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Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction., In uncertain conditions, people make accurate decisions by considering multiple pieces of information. Here, the authors show that pharmacological n-methyl-d-aspartate (NMDA) receptor hypofunction is associated with elevated uncertainty and premature decisions based on unreliable evidence.

Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding., Reconstructing imagined speech from neural activity holds great promises for people with severe speech production deficits. Here, the authors demonstrate using human intracranial recordings that both low- and higher-frequency power and local cross-frequency contribute to imagined speech decoding.

International audience; Categorising voices is crucial for auditory-based social interactions. A recent study by Rupp and colleagues in PLOS Biology capitalises on human intracranial recordings to describe the spatiotemporal pattern of neural activity leading to voice-selective responses in associative auditory cortex.

One way music is thought to convey emotion is by mimicking acoustic features of affective human vocalizations [Juslin and Laukka (2003). Psychol. Bull. 129(5), 770–814]. Regarding fear, it has been informally noted that music for scary scenes in films frequently exhibits a “scream-like” character. Here, this proposition is formally tested. This paper reports acoustic analyses for four categories of audio stimuli: screams, non-screaming vocalizations, scream-like music, and non-scream-like music. Valence and arousal ratings were also collected. Results support the hypothesis that a key feature of human screams (roughness) is imitated by scream-like music and could potentially signal danger through both music and the voice.

Neuroprosthetic speech devices are an emerging technology that can offer the possibility of communication to those who are unable to speak. Patients with ‘locked in syndrome,’ aphasia, or other such pathologies can use covert speech—vividly imagining saying something without actual vocalization—to trigger neural controlled systems capable of synthesizing the speech they would have spoken, but for their impairment.We provide an analysis of the mechanisms and outputs involved in speech mediated by neuroprosthetic devices. This analysis provides a framework for accounting for the ethical significance of accuracy, control, and pragmatic dimensions of prosthesis-mediated speech. We first examine what it means for the output of the device to be accurate, drawing a distinction between technical accuracy on the one hand and semantic accuracy on the other. These are conceptual notions of accuracy.Both technical and semantic accuracy of the device will be necessary (but not yet sufficient) for the user to have sufficient control over the device. Sufficient control is an ethical consideration: we place high value on being able to express ourselves when we want and how we want. Sufficient control of a neural speech prosthesis requires that a speaker can reliably use their speech apparatus as they want to, and can expect their speech to authentically represent them. We draw a distinction between two relevant features which bear on the question of whether the user has sufficient control: voluntariness of the speech and the authenticity of the speech. These can come apart: the user might involuntarily produce an authentic output (perhaps revealing private thoughts) or might voluntarily produce an inauthentic output (e.g., when the output is not semantically accurate). Finally, we consider the role of the interlocutor in interpreting the content and purpose of the communication.These three ethical dimensions raise philosophical questions about the nature of speech, the level of control required for communicative accuracy, and the nature of ‘accuracy’ with respect to both natural and prosthesis-mediated speech.

International audience; In the motor cortex, beta oscillations (∼12-30 Hz) are generally considered a principal rhythm contributing to movement planning and execution. Beta oscillations cohabit and dynamically interact with slow delta oscillations (0.5-4 Hz), but the role of delta oscillations and the subordinate relationship between these rhythms in the perception-action loop remains unclear. Here, we review evidence that motor delta oscillations shape the dynamics of motor behaviors and sensorimotor processes, in particular during auditory perception. We describe the functional coupling between delta and beta oscillations in the motor cortex during spontaneous and planned motor acts. In an active sensing framework, perception is strongly shaped by motor activity, in particular in the delta band, which imposes temporal constraints on the sampling of sensory information. By encoding temporal contextual information, delta oscillations modulate auditory processing and impact behavioral outcomes. Finally, we consider the contribution of motor delta oscillations in the perceptual analysis of speech signals, providing a contextual temporal frame to optimize the parsing and processing of slow linguistic information.

