voice. Furthermore, during passive listening, neither perturbation produced any N100 amplitude differences. This suggests that suppression of the auditory cortex is greatest when afferent sensory feedback matches an expected outcome specifically during speech production (Heinks‐Maldonado et al., 2005).
Functional magnetic resonance imaging (fMRI) studies have broadly concurred with the electrophysiological evidence. For example, Tourville, Reilly, and Guenther (2008) compared the blood‐oxygen‐level‐dependent (BOLD) response in trials when there was a first formant feedback shift to trials in which there was no modification of the auditory feedback. This comparison showed activation in posterior temporal regions consistent with previous findings in which noise masked the speech (Christoffels, Formisano, & Schiller, 2007), auditory feedback was delayed (Hashimoto & Sakai, 2003), vocal pitch feedback was altered (Zarate & Zatorre, 2008 and MEG studies of pitch shifts: Franken et al., 2018). Tourville, Reilly, and Guenther (2008) also reported greater activity in the shift–no shift comparison in the right‐hemisphere ventral premotor cortex for trials in which the first formant was shifted. They interpreted their findings as support of the DIVA model components that perform auditory‐error detection and compensatory motor responses.
These studies and many others support the existence of neural mechanisms that use the auditory character of the talker’s speech output to control articulation. However, the challenge of mapping high‐level computational models to behavioral and neural data remains. The necessity of different levels of description and the units within the levels are difficult to determine. In short, while there may be only a single neural architecture that manages fluent speech, many abstract cognitive models could be consistent with this architecture (see Griffiths, Lieder, & Goodman, 2015, for a discussion of constraints on cognitive modeling). An additional approach is to examine ontogeny for relationships between perception and production.
Auditory feedback and vocal learning
Much is made about the uniqueness of human language and at the speech level, the frequent focus of these uniqueness claims is on the perceptual skills of the developing infant. However, the less emphasized side of communication, speaking, is clearly a specialized behavior. Humans are part of a small cohort of species that are classified as vocal learners and who acquire the sounds in their adult repertoire through social learning (Petkov & Jarvis, 2012). This trait seems to be an example of convergent evolution in a few mammalian (humans, dolphins, whales, seal, sea lions, elephants, and bats) and bird (songbirds, parrots, and hummingbirds) species. The behavioral similarities shown by these disparate species are mirrored by their neuroanatomy and gene expression. In a triumph of behavioral, genetic, and neuroanatomic research, a consortium of scientists has shown similarities in brain pathways for vocal learners that are not observed in species that do not learn their vocal repertoires (Pfenning et al., 2014).
As shown by the studies of deafness summarized earlier, hearing is vital for vocal learning. However, the role that auditory feedback plays in speech development is unclear. There are few human developmental studies that manipulate feedback in the early stages of speech development. Jones and colleagues (Scheerer, Liu, & Jones, 2013; Scheerer, Jacobson, & Jones, 2016, 2019) have shown that children as young as two years of age show compensation for F0 perturbations. However, feedback perturbation of segmental properties such as vowel formant frequency shows a different pattern of results. MacDonald et al. (2012) tested children at two and four years of age, as well as adults in a formant feedback perturbation paradigm. By the age of four, young children acted like adults and partially compensated in response to F1 and F2 perturbations. At the age of two, however, the toddlers showed two significant patterns in response to feedback perturbations (see Figure 4.2). On average there was no evidence of compensatory behavior when the two‐year‐old children were presented with altered feedback of vowel formants. Further, they produced utterances that were remarkably variable. Variability is one of the hallmarks of early speech and birdsong development. But what role does this variability play in development and does feedback processing have a developmental profile? We will return to this question.
The DIVA model proposes that early vocal development involves a closed‐loop imitation process driven initially by two stages of babbling and later a vocal learning stage that directly involves corrective feedback processing. In the first babbling phase, random articulatory motions generate somatosensory and acoustic consequences, and a mapping of the developing vocal tract as a speech device takes place. Separately, infants learn the perceptual categories of their native language. This is crucial to the model. As Guenther (1995, p. 599) states, “the model starts out with the ability to perceive all of the sounds that it will eventually learn to produce.” When the second stage of babbling begins, it involves the mapping between phonetic categories developed through perceptual learning and articulation. The babbling during this period tunes the feedback system to permit corrective responses to detected errors. In the next imitation phase, infants hear adult syllables that they try to reproduce. In a cyclical process involving sensory feedback from actual productions and better feedforward commands, the system shapes the early utterances toward the native language.
Simulations by Guenther and his students support the logic of this account. However, there are significant concerns. First among these is that the data supporting this process are weak or, in the case of MacDonald et al.’s (2012) results, contradict the hypothesis. Early speech feedback processing and the shaping of speech production targets is not well attested. Second, the proposal relies on a strong relationship between the representations of speech perception and speech production. Surprisingly, this relationship is controversial.
Models like DIVA predict a phenomenon that is frequently assumed but is not strongly supported by actual data – babbling drift. The hypothesis that the sounds of babbling drift over time was first proposed by Roger Brown (1958). Brown suggested that the phonetic repertoire in the babbling of infants slowly begins to resemble the phonetics of the language environment that they are exposed to and begins to not include sounds that are absent from the native language. As the review by Best et al. (2016) indicates, the support for this idea is mixed, particularly from transcription studies and perceptual studies where naive listeners attempted to identify the language environment of the infant’s babbling.
There is broad agreement that early babbling has common characteristics across languages and a somewhat limited phonetic repertoire. The evidence from later babbling lacks this broad consensus. Older transcription studies of late babbling were often plagued by small sample sizes and bias issues inherent in transcription. Studies with larger sample sizes, however, still show conflicting patterns of results. For example, de Boysson‐Bardies and Vihman (1991) reported that the prevalence of consonants of different manners and places of articulation in the babbling of 12‐month old infants from English, French, Japanese, and Swedish homes corresponded to the distributions of consonants in their language environments. In contrast, a number of other transcription studies have failed to find such differences (e.g. Kern, Davis, & Zink, 2009; Lee, Davis, & MacNeilage, 2010).
Figure 4.2 Average F1 (circles) and F2 (triangles) frequencies estimates across time for adults (top panel), young children (middle panel), and toddlers (bottom panel). The formant frequencies have been normalized to the average baseline frequencies. The shaded area indicates when subjects were given altered auditory feedback (from MacDonald et al., 2012).
Source: MacDonald et al., © 2012, Elsevier.
Another approach has been to use recordings of infants babbling as perceptual stimuli and ask adult listeners to categorize what native language the infants have. These studies have also shown mixed results, with some
