Why would Musical Training Benefit the Neural Encoding of Speech? The OPERA Hypothesis

Abstract

Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The "OPERA" hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: (1) Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope), (2) Precision: music places higher demands on these shared networks than does speech, in terms of the precision of processing, (3) Emotion: the musical activities that engage this network elicit strong positive emotion, (4) Repetition: the musical activities that engage this network are frequently repeated, and (5) Attention: the musical activities that engage this network are associated with focused attention. According to the OPERA hypothesis, when these conditions are met neural plasticity drives the networks in question to function with higher precision than needed for ordinary speech communication. Yet since speech shares these networks with music, speech processing benefits. The OPERA hypothesis is used to account for the observed superior subcortical encoding of speech in musically trained individuals, and to suggest mechanisms by which musical training might improve linguistic reading abilities.

Keywords: hypothesis; music; neural encoding; neural plasticity; speech.

Figures

Figure 1

A simplified schematic of the ascending auditory pathway between the cochlea and primary auditory cortex, showing a few of the subcortical structures involved in auditory processing, such as the cochlear nuclei in the brainstem and the inferior colliculus in the midbrain. Solid red lines show ascending auditory pathways, dashed lines show descending (“corticofugal”) auditory pathways. (In this diagram, corticofugal pathways are only shown on one side of the brain for simplicity. See Figure 2 for a more detailed network diagram). From Patel and Iversen (2007), reproduced with permission.

Figure 2

Schematic diagram of the auditory system, illustrating the many subcortical processing stations between the cochlea (bottom) and cortex (top). Blue arrows represent ascending (bottom-up) pathways; red arrows represent descending projections. From Chandrasekaran and Kraus (2010), reproduced with permission.

Figure 3

The auditory brainstem response to the spoken syllable /da/ (red) in comparison to the acoustic waveform of the syllable (black). The neural response can be studied in the time domain as changes in amplitude across time (top, middle and bottom-left panels) and in the spectral domain as spectral amplitudes across frequency (bottom-right panel). The auditory brainstem response reflects acoustic landmarks in the speech signal with submillisecond precision in timing and phase locking that corresponds to (and physically resembles) pitch and timbre information in the stimulus. From Kraus and Chandrasekaran (2010), reproduced with permission.

Figure 4

Time–amplitude waveform of a 40-ms synthesized speech stimulus /da/ is shown in blue (time shifted by 6 ms to be comparable with the neural response). The first 10 ms of the syllable are characterized by the onset burst of the consonant /d/; the following 30 ms are the formant transition to the vowel /a/. The time–amplitude waveform of the time-locked brainstem response to the 40-ms /da/ is shown below the stimulus, in black. The onset response (V) begins 6–10 ms following the stimulus, reflecting the time delay to the auditory brainstem. The start of the formant transition period is marked by wave C, marking the change from the burst to the periodic portion of the syllable, that is, the vowel. Waves D, E, and F represent the periodic portion of the syllable (frequency-following response) from which the fundamental frequency (F0) of the stimulus can be extracted. Finally, wave O marks stimulus offset. From Chandrasekaran and Kraus (2010), reproduced with permission.

Figure 5

Frequency-following responses to the spoken syllable /mi/ with a “dipping” (tone 3) pitch contour from Mandarin. The top row shows FFR waveforms of a musician and non-musician subject; the bottom row shows the fundamental frequency of the voice (thin black line) and the trajectories (yellow lines) of the FFR's primary periodicity, from the same two individuals. For the musician, the FFR waveform is more periodic and its periodicity tracks the time-varying F0 contour of the spoken syllable with greater accuracy. From Wong et al. (2007), reproduced with permission.

Figure 6

F0 contours of the three Mandarin tones used in the study of Song et al. (2008): high-level (tone 1), rising (tone 2), and dipping/falling–rising (tone 3). Reproduced with permission.

Figure 7

Brainstem encoding of the fundamental frequency (F0) the Mandarin syllable /mi/ with a “dipping” (tone 3) pitch contour. The F0 contour of the syllable is shown by the thin black line, and the trajectory of FFR periodicity is shown by the yellow line. Relative to pretraining (left panel), the post-training FFR in the same participant (right panel) shows more faithful tracking of time-varying F0 contour of the syllable. Data from a representative participant from Song et al. (2008). Reproduced with permission.

Figure 8

Examples of non-linguistic tonal stimulus waveforms for rise times of 15 (A) and 300 ms (B). From Goswami et al. (2002), reproduced with permission.

Figure 9

Grand average cortical responses from temporal electrodes T3 and T4 (red: right hemisphere, blue: left hemisphere) and broadband speech envelope (black) for the sentence “the young boy left home.” Ninety-five milliseconds of the prestimulus period is plotted. The speech envelope was shifted forward in time 85 ms to enable comparison to cortical responses. From Abrams et al. (2008), reproduced with permission.