## Abstract

Innate auditory sensitivities and familiarity with the sounds of language give rise to clear influences of phonemic categories on adult perception of speech. With few exceptions, current models endorse highly left-hemisphere-lateralized mechanisms responsible for the influence of phonemic category on speech perception, based primarily on results from functional imaging and brain-lesion studies. Here we directly test the hypothesis that the right hemisphere does not engage in phonemic analysis. By using fMRI to identify cortical sites sensitive to phonemes in both word and pronounceable nonword contexts, we find evidence that right-hemisphere phonemic sensitivity is limited to a lexical context. We extend the interpretation of these fMRI results through the study of an individual with a left-hemisphere lesion who is right-hemisphere reliant for initial acoustic and phonetic analysis of speech. This individual's performance revealed that the right hemisphere alone was insufficient to allow for typical phonemic category effects but did support the processing of gradient phonetic information in lexical contexts. Taken together, these findings confirm previous claims that the right temporal cortex does not play a primary role in phoneme processing, but they also indicate that lexical context may modulate the involvement of a right hemisphere largely tuned for less abstract dimensions of the speech signal.

## INTRODUCTION

The categorical perception of phonemes is a widely investigated aspect of the speech perception system. Early formulations of categorical perception proposed that the receptive language system collapses the continuous acoustic speech signal into the discrete phonemic categories of a language. This proposal was based on the finding that linguistically defined phonemes have psychophysical validity: listeners could discriminate acoustically slightly distinct speech sounds when—and only when—the listeners identified those speech sounds as coming from two distinct phonemic categories (Liberman, Harris, Hoffman, & Griffith, 1957). Subsequent work has shown the initial proposal of perfectly discrete speech perception to be underspecified. The degree to which segments are perceived categorically is influenced by numerous factors (Schouten, 2003). Also, subphonemic details that can aid in phoneme identification and lexical disambiguation and be used for speaker, dialect, or mood identification are retained by speech decoding mechanisms (McMurray, Aslin, Tanenhaus, Spivey, & Subik, 2008).

Although speech perception may be somewhat less than categorical, there is a clear categorical influence. The perceptual space is not isomorphous to physical space but warped, with regions of heightened and diminished sensitivities. The influence of phonemic categories can result in a “continuous physical dimension … perceived in a discontinuous manner,” (Pastore, 1987, p. 41), such as the dimension of VOT—the time lag between the onset or initial release of an obstruent consonant and the subsequent vibration of the vocal fold. For example, in the range of VOTs between prevocalic /b/ and /p/, the ability to distinguish tokens with similar VOTs is not constant from minimal to maximal VOT but is lowest near the canonical VOTs for /b/ and /p/ and peaks somewhere between canonical /b/ and /p/, forming a phonemic category boundary.

There are (at least) four classes of explanation for the discontinuity in VOT perception and categorical influences more generally: (1) listeners pick up on real acoustic discontinuities in the signal, (2) nonlinear temporal filters are applied to the signal by early auditory mechanisms, (3) perception relies on contact with the relatively discrete articulatory representations or programs used to produce segments, and (4) well-learned and relatively stable phonemic labels (unrelated to motor representations) influence perception to different degrees as gradient sensory traces fade more or less rapidly, depending on task demands and listening context. The evidence for each of these explanations (reviewed in Rosen & Howell, 1987) suggests that the categorical influence on perception is due to an interaction of all four factors because any subset has limitations in accounting for the 50 years of related results.

Given these multiple cognitive mechanisms, it is unlikely that one brain region is the exclusive “seat” of phonemic processing. This is borne out by fMRI studies that have consistently associated several areas with the categorical influence on perception: left hemisphere (LH), middle and posterior STS, and peri-sulcal regions (Desai, Liebenthal, Waldron, & Binder, 2008; Myers & Blumstein, 2008; Joanisse, Zevin, & McCandliss, 2007; Myers, 2007; Blumstein, Myers, & Rissman, 2005; Dehaene-Lambertz, 2005; Liebenthal, Binder, Spitzer, Possing, & Medler, 2005); LH temporo-parietal regions including the TPJ; and parts of the supramarginal and angular gyri (Joanisse et al., 2007; Raizada & Poldrack, 2007; Blumstein et al., 2005) as well as bilateral frontal regions (Myers & Blumstein, 2008; Myers, 2007; Raizada & Poldrack, 2007; Blumstein et al., 2005; Dehaene-Lambertz, 2005).

