One of the most common assumptions about singing is that poor perception ability drives poor production ability. If people cannot hear pitches accurately, then it stands to reason that they will be inaccurate at imitating those pitches. This is the prediction of the perception-based model (Figure 1, left). Several studies have investigated this hypothesis, and the evidence is mixed. Using a variety of different singing and pitch perception tasks, some studies have found evidence of a correlation between the two abilities (e.g., Amir et al., 2003; Watts et al., 2005; Moore et al., 2007; Estis et al., 2009, 2011). However, many others, using similar designs, have failed to find a significant correlation (e.g., Bradshaw and McHenry, 2005; Dalla Bella et al., 2007; Pfordresher and Brown, 2007; Moore et al., 2008), which argues more for a dual-route model of perception and production (Figure 1, right), making the overall evidence mixed at best.

Two studies addressing this issue are worth pointing out in particular. First, in one of the few studies to use an experimental design, Zarate et al. (2010a) trained participants to better perceive small variations in pitch in the context of micromelodies. However, although they improved at perception, they did not improve in their abilities to produce these same small pitch changes. They concluded that perceptual training does not aid singing ability, thus contradicting the perceptual-based model. Second, in their 2007 study, Pfordresher and Brown found no correlation between pitch perception abilities and their imitation tasks, nor any problems with vocal pitch range in their sample. Thus, they posited that sensori-motor mismappings were the best remaining explanation for poor singing ability in most cases, such that perceived tones were incorrectly mapped onto motor outputs.

In order to sort out the causes of poor singing ability, Hutchins and Peretz (2012a) used a novel methodology involving a new instrument called a slider. This slider produced a synthesized vocal tone that was subject to many of the same limitations as the human voice, including a very fine scale of pitch control. Instead of using their vocal apparatus, though, the participant played the slider by pressing a finger onto a touch-sensitive strip. Thus, it provided a measurement of pitch matching ability independent of the ability to control one's vocal musculature. Pitch-matching ability on the slider was compared to the ability to vocally match a synthesized vocal tone and a prior recording of one's own voice. Participants who could match the pitch with the slider but not with their voice were thus likely to have a vocal-motor control impairment as their primary cause of singing inaccuracies. Those who could match the pitch with the slider and match the recording of their own voice (which had the same timbre as their attempts to match it), but not the synthesized vocal tone, were likely to have a sensori-motor impairment as their primary cause of singing inaccuracies. These singers had a specific difficulty in translating between the timbre of the synthesized voice and the timbre of their own voice. Because their primary deficit was neither in perceiving the relationships among tones, nor in controlling their vocal muscles, but in connecting their perception to an appropriate production, this is considered to be a type of sensori-motor impairment. Finally, those singers who failed at matching pitch both with the slider and the voice are likely to have a perceptual deficit.

The results showed about 20% of singers had a vocal-motor control impairment, 35% had a sensori-motor (timbre) deficit, and only 5% had a perceptual deficit. Participants were universally better at matching pitch with the slider than with their voice, and the results showed a wide range of singing abilities among non-musicians. Singing ability was not aided by multiple attempts, nor was it improved by a visualization of their produced pitch. Although these results show that perception is not a limiting factor in most people's pitch imitation ability, there was nevertheless a modest correlation among non-musicians (r = 0.4) between accuracy on the slider and with their voice. These results point to a strong effect of motor and sensori-motor factors on singing ability, with a moderate influence of perceptual ability. This pattern of results suggests aspects of both the perceptual-based model and the dual-route model of vocal perception and production.

Other studies have also shown effects of the target's timbre on pitch-matching ability. Singers are better able to match the pitch of vocal targets with a similar voice than the pitch of instruments (Watts and Hall, 2008) and better able to match the pitch of their own voice than the pitch of other targets (Moore et al., 2008). Poor singers are especially aided by using a human, rather than synthetic, target pitch (Léveque et al., 2012). Educators also report that children tend to be able to match pitch better when modeling a similar voice (reviewed in Goetze et al., 1990).

