Marjorie Dole
email: marjorie.dole@cea.fr

Emmanuel Coriandre

Célise Vilain

Monica Haldin

Emilie Baciu

Laurent Cousin

Hélène Lamalle

Anne Loevenbruck

Jean-Luc Vilain

Schwartz

Vilain Coriandre
email: coriandre.vilain@gipsa-lab.grenoble-inp.fr

Célise Haldin
email: celise.haldin@univ-grenoble-alpes.fr

Monica Baciu
email: monica.baciu@univ-grenoble-alpes.fr

Emilie Cousin
email: emilie.cousin@univ-grenoble-alpes.fr

Laurent Lamalle
email: laurent.lamalle@univ-grenoble-alpes.fr

Hélène Loevenbruck
email: helene.loevenbruck@univ-grenoble-alpes.fr

Anne Vilain
email: anne.vilain@gipsa-lab.grenoble-inp.fr

Jean-Luc Schwartz
email: jean-luc.schwartz@gipsa-lab.grenoble-inp.fr

Comparing the selectivity of vowel representations in cortical auditory vs. motor areas: A repetition-suppression study

Keywords: Repetition-suppression, selectivity, auditory representations, motor representations, vowel processing

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Questioning accounts of sensory-motor interactions in speech perception

The hypothesis of a role for sensory-motor interactions in speech perception was introduced with the Motor Theory of Speech Perception by Liberman and his colleagues in the Haskins Labs in the 1960s [START_REF] Liberman | Perception of the Speech Code[END_REF], as a way of explaining how the perceptual system resolves the variability in acoustic realizations associated with a given phonological code. The proposal of the Motor Theory of Speech Perception is that the gesture rather than the sound characterizes the phoneme. Consequently, proponents of the Motor Theory of Speech Perception have argued that listeners are able to recover the articulatory [START_REF] Liberman | Perception of the Speech Code[END_REF] or motor [START_REF] Liberman | The motor theory of speech perception revised[END_REF] cause of the speech sound during the decoding process and that the articulatory-motor gesture would provide the underlying invariant structure, thus bringing order to the acoustic disorder.

However, this proposal has been hotly debated and criticized, and "auditory theories of speech perception", in which speech decoding does not rely on speech production knowledge or articulatory-motor representations, have been proposed and defended (e.g. [START_REF] Diehl | Speech perception[END_REF] with a number of experimental and functional arguments (e.g. [START_REF] Kingston | Phonetic knowledge[END_REF][START_REF] Kluender | Speech perception as a tractable problem in cognitive science[END_REF][START_REF] Lotto | Language acquisition as complex category formation[END_REF][START_REF] Massaro | Evaluation and integration of acoustic features in speech perception[END_REF][START_REF] Nearey | The segment as a unit of speech perception[END_REF]. This has led to the introduction of perceptuo-motor theories of speech perception, which attempt to integrate and reconcile these different sets of arguments combining auditory processing and motor knowledge into a coherent framework [START_REF] Schwartz | The Perception-for-Action-Control Theory (PACT): A perceptuo-motor theory of speech perception[END_REF][START_REF] Skipper | Hearing lips and seeing voices : how cortical areas supporting speech production mediate audiovisual speech perception[END_REF].

Over the last twenty years, a large number of experimental data provided by neuroimaging tools have demonstrated the existence of sensory-motor links in the human brain during speech perception tasks (e.g. [START_REF] Benson | Parametrically dissociating speech and nonspeech perception in the brain using fMRI[END_REF][START_REF] Fadiga | Speech listening specifically modulates the excitability of tongue muscles: a TMS study[END_REF][START_REF] Pulvermüller | Motor cortex maps articulatory features of speech sounds[END_REF][START_REF] Watkins | Seeing and hearing speech excites the motor system involved in speech production[END_REF]; see a review in [START_REF] Skipper | The hearing ear is always found close to the speaking tongue: Review of the role of the motor system in speech perception[END_REF], and confirmed that these links do have a potentially causal role in speech perception [START_REF] D'ausilio | The motor somatotopy of speech perception[END_REF][START_REF] D'ausilio | Tongue corticospinal modulation during attended verbal stimuli: Priming and coarticulation effects[END_REF][START_REF] Möttönen | Auditory-motor processing of speech sounds Cerebral Cortex[END_REF][START_REF] Möttönen | [END_REF][START_REF] Sato | A mediating role of the premotor cortex in phoneme segmentation[END_REF][START_REF] Sato | Articulatory bias in speech categorization: evidence from use-induced motor plasticity[END_REF]; see a review in [START_REF] Schomers | Is the Sensorimotor Cortex Relevant for Speech Perception and Understanding? An Integrative Review[END_REF]; and a caveat on the importance of this causal role in [START_REF] Stokes | The motor system's [modest] contribution to speech perception[END_REF]. A striking finding, however, is that motor areas are more activated in noisy [START_REF] Binder | Neural correlates of sensory and decision processes in auditory object identification[END_REF][START_REF] Du | Noise differentially impacts phoneme representations in the auditory and speech motor systems[END_REF][START_REF] Zekveld | Top-down and bottom-up processes in speech comprehension[END_REF] or in atypical listening conditions [START_REF] Callan | Phonetic perceptual identification by native-and second-language speakers differentially activates brain regions involved with acoustic phonetic processing and those involved with articulatoryauditory/orosensory internal models[END_REF][START_REF] Callan | Speech motor brain regions are differentially recruited during perception of native and foreign-accented phonemes for first and second language listeners[END_REF][START_REF] Wilson | Neural responses to non-native phonemes varying in producibility: evidence for the sensorimotor nature of speech perception[END_REF], and that their modulatory role in speech perception is more apparent for ambiguous or noisy stimuli [START_REF] D'ausilio | The motor somatotopy of speech perception[END_REF][START_REF] D'ausilio | Tongue corticospinal modulation during attended verbal stimuli: Priming and coarticulation effects[END_REF][START_REF] Sato | Articulatory bias in speech categorization: evidence from use-induced motor plasticity[END_REF]. The reason for this phenomenon, namely the recruitment of motor regions mainly under adverse listening conditions, remains to be understood. Importantly, the Motor Theory of Speech Perception, which states that the motor system would systematically be recruited during the processing of acoustic stimuli for the extraction of invariant decoding cues, does not predict that there should be an increase in motor recruitment for acoustic stimuli presented under adverse conditions.

The Auditory-Narrow Motor-Wide property in the COSMO computational model

In recent years, we have developed a computational Bayesian model of speech communication, COSMO [START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor and perceptuo-motor theories of speech perception: an exploratory Bayesian modeling study[END_REF], 2015), which provides interesting insights into the possible role of the motor system in speech perception under adverse conditions. COSMO (for "Communicating Objects using Sensory-Motor Operations") explores the hypothesis that there are two possible accesses to the phonological code from the incoming sound, namely an "auditory pathway" and a "motor pathway".

The auditory pathway is based on a direct relationship between sounds and phonological categories. This is expressed, in the Bayesian probabilistic framework in which COSMO was designed, by the probability distribution PA(S/O) where S represents the attributes of the sound (the Sensory input, auditory cues in the following) and O is the object of the communication between the speaker and the listener (object being conceived in a broad sense, from phonemes to conceptsin the present paper, O stands for the phoneme category). The probability distribution PA(S/O) is learnt by the child (or by the COSMO computational agent) from speech input provided by the child's caregivers or peers (or the agent's tutors). The learning process for the auditory pathway is direct and therefore simple. It requires the child to associate stimuli and objectshere sounds and phonemesdirectly, in a supervised way. In the COSMO framework, the auditory pathway is an implementation of aspects of speech perception that are best explained by auditory theories [START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor and perceptuo-motor theories of speech perception: an exploratory Bayesian modeling study[END_REF], 2015). It provides an optimal representation of the stimuli in the environment, with a straightforward relationship from sound to object [START_REF] Kleinschmidt | Robust speech perception: Recognizing the familiar, generalizing to the similar, and adapting to the novel[END_REF].

Conversely, the link between stimuli and objects in the motor pathway summarized in the probability distribution PM(S/O), is indirect. It is mediated by the recovery of motor commands (or articulatory gestures) M from the sound stimulus, generally referred to as the "acoustic-toarticulatory inversion process". Because of the versatility of the speech apparatus, several combinations of articulatory gestures can result in the same acoustic product. Therefore, the articulatory-to-acoustic relation is many-to-one: there are many possible articulatory/motor solutions for a single acoustic outcome. Recovering a gesture or a motor command from the input sound is therefore an ill-posed problem. COSMO solves this issue within the Bayesian formalism [START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor and perceptuo-motor theories of speech perception: an exploratory Bayesian modeling study[END_REF], 2015). Bayesian inversion rules result in what is called a marginalization process, in which the link between stimuli and objects includes all possible values of the motor variable M, according to the following formula:

PM(S/O)  M P(M/O) P(S/M) (Eq. 1).

