Transformation of a temporal speech cue to a spatial neural code in human auditory cortex

Abstract
Introduction
Results
Discussion
Materials and methods
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Abstract

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like /b/ and/p/. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from /ba/ to /pa/. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population’s preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues.

Introduction

During speech perception, listeners must extract acoustic cues from a continuous sensory signal and map them onto discrete phonetic categories, which are relevant for meaning (Stevens, 2002; Liberman et al., 1967). Many such cues to phonological identity are encoded within the fine temporal structure of speech (Shannon et al., 1995; Rosen, 1992; Klatt, 1976). For example, voice-onset time (VOT), defined as the interval between a stop consonant’s release and the onset of vocal fold vibration (acoustically, the burst and the voicing), is a critical cue that listeners use to distinguish voiced (e.g., /b/, /d/, /g/) from voiceless (e.g., /p/, /t/, /k/) stop consonants in English (Liberman et al., 1958; Lisker and Abramson, 1964). When the burst and voicing are roughly coincident (short VOT; ~0 ms), listeners perceive a bilabial stop as a /b/, but when voicing follows the burst after a temporal gap (long VOT; ~50 ms), listeners hear a /p/.

Recent evidence from human electrocorticography (ECoG) has shown that information about a speech sound’s identity is encoded in the amplitude of neural activity at phonetically-tuned cortical sites in the superior temporal gyrus (STG) (Mesgarani et al., 2014). Distinct neural populations in this region respond selectively to different classes of phonemes that share certain spectral cues, such as the burst associated with stop consonants or the characteristic formant structure of vowels produced with specific vocal tract configurations. However, it is unclear whether phonetic categories distinguished by temporal cues (e.g., voiced vs. voiceless stops) are represented within an analogous spatial encoding scheme. If so, this would entail that local neural populations are tuned to detect not merely the presence of certain spectral cues (the burst and voicing), but also their timing relative to one another.

In addition to distinguishing phonetic categories, the exact VOT of a given utterance of a /b/ or a /p/ will vary considerably depending on numerous factors such as speech rate, phonetic context, and speaker accent (Miller et al., 1986; Kessinger and Blumstein, 1997; Klatt, 1975; Lisker and Abramson, 1967; Allen et al., 2003; Flege and Eefting, 1986; Fox et al., 2015). Although only categorical phonetic identity (e.g., whether a particular VOT is more consistent with a /b/ or a /p/) is strictly necessary for understanding meaning, sensitivity to fine-grained sub-phonetic detail (e.g., whether a particular /p/ was pronounced with a 40 ms vs. a 50 ms VOT) is also crucial for robust speech perception, allowing listeners to flexibly adapt and to integrate multiple cues to phonetic identity online in noisy, unstable environments (Miller and Volaitis, 1989; Clayards et al., 2008; Kleinschmidt and Jaeger, 2015; McMurray and Jongman, 2011; Toscano and McMurray, 2010; Fox and Blumstein, 2016). However, the neurophysiological mechanisms that support listeners’ sensitivity (Kuhl, 1991; Carney, 1977; Pisoni and Tash, 1974; Massaro and Cohen, 1983; Andruski et al., 1994; McMurray et al., 2002; Schouten et al., 2003) to such detailed speech representations are not known. We tested whether sub-phonetic information might be encoded in the neural response amplitude of the same acoustically-tuned neural populations that encode phonetic information in human auditory cortex.

To address these questions, we recorded neural activity directly from the cortex of seven human participants using high-density ECoG arrays while they listened to and categorized syllables along a VOT continuum from /ba/ (0 ms VOT) to /pa/ (50 ms VOT). We found that the amplitude of cortical responses in STG simultaneously encodes both phonetic and sub-phonetic information about a syllable’s initial VOT. In particular, spatially discrete neural populations respond preferentially to VOTs from one category (either /b/ or /p/). Furthermore, peak response amplitude is modulated by stimulus VOT within each population’s preferred – but not its non-preferred – voicing category (e.g., stronger response to 0 ms than to 10 ms VOT in voiced-selective [/b/-selective] neural populations). This same encoding scheme emerged in a computational neural network model simulating neuronal populations as leaky integrators tuned to detect either temporal coincidences or gaps between distinct spectral cues. Our results provide direct evidence that phonetic and sub-phonetic information carried by VOT are represented within spatially discrete, phonetically-tuned neural populations that integrate temporally-distributed spectral cues in speech. This represents a crucial step towards a unified model of cortical speech encoding, demonstrating that both spectral and temporal cues and both phonetic and sub-phonetic information are represented by a common (spatial) neural code.

Results

Participants listened to and categorized speech sounds from a digitally synthesized continuum of consonant-vowel syllables that differed linearly only in their voice-onset time (VOT) from /ba/ (0 ms VOT) to /pa/ (50 ms VOT). This six-step continuum was constructed by manipulating only the relative timing of the spectral burst and the onset of voicing while holding all other acoustic properties of the stimuli constant (Figure 1A/B; see Materials and methods) (Klatt, 1980). Analysis of participants’ identification behavior confirmed that stimuli with longer VOTs were more often labeled as /pa/ (mixed effects logistic regression: β_VOT = 0.19, t = 17.78, p=5.6*10⁻⁶³; data for example participant in Figure 1C; data for all participants in Figure 1—figure supplement 1). Moreover, and consistent with past work, listeners’ perception of the linear VOT continuum was sharply non-linear, a behavioral hallmark of categorical perception (Liberman et al., 1957; Liberman et al., 1961; Kronrod et al., 2016). A psychophysical category boundary between 20 ms and 30 ms divided the continuum into stimuli most often perceived as voiced (/b/: 0 ms, 10 ms, 20 ms VOTs) or as voiceless (/p/: 30 ms, 40 ms, 50 ms VOTs).

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Speech sound categories that are distinguished by a temporal cue are spatially encoded in the peak amplitude of neural activity in distinct neural populations.