On the basis of cumulative evidence, a model of the functional neuroanatomy of categorical influences on speech perception begins to take shape. This model tentatively includes left-lateralized primary auditory areas specialized for higher frequency acoustic/phonetic temporal filtering (Liegeois-Chauvel, de Graaf, Laguitton, & Chauvel, 1999; Steinschneider, Schroeder, Arezzo, & Vaughan, 1995), a left middle and posterior temporal lobe mechanism related to speech-specific phonemic analysis1 (Andoh et al., 2006; Boatman & Miglioretti, 2005), a left temporo-parietal locus engaged in sound-to-articulation mapping (Hasson, Skipper, Nusbaum, & Small, 2007; Hickok & Poeppel, 2004, 2007), left frontal influences of articulatory representations (Meister, Wilson, Deblieck, Wu, & Iacoboni, 2007), and bilateral frontal regions related to task-influenced decision processes (Myers, 2007).

Conspicuously absent from this emerging model is the contribution of the right hemisphere (RH). Although some functional anatomical models explicitly highlight RH involvement (Hickok & Poeppel, 2007), a recent review of abstract representations used for speech perception dispenses with the RH entirely (Obleser & Eisner, 2009). In an attempt to evaluate the degree to which this exclusion is warranted, Figure 1 provides a summary of findings from LH and RH temporal and temporo-parietal regions as reported by 19 neuroimaging studies (17 fMRI and 2 PET) categorized by level of processing targeted in the research. The studies were selected on the basis of three criteria: (1) discussion of phonemic processing, (2) RH data collection, and (3) publication of tables of results (a summary of studies can be found in Supplementary Table 1). The basic pattern is illustrated by plotting reported activation peaks color coded by level of processing (colder colors indicating less abstract acoustic dimensions and warmer colors indicating more abstract phonemic dimensions). Sensitivity to spectral complexity and temporal cues (dark blue) is roughly equally distributed between the two hemispheres; as more abstract and less acoustic aspects of speech processing are investigated (warmer colors), the balance changes. All eight studies that perform contrasts related to phoneme-specific processing (red) reported LH temporal or temporo-parietal results, whereas only two reported RH results.

Figure 1.

Review of LH and RH temporal lobe and temporo-parietal findings from 19 imaging studies. Colors indicate the level of processing targeted by the contrast plotted, with the cold colors representing the least abstract, sensitivity to spectral complexity and temporal cues (blue), and sensitivity to speech as compared with matched nonspeech (cyan) to the warmer colors representing sensitivity to syllabic and segmental dimensions (yellow) and phonemic category (red).

Figure 1.

Review of LH and RH temporal lobe and temporo-parietal findings from 19 imaging studies. Colors indicate the level of processing targeted by the contrast plotted, with the cold colors representing the least abstract, sensitivity to spectral complexity and temporal cues (blue), and sensitivity to speech as compared with matched nonspeech (cyan) to the warmer colors representing sensitivity to syllabic and segmental dimensions (yellow) and phonemic category (red).

Research on brain-lesioned individuals has also supported a left-lateralized network. Most relevant neuropsychological studies use the following logic: By testing individuals with LH lesions, one can observe the abilities of the RH. For example, Basso, Casati, and Vignolo (1977) identified the “position and extent of [the] boundary zone” separating /da/ from /ta/ in a VOT continuum for the following groups: LH-lesioned aphasics, LH-lesioned nonaphasics, and RH-damaged nonaphasics. RH-lesioned and LH-lesioned nonaphasic individuals produced typical categorical identification functions, but 74% of LH-lesioned aphasics had some degree of deficit, ranging from slight (identifiable but abnormally wide boundary zone), to severe (no identifiable boundary zone, but linearly correlated with VOT), to very severe (uncorrelated with VOT). Similarly, Blumstein, Baker, and Goodglass (1977) assessed the phonemic categorization deficits of aphasics, showing an overall tendency toward poor identification in Wernicke's aphasics but seemingly spared discrimination. These data appear consistent with an inability of the RH to support the typical phonemic influence on perception.

Given the functional imaging and neuropsychological data, the degree to which anesthesia (Wada) and (epilepsy-related) hemi-decortication findings suggest substantial RH involvement is surprising. For example, Boatman's studies show that neither complete LH anesthetization (Boatman et al., 1998) nor complete removal (Boatman et al., 1999) impaired auditory discrimination of monosyllabic minimal pairs. Specifically, LH anesthetization resulted in a complete absence of auditory comprehension, object naming and contralateral limb strength, indicating that the LH was truly anesthetized and solely responsible for higher level language functions. Nonetheless, the participant not only discriminated between different word minimal pairs but could recognize that two acoustically different yet phonemically identical words were the same, indicating intact phonemic processes supported by the RH (Boatman et al., 1998). This capacity was similarly shown in six children who each received a left hemidecorticectomy. Following surgery, minimal pair discrimination was uniformly intact (Boatman et al., 1999). (The possibility of cortical reorganization or abnormal development in these participants complicates the generalizability of these findings.)

Although the RH can support discrimination, this may not be so when tokens are presented in noise (Boatman, Vining, Freeman, & Carson, 2003; Zaidel, 1978). Zaidel (1978) suggested an account according to which the RH relies primarily on continuous acoustic-dependent representations easily hindered by noise, whereas LH phonetic feature extraction and abstract phoneme representations are more robust to noise.

