failed to produce occipital activation when they were not being attend to (Cate et al., 2009). Moreover, there is some evidence that auditory attention, but not passive exposure to sounds, activates peripheral regions of visual cortex when participants attended to sound sources outside the visual field. Functional connections between auditory cortex and visual cortex subserving the peripheral visual field appear to underlie the generation of auditory occipital activations (Cate et al., 2009). This activation may reflect the priming of visual regions to process soon-to-appear objects associated with unseen sound sources and provides further support for the idea that the auditory “where” subsystem may be in the service of the visual-motor “where” subsystem (Kubovy & Van Valkenburg, 2001). In fact, the functional overlap between the auditory cortical spatial network and the visual orientation network is quite striking, as we have recently shown, suggesting that the auditory spatial network and visual orienting network share a al ain, arnot t, dyson


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common neural substrate (Arnott & Alain, 2011). Along the same line, auditory selective attention to speech modulates activity in the visual word form areas (Yoncheva et al., 2009), suggesting a high level of interaction between sensory systems even at the relatively early stages of processing. Throughout this discussion of intermodal attention, one should also keep in mind that even though auditory information can certainly be obtained in the absence of other sensory input (e.g., sounds perceived in the dark, or with the eyes closed), for the vast majority of neurologically intact individuals, any given auditory experience is often experienced in the presence of other sensory (especially visual) input. Thus, there is good reason to expect that in such cases, the neural architecture of auditory processing may be interwoven with that of other sensory systems, especially in instances in which the two are dependent on one another. Once again, neuroimaging data derived from the experience of auditory material property information are useful in this regard. Unlike auditory localization where a sound’s position can be constructed from interaural timing and intensity differences, the acoustic route to the material properties of any given object depends entirely on previous associations between sound and information from other senses (e.g., hearing the sound that an object makes as one watches someone or something come into contact with that object, or hearing the sound that an object makes as one touches it). Building on research showing that visual material processing appears to be accomplished in ventromedial brain areas that include the collateral sulcus and parahippocampal gyrus (Cant & Goodale, 2007), Arnott and colleagues used fMRI to investigate the brain regions involved in auditory material processing (Arnott et al., 2008). Relative to control sounds, audio recordings of various materials being manipulated in someone’s hands (i.e., paper, plastic, aluminium foil, and Styrofoam) were found to elicit greater hemodynamic activity in the medial region of the right parahippocampus both in neurologically intact individuals and in a cortically blind individual. Most interestingly, a concomitant visual material experiment in which the sighted participants viewed pictures of objects rendered in different materials (e.g., plastic, wood, marble, foil) was found to elicit right parahippocampal activity in an area just lateral to the auditory-evoked region. These results fit well with animal neuroanatomy in that the most medial aspect of the monkey parahippocampus (i.e., area TH) has connections with auditory cortex (Blatt et al., 2003; Lavenex et al., 228

2004; Suzuki et al., 2003), whereas the region situated immediately adjacent to area TH (i.e., area TFm in the macaque or TL in the rhesus monkey) has strong inputs from areas processing visual information, receiving little if any input from auditory areas (Blatt et al., 2003). The data from both the intramodal and intermodal attentional literature reminds us again that attention may have a variety of neural expressions, at both relatively early (e.g., occipital) and late (e.g., parahippocampal) stages of processing, and depends to a large extent on specific task demands (Griffiths et al., 2004). In this respect, attention demonstrates itself as a pervasive and powerful influence on brain functioning.

Divided Attention Divided attention between two concurrent streams of sounds (one in each hemispace) or between auditory modality and visual modality, has been associated with enhanced activity in the precuneus, IPS, FEFs, and middle frontal gyrus compared with focused attention to either one modality or location (Santangelo et al., 2010). Moreover, Lipschutz et al. (2002) found comparable activation during selective and divided attention suggestive of a common neural network. Bimodal divided attention (i.e., attending to auditory and visual stimuli) has also been associated with enhanced activity in the posterior dorsolateral prefrontal cortex (Johnson et al., 2007). Importantly, the same area was not significantly active when participants focused their attention to either visual or auditory stimuli or when they were passively exposed to bimodal stimuli (Johnson & Zatorre, 2006). The importance of the dorsolateral prefrontal cortex (DLPFC) during bimodal divided attention is further supported by evidence from a repetitive transcranial magnetic stimulation (rTMS) study (Johnson et al., 2007). In that particular study, performance during bimodal divided attention was hindered by temporarily disrupting the function of the DLPFC using rTMS compared with control site stimulation.