In this equation (where  means "proportional to", that is equal modulo a normalization factor), the distribution P(M/O) relates motor gestures to objects and is called the motor repertoire, whereas P(S/M) predicts sensory outputs from motor commands and is called the internal forward model. These two distributions must also be learned by the child. However, the learning process for the motor pathway is more complex than for the auditory pathway and cannot be conceived as supervised, since the motor gestures associated with the incoming sounds are not provided by tutors. It is hypothesized that infants gradually combine endogenous exploration and exogenous tuning (Warlaumount, 2020). Endogenous exploration consists in playing with their vocal tract and learning the relationship between motor commands and acoustic outputs, i.e. the internal forward motor model (or P(S/M)). Exogenous tuning consists in learning to pick the appropriate motor commands to best imitate the acoustic targets in their environment, i.e. in tuning the motor repertoire or P(M/O)). Importantly, the summation over the variable M in Eq. ( 1) means that the Bayesian resolution of the inversion process (finding an adequate gesture M for a given sound S) consists in actually exploiting all the possible gestures M on the basis of their likelihood. This learning process has been shown to be computationally tractable [START_REF] Laurent | The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception[END_REF]. However, the complexity of the motor pathway, and particularly of the probabilistic inversion expressed through the marginalization operation in Eq. ( 1), blurs the relationship between sensory inputs and phonetic categories provided by the PM(S/O) distribution. As a consequence, [START_REF] Laurent | The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception[END_REF] revealed that, in comparison with the optimal probability distribution in the auditory pathway PA(S/O), PM(S/O) is less peaked and less well tuned to the acoustic stimuli provided in the learning process. As illustrated in Figure 1A, a typical sound stimulus in the environment (S1) is associated with a high PA(S/O) probability in the auditory pathway, whereas it will be associated with a weaker PM(S/O) probability in the motor pathway. [START_REF] Laurent | The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception[END_REF] suggested that the objects (O) associated with typical stimuli (S1) are therefore optimally recognized in the auditory pathway. Conversely, the objects related to atypical input stimuli (e.g. noisy or pronounced with an accent never experienced by the agent) should be better identified in the motor pathway, due to the wider distribution PM(S/O) (stimulus S2 in Figure 1A). Simulations of identification scores using the COSMO model confirm the presence of these two distinct profiles [START_REF] Moulin-Frier | Adverse conditions improve distinguishability of auditory, motor and perceptuo-motor theories of speech perception: an exploratory Bayesian modeling study[END_REF][START_REF] Laurent | The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception[END_REF]. This pattern has been coined the "Auditory-Narrow Motor-Wide property" (ANMW). To our knowledge, this is the first explanatory account for the increased role of the motor system in noise or under atypical conditions [START_REF] Barnaud | Reanalyzing neurocognitive data on the role of the motor system in speech perception within COSMO, a Bayesian perceptuomotor model of speech communication[END_REF]. Importantly, COSMO also considers a perceptuo-motor decoding process based on the fusion of the auditory and motor decoding pathways, in line with the perceptuo-motor theory proposed by [START_REF] Schwartz | The Perception-for-Action-Control Theory (PACT): A perceptuo-motor theory of speech perception[END_REF]. This perceptuo-motor system takes the best of the properties of its two components, allowing optimal recognition performance for both typical stimuli (through the auditory branch) and atypical stimuli (though the motor branch).

The repetition-suppression paradigm adapted to the ANMW property

The objective of the present study is to provide neurocognitive experimental evidence for the ANMW property. For this aim, a repetition-suppression (RS) paradigm was chosen. This paradigm is based on the robust finding that when a given stimulation is repeated several times, the neural response to the stimulus decreases. This phenomenon operates over a wide range of temporal scales and experimental paradigms, and has been reported at the level of single-cell recordings as well as EEG/MEG or fMRI data [START_REF] Grill-Spector | Repetition and the brain: neural models of stimulus-specific effects[END_REF]. Importantly, recording repetition-suppression effects in fMRI or MEG provides a way to test which variations in the stimulation lead to a decrease in the cerebral response. In the visual system this has led to the characterization of sensitivity to orientation, color or motion [START_REF] Engel | Adaptation of oriented and unoriented color-selective neurons in human visual areas[END_REF][START_REF] Huk | Pattern-motion responses in human visual cortex[END_REF].

The RS paradigm, originally developed in EEG studies, has been transferred to fMRI and adapted to speech for over 20 years (see first studies in [START_REF] Celsis | Differential fMRI responses in the left posterior superior temporal gyrus and left supramarginal gyrus to habituation and change detection in syllables and tones[END_REF][START_REF] Zevin | Dishabituation of the BOLD response to speech sounds[END_REF]. To describe a typical experiment, [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF] investigated fMRI responses to sequences of 2 syllables from a continuum of synthetic stimuli varying between /ga/ and /da/. The presented sequence consisted of either 2 identical syllables, or of a first syllable followed by a slightly different syllable, which either belonged to the same category or to another category. The RS paradigm was expected to lead to a low neural response when the same stimulus was repeated twice, due to suppression of neuronal activity, whereas the variation in the last stimulus was expected to lead to a higher response (less suppression). The fMRI analysis revealed a significant increase in cortical activity in the left anterior and posterior auditory cortex when the last stimulus was varied but remained within the same category, and a further increase in activity in the left premotor cortex when the second stimulus was varied and changed category.

Similar results with a few variations were reported by [START_REF] Raizada | Selective amplification of stimulus differences during categorical processing of speech[END_REF], [START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF], [START_REF] Joanisse | Brain mechanisms implicated in the preattentive categorization of speech sounds revealed using FMRI and a short-interval habituation trial paradigm[END_REF], [START_REF] Myers | Effects of Category Learning on Neural Sensitivity to Non-native Phonetic Categories[END_REF], [START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF], [START_REF] Lawyer | An Investigation of Place and Voice Features Using fMRI-Adaptation[END_REF], [START_REF] Alho | Early-latency categorical speech sound representations in the left inferior frontal gyrus[END_REF]. They all concerned plosives in a consonant-vowel sequence, focusing on the categorization of place of articulation or voicing. They showed differences in the set of regions activated for within-category stimulus variation contrasted to betweencategory variations. Overall, within-category variations mainly induce activity in the temporal cortex (left posterior Superior Temporal Gyrus pSTG and anterior Middle Temporal Gyrus aMTG in [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF]left Superior Temporal Gyrus STG in Alho et al., 2016;and right STG in Myers et al., 2009). Only [START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF] also found activity in the parietal and frontal cortex under this condition (right Supramarginal Gyrus SMG and bilateral Inferior Frontal Gyrus IFG). Between-category variations induce additional activity in temporal (left posterior STG in [START_REF] Zevin | Dishabituation of the BOLD response to speech sounds[END_REF]left pSTS/STG in Altmann et al., 2014;left STS-MTG in Joanisse et al., 2007;bilateral STG in Lawyer & Corina, 2014), parietal (left SMG in Celsis et al., 1999;[START_REF] Raizada | Selective amplification of stimulus differences during categorical processing of speech[END_REF]left Inferior Parietal Cortex IPC in Joanisse et al., 2007) and frontal regions (left Pre-Motor Cortex, PMC in [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF]andleft IFG in Myers et al., 2009 and[START_REF] Alho | Early-latency categorical speech sound representations in the left inferior frontal gyrus[END_REF].

One single study has explored vowel stimuli using this paradigm. [START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF] compared MEG responses to pairs of consonant-vowel stimuli which were either identical or differed by the consonant (e.g. /ba/ vs. /da/) or by the vowel (e.g. /ba/ vs. /bo/). Pairs of different stimuli elicited greater activation than pairs of identical stimuli in the left STG. However, while this increased activity in the left STG was only observed when the difference involved crossing a categorical boundary in the case of consonants (i.e. in the between-category condition), it was shown in both the within-and between-category cases for vowels. Therefore, overall, there is a trend to find increased activity in frontal areas in the betweencategory condition compared to the intra-category condition, but this trend is variable between studies, and it is not reported in the one study concerning vowels. Most of these data are interpreted in terms of pre-categorial vs. categorial processes, with regions specifically activated during a stimulus variation associated with a category change being interpreted as playing a specific role in the categorization process per se. Authors diverge on the anatomical localization of these regions, however (e.g. frontal for [START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF][START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF]vs. temporal or temporo-parietal in Joanisse et al., 2007[START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF]. The lack of effect of category change for vowels is interpreted by [START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF] as related to the less categorial perception of vowels [START_REF] Schouten | The end of categorical perception as we know it[END_REF].

