Automatic and feature-specific prediction-related neural activity in the human auditory system

Carrier-frequency specific omission-related neural activity in ordered sound sequences is independent of omission-predictability

Regularities in our surroundings lead to predictions about upcoming events. Previous research has shown that omitted sounds during otherwise regular tone sequences elicit frequency-specific neural activity related to the upcoming but omitted tone. We tested whether this neural response is depending on the unpredictability of the omission. Therefore, we recorded magnetencephalography (MEG) data while participants listened to ordered or random tone sequences with omissions occurring either ordered or randomly. Using multivariate pattern analysis shows that the frequency-specific neural pattern during omission within ordered tone sequences occurs independent of the regularity of the omissions. These results suggest that the auditory predictions based on sensory experiences are not immediately updated by violations of those expectations.

Decoding predicted musical notes from omitted stimulus potentials

Electrophysiological studies have investigated predictive processing in music by examining event-related potentials (ERPs) elicited by the violation of musical expectations. While several studies have reported that the predictability of stimuli can modulate the amplitude of ERPs, it is unclear how specific the representation of the expected note is. The present study addressed this issue by recording the omitted stimulus potentials (OSPs) to avoid contamination of bottom-up sensory processing with top-down predictive processing. Decoding of the omitted content was attempted using a support vector machine, which is a type of machine learning. ERP responses to the omission of four target notes (E, F, A, and C) at the same position in familiar and unfamiliar melodies were recorded from 25 participants. The results showed that the omission N1 were larger in the familiar melody condition than in the unfamiliar melody condition. The decoding accuracy of the four omitted notes was significantly higher in the familiar melody condition than in the unfamiliar melody condition. These results suggest that the OSPs contain discriminable predictive information, and the higher the predictability, the more the specific representation of the expected note is generated.

Cardio-audio synchronization elicits neural and cardiac surprise responses in human wakefulness and sleep

The human brain can encode auditory regularities with fixed sound-to-sound intervals and with sound onsets locked to cardiac inputs. Here, we investigated auditory and cardio-audio regularity encoding during sleep, when bodily and environmental stimulus processing may be altered. Using electroencephalography and electrocardiography in healthy volunteers (N = 26) during wakefulness and sleep, we measured the response to unexpected sound omissions within three regularity conditions: synchronous, where sound and heartbeat are temporally coupled, isochronous, with fixed sound-to-sound intervals, and a control condition without regularity. Cardio-audio regularity encoding manifested as a heartbeat deceleration upon omissions across vigilance states. The synchronous and isochronous sequences induced a modulation of the omission-evoked neural response in wakefulness and N2 sleep, the former accompanied by background oscillatory activity reorganization. The violation of cardio-audio and auditory regularity elicits cardiac and neural responses across vigilance states, laying the ground for similar investigations in altered consciousness states such as coma and anaesthesia.

Strength of predicted information content in the brain biases decision behavior

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Eavesdropping on Tinnitus Using MEG: Lessons Learned and Future Perspectives

Tinnitus has been widely investigated in order to draw conclusions about the underlying causes and altered neural activity in various brain regions. Existing studies have based their work on different tinnitus frameworks, ranging from a more local perspective on the auditory cortex to the inclusion of broader networks and various approaches towards tinnitus perception and distress. Magnetoencephalography (MEG) provides a powerful tool for efficiently investigating tinnitus and aberrant neural activity both spatially and temporally. However, results are inconclusive, and studies are rarely mapped to theoretical frameworks. The purpose of this review was to firstly introduce MEG to interested researchers and secondly provide a synopsis of the current state. We divided recent tinnitus research in MEG into study designs using resting state measurements and studies implementing tone stimulation paradigms. The studies were categorized based on their theoretical foundation, and we outlined shortcomings as well as inconsistencies within the different approaches. Finally, we provided future perspectives on how to benefit more efficiently from the enormous potential of MEG. We suggested novel approaches from a theoretical, conceptual, and methodological point of view to allow future research to obtain a more comprehensive understanding of tinnitus and its underlying processes.

Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.

Crossmodal visual predictions elicit spatially specific early visual cortex activity but later than real visual stimuli

Previous studies have indicated that crossmodal visual predictions are instrumental in controlling early visual cortex activity. The exact time course and spatial precision of such crossmodal top-down influences on the visual cortex have been unknown. In the present study, participants were exposed to audiovisual combinations comprising one of two sounds and a Gabor patch either in the top left or in the bottom right visual field. Event-related potentials (ERPs) were recorded to these frequent crossmodal combinations (standards) as well as to trials in which the visual stimulus was omitted (omissions) or the visual and auditory stimuli were recombined (deviants). Standards and deviants elicited an ERP between 50 and 100 ms of opposite polarity known as the C1 effect commonly associated with retinotopic processing in early visual cortex. By contrast, a C1 effect was not observed in omission trials. Spatially specific omission and mismatch effects (deviants minus standards) started only later with a latency of 230 ms and 170 ms, respectively. These results suggest that crossmodal visual predictions control visual cortex activity in a spatially specific manner. However, visual predictions do not modulate visual cortex activity with the same timing as visual stimulation activates these areas but rather seem to involve distinct neural mechanisms. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Decoding reveals the neural representation of held and manipulated musical thoughts

Imagine a song you know by heart. With little effort you could sing it or play it vividly in your mind. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded listened and imagined melodies from MEG brain data (N = 71) to show that auditory regions represent the sensory properties of individual sounds, whereas cognitive control (prefrontal cortex, basal nuclei, thalamus) and episodic memory areas (inferior and medial temporal lobe, posterior cingulate, precuneus) hold and manipulate the melody as an abstract unit. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting the volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations and paves the way for future work on neural decoding of auditory imagination.

Extensive representation of sensory deviance in the responses to auditory gaps in unanesthetized rats

Unexpected changes in incoming sensory streams are associated with large errors in predicting the deviant stimulus relative to a memory trace of past stimuli. Mismatch negativity (MMN) in human studies and the release from stimulus-specific adaptation (SSA) in animal models correlate with prediction errors and deviance detection.¹ In human studies, violation of expectations elicited by an unexpected stimulus omission resulted in an omission MMN.^2,3,4,5 These responses are evoked after the expected occurrence time of the omitted stimulus, implying that they reflect the violation of a temporal expectancy.⁶ Because they are often time locked to the end of the omitted stimulus,^4,6,7 they resemble off responses. Indeed, suppression of cortical activity after the termination of the gap disrupts gap detection, suggesting an essential role for offset responses.⁸ Here, we demonstrate that brief gaps in short noise bursts in the auditory cortex of unanesthetized rats frequently evoke offset responses. Importantly, we show that omission responses are elicited when these gaps are expected but are omitted. These omission responses, together with the release from SSA of both onset and offset responses to rare gaps, form a rich and varied representation of prediction-related signals in the auditory cortex of unanesthetized rats, extending substantially and refining the representations described previously in anesthetized rats.

Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding

Prediction provides key advantages for survival, and cognitive studies have demonstrated that the brain computes multilevel predictions. Evidence for predictions remains elusive at the neuronal level because of the complexity of separating neural activity into predictions and stimulus responses. We overcome this challenge by recording from single neurons from cortical and subcortical auditory regions in anesthetized and awake preparations, during unexpected stimulus omissions interspersed in a regular sequence of tones. We find a subset of neurons that responds reliably to omitted tones. In awake animals, omission responses are similar to anesthetized animals, but larger and more frequent, indicating that the arousal and attentional state levels affect the degree to which predictions are neuronally represented. Omission-sensitive neurons also responded to frequency deviants, with their omission responses getting emphasized in the awake state. Because omission responses occur in the absence of sensory input, they provide solid and empirical evidence for the implementation of a predictive process.