(A) Stimuli varied only in voice-onset time (VOT), the duration between the onset of the burst (top) and the onset of voicing (bottom) (a.u. = arbitrary units). (B) Acoustic waveforms of the first 100 ms of the six synthesized stimuli. (C) Behavior for one example participant (mean ± bootstrap SE). Best-fit psychometric curve (mixed effects logistic regression) yields voicing category boundary between 20–30 ms (50% crossover point). (D) Neural responses in the same representative participant show selectivity for either voiceless or voiced VOTs at different electrodes. Electrode size indicates peak high-gamma (HG; z-scored) amplitude at all speech-responsive temporal lobe sites. Electrode color reflects strength and direction of selectivity (Spearman’s ρ between peak HG amplitude and VOT) at VOT-sensitive sites (p<0.05). (E) Average HG responses (± SE) to voiced (0–20 ms VOTs; red) and voiceless (30–50 ms VOTs; blue) stimuli in two example electrodes from (D), aligned to stimulus onset (e1: voiceless-selective, V-; e2: voiced-selective, V+). Horizontal black bars indicate timepoints with category discriminability (p<0.005). Grey boxes mark average peak window (± SD) across all VOT-sensitive electrodes (n = 49). (F) Population-based classification of voicing category (/p/ vs. /b/) during peak window (150–250 ms after stimulus onset). Chance is 50%. Boxes show interquartile range across all participants; whiskers extend to best- and worst-performing participants; horizontal bars show median performance. Asterisks indicate significantly better-than-chance classification across participants (p<0.05; n.s. = not significant). Circles represent individual participants.

Temporal cues to voicing category are encoded in spatially distinct neural populations

To investigate neural activity that differentiates the representation of speech sounds based on a temporal cue like VOT, we recorded high-density electrocorticography in seven participants while they listened to the VOT continuum. We examined high-gamma power (70–150 Hz) (Chang, 2015; Crone et al., 2001; Steinschneider et al., 2008; Ray and Maunsell, 2011), aligned to the acoustic onset of each trial (burst onset), at every speech-responsive electrode on the lateral surface of the temporal lobe of each patient (n = 346 electrodes; see Materials and methods for details of data acquisition, preprocessing, and electrode selection).

We used nonparametric correlation analysis (Spearman’sρ) to identify electrodes where the peak high-gamma amplitude was sensitive to stimulus VOT. Across all participants, we found 49 VOT-sensitive sites, primarily located over the lateral mid-to-posterior STG, bilaterally. Peak response amplitude at these VOT-sensitive electrodes reliably discriminated between voicing categories, exhibiting stronger responses to either voiced (/b/; VOT = 0–20 ms; n = 33) or voiceless (/p/; VOT = 30–50 ms; n = 16) stimuli (Figure 1D; locations of all sites shown in Figures 2A and Figure 1—figure supplement 2). We observed that, within individual participants, electrodes spaced only 4 mm apart showed strong preferences for different voicing categories, and we did not observe any clear overall regional or hemispheric patterns in the prevalence or selectivity patterns of VOT-sensitive electrodes (see Materials and methods for additional information).

Robust category selectivity in voiceless-selective (V-) and voiced-selective (V+) neural populations emerged as early as 50–150 ms post-stimulus onset and often lasted for several hundred milliseconds (example electrodes in Figure 1E). Across all VOT-sensitive electrodes, voicing category selectivity was reliable whether a trial’s voicing category was defined based on the psychophysically-determined category boundary (0–20 ms vs. 30–50 ms VOTs; V- electrodes: z = 3.52, p=4.4×10⁻⁴; V+ electrodes: z = −5.01, p=5.4×10⁻⁷; Wilcoxon signed-rank tests) or based on the actual behavioral response recorded for each trial (V- electrodes: p=4.9×10⁻⁴; V+ electrodes: p=6.1×10⁻⁵; Wilcoxon signed-rank tests).

These results show that spatially distinct neural populations in auditory cortex are tuned to speech sound categories defined by a temporal cue. Critically, if individual neural populations only responded to spectral features (e.g., to the burst or to the onset of voicing), we would not have observed overall amplitude differences in their responses to /b/ versus /p/ categories.

Given this pattern of spatial tuning, we tested whether the voicing category of single trials could be reliably decoded from population neural activity across electrodes. For each participant, we trained a multivariate pattern classifier (linear discriminant analysis with leave-one-out cross validation) to predict trial-by-trial voicing category using high-gamma activity across all speech-responsive electrodes on the temporal lobe during the peak neural response (150–250 ms after stimulus onset; see Materials and methods). We found that, across participants, classification accuracy was significantly better than chance (Wilcoxon signed-rank test: p=0.016; Figure 1F, leftmost box plot), demonstrating that spatially and temporally distributed population neural activity during the peak response contains information that allows for decoding of a temporally-cued phonetic distinction in speech.

Peak neural response amplitude robustly encodes voicing category

Next, we asked which features of the population neural response encode voicing category. Specifically, we evaluated three alternatives for how temporally-cued voicing category is encoded by high-gamma responses in cortex during the peak neural response: (1) the spatial pattern of peak response amplitude across electrodes, (2) the temporal patterns of evoked responses across electrodes during the peak response, or (3) both amplitude and timing of neural activity patterns. We tested these hypotheses by selectively corrupting amplitude and/or temporal neural features that were inputs for the classifier. As with the previous analyses, and following prior work on speech sound encoding (Mesgarani et al., 2014), these analyses (Figure 1F) focused on cortical high-gamma activity during the peak response window (150–250 ms after stimulus onset; but see Figure 3 for analyses of an earlier time window).

To corrupt temporal information, we randomly jittered the exact timing of the neural response for each trial by shifting the 100 ms analysis window by up to ±50 ms. Because the uniform random jitter was applied independently to each trial, this procedure disrupts any temporal patterns during the peak neural response that might reliably distinguish trials of different voicing categories, such as precise (millisecond-resolution) timing of the peak response at an electrode or the dynamics of the evoked response during the peak window, including local temporal dynamics (during a single electrode’s peak response) or ensemble temporal dynamics (the relative timing of responses of spatially-distributed electrodes in the same participant). To corrupt amplitude information, we eliminated any condition-related differences in the peak response amplitude at every electrode. For each electrode, the evoked high-gamma response to all trials within a given voicing category were renormalized so that the average responses to both voicing categories had identical amplitudes at the peak, but could still vary reliably in the timing and dynamics during the peak window. These techniques allowed us to examine the relative contributions of temporal and amplitude information contained within the peak neural response window to the classification of voicing category (see Materials and methods for detailed description of this approach).