In the present study, we specifically attempt to explore the ANMW hypothesis using the RS paradigm on a vowel perception task. To operationalize the ANMW hypothesis in neurocognitive terms, we start from the architecture proposed in COSMO [START_REF] Barnaud | Reanalyzing neurocognitive data on the role of the motor system in speech perception within COSMO, a Bayesian perceptuomotor model of speech communication[END_REF], according to which the auditory knowledge stored in PA(S/O) is represented in temporal regions (superior temporal gyrus and sulcus) while the motor knowledge leading to the distribution PM(S/O) is represented in frontal regions including the motor and premotor cortices and the inferior frontal gyrus. We then further assume that the distributions can be translated into patterns of neural activity, with differential neural selectivity within auditory and motor regions. Selectivity is related to the set of stimuli that provide a given response in a neural channel Ch. It can be described by a probability distribution P(S/Ch). Our hypothesis is that the selectivity distributions are sharp in temporal auditory regions, whereas they are wide in frontal motor regions, because of the marginalization process described in Eq. (1). As a result, a small variation in the acoustic stimulus leads to a change in the neural channel in auditory regions, while a larger variation would be required for a change in motor regions. Therefore, a small acoustic change is expected to result in release of suppression in auditory but not in motor regions.

In the present study, we use stimuli varying from prototypical vowels (i.e. vowels that will be unambiguously identified by listeners, e.g. i0), to vowels progressively deviating from the prototype (i1, i2, i3, etc), to vowels clearly distant from the prototype (i∞). The design and assumptions of the study are presented in Figure 1B. In the top left corner of the plot, we present the standard deviation ellipses of the hypothetical sensory distributions PA(S/O) and PM(S/O) associated with the distribution of responses to sensory inputs around a prototypical /i/ (i0), with a broader ellipse for PM(S/O) than for PA(S/O), in line with the ANMW hypothesis. The upper right part of the plot provides the neuroanatomical translation of the ANMW hypothesis, with a sharp distribution PA(S/O) in auditory regions and a wide distribution PM(S/O) in motor regions. The text block at the bottom right of the plot provides the experimental RS hypothesis. In a sequence of 4 identical vowels such as i0-i0-i0-i0, the fMRI-BOLD activity is expected to be weak due to suppression. When the last stimulus is slightly modified (e.g. i1 or i2) BOLD activity is expected to partially recover, but recovery would likely occur for smaller acoustic modifications in auditory regions compared to motor regions. Therefore, the prediction is that activity could be enhanced in auditory regions for sequences such as i0-i0-i0-i1/2 where i1 or i2 are close to i0. In motor regions, activity should only increase for sequences such as i0-i0-i0-i∞, where i∞ is sufficiently distant from i0. In other words, this study aims at revealing a differential involvement of temporal auditory regions and frontal motor regions in the RS paradigm for vowel stimuli.

Material and methods

Participants

Three groups of participants were recruited for this study, two groups for pilot behavioral tests to select suitable stimuli for the repetition-suppression task, and a third group for the fMRI repetition-suppression task itself. Participants in the three groups were all different. The study was approved by the local ethics committee (CPP Sud Est V, ID RCB 2019-A00293-48) and registered on ClinicalTrials.gov (NCT number 03102983). Written informed consent was obtained from all participants before the study in accordance with Helsinki guidelines.

For the first set of pilot behavioral tests, a first group of 12 French listeners (G1 in the following, age between 23 and 58, 9 female subjects) and a second group of 7 participants (G2 in the following, age between 21 and 52, 5 female subjects), all from Université Grenoble Alpes, participated in the experiments. All participants were native speakers of French with normal audition and normal or corrected-to-normal vision, and reported no history of psychiatric or neurological disorders.

For the fMRI repetition-suppression task, 19 healthy participants (10 females) were initially included but 18 were retained for the analyses, aged 18 to 39 years (G3 in the following; mean age: 24.22, S.D.: 6.29, 9 females). One female participant was excluded from the analyses for methodological reasons. The participants received compensatory retribution for their participation. All were native speakers of French with normal or corrected-to-normal vision. All were right-handed (Edinburgh Laterality Inventory; [START_REF] Oldfield | The assessment and analysis of handedness: the Edinburgh inventory[END_REF] and had audiometric pure-tone thresholds not exceeding 25 dB HL at frequencies 250 Hz, 500 Hz, 750 Hz, 1000Hz, 1500Hz, 2000 Hz, 3000 Hz, 4000 Hz and 6000 Hz. Participants reported no history of psychiatric or neurological disorders.

Stimuli

Synthetic vowel sounds

The experiment included vowel stimuli in the /i/ (front unrounded) and /u/ (back rounded) regions. Stimuli were synthetic, obtained by a Klatt formant synthesizer [START_REF] Klatt | Analysis, synthesis and perception of voice quality variations among male and female talkers[END_REF] available in Praat [START_REF] Boersma | Praat: doing phonetics by computer[END_REF]. All stimuli had the same 200 ms duration and the same pitch (decreasing linearly from 147 Hz at the beginning of the stimulus to 122 Hz at the end), and only the formant values were varied. The formant values were adjusted so as to start at the top of the vowel space, with a minimum F1 value around 250 Hz (stimuli i0 and u0), and then moving along the boundaries of the vowel space in regular F1, F2 and F3 steps, in a perceptual Bark scale [START_REF] Schroeder | Objective measure of certain speech signal degradations based on masking properties of human auditory perception[END_REF]. This provided two sets of stimuli in the front unrounded and in the back rounded region respectively. In the first set, stimuli i0 to i10 had F1 increasing from 2.6 to 4.6 Bark in 0.2 Bark steps, and F2 and F3 decreasing in 0.1 Bark steps from 13.3 to 12.3 and from 15.6 to 14.6 Barks, respectively. In the second set, stimuli u0 to u10 had F1 increasing from 2.6 to 4.6 Bark in 0.2 Bark steps, and F2 and F3 increasing in 0.1 Bark steps from 5.8 to 6.8 and from 13.0 to 14.0 Barks, respectively. Three additional stimuli were prepared so as to provide stimuli that were significantly different from i0 and u0: namely stimuli i∞ and u∞, with an F1 value at 5.8 Barks, and a control stimulus a. All formant values are provided in Table 1.

Selection of suitable individual vowel sounds

Given the way the sounds were synthesized, the expectation was that stimuli i0 and u0 would be respectively perceived as /i/ and /u/. Then, it was expected that front vowel stimuli from i1 to i10 would gradually be perceived as mid-high and mid-open vowels /e/ and /ɛ/. Back vowel stimuli from u1 to u10, were expected to be more and more identified as mid-high /o/ and midopen /ɔ/. Stimuli i∞, u∞ and a, were expected to be respectively perceived as /ɛ/, /ɔ/ and /a/. To assess how the synthetic stimuli were actually perceived, a categorization test was performed by the first group of 12 listeners, G1, with a forced choice procedure, among the following answers: /i/, /e/, /ɛ/, /u/, /o/, /ɔ/ and /a/. The participants were presented with 9 repetitions of each of the 25 stimuli (i0 to i10, u0 to u10, i∞, u∞ and a), with all stimuli presented in a random order. The average categorization percentages for the stimuli i0 to i10 and u0 to u10 are displayed in Figure 2. They show that i0 and u0 are indeed good prototypical exemplars of the high vowels /i/ and /u/, with a switch to the mid-high vowels /e/ and /o/ around i4 and u4, whereas the last stimuli i10 and u10 were categorized as mid-open vowels /ɛ/ and /ɔ/ respectively. As expected, i∞ and a were 100% categorized as /ɛ/ and /a/, whereas u∞ was actually categorized halfway between /ɔ/ and /a/.

Since we had no clear predictions about the optimal distance from the prototypical i0 and u0 stimuli that would allow partial recovery from repetition-suppression, we decided to further evaluate three pairs of stimuli. The first pair consists of i1 and u1, with an F1 distance with prototypes equal to 0.2 Bark, thus quite close to prototypes. The sounds in the second pair, i2 and u2, correspond to an F1 distance with prototypes equal to 0.4 Bark, which is likely to allow clear discrimination already, but should still remain within the high /i/ or /u/ category, given the categorization responses displayed in Figure 2. Finally, the third pair involves two stimuli even further away from the prototypes, i.e. i4 and u4. These sounds belong to the mid-high category, given that i4 was perceived as /e/ and that u4 was perceived at the boundary between /u/ and /o/.