A number of functional imaging studies have investigated the brain areas that support singing production. These studies have localized the “singing network,” which includes the auditory cortex, insula, supplementary motor area and anterior cingulated, as well as parts of the motor cortex specific to the mouth/lips and larynx. (Perry et al., 1999; Brown et al., 2004; Özdemir et al., 2006; Kleber et al., 2007). This network is involved in motor production, motor planning of sequences, motor initiation, and articulation.

Singing ability is also reflected in neural activation patterns. For example, as might be expected, highly trained singers show more recruitment of laryngeal and mouth areas of the somatosensory cortex than less-trained singers, an effect related to the amount of singing practice (Kleber et al., 2010). They also show more activation in non-cortical regions, such as the basal ganglia, the thalamus, and the cerebellum (Kleber et al., 2010). Other studies using a pitch-shift paradigm, in which the singer's auditory feedback is manipulated while producing the tones, have shown that experienced singers recruit more areas of the singing network than untrained singers (Zarate and Zatorre, 2008). This methodology has shown a particularly strong role of the dorsal premotor cortex in regulating and controlling responses to auditory feedback; this area is thus thought to be highly involved in the interface between perception and production (Zarate and Zatorre, 2008; Zarate et al., 2010b).

While there has been a good amount of research on singing ability and the factors underlying singing ability, there has been quite a bit less research done of vocal perception. However, we know a great deal about auditory perception in general. In the case of pitch, we can measure just-noticeable differences (or difference limens); in some cases these can be as low as five cents (Zwicker and Fastl, 1999). Individual differences in pitch difference limens, which can be considerable, could contribute to differences in vocal pitch perception abilities. The timbre of tones can also affect pitch perception abilities. Changes in timbre interfere with pitch judgments (Melara and Marks, 1990a,b,c; Krumhansl and Iverson, 1992), and timbre and pitch have been shown not to be perceptually independent (Melara and Marks, 1990a,b,c; Krumhansl and Iverson, 1992; Pitt, 1994; Warrier and Zatorre, 2002). Musicians seem to be less susceptible to timbral interference of pitch processing, however, (Beal, 1985; Pitt and Crowder, 1992; Pitt, 1994).

There is also considerable variability in preferences and judgments of musical intervals. Listeners will show differences between what they consider to be an acceptably-tuned musical interval or note (Rakowski, 1990; Vurma and Ross, 2006; Hutchins et al., 2012), as well as differences in their identification judgments of intervals (Siegel and Siegel, 1977; Halpern and Zatorre, 1979). There are also individual differences related to musical training in preferences in listening to certain types of consonant vs. dissonant intervals (McDermott et al., 2010).

Experience and training can play a large role in pitch perception ability, as evidenced by the differences between musicians and non-musicians (e.g., Pitt, 1994; Moreno and Besson, 2006; Moreno et al., 2009; McDermott et al., 2010; Hutchins et al., 2012). Even among non-musicians, pitch discrimination abilities can be improved with extra training (Zarate et al., 2010a). Tone-language speakers, too, show better pitch perception abilities, presumably due to their greater experience in pitch processing (Pfordresher and Brown, 2009; Bidelman et al., 2013a). Among bilinguals, there is also evidence of causality running in the opposite direction, such that musical ability is predictive of the ability to discriminate and produce non-native speech sounds, both for linguistic tones (Gottfried et al., 2004; Alexander et al., 2005) and for non-tone phonemes (Slevc and Miyake, 2006). Musically trained participants are also better at detecting pitch changes in speech in a foreign language (Marques et al., 2007).