The neural representations underlying asymmetric cross-modal prediction of words

Cross-modal prediction serves a crucial adaptive role in the multisensory world, yet the neural mechanisms underlying this prediction are poorly understood. The present study addressed this important question by combining a novel audiovisual sequence memory task, functional magnetic resonance imaging (fMRI), and multivariate neural representational analyses. Our behavioral results revealed a reliable asymmetric cross-modal predictive effect, with a stronger prediction from visual to auditory (VA) modality than auditory to visual (AV) modality. Mirroring the behavioral pattern, we found the superior parietal lobe (SPL) showed higher pattern similarity for VA than AV pairs, and the strength of the predictive coding in the SPL was positively correlated with the behavioral predictive effect in the VA condition. Representational connectivity analyses further revealed that the SPL mediated the neural pathway from the visual to the auditory cortex in the VA condition but was not involved in the auditory to visual cortex pathway in the AV condition. Direct neural pathways within the unimodal regions were found for the visual-to-visual and auditory-to-auditory predictions. Together, these results provide novel insights into the neural mechanisms underlying cross-modal sequence prediction.

Multivariate functional neuroimaging analyses reveal that strength-dependent face expectations are represented in higher-level face-identity areas

Perception is an active inference in which prior expectations are combined with sensory input. It is still unclear how the strength of prior expectations is represented in the human brain. The strength, or precision, of a prior could be represented with its content, potentially in higher-level sensory areas. We used multivariate analyses of functional resonance imaging data to test whether expectation strength is represented together with the expected face in high-level face-sensitive regions. Participants were trained to associate images of scenes with subsequently presented images of different faces. Each scene predicted three faces, each with either low, intermediate, or high probability. We found that anticipation enhances the similarity of response patterns in the face-sensitive anterior temporal lobe to response patterns specifically associated with the image of the expected face. In contrast, during face presentation, activity increased for unexpected faces in a typical prediction error network, containing areas such as the caudate and the insula. Our findings show that strength-dependent face expectations are represented in higher-level face-identity areas, supporting hierarchical theories of predictive processing according to which higher-level sensory regions represent weighted priors.

Cortical speech tracking is related to individual prediction tendencies

Listening can be conceptualized as a process of active inference, in which the brain forms internal models to integrate auditory information in a complex interaction of bottom-up and top-down processes. We propose that individuals vary in their "prediction tendency" and that this variation contributes to experiential differences in everyday listening situations and shapes the cortical processing of acoustic input such as speech. Here, we presented tone sequences of varying entropy level, to independently quantify auditory prediction tendency (as the tendency to anticipate low-level acoustic features) for each individual. This measure was then used to predict cortical speech tracking in a multi speaker listening task, where participants listened to audiobooks narrated by a target speaker in isolation or interfered by 1 or 2 distractors. Furthermore, semantic violations were introduced into the story, to also examine effects of word surprisal during speech processing. Our results show that cortical speech tracking is related to prediction tendency. In addition, we find interactions between prediction tendency and background noise as well as word surprisal in disparate brain regions. Our findings suggest that individual prediction tendencies are generalizable across different listening situations and may serve as a valuable element to explain interindividual differences in natural listening situations.

Evidence for predictions established by phantom sound

Predictions, the bridge between the internal and external worlds, are established by prior experience and updated by sensory stimuli. Responses to omitted but unexpected stimuli, known as omission responses, can break the one-to-one mapping of stimulus-response and can expose predictions established by the preceding stimulus built up. While research into exogenous predictions (driven by external stimuli) is often reported, that into endogenous predictions (driven by internal percepts) is rarely available in the literature. Here, we report evidence for endogenous predictions established by the Zwicker tone illusion, a phantom pure-tone-like auditory percept following notch noises. We found that MMN, P300, and theta oscillations could be recorded using an omission paradigm in subjects who can perceive Zwicker tone illusions, but could not in those who cannot. The MMN and P300 responses relied on attention, but theta oscillations did not. In-depth analysis shows that an increase in single-trial theta power, including total and induced theta, with the endogenous prediction, is lateralized to the left frontal brain areas. Our study depicts that the brain automatically analyzes internal perception, progressively establishes predictions and yields prediction errors in the left frontal region when a violation occurs.