With the current report, we aim to further test the hypothesis that the RH does not engage in phonemic analysis. This will help resolve the discrepancies between, on the one hand, the rarity of RH BOLD response reported during phonemic processing and absence of categorization deficits in some LH-lesioned individuals and, on the other, the apparent capacities of the RH to execute phonemic processing as evident from decortication and anesthetization. Although similar tasks were used across these methodologies, one key difference was whether the target phonemes were presented within words or nonwords. The data suggesting an RH inability to support typical phonemic processing in perception used nonwords, whereas those data demonstrating the phonemic capacities of the RH used words.

In the first study presented here, we investigated whether this lexical distinction was important for resolving the issue of RH phonemic processing by using fMRI to identify cortical sites sensitive to phonemes in both word and pronounceable nonword contexts. We next present a case study of an individual DMN, who has a left temporo-parietal lesion. This case is particularly well suited for extending the interpretation of the fMRI results as DMN's lesion affected phonemically sensitive areas identified by fMRI in our first study. This allowed us to assess the integrity of speech perception when regions typically involved in phonemic processing are damaged. We are able to show with fMRI and magneto-encephalography (MEG) that DMN's LH is essentially deafferented for auditory input, rendering him RH reliant for early cortical processing of speech. As a result, we can attribute disparities between DMN's performance and that of typical listeners to disparities between the hemispheres, in the degree to which phonemic categories influence speech perception.

The earlier neuropsychological studies discussed above (Basso et al., 1977; Blumstein et al., 1977) had similar goals, but with several key methodological differences. All previous testing was done with nonwords, preventing investigation of phonemic perception in word contexts. Second, previous testing used synthesized stimuli—but there are marked differences in the perception of synthetic and natural speech among aphasic individuals (Gow & Caplan, 1996; Huntress, Lee, Creaghead, Wheeler, & Braverman, 1990). In contrast, we use naturally produced word and nonword stimuli. Lastly, we use functional imaging to examine the extent of structural and functional LH damage such that inferences about speech processing by the RH can be made with greater certainty.

## STUDY 1: FUNCTIONAL LOCALIZATION OF CORTEX SENSITIVE TO PHONEMIC CATEGORY

### Methods

We used a neural adaptation paradigm (Kourtzi & Grill-Spector, 2005) to identify cortex sensitive to phonemic category in unimpaired listeners (Zevin & McCandliss, 2005). To reveal neural populations selectively responsive to phonemic category, we first presented the same stimulus multiple times to habituate a given phonemic category and then presented a stimulus that differed from the habituating stimulus either acoustically and phonemically or only acoustically. In this way, we identified phonemic category-sensitive regions as those exhibiting phoneme-specific dishabituation: a larger rebound response when both the phonemic category and the acoustics changed, as compared with the response when the acoustics alone changed.

#### Experiment 1: Anatomical and Functional MRI

MRI and fMRI data were collected using the same methods and stimuli reported in Study 1. However, it was not appropriate to perform the same analyses of the functional data as were carried out in Study 1 to identify cortex sensitive to phonemic categorization because DMN's behavioral responses were grossly abnormal. Therefore, the fMRI data were used to contrast the neural response to speech versus silence to examine the integrity of auditory input to the two hemispheres and, in this way, evaluate the extent of deafferentation of the LH. Nonetheless, DMN's behavioral discriminations to the dishabituation task were analyzed and compared with controls. All statistical comparisons between DMN and control groups were performed using the Crawford and Garthwaite (2007) Bayesian point estimate.

#### Experiment 2: Magneto-encephalography

To investigate DMN's neurophysiological responses to speech, MEG measurements were collected on a 160-channel whole-head axial gradiometer MEG system (KIT, Japan) at the University of Maryland. Data were collected with a sampling rate of 500 Hz during passive listening to tones (100 Hz, 250 Hz, 1 kHz, and 4 kHz) and to bisyllabic words and nonwords (45 words, 45 nonwords, mean length of 613 and 610 msec, respectively). Noise reduction was achieved using adaptive noise suppression (Ahmar & Simon, 2005). Waveforms were baseline corrected (100 msec prestimulus onset) and low-pass filtered (30 Hz). For each participant (DMN and five controls), for each response component, and for each hemisphere, the channels most representative of the source and sink distribution were selected for quantitative analysis. Here, we report the peak root mean squared (RMS) amplitudes, latencies at peak, and surface topographies for the M100 response to tones as well as the M350 response to auditorily presented words and nonwords. The M100 was selected as the first RMS peak after 70 msec showing a plausible M100 distribution (see, e.g., Salajegheh et al., 2004) and the M350 as the first RMS peak after 300 msec that showed the M350 distribution (following Fiorentino & Poeppel, 2007; Pylkkanen, Stringfellow, & Marantz, 2002).