One of the most important neurological correlates of pitch processing ability is the auditory brainstem response (ABR). This response mimics the pitch and some timbral characteristics of a presented tone (Krishnan, 2007; Skoe and Kraus, 2010) and occurs very early in processing, being recorded typically with less than a 10 ms lag following the stimulus. One characteristic of the ABR that is of particular interest is the fact that trained musicians show a higher-fidelity ABR with a shorter lag than non-musicians; this higher fidelity ABR correlates with better ability to make behavioral pitch judgments (Kraus et al., 2009; Bidelman et al., 2011). This benefit is not limited to musicians but generalizes to other groups with high expertise in pitch, such as tonal language speakers (Krishnan et al., 2008; Bidelman et al., 2013b). Other studies have shown that the ABR preserves timbral characteristics more accurately in people with musical backgrounds (Kraus et al., 2009; Bidelman and Krishnan, 2010; Strait et al., 2012). This early benefit in pitch and timbre perception seems to precede cortical representations of pitch and timbre and may be transformed to a more conceptual-level representation of the response as it is transmitted upwards (Bidelman et al., 2013a). This response most likely occurs before any task-relevant effects have time to affect the neural representation. Thus, the fidelity of the brainstem response is a good candidate to affect the accuracy of both pitch perception and production, and may be an indicator of the earliest level of perceptual processing.

One way of learning about the causes and effects of pitch perception, as well as its relationship to production and to the domain of language, is by looking at cases where pitch perception is compromised. Congenital amusia, which is a neurogenetic disorder (Peretz et al., 2007) characterized by impaired music perception ability in the absence of brain damage or hearing or cognitive impairments (Peretz, 2008), provides this kind of test case. This condition is formally diagnosed by the Montreal Battery of Evaluation of Amusia (MBEA; Peretz et al., 2003). The majority of congenital amusics seem to suffer from a selective pitch perception deficit. Amusics are impaired at detecting pitch changes of less than a semitone (Peretz et al., 2002; Hyde and Peretz, 2004) and distinguishing between rising and falling pitches (Foxton et al., 2004; Liu et al., 2010). Amusics also seem to be somewhat impaired in timbre perception (Tillmann et al., 2009; Marin et al., 2012) and memory for pitch (e.g., Gosselin et al., 2009; Tillmann et al., 2009; Williamson et al., 2010). Their condition often leads to amusics not enjoying or seeking out music. Subjectively, they report that music seems like noise; thus it is reasonable to suspect a vicious circle here, where amusics tend to listen to music less often, thus gaining less experience with processing it, making listening even less rewarding than it otherwise might have been.

As would be expected from this type of condition, amusics are impaired in their singing abilities as well. Congenital amusics are judged as poor singers (Ayotte et al., 2002) and make considerably more pitch errors in singing a well-known song than do matched controls (Dalla Bella et al., 2009; Tremblay-Champoux et al., 2010). They are also well-below controls at matching single pitches (Hutchins et al., 2010). However, there are some signs that amusics are not uniformly poor at singing. Certain amusics seem to sing considerably better than would be predicted by their poor perceptual abilities (Dalla Bella et al., 2009; Hutchins et al., 2010; Tremblay-Champoux et al., 2010), and amusics as a whole are aided when directly imitating a model, rather than singing from memory (Tremblay-Champoux et al., 2010). For example, one amusic, ML, is able to sing an array of songs just as well as or better than unimpaired individuals despite her inability to hear errors in songs. These types of findings suggest that conscious perceptual ability may not be a hard limit on amusics' singing abilities. Further evidence for this and its implications will be reviewed later in this paper.

Anatomic and functional MRI studies have shown several differences between congenital amusics and unimpaired individuals. Congenital amusics typically show reduced white matter in the right inferior frontal gyrus, as well as thicker cortices in both that area and the right auditory cortex (Hyde et al., 2007). There is some evidence that there may be differences between amusics and controls in the left analogues of those regions as well (Mandell et al., 2007). In the right hemisphere, these two regions also show reduced functional connectivity (Hyde et al., 2011), and diffusion tensor imaging has shown reduced anatomical connectivity in the right arcuate fasciculus connecting these two regions (Loui et al., 2009). There is some evidence that different regions of the arcuate fasciculus may correlate with pitch perception ability and the discrepancy between perception and production ability (Loui et al., 2009), but this has yet to be corroborated.