Temporal Dynamics of Competition between Statistical Learning and Episodic Memory in Intracranial Recordings of Human Visual Cortex

The function of long-term memory is not just to reminisce about the past, but also to make predictions that help us behave appropriately and efficiently in the future. This predictive function of memory provides a new perspective on the classic question from memory research of why we remember some things but not others. If prediction is a key outcome of memory, then the extent to which an item generates a prediction signifies that this information already exists in memory and need not be encoded. We tested this principle using human intracranial EEG as a time-resolved method to quantify prediction in visual cortex during a statistical learning task and link the strength of these predictions to subsequent episodic memory behavior. Epilepsy patients of both sexes viewed rapid streams of scenes, some of which contained regularities that allowed the category of the next scene to be predicted. We verified that statistical learning occurred using neural frequency tagging and measured category prediction with multivariate pattern analysis. Although neural prediction was robust overall, this was driven entirely by predictive items that were subsequently forgotten. Such interference provides a mechanism by which prediction can regulate memory formation to prioritize encoding of information that could help learn new predictive relationships.SIGNIFICANCE STATEMENT When faced with a new experience, we are rarely at a loss for what to do. Rather, because many aspects of the world are stable over time, we rely on past experiences to generate expectations that guide behavior. Here we show that these expectations during a new experience come at the expense of memory for that experience. From intracranial recordings of visual cortex, we decoded what humans expected to see next in a series of photographs based on patterns of neural activity. Photographs that generated strong neural expectations were more likely to be forgotten in a later behavioral memory test. Prioritizing the storage of experiences that currently lead to weak expectations could help improve these expectations in future encounters.

Precise movement-based predictions in the mouse auditory cortex

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. However, it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precise acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, to a precise position within the movement, and to the movement that was coupled to sound during training. Prediction-based suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings identify distinct populations of auditory cortical neurons with movement, expectation, and error signals consistent with a learned internal model linking an action to its specific acoustic outcome.

Cross-modal attentional effects of rhythmic sensory stimulation

Temporal regularities are ubiquitous in our environment. The theory of entrainment posits that the brain can utilize these regularities by synchronizing neural activity with external events, thereby, aligning moments of high neural excitability with expected upcoming stimuli and facilitating perception. Despite numerous accounts reporting entrainment of behavioural and electrophysiological measures, evidence regarding this phenomenon remains mixed, with several recent studies having failed to provide confirmatory evidence. Notably, it is currently unclear whether and for how long the effects of entrainment can persist beyond their initiating stimulus, and whether they remain restricted to the stimulated sensory modality or can cross over to other modalities. Here, we set out to answer these questions by presenting participants with either visual or auditory rhythmic sensory stimulation, followed by a visual or auditory target at six possible time points, either in-phase or out-of-phase relative to the initial stimulus train. Unexpectedly, but in line with several recent studies, we observed no evidence for cyclic fluctuations in performance, despite our design being highly similar to those used in previous demonstrations of sensory entrainment. However, our data revealed a temporally less specific attentional effect, via cross-modally facilitated performance following auditory compared with visual rhythmic stimulation. In addition to a potentially higher salience of auditory rhythms, this could indicate an effect on oscillatory 3-Hz amplitude, resulting in facilitated cognitive control and attention. In summary, our study further challenges the generality of periodic behavioural modulation associated with sensory entrainment, while demonstrating a modality-independent attention effect following auditory rhythmic stimulation.

Constructing the hierarchy of predictive auditory sequences in the marmoset brain

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

Neural Substrates and Models of Omission Responses and Predictive Processes

Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception.