Across participants, we found that, when the classifiers had access to amplitude information but not timing information (+Amplitude/-Timing) during the peak response, performance was significantly better than chance (Wilcoxon signed-rank test: p=0.016; Figure 1F). Furthermore, despite the profound corruption of temporal information in the neural responses, classification accuracy was statistically comparable to the model that had access to both amplitude and timing information (+Amplitude/+Timing; Wilcoxon signed-rank test: p=0.69; Figure 1F), suggesting that amplitude information alone is sufficient for classifying a trial’s voicing category.

In contrast, when amplitude information was corrupted and only temporal patterns in the peak response window were reliable (-Amplitude/+Timing), classifier performance was not different from chance (Wilcoxon signed-rank test: p=0.69; Figure 1F) and was worse for every participant compared to the model with both types of information (Wilcoxon signed-rank test: p=0.016). Finally, we compared the model with only timing information to a model where both amplitude and timing information during the peak window were corrupted (-Amplitude/-Timing). We found that preserving timing information alone had no effect on classification performance compared to the most impoverished model (-Amplitude/-Timing; Wilcoxon signed-rank test: p=0.58; Figure 1F), which also failed to perform better than chance (Wilcoxon signed-rank test: p=0.94; Figure 1F). Together, these results constitute evidence for a spatial/amplitude code for speech categories that differ in a temporal cue. Thus, localized peak high-gamma response amplitude spatially encodes voicing of single trials in STG, analogous to other spectrally-cued phonetic features (Mesgarani et al., 2014). Note that, while spatial (and not temporal) patterns of high-gamma responses robustly encode voicing during this critical peak window, we later describe additional analyses that address possible temporal encoding patterns in the local field potential (Figure 1—figure supplements 3 and 4) and in an earlier time window (Figure 3).

The encoding of stop consonant voicing in the amplitude of evoked high-gamma responses in STG suggests that the representation of temporally-cued phonetic features may be explained within the same neural coding framework as the representation of spectrally-cued phonetic features. However, previous work on the cortical representation of voicing has identified a role for temporal information in the local field potential (LFP) (Steinschneider et al., 1999; Steinschneider et al., 2013), which is dominated by lower- frequencies (Buzsáki et al., 2012; Einevoll et al., 2013).

To link our results with this existing literature, we conducted a series of exploratory analyses of the neural responses to our stimuli using the raw voltage (LFP) signal. For each VOT-sensitive electrode (defined in the high-gamma analysis), we measured the correlations between VOT and peak latency and between VOT and peak amplitude for three peaks in the auditory evoked potential (AEP) occurring approximately 75–100 ms (P_α), 100–150 ms (N_α), and 150–250 ms (P_β) after stimulus onset (Figure 1—figure supplement 3; Howard et al., 2000; Nourski et al., 2015). We found that some VOT-sensitive electrodes encoded VOT in the latency of these peaks (e.g., Figure 1—figure supplement 4, panels E/I/M), replicating previous results (Steinschneider et al., 2011). However, among electrodes that encode VOT in peak high-gamma amplitude, there exist many more electrodes that do not encode VOT in these temporal features of the AEP, and many that also encode VOT in the amplitude of these AEP peaks (Figure 1—figure supplements 3 and 4). This further supports the prominent role that amplitude information plays in the neural representation of voicing and VOT, both in high-gamma and in the LFP. Therefore, subsequent analyses focus on the high-gamma amplitude. (For detailed descriptions of these LFP analyses and their results, see Methods and Figure 1—figure supplements 3 and 4).

Peak response amplitude encodes sub-phonetic VOT information within preferred category

Next, we assessed whether VOT-sensitive neural populations (Figure 2A), which reliably discriminate between phonetic categories (voiced vs. voiceless), also encoded within-category sub-phonetic detail in the peak response amplitude. Specifically, the cortical representation of stimuli from the same voicing category but with different VOTs (e.g., 30, 40, and 50 ms VOTs that all correspond to /p/) could be either categorical (i.e., all elicit the same peak response amplitude) or graded (i.e., peak response amplitude depends on within-category VOT).

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Human auditory cortex encodes both phonetic (between-category) and sub-phonetic (within-category) information in peak response amplitude, which can be modeled by a simple neural network that implements temporal gap and coincidence detection.

(A) Spatial distribution of VOT-sensitive electrodes across all participants (on standardized brain). (B) Average (± SE) normalized HG response to each VOT across all voiceless-selective (V-) electrodes, aligned to stimulus onset. Line style denotes category membership of a given VOT (solid: preferred category; dashed: non-preferred category). Grey box marks average peak window (± SD) across all VOT-sensitive electrodes. (C) Average (± SE) normalized response to each VOT across all voiced-selective (V+) electrodes. (D) Average (± SE) peak response to each VOT stimulus for V- electrodes (left) and V+ electrodes (right) (see Materials and methods). (E) A simple neural network model (top) comprised of five leaky integrator nodes was implemented to examine computational mechanisms that could account for the spatial encoding of a temporal cue (VOT). Arrows and circle represent excitatory and inhibitory connections between nodes. See Materials and methods for details on model parameters. Postsynaptic potentials (PSPs) illustrate the internal dynamics of the gap detector (Gap, middle) and coincidence detector (Coinc, bottom) in response to simulated VOT stimuli (line color). Outputs (panels F/G) are triggered by suprathreshold instantaneous PSPs (ΣPSP≥θ, dark lines) but not by subthreshold PSPs (ΣPSP<θ; semitransparent lines). (F) Model outputs (a.u. = arbitrary units) evoked by simulated VOT stimuli for Gap (one cycle = 10 ms). Note that outputs for 0 ms and 10 ms VOTs are overlapping. No error bars shown because model simulations are deterministic. Grey box marks average peak window (across panels F/G); width matches peak window of real neural data (panels B/C). (G) Model outputs for Coinc (H) Peak response to each simulated VOT stimulus for Gap (left) and Coinc (right).

We examined the average responses to each of the six VOTs separately in the voiceless-selective electrodes (V-; Figure 2B) and the voiced-selective electrodes (V+; Figure 2C). We observed clear differences in activity evoked by different VOTs at the peak response (~200 ms after stimulus onset), even within the same voicing category, consistent with sensitivity to sub-phonetic detail (Blumstein et al., 2005; Toscano et al., 2010; Toscano et al., 2018; Frye et al., 2007). However, the discriminability of responses to within-category VOTs depended on the preferred voicing category of a given electrode.