Deployment of Auditory Attention Our ability to attend a particular sound object or sound location is not instantaneous and may require a number of cognitive alterations. We may need to disengage from what we are doing, switch our attention to a different sensory modality, focus on a different spatial location or object, and then engage our selection mechanisms. It has been generally established that focusing attention on a



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particular input modality, or a particular “what” or “where” feature, modulates cortical activity such that task-relevant representations are enhanced at the expense of irrelevant ones (Alain et al., 2001b; Degerman et al., 2006; Johnson & Zatorre, 2005, 2006). Although the object-based model can adequately account for many findings that involve focusing attention to a task-relevant stream, there is a growing need to better understand how attention is deployed toward an auditory object within an auditory scene and whether the mechanisms at play when directing auditory attention toward spatial and nonspatial cues also apply when the auditory scene comprises multiple sound objects. Although substantial research has been carried out on sustained and selective attention, fewer studies have examined the deployment of attention, especially in the auditory modality. One popular paradigm to assess the deployment of attention consists of presenting an informational cue before either a target sound or streams of sounds in which the likely incoming targets are embedded (Green et al., 2005; Stormer et al., 2009). The mechanisms involved in the control of attention can be assessed by comparing brain activity during the cue period (i.e., the interval between the cue and the onset of the target or stream of sounds) in conditions in which attention is directed to a particular feature (e.g., location or pitch) that defined either the likely incoming target or the streams of sounds to be attended. Such a design has enabled researchers to identify a frontal-parietal network in the deployment of attention to either a particular location (Hill & Miller, 2009; Salmi et al., 2007a, 2009; Wu et al., 2007) or sound identity (Hill & Miller, 2009). A similar frontal-parietal network has been reported during attention switching between locations in both auditory modality and visual modality (Salmi et al., 2007b; Shomstein & Yantis, 2004; Smith et al., 2010), sound locations (left vs. right ear), or sound identities (male vs. female voice) (Shomstein & Yantis, 2006). The network that mediates voluntary control of auditory spatial and nonspatial attention encompasses several brain areas that vary among studies and task and include, but are not limited to, the inferior and superior frontal gyrus, dorsal precentral sulcus, IPS, superior parietal lobule, and auditory cortex. Some of the areas (e.g., anterior cingulate, FEFs, superior parietal lobule) involved in orienting auditory spatial attention are similar to those observed during the orientation of visual spatial attention, suggesting that control of spatial attention may be supported by a

combination of supramodal and modality- specific brain mechanism (Wu et al., 2007). The activations in this network vary as a function of the feature to be attended, with location recruiting the parietal cortex to a greater extent and attention to pitch recruiting the inferior frontal gyrus. These findings resemble studies of auditory working memory for sound location and sound identity (Alain et al., 2008; Arnott et al., 2005; Rama et al., 2004). In addition to brain areas that appear to be specialized in terms of processing isolated featural information in individual modalities, other sites have been identified whose role may be considered more integrative in nature. For example, the left IPS has been found to be equally active when attention is directed to sound identity or sound location (Hill & Miller, 2009). Moreover, the left IPS is also activated by tool- or hand-manipulated sounds, as previously discussed. This suggests that the IPS may be an integrative center that coordinates attention regardless of which class of features is the focus of attention (Hill & Miller, 2009). Additionally, the neural networks involved in the endogenous control of attention differ from those engaged by salient auditory oddball or novel stimuli designed to capture a participant’s attention in an exogenous fashion. Salmi et al. (2009) used fMRI to measure brain activity elicited by infrequently occurring loudness deviation tones (LTDs) while participants were told to focus their attention on one auditory stream (e.g., left ear) and to ignore sounds presented in the other ear (i.e., right ear). The LTD occurred in both streams and served as a means of assessing involuntary (i.e., bottom-up) attentional capture. The authors found impaired performance when the targets were preceded by LTDs in the unattended location, and this effect coincided with enhanced activity in the ventromedial prefrontal cortex (VMPFC), possibly related to evaluation of the distracting event (Figure 11.7). Together, these fMRI studies reveal a complex neural network involved in the deployment of auditory attention. In a recent study, Gamble and Luck (2011) measured auditory ERPs while listeners were presented with two clearly distinguishable sound objects occurring in the left and right hemispace simultaneously. Participants indicated whether a predefined target was present or absent. They found an increased negativity between 200 and 400 ms that was maximum at anterior and contralateral electrodes to the target location, which was followed by a posterior contralateral positivity. These results suggest that auditory attention can be quickly deployed al ain, arnot t, dyson