Electrophysiological evidence also supports the relationship between pitch perception abilities and frontal-auditory connectivity. Amusics show a normal mismatch negativity (MMN) response (a pre-conscious response to deviations in sound generated in the auditory cortex, Näätänen et al., 2007) to small deviations in pitch which they are unable to consciously detect (Moreau et al., 2009; Peretz et al., 2009). These same deviations, however, generate no P3b response, normally indicative of attentive processing (Moreau et al., 2013). These components, then, seem to be markers of conscious and unconscious pitch perception ability. Taken together, the evidence indicates that frontal regions, auditory regions, and the connection between them regulate normal pitch perception ability, and that there may be anatomically and functionally distinct regions responsible for conscious and unconscious pitch processing. While the regions and processes investigated in these studies are not voice-specific, this type of pitch processing is likely a precursor to voice specific perception and production abilities, which may also be anatomically and functionally distinct.

One possible explanation of amusics' better-than-expected singing abilities is that our ability to perceive vocal pitch (and by extension, the processes underlying this ability) may be different from our ability to perceive the pitch of non-vocal tones, such as instruments or synthesized tones. While it is obvious that we can distinguish between the voice and other instruments, not many studies have examined the uniqueness of vocal musical perception. One clue that there may be fundamental differences between vocal and non-vocal pitch perception comes from the tuning perception literature. It has been noticed that pitch errors seem to be less noticeable when produced by a voice than by other instruments (Seashore, 1938; Sundberg, 1979). For example, Lindgren and Sundberg (as cited in Sundberg, 1979, 1982) showed that musically experienced listeners would accept as in-tune up to 50–70 cents of tuning errors in a recording of a highly trained singer. Another study looked at recordings of 10 professional singers performing the same song, and found that listeners were highly variable in their assessments of the tuning, with out-of-tune notes being accepted as in-tune and well-tuned notes sometimes being judged as out-of-tune (Sundberg et al., 1996). In contrast, studies of acceptable tuning in synthesized tones show a much smaller range of acceptable tuning, with listeners accepting only 10–15 cents of error (Fyk- in van Besouw et al., 2008). This seems to indicate that listeners use different criteria when judging the pitch of the voice vs. other instruments.

To investigate this effect in a well-controlled manner, Hutchins and Peretz (2012a) directly compared tuning judgments of real and synthesized voices. Musicians and non-musicians listened to pairs of tones and judged them as the same or different. Listeners were less likely to notice the differences in tuning when the tone pairs were real voices than when they were synthesized voices; this pattern held across musicians and non-musicians. Non-musicians needed the two tones to be 50 cents apart to reliably notice the difference between two real vocal tones, compared with only 30 cents for synthesized vocal tones. This pattern held in musicians as well. Hutchins et al. (2012) found very similar results for tuning judgments of a trained voice vs. a violin and extended these findings to a melodic context. This difference in acceptable and noticeable tuning between voices and other timbres was termed the Vocal Generosity Effect and may be evidence of special processing of voices in a musical context as it is consistent across different voices and instruments.

Different types of tuning errors between vocal and non-vocal stimuli are also found in production. Trained singers tend to show more tuning errors than trained instrumentalists. Trained singers have a propensity to begin a note flat (Seashore, 1938), and analyses of recordings of professional singers show deviations of more than 40 cents, both sharp and flat (Prame, 1997). In contrast, studies of violin and wind instruments show average deviations less than 20 cents. This difference in production ability comes despite the fact that people have considerable amounts of experience using their voice. In experts, though, there is a tendency for instrumentalists to practice much more than vocalists (as the voice tends to tire out after a couple of hours of practice). In addition, singers typically use considerably more vibrato than do performers on other instruments, such as the violin (Prame, 1997; Mellody and Wakefield, 2000). Vibrato is sometimes thought to be a way of hiding tuning errors (Yoo et al., 1998), although listeners are nevertheless capable of making quite accurate tuning judgments even for tones with very high-amplitude vibrato (Shonle and Horan, 1980). However, unlike the case of perception, many of these differences between voice and instruments can be explained by the unique motoric requirements of vocal production, which are substantially different from those required by any other instrument.