#### Experiment 3: Perception of Phonemic Category—Identification and Discrimination

To assess the degree to which phonemic categories influenced DMN's speech perception, we considered DMN's performance on the in-scanner discrimination task as well as VOT identification tasks that included tokens from a range of word and nonword VOT continua. For the identification tasks, stimuli consisted of VOT continua similar to those described in the fMRI methods: three word and three matched nonword continua described earlier made up the primary stimulus set (beach/peach, dent/tent, goat/coat and beesh/peesh, deg/teg, gobe/cobe). An additional three word (bale/pale, base/pace, game/came) and nonword continua (baysh/paysh, bem/pem, gice/kice) were piloted to confirm a highly categorical response from typical listeners and then used with DMN.

In VOT identification tasks, participants were familiarized with the continuum end points then given a two-alternative forced-choice identification task with members of that continuum. For example, for testing of the beach/peach continuum, each participant heard over headphones (Sony MDR V-15) calibrated to a volume comfortable for the listener (approximately 72–79 dB) a member of the beach/peach continuum and saw the choices “beach/b” and “peach/p” on the left- and right-hand side of the screen, respectively. The participant was instructed to press the 1 key if “beach” was the percept or 0 if “peach” was the percept. This was repeated for nine presentations of each member of the nine-step continuum, for a total of 81 randomized trials per continuum. Each trial began 500 msec after the response from the previous trial. The procedure was repeated for each continuum in a random order. For each continuum, we obtained the number of times each token was identified as a particular type as well as response times. Stimuli were presented, and identifications and response times were recorded using Alvin presentation software (Hillenbrand & Gayvert, 2005).

DMN was evaluated with the same procedure but received more extensive familiarization with the continua end points and two testing sessions of each continuum on separate days because of his greater response variability. DMN received one additional identification paradigm in which members of one word continuum (beach/peach) and nonword continuum (beesh/peesh) were mixed, but choices for identification were limited to “beach/b” and “peach/p.” Here, instructions were purposefully vague and did not mention the inclusion of nonwords. As a result, DMN was under the impression he would only hear word stimuli. The goal of this final set of identifications was to help determine the role of stimulus-driven and knowledge-driven influences on lexical effects of phonemic processing.

We adopted a curve-fitting approach to assess the structure of phonemic category representations. Typical VOT identification strongly influenced by phonemic category produces data sharply shifting from near exclusive identification of one category to near exclusive identification of the other. This type of sigmoid response is well fit by logistic functions but poorly fit by linear functions (McMurray & Spivey, 2000). In contrast, gradient or continuous identification yields data that are similarly fit by linear and logistic functions because logistic functions can approximate linear functions, but not the reverse. To evaluate the degree of phonemic category influence on speech perception, both linear and logistic regressions were performed, yielding a linear best-fit line and deviance residual as well as a logistic best-fit curve and deviance residual (calculated for proportional data) for each continuum, for each participant. The difference between linear and logistic deviance yielded a metric of categoricity such that high values indicated high categoricity whereas low deviance values indicated a lack of categoricity. Pearson correlations between VOT and percent unvoiced token identified were also calculated for each continuum and tested for significance. Statistics were calculated using MATLAB 7.0.

### Results

#### Experiment 1: MRI/fMRI

DMN's MRI showed an LH lesion affecting gray and white matter integrity in HG, the middle and posterior aspects of the MTG and STG, extending caudally into the TPJ and rostrally to the rolandic operculum. When normalized, DMN's lesion overlapped with the pMTG/STS area shown in Study 1 to be selectively responsive to phonemic category in normal participants (Figure 4) as well as with posterior temporal findings for phonemic sensitivity in the literature (Desai et al., 2008; Joanisse et al., 2007; Myers, 2007; Blumstein et al., 2005; Dehaene-Lambertz, 2005; Liebenthal et al., 2005).

Figure 4.

Normalized MPRAGE of DMN's LH, as shown in coronal (A) and sagittal (B) slices. The gray border indicates outline of normalized lesion tracing, and the white border indicates outline of the main effect of phonemic dishabituation from Study 1. Coronal slices range from y = −56 to −54. Sagittal slices range from x = −52 to −50.

Figure 4.

Normalized MPRAGE of DMN's LH, as shown in coronal (A) and sagittal (B) slices. The gray border indicates outline of normalized lesion tracing, and the white border indicates outline of the main effect of phonemic dishabituation from Study 1. Coronal slices range from y = −56 to −54. Sagittal slices range from x = −52 to −50.

The degree to which DMN's LH early auditory cortex was still functional was unclear from structural MRI because some highly misshapen gray matter remained in the HG. However, a total absence of white matter in adjacent regions indicated possible deafferentation of LH from auditory input. fMRI data allowed us to address this issue. As mentioned above, bilateral activation of HG was observed in each of our normal participants for speech relative to silence. DMN, however, showed a total lack of left HG response for the contrast of speech > silence, even at liberal thresholds (α < .1), at the same time displaying the typical right HG activation for this contrast, even at strict thresholds (Bonferonni corrected p < .05).