To quantify this observation, at each electrode, we computed the rank-based correlation (Spearman’s ρ) between stimulus VOT and peak response amplitude separately for each voicing category (0–20 ms and 30–50 ms VOTs). This procedure resulted in two correlation coefficients for each VOT-sensitive site (ρ_0-20, ρ_30-50) and corresponding test statistics reflecting the strength of within-category amplitude encoding of stimulus VOT in each voicing category. These test statistics (one per voicing category per VOT-sensitive electrode) then served as the input data for a series of signed-rank statistical tests to assess overall within-category encoding properties of groups of electrodes (e.g., of all V- electrodes) (see Methods for details). For example, consider V- electrodes, which exhibit stronger responses, overall, for voiceless stimuli (30–50 ms VOTs) compared to voiced stimuli (0–20 ms VOTs). Across V- electrodes, we found that voiceless stimuli with longer VOTs (i.e., closer to the preferred category’s 50 ms endpoint VOT) also elicit increasingly stronger responses (Wilcoxon signed-rank test: z = 3.52, p=4.4×10⁻⁴). At the same V- sites, however, within-category VOT does not reliably predict response amplitude among (non-preferred) voiced stimuli (Wilcoxon signed-rank test: z = −1.60, p=0.11; Figure 2B: differences among solid blue lines but not dashed red lines). Across all V- and V+ electrodes, peak high-gamma response amplitude encoded stimulus VOT within the preferred category (Wilcoxon signed-rank test: z = 6.02, p=1.7×10⁻⁹), but not the non-preferred category (Wilcoxon signed-rank test: z = 1.31, p=0.19). While V- electrodes encoded sub-phonetic VOT more robustly within the voiceless category than within the voiced category (Figure 2D, left; Wilcoxon signed-rank test: z = 3.00, p=2.7×10⁻³), the opposite pattern emerged for V+ electrodes, which encoded sub-phonetic VOT more robustly within the voiced category than within the voiceless category (Figure 2D, right; Wilcoxon signed-rank test: z = 3.78, p=1.6×10⁻⁴).

Together, these analyses revealed two key results: (1) VOT encoding in human STG is not purely categorical, but also (2) the relationship between response amplitude and VOT is not linear across the entire continuum (Figure 2D). These results suggest that, even at the level of STG, the brain maintains information about the specific, sub-phonetic details of individual speech sounds. The asymmetrical pattern of within-category encoding suggests that individual neural populations in human auditory cortex encode information about both the category identity of a speech sound and its more fine-grained acoustic properties, or its category goodness (Kuhl, 1991; Blumstein et al., 2005; Myers, 2007).

A simple neural network model of VOT encoding in STG

Thus far, we have demonstrated that a temporal cue that distinguishes speech sounds is represented by a spatial/amplitude code (Ferster and Spruston, 1995; Shadlen and Newsome, 1994) in human STG. To understand how this could be implemented computationally in the brain, we built an architecturally minimalistic neural network (Figure 2E, top). The network was designed to implement a small set of basic computations, motivated by well-established models of temporal processing (Buonomano and Merzenich, 1995; Gao and Wehr, 2015; Eggermont, 2000; Carr, 1993; Konishi, 2003; Rauschecker, 2014; Rauschecker, 1998). Specifically, our model employs discrete integrator units that detect temporal gaps or coincidences between distinct spectral events by incorporating canonical neurophysiological mechanisms that allow current input to modulate a unit’s sensitivity to subsequent input in highly specific ways.

The entire model is comprised of just five localist units: a burst detector, a voicing detector, a gap detector (Gap), a coincidence detector (Coinc), and an inhibitory unit. Conventional leaky integrator dynamics governed continuously varying activation values of each rectified linear unit within the model (McClelland and Rumelhart, 1981; McClelland et al., 2014), with the activity $a_{i} (t)$ of a given unit $i$ at time $t$ depending on its prior activity $a_{i} (t - 1)$ , the weighted sum of its excitatory and inhibitory inputs $\sum_{j} w_{j i} * a_{j} (t - 1)$ , and unit-specific activation parameters (e.g., propagation threshold [ $θ$ ], decay rate). To illustrate intuitively how time-dependent neuronal properties can give rise to spatially-localized temporal cue processing, model parameters and connection weights were set manually (see Methods for details; Figure 2—figure supplement 1; Supplementary file 2). We presented the network with simplified inputs mimicking the spectral and temporal properties of the six VOT stimuli used in the ECoG experiment (Figure 1A; see Materials and methods; Supplementary file 3). Presentation of burst and voicing inputs triggered propagation of activation that spread through the network, and our analyses assessed how the resulting activation dynamics differed depending on VOT.

The simulated responses of Gap and Coinc to VOTs of 0–50 ms are shown in Figure 2F/G. We observed striking qualitative similarities between Gap simulated outputs (Figure 2F) and the real neural responses of V- electrodes (Figure 2B), and between Coinc outputs (Figure 2G) and the V+ electrodes (Figure 2C). By design, voicing category is clearly distinguished in both Gap and Coinc, with Gap responding more strongly to longer (voiceless) VOTs (30–50 ms), and Coinc responding more strongly to shorter (voiced) VOTs (0–20 ms). This demonstrates that spatial encoding of temporal cues (gaps vs. coincidences) can arise naturally within a simple, biologically-inspired neural network (Buonomano and Merzenich, 1995; Gao and Wehr, 2015; Eggermont, 2000; Carr, 1993; Konishi, 2003; Rauschecker, 2014; Rauschecker, 1998).

Perhaps more surprisingly, we also found that both Gap and Coinc detector units exhibit sensitivity to within-category VOT distinctions (Figure 2H). These partially graded activations mirror the pattern observed in the neural data (Figure 2D), where V- electrodes and Gap units are only sensitive to differences among long (voiceless) VOTs, and V+ electrodes and Coinc units are only sensitive to differences among short (voiced) VOTs.

These relatively sophisticated dynamics are the natural result of well-established computational and physiological mechanisms. Within the model, the burst and voicing detector units are tuned to respond independently to distinct spectral cues in the simulated acoustic input. Hence, the relative timing of their responses, but not their amplitudes, differ as a function of VOT. Both the gap (Gap) and the coincidence (Coinc) detector units receive excitatory input from both the burst and voicing detector units, but Gap and Coinc. differ in how they integrate these inputs over time. Specifically, as described below, while initial excitatory input (from the burst detector) temporarily decreases the sensitivity of Gap to immediate subsequent excitatory input (from the voicing detector), the opposite is true of Coinc.