To characterize the perceptual distance between the selected stimuli {i0, i1, i2, i4} on the front side and {u0, u1, u2, u4} on the back side, we performed two additional perceptual tests on these two sets. The first test consisted in an ABX discrimination test with A or B the i0 (resp. u0) prototype, B or A one stimulus in the {i0, i1, i2, i4} (resp. {u0, u1, u2, u4}) set, and X one of the two stimuli in the pair. The task was to decide whether X was closer to A or B. There were 10 occurrences of each pair with X randomly selected within the pair. All stimuli in the test were grouped in a single block and presented in a random order, different for each participant. The second additional test was a quantitative assessment of the perceptual distance to the prototype. For this test we added the stimuli i∞, u∞. The task consisted in listening to pairs such as i0-iX or u0-uX, X being a value within the {0, 1, 2, 4, ∞} set, and in providing a subjective evaluation of the perceptual distance of the two stimuli in the pair on a scale varying between 1 (no audible difference) and 7 (maximal possible perceptual distance). For each pair, the two orders of stimuli in the pair were presented 5 times, with all pairs mixed in a single block in a random order different for each participant.

The 7 participants in group G2 participated in the two additional tests. The results are displayed in Figure 3. They show that stimuli i1 and u1 are poorly discriminated from the prototypes in the ABX test and perceptually quite close (especially for u1, with discrimination from u0 close to chance and distance close to 1, meaning no audible difference). Stimuli i2 and u2 are rather well discriminated, although far from perfectly, but display a rather small perceptual distance (around 2). Stimuli i4 and u4 are clearly discriminated (although not perfectly) and display rather large distances (around 5), though still far from the maximal distance obtained for stimuli i∞ or u∞.

This set of perceptual evaluations led us to discard stimuli i1 and u1, which might not lead to any recovery from the repetition-suppression effect, and to select the set of stimuli {i0, i2, i4, i∞} on one side and {u0, u2, u4, u∞} on the other side of the vowel (F1, F2) space for the repetitionsuppression fMRI experiment to be presented in the next section.

Selected trains of stimuli

Finally, the stimuli for the RS task were composed of trains of 4 vowels consisting in 3 identical prototypical vowels (i0 or u0), followed by a fourth vowel that could be either the same prototypical stimulus (i0 / u0) or a different stimulus (i2, i4, i∞ / u2, u4, u∞), with increasing distance between the first 3 vowels and the last one, resulting in the following four conditions: Repetition (REP): i0-i0-i0-i0 / u0-u0-u0-u0, Non-Repetition 1 (NREP1): i0-i0-i0-i2 / u0-u0-u0-u2, Non-Repetition 2 (NREP2): i0-i0-i0-i4 / u0-u0-u0-u4, and Non-Repetition ∞ (NREP∞): i0-i0-i0-i∞ / u0-u0-u0-u∞.

Catch trials consisting of trains of i0-i0-i0-a and u0-u0-u0-a stimuli were also added to maintain participants' attention on the auditory stimuli. In each train of vowels, the vowel duration was 200 ms and the interstimulus interval was 50 ms, for a total stimulus duration of 950 ms.

fMRI repetition-suppression protocol

Task and procedure

Vowel Perception experiment

Functional MRI experiments underwent by the G3 participants were performed at the Centre for neuroimaging of the University Hospital CHU Grenoble Alpes (IRMaGe, Grenoble, France). The participants lying in the MRI scanner listened to auditory stimuli via an audio system compatible with high magnetic fields (MR Confon). Visual instructions were displayed through a mirror located in front of their eyes, reflecting a screen which was positioned behind the MRI scanner. The task consisted in carefully listening to the 4-vowel sequences and pressing a key when the last vowel was an /a/ (catch trials). There were altogether 4 conditions of interest plus one condition for catch trials and a baseline consisting of a silent condition, with 54 trials per condition and a total of 324 trials (see Table 2). The 324 stimuli were divided into three runs of 108 items each, separated by a short pause; the order of sessions was counterbalanced between participants. In each session, the order of conditions was counterbalanced, with the constraint of always alternating /i/-and /u/-stimuli. A motor localizer session was added at the end of the fMRI recording session. The total experiment duration for the three sessions was about 45 min.

Condition

Post-Scan behavioral test

Importantly, given that the fMRI task involved perceptual detection of catch-trials /a/ stimuli, there was no control of the perception of i0-u0, i2-u2, i4-u4 or i∞-u∞ stimuli. Therefore, right after the fMRI experiment, a post-scan check was run outside the magnet. Specifically, participants performed a behavioural test in a quiet room in order to evaluate their subjective perception of between-vowel distances. To this aim, they performed the quantitative perceptual distance evaluation task applied for the preparation of the stimuli with the G2 group (see Section 2.2). They were presented with the following pairs of vowels: i0-i0, u0-u0, i0-i2, u0-u2, i0-i4, u0-u4, i0-i∞, u0-u∞, in both orders, e.g. i0-i2 or i2-i0. Seventy pairs of stimuli (5 repetitions for each pair and each order) were thus presented in a random order. Participants were asked to subjectively evaluate the distance between the vowels in the pair, on a 1 to 7 scale. Due to technical problems, 2 of the 18 participants were unable to complete this post-scan test. For the other 16 participants, the responses were similar to those provided in the pilot study (displayed in Figure 3).

fMRI data acquisition

MR images were acquired with a whole-body 3T MR Philips imager (Achieva 3.0T TX Philips, Philips Medical Systems, Best, NL) with a 32-channel head coil for all of the participants. The chronology of fMRI sequences was as follows: vowel perception fMRI run #1, vowel perception fMRI run #2, anatomical MRI, vowel perception fMRI run #3, motor localizer fMRI and B0 fieldmap. A T2*weighted (Gradient Echo, GE) echo-planar imaging (EPI) sequence sensitive to blood oxygen level dependent (BOLD) contrast was used for the functional scans.

For the vowel perception experiment, the acquisition parameters were: 9 s repetition time (TR), 30 ms echo time, 90° flip angle, 240 mm x 240 mm in-plane field of view, 80 x 78 acquisition matrix size, 2.5 SENSE factor. 53 axial slices (2.80 mm thickness, separated by a 0.20 mm gap) covering the entire brain and parallel to the anterior commissure-posterior commissure plane were acquired in a sequential mode (ordered from Head to Feet); reconstructed voxel size was 3x3x3 mm 3 . A total of 108 dynamic volumes per run were acquired in a sparse imaging procedure, which introduces a silent gap between subsequent/successive volume acquisition. Functional scanning therefore occurred over a fraction of the TR (2720 ms per volume, over the 9000 ms TR), alternating with silent inter-scanning periods during which the auditory stimuli were presented. The time interval between the onset of auditory stimuli and the midpoint of the following acquisition scan was varied randomly between 4s, 5s, or 6s to cover the typical delay range where the maximum of the BOLD hemodynamic response function occurs (the sparse sampling acquisition technique used in this study was similar to the one described in [START_REF] Grabski | Functional MRI assessment of orofacial articulators: neural correlates of lip, jaw, larynx and tongue movements[END_REF]. Figure 4 summarizes the detail of the acquisition sequence. Complementary to this vowel perception experiment, participants completed a motor localizer test. This test was run in order to draw functional regions of interest which served for further analyses. However, since the analyses focused on these regions of interest did not bring any additional information, the motor localizer and the related analyses are not described in the present paper.

A 3D T1-weighted high-resolution three-dimensional anatomical volume was also acquired using a MP-RAGE sequence: 1170 ms TI, 10.5 ms TR, 4.9 ms TE, 8° flip angle, 220 mm x 220 mm in-plane field of view with 175 mm H-F coverage, 316 x 315 x 250 acquisition matrix, 1.5 x 1.5 SENSE factors, isotropic 0.7 mm 3 reconstructed voxel size.

To correct images for geometric distortions, a B0 fieldmap was obtained from two gradient echo data sets acquired with a FLASH sequence. The fieldmap was subsequently used during data pre-processing.

fMRI Data analyses

Statistical analyses were performed using SPM12 statistical parametric mapping software The first 5 volumes of each scanning session, during which MR signal reaches steady-state, were discarded. fMRI data underwent two categories of analyses: spatial pre-processing and statistical analyses on the spatial pre-processed data.

Spatial pre-processing

Data pre-processing included realignment on the mean volume of each session, unwarping, coregistration on the anatomical volume and normalisation using DARTEL Tool from SPM12.

On the latter point, structural T1-weighted scans of the 18 participants were segmented into different tissue types. Intensity average of the grey and white matter images were generated to use as an initial template for DARTEL registration (6 iterations). This template was aligned with the MNI Template using affine transform, and each functional scan was then aligned with this template. Finally, images were smoothed with an 8-mm FWHM Gaussian kernel. No highpass filtering was applied.

Statistical analyses

Data analysis was performed using the general linear model (GLM, [START_REF] Friston | Analysis of fMRI time-series revisited[END_REF] as implemented in SPM12.

First-level (individual) analyses

For each participant, the GLM modelled each experimental condition as five regressors (REP, NREP1, NREP2, NREP∞, Baseline) depending on the stimulus onset time and convolved with a canonical hemodynamic response function (HRF). Stimulus duration was set in the model at 1s. Six regressors were added for the six realignment parameters. The GLM was then used to generate parameter estimates of activity at each voxel and for each condition. Statistical parametric maps were generated from linear contrasts between the HRF parameter estimates for the different conditions and for between-conditions contrasts.