Crying is the principal means by which newborn infants shape parental behavior to meet their needs. While this mechanism can be highly effective, infant crying can also be an aversive stimulus that leads to parental frustration and even abuse. Fathers have recently become more involved in direct caregiving activities in modern, developed nations, and fathers are more likely than mothers to physically abuse infants. In this study, we attempt to explain variation in the neural response to infant crying among human fathers, with the hope of identifying factors that are associated with a more or less sensitive response. We imaged brain function in 39 first-time fathers of newborn infants as they listened to both their own and a standardized unknown infant cry stimulus, as well as auditory control stimuli, and evaluated whether these neural responses were correlated with measured characteristics of fathers and infants that were hypothesized to modulate these responses. Fathers also provided subjective ratings of each cry stimulus on multiple dimensions. Fathers showed widespread activation to both own and unknown infant cries in neural systems involved in empathy and approach motivation. There was no significant difference in the neural response to the own vs. unknown infant cry, and many fathers were unable to distinguish between the two cries. Comparison of these results with previous studies in mothers revealed a high degree of similarity between first-time fathers and first-time mothers in the pattern of neural activation to newborn infant cries. Further comparisons suggested that younger infant age was associated with stronger paternal neural responses, perhaps due to hormonal or novelty effects. In our sample, older fathers found infant cries less aversive and had an attenuated response to infant crying in both the dorsal anterior cingulate cortex (dACC) and the anterior insula, suggesting that compared with younger fathers, older fathers may be better able to avoid the distress associated with empathic over-arousal in response to infant cries. A principal components analysis revealed that fathers with more negative emotional reactions to the unknown infant cry showed decreased activation in the thalamus and caudate nucleus, regions expected to promote positive parental behaviors, as well as increased activation in the hypothalamus and dorsal ACC, again suggesting that empathic over-arousal might result in negative emotional reactions to infant crying. In sum, our findings suggest that infant age, paternal age and paternal emotional reactions to infant crying all modulate the neural response of fathers to infant crying. By identifying neural correlates of variation in paternal subjective reactions to infant crying, these findings help lay the groundwork for evaluating the effectiveness of interventions designed to increase paternal sensitivity and compassion.

SummaryScreaming is arguably one of the most relevant communication signals for survival in humans. Despite their practical relevance and their theoretical significance as innate [1] and virtually universal [2, 3] vocalizations, what makes screams a unique signal and how they are processed is not known. Here, we use acoustic analyses, psychophysical experiments, and neuroimaging to isolate those features that confer to screams their alarming nature, and we track their processing in the human brain. Using the modulation power spectrum (MPS [4, 5]), a recently developed, neurally informed characterization of sounds, we demonstrate that human screams cluster within restricted portion of the acoustic space (between ∼30 and 150 Hz modulation rates) that corresponds to a well-known perceptual attribute, roughness. In contrast to the received view that roughness is irrelevant for communication [6], our data reveal that the acoustic space occupied by the rough vocal regime is segregated from other signals, including speech, a pre-requisite to avoid false alarms in normal vocal communication. We show that roughness is present in natural alarm signals as well as in artificial alarms and that the presence of roughness in sounds boosts their detection in various tasks. Using fMRI, we show that acoustic roughness engages subcortical structures critical to rapidly appraise danger. Altogether, these data demonstrate that screams occupy a privileged acoustic niche that, being separated from other communication signals, ensures their biological and ultimately social efficiency.

Recent psychophysics data suggest that speech perception is not limited by the capacity of the auditory system to encode fast acoustic variations through neural γ activity, but rather by the time given to the brain to decode them. Whether the decoding process is bounded by the capacity of θ rhythm to follow syllabic rhythms in speech, or constrained by a more endogenous top-down mechanism, e.g., involving β activity, is unknown. We addressed the dynamics of auditory decoding in speech comprehension by challenging syllable tracking and speech decoding using comprehensible and incomprehensible time-compressed auditory sentences. We recorded EEGs in human participants and found that neural activity in both θ and γ ranges was sensitive to syllabic rate. Phase patterns of slow neural activity consistently followed the syllabic rate (4–14 Hz), even when this rate went beyond the classical θ range (4–8 Hz). The power of θ activity increased linearly with syllabic rate but showed no sensitivity to comprehension. Conversely, the power of β (14–21 Hz) activity was insensitive to the syllabic rate, yet reflected comprehension on a single-trial basis. We found different long-range dynamics for θ and β activity, with β activity building up in time while more contextual information becomes available. This is consistent with the roles of θ and β activity in stimulus-driven versus endogenous mechanisms. These data show that speech comprehension is constrained by concurrent stimulus-driven θ and low-γ activity, and by endogenous β activity, but not primarily by the capacity of θ activity to track the syllabic rhythm. SIGNIFICANCE STATEMENT Speech comprehension partly depends on the ability of the auditory cortex to track syllable boundaries with θ-range neural oscillations. The reason comprehension drops when speech is accelerated could hence be because θ oscillations can no longer follow the syllabic rate. Here, we presented subjects with comprehensible and incomprehensible accelerated speech, and show that neural phase patterns in the θ band consistently reflect the syllabic rate, even when speech becomes too fast to be intelligible. The drop in comprehension, however, is signaled by a significant decrease in the power of low-β oscillations (14–21 Hz). These data suggest that speech comprehension is not limited by the capacity of θ oscillations to adapt to syllabic rate, but by an endogenous decoding process.