In particular, prior work has shown that one computational implementation of gap detection involves configuration of a slow inhibitory postsynaptic potential (IPSP) microcircuit (Figure 2E, middle) (Buonomano and Merzenich, 1995; Gao and Wehr, 2015; Douglas and Martin, 1991; McCormick, 1989). In our model, activity in the burst detector following burst onset elicits fast suprathreshold excitatory postsynaptic potentials (EPSPs) in both Gap and the inhibitory unit, immediately followed by a longer-latency (‘slow’) IPSP in Gap. This slow IPSP renders Gap temporarily insensitive to subsequent excitatory input from the voicing detector, meaning that voicing-induced excitation that arrives too soon (e.g., 10 ms) after the burst input, when inhibition is strongest, is not able to elicit a second suprathreshold EPSP in Gap. Consequently, all short VOTs (below some threshold) elicit uniformly weak responses in Gap that reflect only the initial excitatory response to the burst (see, e.g., indistinguishable responses to 0 ms and 10 ms VOTs in Figure 2F). However, as Gap gradually recovers from the burst-induced slow IPSP, later-arriving voicing input (i.e., longer VOTs) tends to elicit suprathreshold responses that grow increasingly stronger with longer gaps, until Gap has reached its pre-IPSP (resting) baseline. In this way, our implementation of gap detection naturally captures three key patterns observed across V- electrodes (Figure 2H, left; Figure 2D, left): (1) amplitude encoding of a temporally cued category (selectivity for gaps over coincidences); (2) amplitude encoding of within-category differences in the preferred category (amplitude differences among gaps of different durations); and (3) no amplitude encoding of differences within the non-preferred category (uniformly lower amplitude responses to short VOTs of any duration).

In contrast, coincidence detection (Carr, 1993; Konishi, 2003; Rauschecker, 2014; Margoliash and Fortune, 1992; Peña and Konishi, 2001; Pena and Konishi, 2002; Figure 2E, bottom) emerges in the model because activity in the burst detector evokes only a subthreshold EPSP in Coinc, temporarily increasing the sensitivity of Coinc to immediate subsequent excitatory input (from the voicing detector). During this period of heightened sensitivity, voicing-induced excitatory input that arrives simultaneously or after short lags can elicit larger amplitude (additive) EPSPs than could voicing-induced excitatory input alone. Because the magnitude of the initial burst-induced EPSP gradually wanes, the summation of EPSPs (from the burst and voicing) is greatest (and hence elicits the strongest response) for coincident burst and voicing (0 ms VOT), and the magnitude of the Coinc response to other voiced stimuli (e.g., 10–20 ms VOTs) becomes weaker as the lag between burst and voicing increases. Finally, in voiceless stimuli, since voicing arrives late enough after the burst (30+ ms) that there is no residual boost to the Coinc baseline post-synaptic potential, elicited responses are entirely driven by a suprathreshold voicing-induced EPSP that reaches the same peak amplitude for all voiceless stimuli. Thus, our implementation of coincidence detection captures three key patterns observed in V+ electrodes (Figure 2H, right; Figure 2D, right): (1) amplitude encoding of a temporally cued category (selectivity for coincidences over gaps); (2) amplitude encoding of within-category differences in the preferred category (amplitude differences among stimuli with short VOTs); and (3) no amplitude encoding of differences within the non-preferred category (uniformly lower amplitude responses to long VOTs of any duration).

In summary, the neurophysiological dynamics underlying local STG encoding of VOT can be modeled using a simple, biologically-inspired neural network. The computational model captures both the between-category (phonetic) and within-category (sub-phonetic) properties of observed neural representations via well-established physiological mechanisms for gap and coincidence detection (Buonomano and Merzenich, 1995; Gao and Wehr, 2015; Eggermont, 2000; Carr, 1993; Konishi, 2003; Rauschecker, 2014; Rauschecker, 1998).

Mechanisms that explain local category selectivity also predict early temporal dynamics

Thus far, we have focused on the encoding of speech sounds that differ in VOT based on activity patterns around the peak of the evoked response. However, in comparing the real and simulated neural data (Figure 2), we also observed a qualitative resemblance with respect to the onset latencies of evoked responses. Specifically, the timing of the evoked neural responses (relative to burst onset) appeared to depend on stimulus VOT in V+ electrodes and in the coincidence detector (Coinc) unit (Figure 2C/G), but not in V- electrodes or in the gap detector (Gap) unit (Figure 2B/F). This pattern could suggest that early temporal dynamics of the evoked response contribute to the pattern of category selectivity observed at the peak.

We examined the neural activity evoked by each VOT stimulus in V- and V+ electrodes at the onset of the response, typically beginning approximately 75–125 ms after stimulus (burst) onset. In the same two example electrodes from Figure 1E, we observed clear differences in the relationship between response onset latency and VOT (Figure 3A). To quantify the onset latency for each electrode to each VOT stimulus, we found the first timepoint after stimulus onset where the evoked high gamma response exceeded 50% of the electrode’s overall peak amplitude (grand mean across conditions). The rank correlation between VOT and response onset latency for e1 (a V- electrode) was substantially lower (Spearman’s ρ = 0.42) than for e2 (a V+ electrode; ρ = 0.89).

Figure 3

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Early temporal dynamics of stimulus-evoked neural responses differ between voiceless-selective (V-) and voiced-selective (V+) electrodes.

(A) Normalized trial-averaged HG responses to each VOT stimulus (line color) in two example electrodes (e1 and e2; same electrodes shown in Figure 1D/E). The time window (x-axis) is relative to onset of the burst and precedes the peak response. Horizontal bars show estimates (bootstrapped mean ± SE) of response onset latency for each VOT (first timepoint exceeding 50% of electrode’s average peak HG). Mean bootstrapped rank-based correlation (Spearman’s ρ) between VOT and response onset latency shown for e1 (blue) and e2 (red). (B) Across all V- electrodes, the bootstrapped correlation coefficients did not differ significantly from 0, suggesting that onset latency was time-locked to the burst. In contrast, across all V+ electrodes, the bootstrapped correlation coefficients were reliably positive (longer latencies for longer VOTs), and greater than for V- electrodes. Circles represent individual electrodes (filled: example electrodes in A). Boxes show interquartile range; whiskers extend to maximum/minimum of each group (excluding two outlier V+ electrodes); vertical bars are medians. Asterisks indicate significance (p<10⁻⁴; n.s. = not significant).

