Rapid computations of spectrotemporal prediction error support perception of degraded speech.

Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning

Background: The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings: We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (~8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (~4 hr in total) relative to a group that trained for a single session (~1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance: Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions

Sustained neural rhythms reveal endogenous oscillations supporting speech perception.

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or "entrained") to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research-including neural entrainment and tACS-and reveal endogenous neural oscillations as a key underlying principle for speech perception

Motivation and intelligence drive auditory perceptual learning

Background: Although feedback on performance is generally thought to promote perceptual learning, the role and necessity of feedback remain unclear. We investigated the effect of providing varying amounts of positive feedback while listeners attempted to discriminate between three identical tones on learning frequency discrimination. Methodology/Principal Findings: Using this novel procedure, the feedback was meaningless and random in relation to the listeners’ responses, but the amount of feedback provided (or lack thereof) affected learning. We found that a group of listeners who received positive feedback on 10% of the trials improved their performance on the task (learned), while other groups provided either with excess (90%) or with no feedback did not learn. Superimposed on these group data, however, individual listeners showed other systematic changes of performance. In particular, those with lower non-verbal IQ who trained in the no feedback condition performed more poorly after training. Conclusions/Significance: This pattern of results cannot be accounted for by learning models that ascribe an external teacher role to feedback. We suggest, instead, that feedback is used to monitor performance on the task in relation to its perceived difficulty, and that listeners who learn without the benefit of feedback are adept at self-monitoring of performance, a trait that also supports better performance on non-verbal IQ tests. These results show that ‘perceptual’ learning is strongly influenced by top-down processes of motivation and intelligence

Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming.

Human listeners achieve quick and effortless speech comprehension through computations of conditional probability using Bayes rule. However, the neural implementation of Bayesian perceptual inference remains unclear. Competitive-selection accounts (e.g., TRACE) propose that word recognition is achieved through direct inhibitory connections between units representing candidate words that share segments (e.g., hygiene and hijack share /haidʒ/). Manipulations that increase lexical uncertainty should increase neural responses associated with word recognition when words cannot be uniquely identified. In contrast, predictive-selection accounts (e.g., Predictive-Coding) propose that spoken word recognition involves comparing heard and predicted speech sounds and using prediction error to update lexical representations. Increased lexical uncertainty in words, such as hygiene and hijack, will increase prediction error and hence neural activity only at later time points when different segments are predicted. We collected MEG data from male and female listeners to test these two Bayesian mechanisms and used a competitor priming manipulation to change the prior probability of specific words. Lexical decision responses showed delayed recognition of target words (hygiene) following presentation of a neighboring prime word (hijack) several minutes earlier. However, this effect was not observed with pseudoword primes (higent) or targets (hijure). Crucially, MEG responses in the STG showed greater neural responses for word-primed words after the point at which they were uniquely identified (after /haidʒ/ in hygiene) but not before while similar changes were again absent for pseudowords. These findings are consistent with accounts of spoken word recognition in which neural computations of prediction error play a central role.SIGNIFICANCE STATEMENT Effective speech perception is critical to daily life and involves computations that combine speech signals with prior knowledge of spoken words (i.e., Bayesian perceptual inference). This study specifies the neural mechanisms that support spoken word recognition by testing two distinct implementations of Bayes perceptual inference. Most established theories propose direct competition between lexical units such that inhibition of irrelevant candidates leads to selection of critical words. Our results instead support predictive-selection theories (e.g., Predictive-Coding): by comparing heard and predicted speech sounds, neural computations of prediction error can help listeners continuously update lexical probabilities, allowing for more rapid word identification

A phenomenological cartography of misophonia and other forms of sound intolerance

People with misophonia have strong aversive reactions to specific “trigger” sounds. Here we challenge this key idea of specificity. Machine learning was used to identify a misophonic profile from a multivariate sound-response pattern. Misophonia could be classified from most sounds (traditional triggers and non-triggers) and, moreover, cross-classification showed that the profile was largely transferable across sounds (rather than idiosyncratic for each sound). By splitting our participants in other ways, we were able to show—using the same approach—a differential diagnostic profile factoring in potential co-morbidities (autism, hyperacusis, ASMR). The broad autism phenotype was classified via aversions to repetitive sounds rather than the eating sounds most easily classified in misophonia. Within misophonia, the presence of hyperacusis and sound-induced pain had widespread effects across all sounds. Overall, we show that misophonia is characterized by a distinctive reaction to most sounds that ultimately becomes most noticeable for a sub-set of those sounds

The Neural Time Course of Semantic Ambiguity Resolution in Speech Comprehension.