A recent report on postinfarct hemodynamics suggested that perilesional BOLD responses may go undetected by models applying canonical HRF convolutions (Bonakdarpour, Parrish, & Thompson, 2007). To examine this possibility, we directly measured DMN's BOLD response and carried out an ROI analysis measuring the first eigenvariate for spherical ROIs of 10-mm radius centered at the anatomically defined HG bilaterally. Because the left HG was less defined than the intact right HG, several ROIs for the left HG were tested, with the ROI producing the largest signal reported. ROI analysis showed a right HRF for DMN similar to controls and a slow, largely attenuated left HRF (Figure 5). A one-way ANOVA on the time course of the left HG ROI was not significant for a main effect of time bin, F(4, 710) = 1.92, p = .105, whereas the RH ROI was highly significant, F(4, 710) = 52.43, p < .0001, suggesting that the LH was either devoid of bottom–up input or was receiving extremely abnormal input. These findings were further investigated using MEG during passive listening to tones and speech.

Figure 5.

Event-related response to speech in left and right HG ROIs for control participants in gray and DMN in black. Error bars denote one standard error of DMN's mean response for each time bin for each hemisphere.

Figure 5.

Event-related response to speech in left and right HG ROIs for control participants in gray and DMN in black. Error bars denote one standard error of DMN's mean response for each time bin for each hemisphere.

#### Experiment 2: MEG

We report the latencies, amplitudes, and surface topographies of the M100 response to passively presented tones and the M350 response to passively presented words and nonwords for DMN and five control participants. Each control participant showed typical surface distributions (Figure 6) and evidence of bilateral auditory-evoked M100 responses to 1 kHz tones (Figure 7A, LH latency/amplitude: μ = 97.2 msec/116 fT, range = 88–119 msec/69–157 fT; LH latency/amplitude: μ = 101 msec/116 fT, range = 89–115 msec/60–177 fT).

Figure 6.

Surface topographies for passive listening. Each topography is shown at the time point of peak amplitude for LH channels (scale = 15 fT/step). DMN's data are displayed in the first row, and each subsequent row corresponds to a different control participant.

Figure 6.

Surface topographies for passive listening. Each topography is shown at the time point of peak amplitude for LH channels (scale = 15 fT/step). DMN's data are displayed in the first row, and each subsequent row corresponds to a different control participant.

Figure 7.

Peak amplitudes and latencies at peak for the M100 response to passively heard tones (A) and the M350 response to passively heard words (B). Control participants' responses are limited to 1 kHz tones displayed in gray and DMN's responses in black. Leftward-facing arrows denote responses from the LH, whereas rightward-facing arrows denote responses from the RH. No markers are present for DMN's LH 125-Hz M100 or nonword LH M350 as no identifiable response could be found. Note that the scales are different in panels A and B.

Figure 7.

Peak amplitudes and latencies at peak for the M100 response to passively heard tones (A) and the M350 response to passively heard words (B). Control participants' responses are limited to 1 kHz tones displayed in gray and DMN's responses in black. Leftward-facing arrows denote responses from the LH, whereas rightward-facing arrows denote responses from the RH. No markers are present for DMN's LH 125-Hz M100 or nonword LH M350 as no identifiable response could be found. Note that the scales are different in panels A and B.

DMN showed typical amplitudes in the RH but markedly low amplitudes across left channels. For the 125-Hz tone, no M100 could be detected in DMN's LH channels, whereas typical topography and amplitude was detected for the RH source across tone frequency (125 Hz—104 fT, 250 Hz—147 fT, 1 kHz—165 fT, and 4 kHz—85 fT). For the higher frequency tones, nonzero M100 components were identified for DMN on the basis of topography and timing, but the drastically attenuated responses were well below the control range (250 Hz—46 fT, 1 kHz—48 fT, and 4 kHz—39 fT), and time series data were not convincingly dipolar. This supports the fMRI finding that the LH is either lacking in auditory input or that the input is grossly abnormal.

A robust LH M350 in response to passively heard words and nonwords was found for each of the controls (Figure 7B, words latency/amplitude: μ = 359 msec/86 fT, range = 324–408 msec/68–109 fT; nonwords latency/amplitude: μ = 331 msec/73 fT, range = 310–370 msec/57–89 fT), with similar LH surface distributions to each participant's LH M100 (Figure 6). Interestingly, DMN showed a normal range M350 response to words (latency/amplitude = 372 msec/78.4 fT) but differed from controls in two aspects: (1) he had no identifiable M350 response to nonwords, and (2) whereas each control showed similar LH M100 and M350 topography, each individual's M350 response to both words and nonwords was reduced in amplitude relative to their M100 response (range = 1–85 fT greater for M100). In contrast, DMN showed markedly larger M350 amplitude relative to the attenuated M100 (+30.2 fT).