A bootstrapped rank-based correlation coefficient was computed for each V- and V+ electrode (1000 resamples; see Methods). We found that response onset latency was strongly associated with VOT for V+, but not V-, electrodes (Wilcoxon signed-rank tests: V+, p=1.6×10⁻⁶; V-, p=0.57), and this difference between the two electrode types was highly reliable (Mann-Whitney rank-sum test: p=1.7×10⁻⁵) (Figure 3B).

The association between VOT and response latency also differed in Gap versus Coinc units in the model simulations (Figure 2F/G), with VOT-dependent response latencies emerging for Coinc, but not Gap. Closer examination of the model’s internal dynamics reveals how the same time-dependent mechanisms that give rise to peak amplitude encoding of VOT are also responsible for these early temporal dynamics. As described above, the category selectivity of Gap (voiceless) and Coinc (voiced) results from how each unit’s subsequent activity is modulated after detection of the burst. While the burst always elicits a fast suprathreshold response in Gap (irrespective of VOT), the Coinc response to the burst alone is subthreshold (Figure 2E, middle vs. bottom). Consequently, the initial Gap response is evoked by the burst of any VOT stimulus, so the response onset latency (when aligned to burst onset) does not depend on VOT (Figure 2F). Conversely, the earliest suprathreshold Coinc response is triggered by the onset of voicing, so the response onset latency (relative to burst onset) is later for longer VOTs (Figure 2G). Thus, the same well-established physiological mechanisms that give rise to peak amplitude encoding of temporally-cued voicing categories also predict the early temporal dynamics we observe in real neural data.

Finally, Figure 3 shows that, unlike during the peak response window (150–250 ms after stimulus onset; Figure 1F), temporal information does encode VOT during an earlier window around the neural response onset in some neural populations. Indeed, both sub-phonetic and phonetic category-level information are carried by the onset latency of V+ electrodes, with evoked responses arising later at these sites for stimuli with progressively longer VOTs. Critically, the modeling results indicate that both the amplitude encoding patterns during the peak window and the temporal encoding patterns during the earlier onset window are captured by the same canonical neurophysiological mechanisms.

Discussion

This study investigated how voice-onset time (VOT), a temporal cue in speech, is represented in human auditory cortex. Using direct intracranial recordings, we found discrete neural populations located primarily on the bilateral posterior and middle STG that respond preferentially to either voiced sounds, where the onset of voicing is coincident with the burst or follows it after a short lag (20 ms or less), or voiceless sounds, where the onset of voicing follows the burst after a temporal gap of at least 30–50 ms.

Past work has also found that phonetic information about speech sounds is encoded in the amplitude of evoked neural responses at spatially localized cortical sites (Mesgarani et al., 2014). In that work, however, STG activity was shown to encode the spectral properties of speech sounds most robustly, such as whether a phoneme is a vowel or a consonant and whether a consonant’s spectrum is broadband (as in plosives, like /b/ and /p/) or is dominated by acoustic energy at high frequencies (as in fricatives, like /f/ and /s/).

The present results extend these earlier findings in a critical way, suggesting that the cortical representation of both spectral and temporal cues in speech follow a common spatial coding scheme. This result is also consistent with prior reports that neural response amplitude depends on VOT (Mesgarani et al., 2014), but such results have often involved natural speech stimuli where voicing categories varied along many other spectral acoustic dimensions besides the temporal cue (Lisker, 1986; Soli, 1983; Stevens and Klatt, 1974; Summerfield and Haggard, 1977). Here, the digitally synthesized VOT stimuli were tightly controlled to vary only in the relative timing of two invariant spectral cues (burst and voicing), thereby demonstrating that this temporal speech cue is encoded in the peak high-gamma response amplitude of spatially distinct neural populations in human STG.

While the present results clearly implicate a spatial/amplitude code in the cortical representation of VOT, other work has described VOT-dependent temporal response patterns that can also be used to encode voicing categories (Eggermont, 1995; Eggermont and Ponton, 2002; Liégeois-Chauvel et al., 1999). For instance, Steinschneider and colleagues have observed neurons and neuronal populations in primate and human auditory cortices in which short VOTs elicit a single-peaked neural response, while longer VOTs elicit a double-peaked response (Steinschneider et al., 1999; Steinschneider et al., 2013; Steinschneider et al., 2011; Steinschneider et al., 2005; Steinschneider et al., 1994; Steinschneider et al., 1995; Steinschneider et al., 2003). Under this ‘local’ temporal coding model, the precise temporal dynamics of the response evoked at a single cortical site could distinguish voiced from voiceless VOTs. Our examination of the timing and amplitude of three peaks in the auditory evoked local field potentials of VOT-sensitive electrodes confirmed that such patterns do appear in some electrodes (Figure 1—figure supplements 3 and 4), clearly demonstrating that temporal and amplitude codes for VOT are not mutually exclusive (see also temporal encoding patterns in onset latencies of V+ electrodes; Figure 3). However, as with spectrally-defined phonetic contrasts (e.g., plosive vs. fricative; Mesgarani et al., 2014), it clear that the amplitude of the peak high-gamma (and, in many cases, of the LFP) response emerged as a robust representation of voicing category and of VOT.

VOT could also be encoded in the relative timing of responses in spatially-distributed, spectrally-tuned burst- and voicing-selective neural populations. Under this ‘ensemble’ temporal coding model (Theunissen and Miller, 1995; Engineer et al., 2008), the pattern of neural activity evoked by voiced VOTs (characterized by roughly coincident burst and voicing cues) would differ from the pattern evoked by voiceless VOTs in the precise temporal latency of the response in a vowel-selective neural population (a voicing detector) compared to the response in a plosive-selective neural population (a burst detector). However, the fact that we found cortical sites in every participant that exhibited robust category-dependent differences in their peak response amplitude rules out the possibility that at least these neural populations are merely responding to spectral cues in the burst or voicing alone.