Semantically ambiguous words challenge speech comprehension, particularly when listeners must select a less frequent (subordinate) meaning at disambiguation. Using combined magnetoencephalography (MEG) and EEG, we measured neural responses associated with distinct cognitive operations during semantic ambiguity resolution in spoken sentences: (i) initial activation and selection of meanings in response to an ambiguous word and (ii) sentence reinterpretation in response to subsequent disambiguation to a subordinate meaning. Ambiguous words elicited an increased neural response approximately 400-800 msec after their acoustic offset compared with unambiguous control words in left frontotemporal MEG sensors, corresponding to sources in bilateral frontotemporal brain regions. This response may reflect increased demands on processes by which multiple alternative meanings are activated and maintained until later selection. Disambiguating words heard after an ambiguous word were associated with marginally increased neural activity over bilateral temporal MEG sensors and a central cluster of EEG electrodes, which localized to similar bilateral frontal and left temporal regions. This later neural response may reflect effortful semantic integration or elicitation of prediction errors that guide reinterpretation of previously selected word meanings. Across participants, the amplitude of the ambiguity response showed a marginal positive correlation with comprehension scores, suggesting that sentence comprehension benefits from additional processing around the time of an ambiguous word. Better comprehenders may have increased availability of subordinate meanings, perhaps due to higher quality lexical representations and reflected in a positive correlation between vocabulary size and comprehension success

Does training with amplitude modulated tones affect tone-vocoded speech perception?

Temporal-envelope cues are essential for successful speech perception. We asked here whether training on stimuli containing temporal-envelope cues without speech content can improve the perception of spectrally-degraded (vocoded) speech in which the temporal-envelope (but not the temporal fine structure) is mainly preserved. Two groups of listeners were trained on different amplitude-modulation (AM) based tasks, either AM detection or AM-rate discrimination (21 blocks of 60 trials during two days, 1260 trials; frequency range: 4Hz, 8Hz, and 16Hz), while an additional control group did not undertake any training. Consonant identification in vocoded vowel-consonant-vowel stimuli was tested before and after training on the AM tasks (or at an equivalent time interval for the control group). Following training, only the trained groups showed a significant improvement in the perception of vocoded speech, but the improvement did not significantly differ from that observed for controls. Thus, we do not find convincing evidence that this amount of training with temporal-envelope cues without speech content provide significant benefit for vocoded speech intelligibility. Alternative training regimens using vocoded speech along the linguistic hierarchy should be explored

Does training with amplitude modulated tones affect tone-vocoded speech perception?

Temporal-envelope cues are essential for successful speech perception. We asked here whether training on stimuli containing temporal-envelope cues without speech content can improve the perception of spectrally-degraded (vocoded) speech in which the temporal-envelope (but not the temporal fine structure) is mainly preserved. Two groups of listeners were trained on different amplitude-modulation (AM) based tasks, either AM detection or AM-rate discrimination (21 blocks of 60 trials during two days, 1260 trials; frequency range: 4Hz, 8Hz, and 16Hz), while an additional control group did not undertake any training. Consonant identification in vocoded vowel-consonant-vowel stimuli was tested before and after training on the AM tasks (or at an equivalent time interval for the control group). Following training, only the trained groups showed a significant improvement in the perception of vocoded speech, but the improvement did not significantly differ from that observed for controls. Thus, we do not find convincing evidence that this amount of training with temporal-envelope cues without speech content provide significant benefit for vocoded speech intelligibility. Alternative training regimens using vocoded speech along the linguistic hierarchy should be explored

Neural dynamics of change detection in crowded acoustic scenes

Two key questions concerning change detection in crowded acoustic environments are the extent to which cortical processing is specialized for different forms of acoustic change and when in the time-course of cortical processing neural activity becomes predictive of behavioral outcomes. Here, we address these issues by using magnetoencephalography (MEG) to probe the cortical dynamics of change detection in ongoing acoustic scenes containing as many as ten concurrent sources. Each source was formed of a sequence of tone pips with a unique carrier frequency and temporal modulation pattern, designed to mimic the spectrotemporal structure of natural sounds. Our results show that listeners are more accurate and quicker to detect the appearance (than disappearance) of an auditory source in the ongoing scene. Underpinning this behavioral asymmetry are change-evoked responses differing not only in magnitude and latency, but also in their spatial patterns. We find that even the earliest (~ 50 ms) cortical response to change is predictive of behavioral outcomes (detection times), consistent with the hypothesized role of local neural transients in supporting change detection