Firstly, to globally evaluate the brain regions associated with each condition of interest, each condition was contrasted against the silent baseline: REP > Baseline, NREP1 > Baseline, NREP2 > Baseline and NREP∞ > Baseline. Secondly, the repetition-suppression effect was evaluated as a function of increasing acoustic distance. Following our hypotheses, neural activity should be enhanced in auditory regions for the NREP1 condition in comparison to the REP condition, and in auditory and motor regions for the NREP∞ condition compared to the REP condition. The status of the NREP2 condition between these two patterns was not predictable a priori. To assess this set of hypotheses, each non-repetition condition was thus contrasted to repetition: NREP1 > REP, NREP2 > REP, NREP∞ > REP. To evaluate the effect of the increasing acoustical distance, the non-repetition consecutive conditions (in terms of increasing distance) were also contrasted to each other, i.e. NREP2 > NREP1 and NREP∞ > NREP2. Finally, the (exploratory) contrast NREP∞ > NREP1 was also included for the sake of completeness.

Second-level (group) analyses

For both analyses, resulting images from each subject were entered in a second-level (random effect) model [START_REF] Friston | How many subjects constitute a study?[END_REF]. A one-sample t-test was used; resulting contrasts were thresholded at a whole-brain p<0.05 family-wise error (FWE) corrected at the voxel level (T > 6.91) and at p<0.05 FWE corrected at the cluster-level for the first part of the analyses (section 3.1). For the second part of the analyses, contrasts were thresholded at p<0.0001 uncorrected at the voxel-level (T > 4.79), and p<0.05 FWE corrected at the cluster-level (section 3.2).

Local maxima are reported in the MNI space. Localisations were obtained using AAL [START_REF] Tzourio-Mazoyer | Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[END_REF] and, when necessary, the Yale BioImage suite package (mni2tal mapping from Lancadie et al., 2008). The resulting tables present extended local maxima for each cluster of activation.

Results

REP, NREP1, NREP2 and NREP∞ compared to Baseline

The results of the first analysis contrasting (each condition REP, NREP1, NREP2 and NREP∞ versus Baseline) are provided in Figure 5 and Figure 6 and Table 3. Compared to Baseline, the perception of vowels in the REP condition yielded bilateral activation in the auditory cortex (BA 41), superior temporal gyrus (STG, BA 22) and left precentral gyrus (BA6). When the distance between the first three vowels and the last one increased, i.e. in the NREP1 and NREP2 conditions compared to Baseline, the bilateral STG remained activated, and activity in frontal areas seemed to extend, including the right premotor region, right medial cingulate gyrus, right inferior frontal gyrus (BA45) and left cerebellum-Crus1 (see Table 3). The NREP∞ condition activated a larger network, including supplementary motor area (SMA, medial BA6), left and right precentral (M1, BA4) gyri, left mid-cingulate gyrus, left IFG (BA 44), right insula, and left and right cerebellum (pars 6).

To better visualize the location of activations for the four different contrasts, they are projected onto the same template in

Between-condition contrasts

The results of the second part of the analysis assessing the difference in the repetitionsuppression effect as a function of acoustic distance are provided in Table 4, and the networks of brain areas significantly activated in the comparisons between the 3 NREP conditions and the REP condition are displayed in Figure 8.

The NREP1 > REP contrast displayed significant increase in activation in the right superior temporal gyrus (BA22) extending in the middle temporal gyrus (BA 21), and the right hippocampus. At the threshold of p<0.0.5 FWE corrected at the voxel level, there was no significant increase in activation when NREP2 was contrasted to REP or when NREP2 was compared to NREP1. As Figure 8 shows, at a less stringent threshold (p<0.001 uncorrected), we can observe a significant activation in the right STG.

The NREP∞ > REP contrast displayed significant differences in a larger network of brain regions, including bilateral superior temporal gyrus extending to the bilateral inferior frontal gyrus (BA 47) and insula, left and right cerebellum, left medial superior frontal gyrus (BA 8) extending to the left mid-cingulate, and left and right calcarine sulcus (BA 17). The NREP∞ > NREP2 contrast was associated to a significant increase in activation in the left and right insula (BA13), bilateral superior temporal gyrus (BA 41 and 22), right inferior frontal (BA45) gyrus and supplementary motor area (BA6). The NREP∞ > NREP1 contrast provided significant differences only in the left and right supplementary motor area.

The reverse contrasts (REP > NREP1, REP > NREP2, and REP > NREP∞, NREP1 > NREP2, NREP1 > NREP∞ and NREP2 > NREP∞) yielded no significant increase in activation.

Discussion

The basic network of vowel perception

As a preliminary note, the auditory speech conditions REP, NREP1, NREP2 and NREP∞, yielded a significant increase in BOLD response compared with the silent baseline, in a network that includes the auditory cortex and superior temporal gyrus bilaterally, the inferior frontal gyrus (BA44/45) bilaterally, the bilateral premotor cortex, SMA, insula and cerebellum. This sensory-motor network linking auditory regions with somatosensory-motor regions through the dorsal pathway has been described in a number of previous studies [START_REF] Hickok | The cortical organization of speech processing[END_REF][START_REF] Skipper | Listening to talking faces: motor cortical activation during speech perception[END_REF][START_REF] Skipper | The hearing ear is always found close to the speaking tongue: Review of the role of the motor system in speech perception[END_REF][START_REF] Schomers | Is the Sensorimotor Cortex Relevant for Speech Perception and Understanding? An Integrative Review[END_REF]. It is also consistent with all other studies on the RS paradigm using phonetic stimuli, as mentioned in Section 1.3, and with a number of studies focused on vowel perception (e.g. [START_REF] Arnaud | Repetition-suppression for speech processing in the associative occipital and parietal cortex of congenitally blind adults[END_REF][START_REF] Grabski | A mediating role of the auditory dorsal pathway in selective adaptation to speech: A state-dependent transcranial magnetic stimulation study[END_REF][START_REF] Grabski | Adaptive phonemic coding in the listening and speaking brain[END_REF][START_REF] Husain | Neural bases of categorization of simple speech and nonspeech sounds[END_REF][START_REF] Joanisse | Overlapping neural regions for processing rapid temporal cues in speech and nonspeech signals[END_REF]Rampini et al., 2017).

Repetition-suppression and the ANMW hypothesis

The novelty of the present study concerns the distinct patterns of activation in the temporal and frontal regions associated with a repetition-suppression paradigm involving vowel stimuli. Our major finding is that a small acoustic variation between the final stimulus and the three preceding ones in a vowel sequence (condition NREP1 compared with REP) produces a significant increase in activity (i.e. release of suppression) in a set of regions associated with auditory processing, namely the right superior temporal gyrus, Heschl's gyrus and the right hippocampus. Larger acoustic distances (NREP∞) provide an additional significant increase in activity in regions associated with speech-related planning and motor programming (right insula and right pars orbitalis and pars opercularis, left insula, right medial superior frontal gyrus, supplementary motor area, as well as bilateral cerebellum), in addition to those considered to be involved in auditory decoding (left STG).

The distinct patterns of repetition-suppression recovery associated with small and large acoustic distances have never been described previously with vowel stimuli. As presented in Section 1.3, the only previous study using a similar paradigm with vowels is the MEG study by [START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF]. Their study revealed a region within the left superior temporal cortex that was differentially activated for pairs of stimuli consisting of the same or different consonant-vowel (CV) syllables, at around 430 to 500 ms after the onset of the second stimulus of the pair. Yet, although this difference in activation occurred only for categorical variations in the consonant of the CV stimuli, it was observed both for within-and for between-category variations of the vowel. The authors interpreted this result as evidence for a categorical effect at the neural level for consonant perception, but not for vowel perception. In their view, their finding is consistent with observed behavioral differences in the categorization of consonants and vowels, consonants being "represented in a more categorical-like manner than vowels".

In contrast, effects similar to those obtained in the present study have been found in previous studies with consonants (e.g. in [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF], or Alho et al., 2016, see Section 1.3). Yet, the results seem to differ largely from one experiment to another, as shown in Section 1.3.

To get a clearer picture of the similarities and differences between the studies, in Table 5, we present a summary of all available fMRI or MEG repetition-suppression studies which involve sequences of phonetic stimuli varying around a prototype. Strikingly, in spite of the large variations in the pattern of activations in these experiments, it appears that all experimental data can be summarized by 3 rules which apply systematically to all these studies. First, all data converge on the fact that when the last stimulus in a sequence differs from the previous one(s), cortical activity increases. As can be observed in Table 5 (column "Results"), activation is larger in conditions D (last stimulus Different), W (last stimulus different but Within the same category), or B (last stimulus different with Between category distinction) than S (all stimuli are the Same). Furthermore, when two types of deviations for the last stimulus are compared, larger deviation leads to an additional increase in cortical activity. There is increased cortical activity in conditions D-far compared to D-close (two "Different" conditions with larger differences in one case, D-far, than in the other, D-close) and in condition B compared to condition W. This is actually the precise manifestation of the RS effect, and it is systematic in all these studies. Therefore, this first observation could be summarized as the "repetitionsuppression rule".