Perception in real-time: predicting the present, reconstructing the past

We feel that we perceive events in the environment as they unfold in real-time. However, this intuitive view of perception is impossible to implement in the nervous system due to biological constraints such as neural transmission delays. I propose a new way of thinking about real-time perception: at any given moment, instead of representing a single timepoint, perceptual mechanisms represent an entire timeline. On this timeline, predictive mechanisms predict ahead to compensate for delays in incoming sensory input, and reconstruction mechanisms retroactively revise perception when those predictions do not come true. This proposal integrates and extends previous work to address a crucial gap in our understanding of a fundamental aspect of our everyday life: the experience of perceiving the present.

Mental compression of spatial sequences in human working memory using numerical and geometrical primitives

How does the human brain store sequences of spatial locations? We propose that each sequence is internally compressed using an abstract, language-like code that captures its numerical and geometrical regularities. We exposed participants to spatial sequences of fixed length but variable regularity while their brain activity was recorded using magneto-encephalography. Using multivariate decoders, each successive location could be decoded from brain signals, and upcoming locations were anticipated prior to their actual onset. Crucially, sequences with lower complexity, defined as the minimal description length provided by the formal language, led to lower error rates and to increased anticipations. Furthermore, neural codes specific to the numerical and geometrical primitives of the postulated language could be detected, both in isolation and within the sequences. These results suggest that the human brain detects sequence regularities at multiple nested levels and uses them to compress long sequences in working memory.

Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli

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Does stimulus timing impact the processing of predicted visual features?.

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We evaluated if expectation suppression effects are modulated by temporal predictability.

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Expectation suppression was robust in both visual ERFs and feature decoding accuracy.

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Visual responses to predictable stimuli are greater for predictable vs. unpredictable timing.

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Sensory evidence is given less weight when timing is uncertain.

Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencephalography (MEG) study, we investigated how processing feature specific information (“what”) is affected by temporal predictability (“when”). Participants (N = 16) were presented with four consecutive Gabor patches (entrainers) with constant spatial frequency but with variable orientation and temporal onset. A fifth target Gabor was presented after a longer delay and with higher or lower spatial frequency that participants had to judge. We compared the neural responses to entrainers where the Gabor orientation could, or could not be temporally predicted along the entrainer sequence, and with inter-entrainer timing that was constant (predictable), or variable (unpredictable). We observed suppression of evoked neural responses in the visual cortex for predictable stimuli. Interestingly, we found that temporal uncertainty increased expectation suppression. This suggests that in temporally uncertain scenarios the neurocognitive system invests less resources in integrating bottom-up information. Multivariate pattern analysis showed that predictable visual features could be decoded from neural responses. Temporal uncertainty did not affect decoding accuracy for early visual responses, with the feature specificity of early visual neural activity preserved across conditions. However, decoding accuracy was less sustained over time for temporally jittered than for isochronous predictable visual stimuli. These findings converge to suggest that the cognitive system processes visual features of temporally predictable stimuli in higher detail, while processing temporally uncertain stimuli may rely more heavily on abstract internal expectations.

Visual Influences on Auditory Behavioral, Neural, and Perceptual Processes: A Review