The ability to generate temporal predictions is fundamental for adap-tive behavior. Precise timing at the time-scale of seconds is critical,for instance to predict trajectories or to select relevant information.What mechanisms form the basis for such accurate timing? Recentevidence suggests that (1) temporal predictions adjust sensoryselection by controlling neural oscillations in time and (2) the motorsystem plays an active role in inferring “when” events will happen.We hypothesized that oscillations in the delta and beta bands areinstrumental in predicting the occurrence of auditory targets. Partici-pants listened to brief rhythmic tone sequences and detected targetdelays while undergoing magnetoencephalography recording. Priorto target occurrence, we found that coupled delta (1–3 Hz) and beta(18–22 Hz) oscillations temporally align with upcoming targets andbias decisions towards correct responses, suggesting that delta–beta coupled oscillations underpin prediction accuracy. Subsequentto target occurrence, subjects update their decisions using the mag-nitude of the alpha-band (10–14 Hz) response as internal evidence oftarget timing. These data support a model in which the orchestrationof oscillatory dynamics between sensory and motor systems isexploited to accuratelyselect sensory information in time.Keywords: auditory, motor, neuronal oscillations, sensorimotor, timingIntroductionAccurately predicting when future events will occur facilitatessensory processing, speeds up behavior, and optimizes theallocation of attentional resources in time (Correa et al. 2005;Nobre et al. 2012). Predictive timing, by analogy to the notionof predictive coding (Knill and Richards 1996; Friston 2005)inthe time domain, requires the construction of an internalmodel of observed temporal regularities to infer precisely theoccurrence of future events. Because these regularities canhappen at different time-scales (seconds, hours, days…), dis-tinct neural systems and computations may be used to generatetemporally adaptive predictions (Ivry and Schlerf 2008; Moril-lon et al. 2009). The seconds time-scale, in particular, is highlyrelevant for online human behavior, as a large number of phe-nomena pertaining to perception and action (i.e., speech,movement etc.) occur at this scale. Here we investigate howthe brain extracts temporal regularities that emerge from theperception of isochronous beats at thistime-scale.The inclination to automaticallysynchronizeour movementsto external rhythms suggests that temporal regularities are par-ticularly relevant to the motor system (Schubotz et al. 2000;Grahn and Brett 2007; Bengtsson et al. 2009; Teki et al. 2011).This system is arguably at the origin of the contingent negativevariation (CNV), an anticipatory electrophysiological compo-nent that precedes relevant sensory or motor events (Walteret al. 1964; Pfeutyet al. 2003; Praamstra et al. 2006; Cravo et al.2011) and facilitates performance when task timing is predict-able (Hillyard 1973; Niemi and Naatanan 1981; Stefanics et al.2010). It is also involved in duration perception, in particularfor durations shorter than 2 s (Ivry and Schlerf 2008; Morillonet al. 2009). Recent considerations have suggested that themotor system internally simulates movement synchronizedwith future events to anticipate their occurrence and facilitatetheir processing (Schubotz 2007; Tian and Poeppel 2010;Arnal 2012; Arnal and Giraud 2012). According to this idea,corollary discharges that are contingent on movement simula-tion propagate to sensory areas to align ongoing activity withpredicted events. Here, we hypothesized that temporal predic-tions are instantiated through sensorimotor oscillatory interac-tions. Such a mechanism would arguably permit the extractionof temporal regularities in order to infer when an event shouldoccur.Cortical oscillations are usually seen as a means towardsflexibly communicating between distant neuronal populations(Engel et al. 2001; Fries 2005). Recent findings also suggestthat oscillations, which reflect fluctuations of neuronal excit-ability, can be temporally adjusted to optimize sensory selec-tion (Schroeder and Lakatos 2009). Accordingly, expectationsalign the phase of delta oscillations in time, which in turnaccelerates response timing (Lakatos et al. 2008; Stefanics et al.2010). Furthermore, ongoing delta phase modulates theweighting of sensory events in the decision-making processduring sequential information processing (Wyart et al. 2012;Cravo et al. 2013). Beta oscillations arguably play a comple-mentary function during temporal expectations and the accu-mulation of sensory evidence (Donner et al. 2009; Saleh et al.2011; Fujioka et al. 2012; de Lange et al. 2013).In sum, sensory selection (stimulus detection or sensoryweighting) can be passively or actively (through prediction)regulated through modulation of the prestimulus oscillatorystate. However, whether they govern subjects’ accuracy remainsunclear. That is, whether oscillations play an instrumental rolein determining whether an event occurs at the expected timehasnotbeenelucidated.This study aimed at determining the neurophysiologicalmechanisms underpinning (1) the generation of a temporalprediction and (2) the subsequent evaluation of this predic-tion. We used a delayed-target detection task in which subjectswererequiredtodetectwhetherthelasttoneofanisochronoussequence was delayed with regard to the beat (Fig. 1A). Ouranalysis design (Fig. 1B) allows us to distinguish how 2 crucialstages of the processing chain are implemented: (1) the pre-dictive stage, which reflects the neural activity patterns used topredict “when” and (2) the decisional stage, in which subjects’decisions (i.e., subjective reports) are made (see Materials andmethods and Fig. 1C).