Second, in the last column of Table 5, we consider the selectivity in the "auditory temporal" (AT) regions, grouping the superior temporal gyrus and sulcus, and in the "speech planningmotor programming" (MP) regions, grouping the inferior frontal gyrus, premotor cortex, supplementary motor area, insula and cerebellum. More precisely, we compare in this last column the activity increase in these two sets of regions, when the last stimulus in the sequence shifts from Same to Different and, when possible, from Close to Far (or from Within to Between). Strikingly, a systematic regularity emerges which provides the second summarizing rule: RS recovery systematically happens in the auditory temporal regions before the motor regions. In other words, RS recovery is triggered for smaller acoustic variations in the auditory temporal regions than in the motor regions. Indeed, when only two conditions are compared (Same and Different), the increase in activity in the Different condition occurs in Auditory Temporal regions (for [START_REF] Zevin | Dishabituation of the BOLD response to speech sounds[END_REF][START_REF] Lawyer | An Investigation of Place and Voice Features Using fMRI-Adaptation[END_REF], adding MP regions in the cerebellum in the second case). When three conditions are examined, the observed pattern is either "no change for Within and a change in Auditory Temporal regions for Between conditions" [START_REF] Joanisse | Brain mechanisms implicated in the preattentive categorization of speech sounds revealed using FMRI and a short-interval habituation trial paradigm[END_REF]Altmann et al., 2014, for plosives) or "change in Auditory Temporal regions for Within/D-close and a change in Motor regions for Between/D-far" [START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF] although in this case there was synchronous change in Auditory Temporal and Motor regions for Within; [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF][START_REF] Alho | Early-latency categorical speech sound representations in the left inferior frontal gyrus[END_REF]; and the present study).

Crucially, this selectivity pattern is the exact application of the ANMW hypothesis at the heart of the present study, so we will call it the "ANMW rule". This rule applies to all 10 studies in Table 5, including the present one. The present study adds one key point to this pattern, that is the fact that the ANMW-rule also applies to vowel stimuli, while previous evidence for this rule only concerned consonants.

Third, attention seems to modulate the pattern of responses in a consistent way, with less activity overall and therefore less difference between conditions when attention is decreased. Although the way in which attention is controlled is highly variable across these studies (see column "Paradigms" in Table 5), the trend is clear. It applies to the comparison of conditions "ATTEND" and "IGNORE" in the study by [START_REF] Alho | Early-latency categorical speech sound representations in the left inferior frontal gyrus[END_REF]. It also emerges when comparing the studies by [START_REF] Joanisse | Brain mechanisms implicated in the preattentive categorization of speech sounds revealed using FMRI and a short-interval habituation trial paradigm[END_REF] and [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF] who used similar stimuli, but differed in attention involvement. In Joanisse et al.'s (2007) study, where participants were instructed not to pay attention to the sound, there were no difference between conditions W and S, but differences emerged in the Auditory Temporal region when comparing B and W. In contrast, [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF] proposed a distraction task which actually led participants to listen carefully to the acoustic stimuli although the focus was not on categorization per se. They found a difference between conditions S and W in Auditory Temporal, and then between W and B in Motor Programming regions. The attentional modulation of cortical activity, globally and across conditions, is consistent with general findings on the role of attention in modulating activity and selectivity in neural processes [START_REF] Spitzer | Increased attention enhances both behavioral and neuronal performance[END_REF][START_REF] Hillyard | Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence[END_REF][START_REF] Murray | Attention increases neural selectivity in the human lateral occipital complex[END_REF]. It is also in line with numerous reports of fMRI speech perception experiments showing a modulation of cortical responses with selective attention in the superior temporal and inferior frontal cortex (e.g. [START_REF] Hugdahl | The effects of attention on speech perception: An fMRI study[END_REF][START_REF] Sabri | Attentional and linguistic interactions in speech perception[END_REF][START_REF] Wild | Effortful Listening: The Processing of Degraded Speech Depends Critically on Attention[END_REF]. This third rule can therefore be called the "attention rule".

Three additional elements in the current data deserve to be discussed. The first one concerns the lack of increase in activation in the NREP2 > REP contrast while there is a significant increase in activation in the NREP1 > REP contrast (see Table 4). This is unlikely to be due to the stimuli, given the increase in acoustic and perceptual distances from stimuli i0-u0 to i2-u2 (used in NREP1) and then to i4-u4 (used in NREP2, see Figures 2 and3). A possible interpretation could be that in conditions REP and NREP1, where the acoustic difference between the first three stimuli and the last one is either null (REP) or small (NREP1), the listeners search more actively for a perceptual difference at the end of the sequence. This would lead to an increase in attentional processing in these two conditions compared with the other two conditions NREP2 and NREP∞, where the last stimulus clearly differs from the first three ones. This could explain an increase in activation in conditions REP and NREP1 compared with condition NREP2 (and NREP∞). This increased processing would mask the increase in neural activity in NREP2 that should result from the release in RS. Two opposite mechanisms (less attention but more release in RS in NREP2 relative to REP and NREP1) would be at play which could explain the observed activation contrasts.

This suggests that the effects of the hypothetical ANMW principle and of attentional processes could interact in a possibly complex way. Moreover, it must be noticed that the pattern of beta estimates within each set of voxels (see Figure 6) does not display smooth monotonically increasing patterns as could be assumed based on the selectivity patterns in Figure 1A even if we take into account the potential role of attentional processes possibly differing in conditions REP and NREP1 compared with conditions NREP2 and NREP∞. This is likely due to the complex and partly unknown relationships relating neural individual and collective activity to BOLD response in a given cortical region [START_REF] Vanzetta | A BOLD Assumption[END_REF][START_REF] Zhang | The relationship between BOLD and neural activity arises from temporally sparse events[END_REF]. Future studies explicitly monitoring or evaluating the amount of cognitive effort and attention in relation to the ANMW principle should be set up to further analyse these interactions and the underlying pattern of cortical activation in temporal vs. frontal areas.

Second, it is not without interest that, in addition to the observed increased activity in the frontal regions for between-category vowel processing (the NREP∞ condition), increased activation was also observed in the bilateral cerebellum. In line with propositions made by [START_REF] Grandchamp | The ConDialInt Model: Condensation, Dialogality, and Intentionality Dimensions of Inner Speech Within a Hierarchical Predictive Control Framework[END_REF], this cerebellar recruitment can be interpreted as the involvement of an internal model when processing a new vowel category. The internal model would provide cortical frontal regions with a motor specification inverted from the new sound.

Finally, there is a trend in the present data to observe greater differences in cortical activity in the right hemisphere compared with the left one, since a difference between NREP1 and REP in the STG was found only in the right hemisphere, while activation in the IFG in the NREP∞ condition was increased bilaterally, as compared with the two other conditions. This pattern is actually not really surprising, since the stimuli used in the present study are synthetic and based on small variations around the prototypical i0/u0 stimuli. They are therefore atypical speech sounds likely to induce larger activity in the right hemisphere. Numerous studies have found a shift in the lateralization of cerebral processing from left to right hemisphere for degraded (noisy) speech stimuli [START_REF] Bidelman | Functional changes in inter-and intra-hemispheric cortical processing underlying degraded speech perception[END_REF]. Other studies have found right hemisphere predominance for the processing of speaker indexical information included in speech sounds [START_REF] Mcgettigan | Cortical asymmetries in speech perception: what's wrong, what's right and what's left?[END_REF] or the processing of phonetic variability between talkers [START_REF] Luthra | The Role of the Right Hemisphere in Processing Phonetic Variability Between Talkers[END_REF]. A right hemisphere shift is also observed in some of the studies listed in Table 5 (e.g. for [START_REF] Zevin | Dishabituation of the BOLD response to speech sounds[END_REF][START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF]Lawyer & Corina, 2004).

Alternative interpretations

The set of experimental data on phonetic repetition-suppression described in Table 5 can be and has been interpreted with other systems of explanations than the present set of three rules including the "ANMW rule".