In a naturalistic environment, auditory cues are often accompanied by information from other senses, which can be redundant with or complementary to the auditory information. Although the multisensory interactions derived from this combination of information and that shape auditory function are seen across all sensory modalities, our greatest body of knowledge to date centers on how vision influences audition. In this review, we attempt to capture the state of our understanding at this point in time regarding this topic. Following a general introduction, the review is divided into 5 sections. In the first section, we review the psychophysical evidence in humans regarding vision's influence in audition, making the distinction between vision's ability to enhance versus alter auditory performance and perception. Three examples are then described that serve to highlight vision's ability to modulate auditory processes: spatial ventriloquism, cross-modal dynamic capture, and the McGurk effect. The final part of this section discusses models that have been built based on available psychophysical data and that seek to provide greater mechanistic insights into how vision can impact audition. The second section reviews the extant neuroimaging and far-field imaging work on this topic, with a strong emphasis on the roles of feedforward and feedback processes, on imaging insights into the causal nature of audiovisual interactions, and on the limitations of current imaging-based approaches. These limitations point to a greater need for machine-learning-based decoding approaches toward understanding how auditory representations are shaped by vision. The third section reviews the wealth of neuroanatomical and neurophysiological data from animal models that highlights audiovisual interactions at the neuronal and circuit level in both subcortical and cortical structures. It also speaks to the functional significance of audiovisual interactions for two critically important facets of auditory perception-scene analysis and communication. The fourth section presents current evidence for alterations in audiovisual processes in three clinical conditions: autism, schizophrenia, and sensorineural hearing loss. These changes in audiovisual interactions are postulated to have cascading effects on higher-order domains of dysfunction in these conditions. The final section highlights ongoing work seeking to leverage our knowledge of audiovisual interactions to develop better remediation approaches to these sensory-based disorders, founded in concepts of perceptual plasticity in which vision has been shown to have the capacity to facilitate auditory learning.

Neural Decoding Reveals Concurrent Phonemic and Subphonemic Representations of Speech Across Tasks

Robust and efficient speech perception relies on the interpretation of acoustically variable phoneme realizations, yet prior neuroimaging studies are inconclusive regarding the degree to which subphonemic detail is maintained over time as categorical representations arise. It is also unknown whether this depends on the demands of the listening task. We addressed these questions by using neural decoding to quantify the (dis)similarity of brain response patterns evoked during two different tasks. We recorded magnetoencephalography (MEG) as adult participants heard isolated, randomized tokens from a /ba/-/da/ speech continuum. In the passive task, their attention was diverted. In the active task, they categorized each token as ba or da. We found that linear classifiers successfully decoded ba vs. da perception from the MEG data. Data from the left hemisphere were sufficient to decode the percept early in the trial, while the right hemisphere was necessary but not sufficient for decoding at later time points. We also decoded stimulus representations and found that they were maintained longer in the active task than in the passive task; however, these representations did not pattern more like discrete phonemes when an active categorical response was required. Instead, in both tasks, early phonemic patterns gave way to a representation of stimulus ambiguity that coincided in time with reliable percept decoding. Our results suggest that the categorization process does not require the loss of subphonemic detail, and that the neural representation of isolated speech sounds includes concurrent phonemic and subphonemic information.

The time-course of prediction formation and revision in human visual motion processing

Establishing the real-time position of a moving object poses a challenge to the visual system due to neural processing delays. While sensory information is travelling through the visual hierarchy, the object continues moving and information about its position becomes outdated. By extrapolating the position of a moving object along its trajectory, predictive mechanisms might effectively decrease the processing time associated with these objects. Here, we use time-resolved decoding of electroencephalographic (EEG) data from an apparent motion paradigm to demonstrate the interaction of two separate predictive mechanisms. First, we reveal predictive latency advantages for position representations as soon as the second object in an apparent motion sequence - even before the stimulus contains any physical motion energy. This is consistent with the existence of omni-directional, within-layer waves of sub-threshold activity that bring neurons coding for adjacent positions closer to their firing threshold, thereby reducing the processing time of the second stimulus in one of those positions. Second, we show that an additional direction-specific latency advantage emerges from the third sequence position onward, once the direction of the apparent motion stimulus is uniquely determined. Because the receptive fields of early visual areas are too small to encompass sequential apparent motion positions (as evidenced by the lack of latency modulation for the second stimulus position), this latency advantage most likely arises from descending predictions from higher to lower visual areas through feedback connections. Finally, we reveal that the same predictive activation that facilitates the processing of the object in its expected position needs to be overcome when the object's trajectory unexpectedly reverses, causing an additional latency disadvantage for stimuli that violate predictions. Altogether, our results suggest that two complementary mechanisms interact to form and revise predictions in visual motion processing, modulating the latencies of neural position representations at different levels of visual processing.