Notably, if either (or both) of these models – a local or ensemble temporal code – was primarily responsible for the neural representation of VOT in the high-gamma range, then the selective corruption of temporal information in a classifier (Figure 1F) should have reduced neural decoding of voicing category to chance levels, while corrupting peak amplitude information should have had little or no effect. We found the opposite pattern of results: corrupting peak amplitude information had a devastating effect on the decoding of voicing category, while corrupting the fine temporal patterns that could have discriminated between voicing categories had no measurable impact on classifier performance. To be clear, our work does not rule out the possibility that local or ensemble temporal codes may also play a role in the cortical representation of VOT. However, it does highlight spatially-localized peak neural response amplitude as a robust code for VOT. Thus, in contrast to prior work theorizing parallel, but fundamentally different, coding schemes for spectrally- and temporally-cued phonetic features (Steinschneider et al., 1999; Steinschneider et al., 2013), we demonstrate evidence for a shared representation of both by high-gamma responses in the human superior temporal lobe.

In order to explicitly test potential computational and physiological mechanisms that could give rise to the observed spatial coding scheme, we implemented an architecturally simple neural network model. Although it is well known that spectral information is represented by a spatial neural code from the earliest stages of auditory transduction in the cochlea (Eggermont, 2001; Oxenham, 2018), the emergence of a spatial code for the representation of temporally-distributed cues in a transient acoustic signal poses a nontrivial computational problem. Our model highlights one parsimonious approach by which selectivity for either temporal gaps or coincidences could be implemented by biologically-inspired neurophysiological microcircuits (Buonomano and Merzenich, 1995; Gao and Wehr, 2015; Eggermont, 2000; Carr, 1993; Konishi, 2003; Rauschecker, 2014; Rauschecker, 1998).

We found that, just like in the neural data, gap and coincidence detector units responded to simulated voiced (/b/) and voiceless (/p/) stimuli with different response amplitudes. As such, we need not invoke any specialized temporal code to account for the representation of temporally cued phonetic features. Rather, our results provide evidence implicating a common neural coding scheme in the neural representation of behaviorally relevant speech features, whether they are embedded within the instantaneous spectrum or the fine temporal structure of the speech signal. Recent ECoG evidence suggests an even more expansive view of the fundamental role of spatial coding in cortical speech representation (Yi et al., 2019) in which different neural populations also encode pitch (Tang et al., 2017) and key properties of the speech envelope such as onsets and auditory edges (Hamilton et al., 2018; Oganian and Chang, 2019).

Crucially, although the neural network was only designed to discriminate between categories (i.e., gaps vs. coincidences), we also observed graded amplitude differences in response to different VOTs (Figure 2H), but only in an electrode’s preferred category. These within-category patterns emerged naturally from the same computational properties that allowed the network to capture basic between-category encoding: (1) the relative responsiveness of each temporal integrator unit (Gap, Coinc) to its various inputs (burst, voicing, and inhibition); (2) the time-dependent properties inherent to neuronal activation dynamics (e.g., decay of postsynaptic potentials towards a unit’s resting activation level); and (3) the nonlinear transformation of postsynaptic inputs into response outputs (rectified linear activation function controlled by a unit’s propagation threshold).

This asymmetric within-category encoding scheme closely resembled the pattern observed in real neurophysiological data, where peak response amplitude to VOTs within the same voicing category only differed within a neural population’s preferred category (Figure 2D). This result clearly demonstrates that human nonprimary auditory cortex maintains a robust, graded representation of VOT that includes the sub-phonetic details about how a particular speech token was pronounced (Blumstein et al., 2005; Toscano et al., 2010; Toscano et al., 2018; Frye et al., 2007). Even though sub-phonetic information is not strictly necessary for mapping sound to meaning in stable, noise-free listening environments, this fine-grained acoustic detail has demonstrable effects on listeners’ behavior (Kuhl, 1991; Carney, 1977; Pisoni and Tash, 1974; Massaro and Cohen, 1983; Andruski et al., 1994; McMurray et al., 2002; Schouten et al., 2003), and modern theories of speech perception agree that perceptual learning (e.g., adaptation to accented speakers) and robust cue integration would be impossible if the perception of speech sounds were strictly categorical (Miller and Volaitis, 1989; Clayards et al., 2008; Kleinschmidt and Jaeger, 2015; McMurray and Jongman, 2011; Toscano and McMurray, 2010; McClelland and Elman, 1986; Norris and McQueen, 2008; Norris et al., 2016; Magnuson et al., 2020). Crucially, these data suggest that the same spatial/amplitude code that is implicated in the representation of phonetic information (from spectral or temporal cues) can also accommodate the representation of sub-phonetic information in the speech signal.

The onset latency results (Figure 3) established an entirely novel correspondence between the real and simulated results that extended beyond the peak response window. Response onset latencies of V- electrodes were time-locked to the burst (Figures 2B and 3), while responses of V+ electrodes were time-locked to voicing onset (Figures 2C and 3). These highly reliable neurophysiological results neatly match specific predictions of our parsimonious model without the need to postulate additional mechanisms (Figure 2F/G).

The correspondence between simulated and real neural data in the onset latency results may also have implications for the question of whether the observed temporal integration is occurring locally in STG or is inherited from earlier levels of auditory processing (e.g., from midbrain or primary auditory cortex). The model’s gap and coincidence detectors (Gap, Coinc) are designed to directly simulate neural populations in the STG. Their inputs from the burst and voicing detectors are only spectrally processed, so in the model, the temporal onset latency dynamics (Figure 2F/G) first arise in Gap and Coinc. As such, the fact that the model’s prediction is borne out in the neural data in STG (Figure 2B, C and 3) is consistent with local temporal integration in STG. While these modeling results do not definitively rule out temporal integration at lower levels of the ascending auditory pathway, its potentially local emergence in high-order auditory cortex illustrates how even relatively simple computational models can be used to generate novel hypotheses, which can ultimately be tested in real neurophysiological data.

Overall, the results of these model simulations illustrate how the same network properties that transform temporal cues into a spatial code are also able to naturally explain at least three additional patterns observed within category-selective neural populations: (1) the graded encoding of VOT within a population’s preferred category; (2) the lack of graded encoding of VOT within a population’s non-preferred category; and (3) the early temporal dynamics of neural responses, which depend on a population’s category-selectivity. Thus, the model provides an explicit, mathematical account of multiple seemingly disparate observations about the neurophysiological data, all of which arise directly from a parsimonious implementation of gap- and coincidence-detection with well-established, theoretically-motivated neuronal circuits.