#### Experiment 3: Perception of Phonemic Category—Identification and Discrimination

DMN's discrimination of tokens from word and nonword continua (in the task presented for fMRI scanning) revealed that relative to controls, DMN was significantly less sensitive to between-category differences for words (p < .005) and nonwords (p < .05) but was within normal range for sensitivity to within-category and end-point differences (Figure 8). Although DMN was numerically better at word-embedded phoneme discrimination (d′ = .41) than non-word-embedded phoneme discrimination (d′ = −.15), this difference was not statistically reliable. DMN was within normal RT range for end-point word trials but significantly slower to respond to end-point nonword (p < .05), between-category word (p < .001), between-category nonword (p < .05), within-category word (p < .05), within-category nonword (p < .05) trials.

Figure 8.

Box and whisker plots showing the median, upper and lower quartiles, and range of in-scanner discrimination sensitivities (quantified as d′) from normal listeners for word stimuli on the left and nonword stimuli on the right. Indentations in the boxes denote the 95% confidence intervals of medians, such that boxes with indentations that do not overlap (e.g., nonwords, between and within) have significantly different medians (p < .05), whereas those that do overlap (e.g., word-between and nonword-between) do not. For comparison, sensitivity for discrimination of continuum end points is also shown as well as sensitivities for DMN represented by the x.

Figure 8.

Box and whisker plots showing the median, upper and lower quartiles, and range of in-scanner discrimination sensitivities (quantified as d′) from normal listeners for word stimuli on the left and nonword stimuli on the right. Indentations in the boxes denote the 95% confidence intervals of medians, such that boxes with indentations that do not overlap (e.g., nonwords, between and within) have significantly different medians (p < .05), whereas those that do overlap (e.g., word-between and nonword-between) do not. For comparison, sensitivity for discrimination of continuum end points is also shown as well as sensitivities for DMN represented by the x.

With regard to VOT identifications, we found that for each continuum, for each control participant, linear fits produced greater deviance than did logistic fits (Figure 9B and D). Accordingly, their identification functions were significantly better fit by logistic as compared with linear functions (Wilcoxon sign-rank test on deviance differences, p < .05 for each control), demonstrating the standard influence of phonemic category on identification for phonemes in words or nonword onsets. DMN showed two departures from this typical response.

Figure 9.

Best-fit logistic functions for DMN (solid lines) and control identification (dashed) for word-embedded obstruent consonants (A) and matched nonword embedded obstruents (C) presented from continua. Color fields cover the range of control response. Box and whisker plots in panels B and D show the difference in deviances between linear function and logistic function fits for controls and DMN (marked as a ⊗) for words and nonwords, respectively (median, upper and lower quartiles, and range). Large deviance differences indicate a large categorical influence on identification, whereas small deviance differences indicate a small influence.

Figure 9.

Best-fit logistic functions for DMN (solid lines) and control identification (dashed) for word-embedded obstruent consonants (A) and matched nonword embedded obstruents (C) presented from continua. Color fields cover the range of control response. Box and whisker plots in panels B and D show the difference in deviances between linear function and logistic function fits for controls and DMN (marked as a ⊗) for words and nonwords, respectively (median, upper and lower quartiles, and range). Large deviance differences indicate a large categorical influence on identification, whereas small deviance differences indicate a small influence.

First, DMN did not show the characteristic advantage for logistic function fit. Instead, DMN's word-embedded phoneme identifications were gradient: His responses were equally well fit by logistic and linear functions (Wilcoxon signed rank test for difference, p > .68), and each function had a strong linear correlation (Pearson correlation coefficients ranging from .63 to .98, p values ranging from .001 to .07). The difference in deviances between logistic and linear fits for DMN was significantly less than that of the controls (p < .05) in each of the word and nonword continua. The same results were observed with the additional word continua completed only by DMN. Second, DMN showed a marked difference in identification when the onset was nonword embedded (compared with word-embedded). Although DMN's word-embedded phoneme identification was gradient (significantly linearly correlated with VOT but not better fit by a logistic), there was no correlation between VOT and DMN's non-word-embedded phoneme identification (and logistic and linear fits were equally poor).

In a final identification paradigm, word and nonword tokens (from beach/peach and beesh/peesh continua) were mixed and presented in random order; DMN was not told of the inclusion of nonwords, and response choices were limited to word responses (“beach/b,” “peach/p”). When asked, DMN did not report anything odd about the paradigm nor did he think any nonwords were present. Although onset phonemes were acoustically identical in the word and nonword continua and DMN had no conscious awareness of the inclusion of nonwords, performance differed for the word and nonword continua (Figure 10), indicating stimulus-driven differences in identification. Although both word and nonword identifications were gradient (similarly well fit by linear and logistic best-fit functions and significantly correlated with VOT), word-embedded phoneme identification produced a 36% steeper slope than non-word-embedded phoneme identification, demonstrating differences in the use of acoustic cues across word and nonword contexts. In addition, this task also provided some indication of knowledge-driven or top–down influence on identification: When DMN thought he was hearing and responding to words (as in this task), nonword identification was correlated with VOT (r = .83, p < .01), but when DMN thought he was hearing and responding to nonwords (as he was in the other VOT identification tasks), there was no correlation between nonword identification and VOT (r = .2, p < .6).