If the voice is processed differently from other instruments, then we should see special neural processes and regions devoted to vocal perception and production. And indeed, there is evidence for just such effects. Belin et al. (2000) showed evidence for subregions of the auditory cortex particularly sensitive to voice perception, called temporal voice areas. These are located bilaterally along the mid superior temporal sulcus, and respond to the voice independent of its linguistic content. Temporal voice areas become less active as the vocal signal is degraded by filtering, indicating a sensitivity to the quality of the input that was reflected in both fMRI and behavioral voice discrimination judgments. Electrophysiological studies also indicate special processing of the voice, with vocal sounds eliciting a fronto-temporal positivity/occipital negativity when compared to environmental sounds or birdsong, peaking around 200 ms post-stimulus (Charest et al., 2009). Another study found a similar frontal positivity of sung tones compared to instrumental sounds, but a bit later, likely due to the more similar acoustic characteristics of these stimuli (Levy et al., 2001), although an MEG study failed to show any differences between similar types of stimuli (Gunji et al., 2003). To the best of our knowledge, no one has yet run an fMRI study comparing activation from perceiving humming to that of perceiving instruments to look for vocal-specific regions involved in music processing. Given the specificity of the motor demands of singing, we would expect to find some such regions; such an experiment would provide an important contribution to the field.

Some of the best evidence arguing for a dual-route model of perception and production comes from congenital amusics. Although most congenital amusics, who have severely impaired pitch perception abilities, are impaired in their singing ability, there is evidence that some amusics nevertheless retain the ability to sing accurately. Dalla Bella et al. (2009) identified three amusics (out of eleven tested) who were unimpaired at singing the correct intervals in a well-known song, including one who was unimpaired even without the aid of the lyrics—a condition in which most amusics fail to complete more than a few notes of the song. Hutchins et al. (2010) tested congenital amusics in a single-pitch matching task and found that despite amusics' overall inaccurate performances, they showed a consistent, linear relationship between the imitations and the target tones.

These studies hint that amusics may demonstrate better overall singing ability than would be predicted from their abilities on perceptual tasks. Recently, a number of studies have attempted to directly compare perception and production abilities in amusia, to serve as direct tests of vocal perception and production models. Loui et al. (2008) presented three amusics with two note sequences and asked amusics to imitate the interval, then to describe whether the second note had been higher or lower than the first. The amusics were impaired at describing the direction of the second note, but they performed similarly to controls at singing an interval that went in the correct direction, although they were still inaccurate at producing an interval of the correct distance.

Some of our recent work also demonstrates a similar discrepancy between pitch perception and production ability in amusics. In one ongoing study (Hutchins and Peretz, 2010), we tested amusics' pitch matching abilities with the slider and a vocal imitation condition (the same as used in Hutchins and Peretz, 2012a, Experiment 1; see above). As expected, amusics as a group performed worse than matched controls at both slider and vocal pitch matching. However, we found two participants who performed at levels comparable to normal participants on the vocal imitation task and, notably, better than their performance on the slider. This is a pattern of results not found among normal participants, who almost invariably show excellent pitch matching performance on the slider, even among non-musicians. This demonstrates that for these two amusics, their vocal pitch matching ability was not constrained by their pitch perception ability, arguing against the perceptual-based model of pitch perception and production.

Another of our studies looked at the pitch shift effect. This effect is an automatic compensatory response to a sudden shift in pitch of the feedback of a sung or spoken utterance. When most participants hear such a shift in their own voice, there is a quick reaction to change the pitch of their voice in the opposite direction. We tested amusics and controls in a pitch shift paradigm, where a pitch shift would occur in the middle of an imitative response. Our results showed that a subset of amusics showed a preserved pitch shift effect, showing normal pitch shift responses to both large (2 semitone) and small (25 cent) shifts. This is strong evidence that amusics do process even small pitch shifts when they are relevant to vocal-motor control. In addition, this study also found evidence of a correlation between the pitch shift effect and pitch matching accuracy (absent of any shift), strengthening the idea that this retained pitch shift response is related to generally preserved vocal-motor control. Together, this presents a strong contrast with amusics' previously documented disabilities in consciously perceiving small pitch changes.