Predictive activation of sensory representations as a source of evidence in perceptual decision-making

Our brains can represent expected future states of our sensory environment. Recent work has shown that, when we expect a specific stimulus to appear at a specific time, we can predictively generate neural representations of that stimulus even before it is physically presented. These observations raise two exciting questions: Are pre-activated sensory representations used for perceptual decision-making? And, do we transiently perceive an expected stimulus that does not actually appear? To address these questions, we propose that pre-activated neural representations provide sensory evidence that is used for perceptual decision-making. This can be understood within the framework of the Diffusion Decision Model as an early accumulation of decision evidence in favour of the expected percept. Our proposal makes novel predictions relating to expectation effects on neural markers of decision evidence accumulation, and also provides an explanation for why we sometimes perceive stimuli that are expected, but do not appear.

Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex

The way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus-specific activity selectively in the deep layers of the primary visual cortex (V1). These findings are in line with predictive processing theories proposing that neurons in the deep cortical layers represent perceptual hypotheses and thereby shed light on the computational architecture of cortex.

The way we perceive the world is strongly influenced by our expectations, but the neural circuitry through which the brain achieves this remains unknown. A study using ultra-high field fMRI reveals that prior expectations evoke stimulus-specific signals in the deep layers of the primary visual cortex.

Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone ‘cues’ and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential ‘prediction signal’ that reflects this fundamental learning process.

Neural and Behavioral Evidence for Frequency-Selective Context Effects in Rhythm Processing in Humans

When listening to music, people often perceive and move along with a periodic meter. However, the dynamics of mapping between meter perception and the acoustic cues to meter periodicities in the sensory input remain largely unknown. To capture these dynamics, we recorded the electroencephalography while nonmusician and musician participants listened to nonrepeating rhythmic sequences, where acoustic cues to meter frequencies either gradually decreased (from regular to degraded) or increased (from degraded to regular). The results revealed greater neural activity selectively elicited at meter frequencies when the sequence gradually changed from regular to degraded compared with the opposite. Importantly, this effect was unlikely to arise from overall gain, or low-level auditory processing, as revealed by physiological modeling. Moreover, the context effect was more pronounced in nonmusicians, who also demonstrated facilitated sensory-motor synchronization with the meter for sequences that started as regular. In contrast, musicians showed weaker effects of recent context in their neural responses and robust ability to move along with the meter irrespective of stimulus degradation. Together, our results demonstrate that brain activity elicited by rhythm does not only reflect passive tracking of stimulus features, but represents continuous integration of sensory input with recent context.

Omission related brain responses reflect specific and unspecific action-effect couplings

When an auditory stimulus is predicted but unexpectedly omitted, an omission response can be observed in the EEG. This endogenous response to the absence of a stimulus demonstrates the important role of prediction in perception. SanMiguel et al. (2013a) showed that in order to observe an omission response, a specific prediction concerning the identity of an upcoming stimulus is necessary. They used button presses coupled to either a single sound (predictable identity), or a random sound (unpredictable identity). In the event-related potentials, a sequence of omission responses consisting of oN1, oN2, and oP3 was observed in the single condition but not in the random condition. Given the importance of omission studies to understand the role of prediction in perception, we replicated this study. We enhanced statistical power by doubling the sample size and adjusting data pre-processing, and applied temporal principal component analysis and replication Bayes statistics. Results in the single sound condition were successfully replicated. Principal component analysis additionally revealed attenuated oN1 and oP3 omission responses in the random sound condition. These results suggest the existence of both specific and unspecific predictions along the sound processing hierarchy, where precision weighting possibly influences the strength of prediction error. Results are discussed in the framework of predictive coding and are congruent with everyday life, where uncertainty often requires broader or more general predictions.