The model we present is just one of many possible architectures that could capture these interesting properties of the neural response. For example, mechanisms like temporal delay lines (Carr, 1993; Rauschecker, 2014) could also be used to implement gap detection. Broadly, we chose to implement a simple hand-tuned neural network model to maximize our ability to explore the detailed dynamics we observed in the neural data. Our approach follows a rich history of using these types of hand-tuned models to explain a wide array of cognitive and perceptual phenomena (including the perception of VOT in speech), as exemplified by the influential TRACE model of speech perception (McClelland and Elman, 1986). An alternative approach to modeling VOT perception is to train a neural network to distinguish voiced from voiceless sounds based on distributed activation dynamics within biologically-grounded spectral processing maps (Damper, 1994). Our model borrows aspects of these two approaches (hand-tuning; biological plausibility) and it extends this past work by directly modeling the time-dependent mechanisms that could give rise to continuously-varying neural responses in STG.

While the model captured several notable features of the neural data (including some for which it was not explicitly designed), we observed at least one inconsistency between the simulated and real neural responses. The model predicted VOT-dependence in the latency of the peak response in both Gap and Coinc units (Figure 2F/G), but we did not find evidence for these fine-grained patterns in the high-gamma data (Figure 2B/C; see also lack of category-dependent temporal patterns during peak window: Figure 1F). However, it is unclear whether this is a false prediction of the model, or whether we did not observe the effect in the neural data because of, for example, poor signal-to-noise ratio for this effect. Regardless of whether the discrepancy arises from the model or the real data, it represents a gap in our mechanistic understanding of the processing of this phenomenon, and should therefore be a target for further research.

Although topographic functional organization is pervasive among many spatial neural coding schemes described in sensory neuroscience, including for the representation of spectral and temporal acoustic cues in audition (e.g., tonotopy in mammalian auditory cortex; Eggermont, 2001; Oxenham, 2018 or chronotopy in bats; Kössl et al., 2014; Portfors and Wenstrup, 2001), this functional organization seems not to extend to the spatial code for speech on the lateral temporal cortex in humans. As with tuning for spectrally-cued phonetic features (Mesgarani et al., 2014; Hamilton et al., 2018) (e.g., plosives vs. fricatives), VOT-sensitive neural populations in the present study were scattered throughout posterior and middle superior temporal gyrus with no discernible topographical map of selectivity or evidence for lateralized asymmetries (Liégeois-Chauvel et al., 1999; Zatorre and Belin, 2001), although data limitations prevent us from ruling out this possibility entirely (for detailed results, see Material and methods).

Most of the present analyses focused on the high-gamma component of the neural response, but this work does not discount a potential role for lower-frequency oscillations in speech perception (Fries, 2009; Giraud and Poeppel, 2012) or in the perception of phonemes (Kösem et al., 2018; Peelle and Davis, 2012). Indeed, it is clear from the exploratory analyses of auditory evoked local field potentials (Figure 1—figure supplements 3 and 4) that there do exist complex associations between VOT and the amplitude/temporal information carried in lower-frequency ranges. Future work should systematically investigate the relationship between high-gamma and other neural signals (such as the local field potential), their relative contributions to the perceptual experience of and neural representation of speech, and the importance of detailed temporal information in each (see, e.g., Nourski et al., 2015).

Finally, it is critical to distinguish our results from studies describing neural correlates of categorical speech perception, per se (e.g., Chang et al., 2010). Neural responses to different VOT tokens that are members of the same voicing category can only be considered truly categorical if the responses are indiscriminable (e.g., Liberman et al., 1957; Macmillan et al., 1977). In our results, acoustically distinct members of the same phonetic category are distinguishable in neural populations that are selective for that voicing category (Figure 2). In light of this graded VOT representation, the present results are best interpreted as elucidating neural mechanisms of category perception, but not necessarily categorical perception, of voiced vs. voiceless stop consonants. While limited coverage beyond the superior temporal lobe precludes us from ruling out the influence of top-down categorical perception (Lee et al., 2012; Myers et al., 2009; Evans and Davis, 2015) (possibly originating in frontal cortex; Sohoglu et al., 2012; Leonard et al., 2016; Cope et al., 2017; Park et al., 2015) on our results, it is notable that the model we present (which does not posit top-down effects) suggests that top-down effects may not be a necessary condition for explaining the observed non-linear encoding patterns (see also McClelland et al., 2006; McQueen et al., 2006; Norris et al., 2000; McClelland and Elman, 1986; Norris and McQueen, 2008).

In conclusion, the present results show that spatially-discrete neural populations in human auditory cortex are tuned to detect either gaps or coincidences between spectral cues, and these sites simultaneously represent both phonetic and sub-phonetic information carried by VOT, a temporal speech cue found in almost all languages (Lisker and Abramson, 1964; Cho and Ladefoged, 1999). This demonstrates a common (spatial) neural code in STG that accounts for the representation of behaviorally relevant phonetic features embedded within the spectral and temporal structure of speech. From a simple model that transforms a temporal cue into a spatial code, we observed complex dynamics that show how a highly variable, continuous sensory signal can give rise to partially abstract, discrete representations. In this way, our findings also add to a growing body of work highlighting the critical role of human STG as a sensory-perceptual computational hub in the human speech perception system (Yi et al., 2019; Tang et al., 2017; Chang et al., 2010; Leonard et al., 2016; DeWitt and Rauschecker, 2012; Obleser and Eisner, 2009; Leonard and Chang, 2014; Sjerps et al., 2019).

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Speech sound categories that are distinguished by a temporal cue are spatially encoded in the peak amplitude of neural activity in distinct neural populations.

Human auditory cortex encodes both phonetic (between-category) and sub-phonetic (within-category) information in peak response amplitude, which can be modeled by a simple neural network that implements temporal gap and coincidence detection.

Early temporal dynamics of stimulus-evoked neural responses differ between voiceless-selective (V-) and voiced-selective (V+) electrodes.

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Neal P Fox

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Matthew Leonard

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Matthias J Sjerps

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Edward F Chang

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