A growing body of research suggests that intrinsic neuronal slow (< 10 Hz) oscillations in auditory cortex appear to track incoming speech and other spectro-temporally complex auditory signals. Within this framework, several recent studies have identified critical-band temporal envelopes as the specific acoustic feature being reflected by the phase of these oscillations. However, how this alignment between speech acoustics and neural oscillations might underpin intelligibility is unclear. Here we test the hypothesis that the ‘sharpness’ of temporal fluctuations in the critical band envelope acts as a temporal cue to speech syllabic rate, driving delta-theta rhythms to track the stimulus and facilitate intelligibility. We interpret our findings as evidence that sharp events in the stimulus cause cortical rhythms to re-align and parse the stimulus into syllable-sized chunks for further decoding. Using magnetoencephalographic recordings, we show that by removing temporal fluctuations that occur at the syllabic rate, envelope-tracking activity is reduced. By artificially reinstating these temporal fluctuations, envelope-tracking activity is regained. These changes in tracking correlate with intelligibility of the stimulus. Together, the results suggest that the sharpness of fluctuations in the stimulus, as reflected in the cochlear output, drive oscillatory activity to track and entrain to the stimulus, at its syllabic rate. This process likely facilitates parsing of the stimulus into meaningful chunks appropriate for subsequent decoding, enhancing perception and intelligibility.

According to the predictive coding theory, top-down predictions are conveyed by backward connections and prediction errors are propagated forward across the cortical hierarchy. Using MEG in humans, we show that violating multisensory predictions causes a fundamental and qualitative change in both the frequency and spatial distribution of cortical activity. When visual speech input correctly predicted auditory speech signals, a slow delta regime (3-4 Hz) developed in higher-order speech areas. In contrast, when auditory signals invalidated predictions inferred from vision, a low-beta (14-15 Hz) / high-gamma (60-80 Hz) coupling regime appeared locally in a multisensory area (area STS). This frequency shift in oscillatory responses scaled with the degree of audio-visual congruence and was accompanied by increased gamma activity in lower sensory regions. These findings are consistent with the notion that bottom-up prediction errors are communicated in predominantly high (gamma) frequency ranges, whereas top-down predictions are mediated by slower (beta) frequencies.