Anticipation-induced delta phase reset improves human olfactory perception

Anticipating an odor improves detection and perception, yet the underlying neural mechanisms of olfactory anticipation are not well understood. In this study, we used human intracranial electroencephalography (iEEG) to show that anticipation resets the phase of delta oscillations in piriform cortex prior to odor arrival. Anticipatory phase reset correlates with ensuing odor-evoked theta power and improvements in perceptual accuracy. These effects were consistently present in each individual subject and were not driven by potential confounds of pre-inhale motor preparation or power changes. Together, these findings suggest that states of anticipation enhance olfactory perception through phase resetting of delta oscillations in piriform cortex.

Use of human intracranial electroencephalography methods, including rare direct recordings from human olfactory cortex, shows that anticipation of odor resets the phase of delta oscillations prior to the arrival of an odor.

A backward encoding approach to recover subcortical auditory activity

Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABR's have to be derived by averaging over activity generated by thousands of artificial sounds such as clicks or tone bursts. This approach cannot be easily applied to natural listening situations (e.g. speech, music), which limits auditory cognitive neuroscientific studies to investigate mostly cortical processes. We propose that by individually training backward encoding models to reconstruct evoked ABRs from high-density electrophysiological data, spatial filters can be tuned to auditory brainstem activity. Since these individualized filters can be applied (i.e. generalized) to any other data set using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept by using backward encoding models generated using a click stimulation rate of 30 ​Hz to predict ABR activity recorded using EEG from an independent measurement using a stimulation rate of 9 ​Hz. We show that individually predicted and measured ABR's are highly correlated (r ​~ ​0.7). Importantly these predictions are stable even when applying the trained backward encoding model to a low number of trials, mimicking a situation with an unfavorable signal-to-noise ratio. Overall, this work lays the necessary foundation to use this approach in more interesting listening situations.

Auditory cortical alpha/beta desynchronization prioritizes the representation of memory items during a retention period

To-be-memorized information in working-memory could be protected against distracting influences by processes of functional inhibition or prioritization. Modulations of oscillations in the alpha to beta range in task-relevant sensory regions have been suggested to play an important role for both mechanisms. We adapted a Sternberg task variant to the auditory modality, with a strong or a weak distracting sound presented at a predictable time during the retention period. Using a time-generalized decoding approach, relatively decreased strength of memorized information was found prior to strong distractors, paralleled by decreased pre-distractor alpha/beta power in the left superior temporal gyrus (lSTG). Over the entire group, reduced beta power in lSTG was associated with relatively increased strength of memorized information. The extent of alpha power modulations within participants was negatively correlated with strength of memorized information. Overall, our results are compatible with a prioritization account, but point to nuanced differences between alpha and beta oscillations.

Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system

Neural responses to auditory surprise are typically studied with highly unexpected, disruptive sounds. Consequently, little is known about auditory prediction in everyday contexts that are characterized by fine-grained, non-disruptive fluctuations of auditory surprise. To address this issue, we used IDyOM, a computational model of auditory expectation, to obtain continuous surprise estimates for a set of newly composed melodies. Our main goal was to assess whether the neural correlates of non-disruptive surprising sounds in a musical context are affected by musical expertise. Using magnetoencephalography (MEG), auditory responses were recorded from musicians and non-musicians while they listened to the melodies. Consistent with a previous study, the amplitude of the N1m component increased with higher levels of computationally estimated surprise. This effect, however, was not different between the two groups. Further analyses offered an explanation for this finding: Pitch interval size itself, rather than probabilistic prediction, was responsible for the modulation of the N1m, thus pointing to low-level sensory adaptation as the underlying mechanism. In turn, the formation of auditory regularities and proper probabilistic prediction were reflected in later components: The mismatch negativity (MMNm) and the P3am, respectively. Overall, our findings reveal a hierarchy of expectations in the auditory system and highlight the need to properly account for sensory adaptation in research addressing statistical learning.

Predictions drive neural representations of visual events ahead of incoming sensory information

The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.