First, a number of authors proposed a dichotomy between pre-categorical auditory/phonetic processing in posterior areas (typically STG/STS/SMG), and categorical processing and decision in anterior areas (including IFG/PMC). This is the argument developed in particular by [START_REF] Myers | Inferior frontal regions underlie the perception of phonetic category invariance[END_REF], [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF] or [START_REF] Alho | Early-latency categorical speech sound representations in the left inferior frontal gyrus[END_REF]. The underlying rationale is that phonological categorisation occurs in the inferior frontal regions, where "integration of auditory and motor information" takes place [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF]. This view is in line with a number of data on the role of anterior regions in successful phonological categorization, for both auditory [START_REF] Alho | Attention-related modulation of auditory-cortex responses to speech sounds during dichotic listening[END_REF][START_REF] Alho | Stimulus-dependent activations and attention-related modulations in the auditory cortex: a meta-analysis of fMRI studies[END_REF] and audiovisual stimuli [START_REF] Hasson | Abstract coding of audiovisual speech: beyond sensory representation[END_REF]. a number of other studies also provide evidence for categorisation processes in the STG/SMG complex (e.g. [START_REF] Chang | Categorical speech representation in human superior temporal gyrus[END_REF][START_REF] Dewitt | Phoneme and word recognition in the auditory ventral stream[END_REF][START_REF] Jacquemot | Phonological grammar shapes the auditory cortex: a functional magnetic resonance imaging study[END_REF], and specifically converge on the role of the SMG in the representation and manipulation of phonological units in speech perception [START_REF] Paulesu | The neural correlates of the verbal component of working memory[END_REF][START_REF] Caplan | Analysis of lesions by MRI in stroke patients with acoustic-phonetic processing deficits[END_REF][START_REF] Dehaene-Lambertz | Neural correlates of switching from auditory to speech perception[END_REF][START_REF] Jacquemot | What is the relationship between phonological short-term memory and speech processing?[END_REF]. As a matter of fact, a number of studies listed in Table 5 show temporal rather than frontal regions to be specifically associated with Betweencategory sequences (e.g. [START_REF] Zevin | Dishabituation of the BOLD response to speech sounds[END_REF][START_REF] Joanisse | Brain mechanisms implicated in the preattentive categorization of speech sounds revealed using FMRI and a short-interval habituation trial paradigm[END_REF][START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF][START_REF] Lawyer | An Investigation of Place and Voice Features Using fMRI-Adaptation[END_REF].

A second line of interpretation would rather relate frontal activity in the Between-category conditions to active decision-making and executive processes (e.g. [START_REF] Binder | Neural correlates of sensory and decision processes in auditory object identification[END_REF][START_REF] Blumstein | The perception of voice onset time: an fMRI investigation of phonetic category structure[END_REF][START_REF] Joanisse | Brain mechanisms implicated in the preattentive categorization of speech sounds revealed using FMRI and a short-interval habituation trial paradigm[END_REF], possibly including access to the phonological loop in working memory [START_REF] Baddeley | Exploring the Articulatory Loop[END_REF] and access to the output phonological buffer in the IFG [START_REF] Jacquemot | What is the relationship between phonological short-term memory and speech processing?[END_REF]. This account could be viewed as consistent with the pattern of results in Table 5, in which most conditions that included an explicit auditory task prompting the listener to pay attention to the stimuli yielded activity in IFG/PMC in the Between condition (apart from the MEG data in [START_REF] Altmann | Categorical speech perception during active discrimination of consonants and vowels[END_REF]. However, all of these studies, apart from the present one, involved an auditory task in which the participants were indeed asked to listen carefully to the stimuli, not for categorization or discrimination, but rather for a decision on some other component of the sound (pitch or duration). This led [START_REF] Chevillet | Automatic Phoneme Category Selectivity in the Dorsal Auditory Stream[END_REF] to argue that IFG activity in their study could not be conceived as attention-related, since the task at play did not involve phonological categorization, and to conclude that the PMC activity was related to "automatic sensorimotor integration of speech".

Overall, the rather complex pattern of data in Table 5 seems difficult to reconcile with either of these two interpretations. It is instead more compatible with the gradient view that underlies the two ground rules proposed to operate in addition to the "RS rule", namely a difference in selectivity in temporal vs. frontal regions at play in the ANMW hypothesis, and a gradient role of attention. Yet, of course, the different arguments are not mutually exclusive, and it is likely that differential selectivity (as in ANMW), auditory-motor integration mechanisms and phonological executive processes are all at play across the RS studies reviewed in this discussion.

Conclusion

In this work, we have designed an original fMRI RS experiment based on the predictions from a computational model. The prediction is related to the so-called Auditory-Narrow Motor-Wide property, according to which auditory processing of speech soundspresumably related to activations in temporal regions in the human cortexwould be narrower, i.e. more tuned to prototypical sounds, than motor processingpresumably related to frontal regions. We designed the study on this basis, to explore whether responses to vowel sounds in a repetition-suppression paradigm would provide distinct recovery patterns in temporal and frontal regions. The data we obtained generally confirm this prediction. A small modification of the acoustic input led to a significant increase in the BOLD response in temporal regions (in the right superior temporal gyrus and Heschl's gyrus) whereas a significant increase in regions likely associated to speech planning and motor programming (right insula and right pars orbitalis and pars opercularis, left insula, right medial superior frontal gyrus, supplementary motor area and cerebellum) occurred for a larger modification of the vowel input. Interestingly, our analysis of previous neuroimaging studies reveals that this pattern, which we have termed the ANMW rule, seems to apply to all other previous studies.

These data are in line with the behavior of the COSMO model and suggest that the interpretation of greater cortical activity in motor regions for atypical stimulie.g. noisy or accentedmay be related to the ANMW property. Regardless of the modelling aspects, these data add to the overall picture of repetition-suppression fMRI experiments, adding data for vowel stimuli to the large amount of data already available for consonants.

Figure 1 .

 1 Figure 1. The probability distributions PA(S/O) and PM(S/O) and the "Auditory-Narrow Motor-Wide" property in COSMO. A: A "typical" stimulus S1 is better recognized by the auditory pathway while an "atypical" stimulus S2 is better recognized by the motor pathway. B: Rationale and hypotheses of the present study.

Figure 2 .

 2 Figure 2. Categorization responses for front unrounded stimuli i0 to i10 (left) and back rounded stimuli u0 to u10 (right) averaged on the 12 listeners. Error bars correspond to standard error of the mean.

Figure 3 .

 3 Figure 3. Top: Discrimination from i0 (left) and u0 (right) in an ABX experiment, 50% corresponds to chance and 100% to perfect discrimination. Bottom: Normalized perceptual distance from i0 (left) and u0 (right), from1 (no audible difference) to 7 (maximal distance). Error bars correspond to standard error of the mean.

  -i0-i0 (27 trials) u0-u0-u0-u0 (27 trials) NREP1 (54 trials) i0-i0-i0-i1 (27 trials) u0-u0-u0-u1 (27 trials) NREP2 (54 trials) i0-i0-i0-i2 (27 trials) u0-u0-u0-u2 (27 trials) NREP∞ (54 trials) i0-i0-i0-i∞ (27 trials) u0-u0-u0-u∞ (27 trials) Catch Trials (54 trials) i0-i0-i0-a (27 trials) u0-u0-u0-a (27 trials)Silent condition (54 trials) --

Figure 4 .

 4 Figure 4. Timeline of the fMRI acquisition experiment.

Figure 5 .

 5 Figure 5. Maps of brain regions significantly activated for conditions REP, NREP1, NREP2 and NREP∞ (in successive rows). Maps are thresholded at p<0.05 FWE corrected at the cluster and voxel-levels (t(17)>6.91). Activations are superposed on the canonical SPM12 MNI template.

Figure 6 .

 6 Figure 6. Parameter estimates (group means and individual data points) extracted from: 1: [-48 0 42] (Left Premotor Cortex); 2: [-51 6 18] (Left Inferior Frontal Gyrus); 3: [-57 -21 3] (Left Superior Temporal Gyrus); 4: [51 -3 39] (Right Premotor Cortex); 5: [45 15 21] (Right Inferior Frontal Gyrus); 6 [60 -9 9] (Right Superior Temporal Gyrus); 7: [-3 12 54] (Supplementary Motor Area). Error bars correspond to 95% confidence interval. Red colors indicate frontal and motor regions, and blue colors indicate auditory regions.

Figure 7 .

 7 As can be seen on the figure, when progressively increasing the acoustic distance with REP, activation in the right STG seems to progress anteriorly, from y = [1; -38] in the REP > Baseline contrast, to y = [5; -34] in the NREP1 > Baseline contrast, and y = [11; -40] in the NREP∞ > Baseline contrast. In the left STG, activation diffuses both anteriorly and posteriorly, from y =[-6; -43] in the REP > Baseline contrast, to y= [5; -41] in the NREP1 > Baseline contrast and y = [7; -44] in the NREP∞ > Baseline contrast. Concerning motor regions (premotor and supplementary motor area), while there is almost no increase of activation in the REP > Baseline contrast, activation slightly increases in the NREP1 > Baseline contrast and is higher in the NREP∞ > Baseline contrast. No additional activation was observable for the NREP2 > Baseline contrast in comparison to the REP > Baseline contrast at a p<0.05 FWE-corrected level.