Figure 10.

Results from DMN's mixed word/nonword identification (dark lines). Although both words and nonwords were presented, DMN thought he was hearing and responding to words only. Nonword only results (gray) are shown for reference.

Figure 10.

Results from DMN's mixed word/nonword identification (dark lines). Although both words and nonwords were presented, DMN thought he was hearing and responding to words only. Nonword only results (gray) are shown for reference.

### Study 2 Summary

Functional imaging (fMRI and MEG) revealed that DMN was RH reliant for, at a minimum, the initial acoustic and phonetic analyses of auditory input, rendering him an informative case study for understanding the speech processing capacities of the RH. Specifically, both his BOLD responses to speech from the left HG as well as his M100 auditory-evoked responses (AER) to tones were absent or weak. The source of the auditory-evoked M100 has been localized to the superior temporal area bilaterally and reflects acoustic processing particularly important for speech (Gage, Poeppel, Roberts, & Hickok, 1998).

Given the cumulative evidence for phonemic category sensitivity in the left posterior temporal lobe, DMN's poor performance with tokens from VOT continua may not be entirely unexpected. What is particularly noteworthy is that although DMN could differentiate between VOT end-point minimal pairs, he did not show the typical phonemic category influence on discrimination or identification, producing instead gradient identification functions only for word- and not non-word-embedded phonemes. His performance, therefore, reveals not only the RH hemisphere capacity for gradient subphonemic processing but also that this type of processing interacts with the lexical status of the acoustic stimulus. The topics of subphonemic RH processing and mechanisms underlying lexical effects in phoneme perception will be taken up in the General Discussion section.

## GENERAL DISCUSSION

On the basis of converging evidence from different populations (neurologically intact and lesioned individuals) and methods, we examined the claim that “what underlies the left (hemisphere) dominance for speech consonants in the temporal lobes is their categorical perception” (Liebenthal et al., 2005).

We found that BOLD response in the superior posterior left temporal lobe showed selective sensitivity to phonemic status. Supporting the critical role of the LH in phoneme perception was the performance of DMN, an individual with a lesion affecting this region, whose performance revealed that the RH alone was insufficient for typical phonemic category effects. With regard to the RH's role, we found that it was not only active in normal listeners but that, when functioning in the context of a deafferented LH, it allowed the processing of gradient phonetic information, at least when provided with lexical support.

Highly relevant to DMN's gradient perception are the findings of Desai et al. (2008), who asked neurologically intact participants to identify and discriminate tokens from phonetic continua made from sine-wave speech before and after familiarization with sine-wave speech. This paradigm allows a comparison of performance with the same acoustic stimuli with and without the contributions of learned phoneme categories or lexical knowledge. Before familiarization, participants responded similarly to DMN, producing gradient identification functions well correlated with acoustic dimensions. After familiarization, they produced categorical identification and increasingly recruited the left posterior STS and STG, further linking this region, lesioned in DMN, to the influence of phonemic category.

### Subphonemic Processing and the RH

DMN's gradient RH responsivity to VOT continua is consistent with other evidence that, despite the lack of typical phonemic category influence in the RH, it is capable of a good deal of the subphonemic processing needed for successful decoding. For example, although dichotic listening paradigms classically show a right ear advantage for speech, indicating an LH bias for processing, a left ear advantage for VOT perception has been demonstrated (Cohen, 1981). Complementing these behavioral findings, Molfese (1980) and Simos et al. (2000) reported exclusively RH AER to VOT and tone onset time perception across several paradigms, indicating an RH facility for representing some phonetic features. Within-category acoustic-phonetic representations have been shown in the BOLD responses from the right STG (Myers et al., 2009) and subdural electrodes on the surface of the right STG (Steinschneider, Volkov, Noh, Garell, & Howard, 1999), and information associated with the second and third formants of syllables has been detected in right primary auditory cortex (Raizada, Tsao, Liu, & Kuhl, 2009). Given the ability of right temporal cortex to process phonetic cues, why should phonemic sensitivity be partitioned along hemispheric lines?