We also see evidence for dissociation of vocal perception and production abilities in amusics' use of pitch in speech. Unlike in tone languages, pitch is non-lexical in most European languages. However, it plays a strong role in prosody and can determine the meaning of certain types of statement/question pairs. Liu et al. (2010) showed that amusics were somewhat poorer than controls at discriminating between statements and questions differing only in pitch contour. However, just as with intervals (Loui et al., 2008), they were better at imitating the pitch contour of these same sentences (although still below the level of matched controls). Hutchins and Peretz (2012b) tested amusics with speech examples containing pitch changes that did not systematically alter the meaning of the sentence. In this experiment, amusics showed an impaired ability to perceive pitch changes between sentences, but no impairment at imitating those same pitch differences, compared to controls. Similarly, in the pitch shift study (Hutchins and Peretz, 2013), we found no difference between pitch shift responses to spoken vs. sung utterances. The fact that pitch perception-production dissociation occurs across music and speech indicates that it is a function of vocal pitch perception and control, rather than a function of music.

Neural evidence also supports the dissociation between pitch perception and production in amusics. Loui et al. (2009) found that pitch perception abilities were correlated with tract density along the superior route of the arcuate fasciculus, whereas the lower route was correlated with the difference between their perception and production abilities. While a somewhat complicated story (all the more so because the association runs in the reverse direction to some other theories of dual-route processing, e.g., Goodale and Milner, 1992; Hickok and Poeppel, 2004), this is the first evidence of direct correlations between these dissociations in amusics and specific neuroanatomical structures.

A few studies have shown similar evidence for dissociations between perception and production abilities in an unimpaired population. In one study, Hafke (2008) used a vocal pitch shift paradigm to test trained singers. She found that they showed a normal pitch shift effect, even when the shifts were so small that the participants were unaware that they had occurred at all. This is similar to the pattern of results found among congenital amusics (Hutchins and Peretz, 2013). Vurma (2010) showed a related effect, demonstrating that trained singers' musical interval production abilities are more finely honed than their abilities to perceive the same intervals. Results such as these indicate that the independence of vocal-motor pitch control from conscious pitch perception is not limited to cases such as amusia, which again argues against a perceptual-based model.

The reverse pattern, better conscious perception than production ability, is even more common in normal participants. Hutchins and Peretz (2012a) showed that almost every participant was more capable of matching pitch with an instrument than with their voice in many cases over an order of magnitude better. This pattern held true for musicians and non-musicians alike and demonstrated that poor vocal pitch accuracy does not lead to poor pitch perception ability, as would be predicted by a motor theory. However, there was a moderate correlation between instrumental and vocal pitch matching abilities, arguing against a dual-route theory. A few other studies have found evidence of such perception-production connections (e.g., Amir et al., 2003; Watts et al., 2005; Moore et al., 2007; Estis et al., 2009, 2011), though others have failed to do so (Bradshaw and McHenry, 2005; Dalla Bella et al., 2007; Pfordresher and Brown, 2007; Moore et al., 2008). The preponderance of evidence shows a weak connection between pitch perception and singing ability, but also indicates that poor pitch perception ability is not necessarily the main cause of poor singing ability.

Similar evidence of this dissociation comes from second language learners. Many late second language learners will gain the ability to comprehend a second language, but will nevertheless be unable to speak it with any degree of fluency. Other second language learners, however, will show an opposite pattern, where their production ability will outstrip their comprehension ability. This latter pattern is typically shown by people who need to perform or deliver information in a second language, such as the singer who performs a Mozart opera without speaking a word of German, whereas the former is more characteristic of an immigrant immersed in a second language who does not have the opportunity or inclination to speak it often. Again, like with pitch in singing, perception and production ability in a second language will broadly correlate, but are nevertheless dissociable abilities.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.