Figure 7 .

 7 Figure 7. Maps of brain regions significantly activated for REP, NREP1, NREP2, and NREP∞ conditions relative to Baseline, superposed on the same ch2bet template. Maps are thresholded at p<0.05 FWE corrected at both the cluster and the voxel levels.

Figure 8 .

 8 Figure 8. Maps of brain regions significantly activated for NREP1 > REP (upper panel), NREP2 > REP (middle panel), and NREP∞ > NREP (lower panel) contrasts. Maps are thresholded at p<0.0001 uncorrected at the voxel level and p<0.05 FWE corrected at the cluster level (t(17)>4,71). Activations are projected on the ch2bet template. * For representation purpose, NREP2>REP is thresholded at p<0.001 uncorrected at the voxel level and p<0,05 FWE corrected at the cluster level.

Table 2 -

 2 Summary of the conditions used in the fMRI experiment

  (Wellcome Department of Cognitive Neurology, UK, www.fil.ion.ucl.ac.uk/219 spm/software/spm8/) running under Matlab 7.9 (The Mathworks Inc., Natick, USA). In addition, we used the SPM extension Anatomical Automatic Labelling (AAL,[START_REF] Tzourio-Mazoyer | Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[END_REF] for effect localizations, and, when necessary, the Yale BioImage suite package (mni2tal mapping from[START_REF] Lacadie | More accurate Talairach coordinates for neuroimaging using nonlinear registration[END_REF], http://www.bioimagesuite.org).

Table 3 .

 3 MNI coordinates (extended local maxima) for each condition contrasted against baseline. We used the SPM AAL extension for effects localization, and when necessary, Yale BioImage suite package for Brodman's areas identification. Threshold is fixed at p<0.05 FWE corrected at the voxel level and

	Condition	Region Label	BA		Peak Voxel (MNI coordinates)		Cluster Size	T score
							(voxels)
				X	y	z
	REP	Right superior temporal gyrus (extending in rolandic operculum and right Heschl)	41, 22, 1	57	-9	9	164	11.78
		Left superior temporal gyrus (extending in rolandic operculum, Heschl gyrus and postcentral gyrus)	41, 22, 1	-57	-21	3	269	10.43
		Right angular gyrus*	39	33	-54	33	3	7.78
		Right precentral gyrus (premotor and Supplementary motor area)	6	51	-3	39	3	7.73
		Left precentral gyrus (premotor and Supplementary motor area)	6	-48	0	45	5	7.35
		Left precentral gyrus (premotor and Supplementary motor area)	6	-48	-9	42	1	7.10
		Right Inferior frontal gyrus -pars triangularis	44	45	15	21	1	7.03
		Left Inferior frontal gyrus -pars triangularis	44	-48	3	24	1	6.94
	NREP1	Left superior temporal gyrus (extending in middle temporal, Heschl and rolandic operculum)	41, 22	-57	-21	3	256	12.59
		Right superior temporal gyrus (extending in rolandic operculum and right Heschl)	22, 41, 1	57	-27	3	277	11.77
		Supplementary Motor Area	6	-3	12	57	41	10.00
		Right precentral gyrus (right premotor)	6	51	-3	39	7	7.58
		Left superior temporal gyrus	22	-51	-3	-3	14	7.54
		Left inferior frontal gyrus -pars opercularis	44	-51	9	18	4	7.45
		Left precuneus	7	-9	-66	39	1	7.30
		Right middle frontal gyrus	10	39	45	18	1	7.16
		Right inferior frontal gyrus -pars opercularis	44	45	15	21	2	7.09
		Right medial cingulate	-	0	-30	27	5	7.08
		Right inferior frontal gyrus (pars triangularis) extending in right insula	45	39	21	6	9	7.07
		Right precuneus	7	12	-69	30	2	6.90
	NREP2	Right superior temporal gyrus (extending in Heschl and Rolandic operculum)	22, 41	60	-24	3	262	12.59
		Left superior temporal gyrus (extending in Heschl gyrus and rolandic operculum)	41	-48	-18	3	207	10.67
		Right insula	-	27	24	6	5	8.82
		Left cerebellum -Crus1	-	-45	-60	-33	6	8.32
		Left Supplementary Motor Area	6	0	12	54	10	7.85
		Right cerebellum -pars 6	-	12	-72	-21	3	7.81
		Right precuneus (extending in left)	7	6	-72	48	6	7.53

cluster level (t(17)>6.91). T-values represent the highest t-value at the voxel level. *nearest grey matter

Table 4 .

 4 MNI coordinates (extended local maxima) for between condition contrasts. We used the SPM AAL extension for effects localization, and when necessary, Yale BioImage suite package for Brodman's areas identification. Threshold is fixed at p<0.0001 uncorrected at the voxel level and p<0.05 FWE corrected at the cluster level (t(17)>4.71). T-values represent the highest t-value at the voxel level.

	Condition	Region Label	BA	Peak Voxel (MNI coordinates)		Cluster Size	T score
							(voxels)
				x	y	z
	NREP1 > REP	Right superior temporal gyrus (extending in middle temporal gyrus and temporal pole)	22	54	-3	-15	54	9.80
		Right hippocampus (extending in right amygdala and putamen)	-	33	-9	-15	25	5.77
		Right superior temporal gyrus (extending in middle temporal gyrus)	41	66	-21	3	28	5.73
	NREP2 > REP	No suprathreshold cluster
	NREP∞ > REP	Right superior temporal gyrus (extending in right insula, Helschl gyrus and right inferior	22, 41,	51	-12	-3	760	11.56
		frontal gyrus)	13
		Left superior temporal gyrus (extending in left insula and inferior frontal gyrus -pars	22	-54	-12	0	441	10.97
		opercularis)
		Right calcarine sulcus	17	15	-69	9	57	7.01
		Right cerebellum -pars 6	-	27	-63	-24	36	6.90
		Left and right medial superior frontal gyrus (extending in supplementary motor area and	8	-6	15	42	87	6.73
		cingulate)
		Left cerebellum -crus1 (extending in pars 6)	-	-45	-60	-30	55	6.37
		Left calcarine (extending in left middle occipital gyrus)	18	-12	-99	-3	19	6.06
	NREP∞ > REP1	Left and right supplementary motor area	8	6	21	45	65	6.04
	NREP2 > NREP1	No suprathreshold cluster
	NREP∞ > NREP2	Right insula (extending in inferior frontal gyrus -pars opercularis)	13, 45	30	21	-3	184	8.49
		Left superior temporal gyrus (extending in left middle temporal gyrus)	41, 22	-54	-18	3	124	7.61
		Left insula (extending in inferior frontal gyrus -pars orbitalis)	13	-30	21	-6	57	6.58
		Right supplementary motor area (extending left supplementary motor area and in left and right	6, 8	6	12	45	83	6.53
		cingulate)
		Right Inferior frontal gyrus -pars opercularis (extending in pars triangularis)	44	42	6	21	21	6.37
		Right superior temporal gyrus (extending in Heschl gyrus)	22	51	-9	-9	26	6.30
		Left calcarine (extending in medial occipital gyrus)	18	-9	-96	0	17	5.90

Table 5 -

 5 Summary of main findings in previous RS studies exploring same-different paradigms with phonetic stimuli. Column 2 -fMRI: functional Magnetic Resonance Imaging; MEG: Magnetoencephalography. Column 3 -CV: Consonant-Vowel; Ca: Consonant-Vowel with vowel [a]. Column 4 -S: Same; D: Different; D-close: Different with close stimuli; D-far: Different with far stimuli; W: Within; B: Between. Column 6 -SMG: Supramarginal Gyrus; STG: Superior Temporal Gyrus (a-anterior, p-posterior); MTG: Middle Temporal Gyrus (a-anterior); IFG: Inferior Frontal Gyrus; INS: insula; PMC: Premotor Cortex; ST: Superior Temporal gyrus/sulcus; STS: Superior Temporal Sulcus; MFG: Middle Frontal Gyrus; POp: Pars Opercularis; MTC: Middle Temporal Cortex; pTC: posterior Temporal Cortex; SMA: Supplementary Motor Area. X>Y: larger response in agiven region for condition X compared with condition Y; X=Y: no significant difference in response in a given region for condition X compared with condition Y. Column 7 -AT: difference between conditions in Auditory Temporal regions; MP: difference between conditions in speech planning -Motor Programming regions. 0: no difference between same (S) and different (W, B, D) conditions. X then Y: difference between same and close conditions in regions X, difference between close and far conditions in regions Y.

Acknowledgement:

This work was performed on the IRMaGe platform member of France Life Imaging network (grant ANR-11-INBS-0006). This work was supported by the European Research Council under the 7th European Community Program (FP7 / 2007-2013 Grant Agreement No.339152 -"Speech Unit(e)s").