There are several plausible explanations for greater LH phonemic sensitivity, all relating to the way phonemic category information is learned. In one account, average temporal window size over which acoustic information is integrated or sampled is longer in the RH (Poeppel, 2003), which is likely to be deleterious to category formation. There are other similar proposals in the literature about this lateralization phenomenon (e.g., Zatorre, 1997), but the data reported here do not differentiate between them. Another account relies on general principles of learning: phonemic sensitivity in perception can develop from sensitivity to statistical structure in the environment (Salminen, Tiitinen, & May, 2009) combined with competitive processing resembling lateral inhibition (Wilson, Wolmetz, & Smolensky, 2008). It may be that RH cortex involved in decoding is simply less plastic than LH counterparts or that cytoarchitectonics or signaling is such that processing between assemblies is less competitive. A final factor may be the connectivity of this area with regions involved in production: LH phonemic representations may be more directly mapped on to relatively discrete left-lateralized motor plans than RH graded acoustic representations. In this way, laterality for phonemic representations would be a consequence of laterality for motor output.

The lack of phonemic influence on RH decoding may contribute to the perceptual impairments caused by LH neural damage but should not be considered a weakness for the typical listener. In some circumstances, within-category information remains on-line past the utterance (McMurray et al., 2008) and can aid in lexical disambiguation, syllabification, speaker-specific tuning, and other speech functions beyond the phoneme. It could be that the same sets of cells simply cannot simultaneously allow for a phonemic influence and encode and maintain the subphonemic details.

### Lexical Support for Phoneme Perception

With lexical support, DMN showed evidence of gradient acoustic/phonetic processing but little or no evidence of phonemic influence. Without lexical support, DMN was essentially deaf to acoustic/phonetic detail. What's more, nonword processing seems to have broken down relatively early, as he showed a typical M350 response to words but no identifiable M350 to nonwords. These characteristics are similar to those of an individual studied by Caplan and Utman (1994) who, after a left peri-sylvian lesion, could only discriminate voiced from voiceless phonemes when they appeared in words. These deficits indicate that (1) the posterior superior left temporal lobe and temporo-parietal cortex, in addition to mediating phonemic category influence, are crucial for some aspects of nonword perception and (2) in cases of impairment, lexical support facilitates the use of acoustic/phonetic detail in speech perception.

Lexical support could come in the form of feedback or lexical anchoring. In the lexical anchoring account, gradient sublexical representations were sufficient to contact the lexicon, and DMN's behavioral responses were based on subsequent lexical activation; as a result, his word responses were linearly related to VOT. Nonwords either did not contact word representation or did not do so to the same degree as words, so they showed no evidence of structured perception. In the feedback account, lexical support staves off decay of sublexical information (Norris, McQueen, & Cutler, 2000; or referred to as “resonance boost” by Grossberg, Boardman, & Cohen, 1997). In the impaired system, without phonemic organization of the perceptual space, fragile sublexical information decays rapidly, but with lexical support, sublexical acoustic/phonetic information decays slowly enough to permit gradient responses. In other words, without lexical support, transient acoustic/phonetic information does not persist long enough to impact behavioral responses.

In the case of DMN, both feedback and lexical anchoring seem at play. Feedback is indicated by DMN's increased ability to process phonetic cues when he perceived them as being word embedded (even when they were not). Lexical anchoring may account for the persistent word advantage. Imaging data presented here suggest that the posterior right temporal lobe has a role in this lexical mediation of phonetic perception. Further support for this interpretation comes from DMN's apparently intact M350 to auditorily presented words—a component with a similar dipole source and surface distribution to the M100 thought to reflect activation of lexical candidates (Pylkkanen et al., 2002). These results indicate that some regions of DMN's left temporal lobe, although inactive during early LH prelexical processes, participated in later lexical processing, presumably via RH input. Nonetheless, the details of RH–LH interaction remain the topic of future research.

### Conclusions

We show that the right posterior temporal lobe is active during phoneme discrimination in normal listeners and contributes to speech perception in the context of a deafferentated LH. The RH was shown to be capable of gradient response, faithful to the acoustic properties of the speech input, when lexical support was available. The picture that emerges is one in which, in normal listeners, fast speech comprehension is achieved with RH acoustic/phonetic representations of speech working in concert with LH mechanisms more sensitive to phonemic category.

## Acknowledgments

This research was supported by NIH grant no. DC006740. The authors thank Dana Boatman for generously providing auditory testing, Susannah Hoffman for assistance with MEG data collection and analysis, and members of the JHU CogNeuro Lab for continued feedback and suggestions. The authors especially thank DMN for his devoted participation.

Reprint requests should be sent to Michael Wolmetz, Department of Cognitive Science, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, or via e-mail: mikew@jhu.edu.

## Note

1.

To be clear, by phonemic analysis, we refer to representations and processes that utilize stored sublexical distributional information about phonemic categories. Here, a phonemic category is thought of as probability mass across various feature values. We have opted to use the term phonemic but do not intend to distinguish between phonemes, allophones, or bundles of features in the current report. By acoustic/phonetic, we refer to less abstract processes or representations of a finer grain size (acoustic dimensions or individual features) unbiased by category information.

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