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Figure 11.7 Blood oxygenation level–dependent effects showing brain areas that are activated by bottom-up data-driven stimulus designed to capture a participant’s attention (i.e., loud deviant sounds) as well as those reflecting top-down controlled processes engaged a spatial cuing task. Note the overlap between the frontal eye field (FEF), the temporal parietal junction (TPJ), and the superior parietal lobule (SPL) during bottom-up and top-down controlled auditory spatial attention. Cb, cerebellum; CG/medFG, cingulated/medial frontal gyrus; IFG/MFG, inferior frontal gyrus/middle frontal gyrus; IPS, intraparietal sulcus; OC, occipital cortex; PMC, premotor cortex. (Adapted from Salmi et al., 2009.)

to the sound object location. More important, these findings suggest that scalp-recording of ERP may provide a useful tool for studying the deployment of auditory attention in real-life situations in which multiple sound objects are simultaneously present in the environment. This is an important issue to address given that auditory perception often occurs in a densely cluttered, rapidly changing acoustic environment, where multiple sound objects compete for attention.

Deployment of Attention in Time Although the data clearly provide evidence for neural modulation with respect to auditory spatial attention, it has been argued that the role of location in audition is less critical than in vision (Woods et al., 2001), and that in contrast to the high spatial resolution of the visual system, the auditory system shows similarly acute sensitivity with respect to the temporal domain (Welch & Warren, 1980). Ventriloquism and sound-induced visual temporal illusions (Shams et al., 2000; Recanzone, 2003) are good examples of this property. Sanders and Astheimer (2008) showed that listeners can selectively direct their attention to specific time 230

points that differ by as little as 500 ms, and that doing so improves target detection, affects baseline neural activity preceding stimulus presentation, and modulates auditory evoked potentials at a perceptually early stage (Figure 11.8). Rimmele, Jolsvai, and Sussman (2011) set up spatial and temporal expectation using a moving auditory stimulus. They found that implicitly directing attention to a specific moment in time modulated the amplitude of auditory ERPs, independently from spatially directing attention. These studies show that listeners can flexibly allocate temporally selective attention over short intervals (for a more extensive review, see Jones, 1976; Jones & Boltz 1989).

Future Directions Over the past decade, we have seen a significant increase in research activity regarding the mechanisms supporting the varieties of auditory attention. Attention to auditory material engages a broadly distributed neural network that varies as a function of task demands, including selective, divided, and sustained attention. An important goal for future research will be to clarify the role of auditory cortical areas as well as those beyond auditory cortices



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Figure 11.8 A, Schematic of the paradigm used to investigate the deployment of auditory attention in time. B, Accuracy in detecting targets at the designated time. C, The first row shows scalp-recorded auditory evoked potential at the midline central site (i.e., Cz) for the whole epoch. The amplitude of the contingent negative variation (CNV) increased as the designate attended time increased. The second row shows the transient N1 and P2 wave at the midline frontal site (i.e., Fz) elicited by the stimuli when they occurred at the attended time (thick line). Note that the N1 amplitude was larger when the stimulus occurred at the attended time than when attention was allocated to a different time. (Adapted from Sanders & Astheimer, 2008.)

(e.g., parietal cortex) in auditory attention. This may require a combination of neuroimaging techniques such as EEG, TMS, and fMRI, as well as animal studies using microstimulation and selective-deactivation (e.g. cooling) techniques combined with behavioral measures. Current research using relatively simple sounds (e.g., pure tone) suggests that selective attention may involve facilitation and suppression of task-relevant and task-irrelevant stimuli, respectively. However, further research is needed to determine the extent to which attentional mechanisms derived from paradigms using relatively simple stimuli account for the processes involved in more complex and realistic listening situations often illustrated using the cocktail party example. Speech is a highly familiar stimulus, and our auditory system has had the opportunity to learn about speech-specific properties (e.g., f0,

formant transitions) that may assist listeners while they selectively attend to speech stimuli (Rossi-Katz & Arehart, 2009). For instance, speech sounds activate schemata that may interact with more primitive mechanisms, thereby influencing our incoming acoustic data to perceptually organize and select for further processing. It is unlikely that such schemata play an equivalent role in the processing of pure tones, so the relationship between bottom-up and top-down contributions in the deployment of attention may be different according to the naturalism of the auditory environment used. Lastly, spoken communication is a multimodal and highly interactive process whereby visual input can help listeners identify speech in noise and can also influence what is heard. Hence, it is also important to examine the role of visual information during selective attention to speech sounds. al ain, arnot t, dyson


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In short, it is clear that auditory attention plays a central (and perhaps even primary) role in guiding our interaction with the external world. However, in reviewing the literature, we have noted how auditory attention is intimately connected with other fundamental issues such as multimodal integration, the relationship between perception and action-based processing, and how mental representations are maintained across both space and time. In advancing the field, it will be important not to ignore the complexity of the problem, such that our understanding of the neural substrates that underlie auditory attention reflect this core mechanism at its most ecologically valid expression.

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