Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.     

Multisensory Oddity Detection as Bayesian Inference

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm – that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments – involving cues across and within modalities – for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new ‘oddity detection’ paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.     

An fMRI Study of Audiovisual Speech Perception Reveals Multisensory Interactions in Auditory Cortex

Research on the neural basis of speech-reading implicates a network of auditory language regions involving inferior frontal cortex, premotor cortex and sites along superior temporal cortex. In audiovisual speech studies, neural activity is consistently reported in posterior superior temporal Sulcus (pSTS) and this site has been implicated in multimodal integration. Traditionally, multisensory interactions are considered high-level processing that engages heteromodal association cortices (such as STS). Recent work, however, challenges this notion and suggests that multisensory interactions may occur in low-level unimodal sensory cortices. While previous audiovisual speech studies demonstrate that high-level multisensory interactions occur in pSTS, what remains unclear is how early in the processing hierarchy these multisensory interactions may occur. The goal of the present fMRI experiment is to investigate how visual speech can influence activity in auditory cortex above and beyond its response to auditory speech. In an audiovisual speech experiment, subjects were presented with auditory speech with and without congruent visual input. Holding the auditory stimulus constant across the experiment, we investigated how the addition of visual speech influences activity in auditory cortex. We demonstrate that congruent visual speech increases the activity in auditory cortex.     

Multisensory interplay reveals crossmodal influences on 'sensory-specific' brain regions, neural responses, and judgments

Although much traditional sensory research has studied each sensory modality in isolation, there has been a recent explosion of interest in causal interplay between different senses. Various techniques have now identified numerous multisensory convergence zones in the brain. Some convergence may arise surprisingly close to low-level sensory-specific cortex, and some direct connections may exist even between primary sensory cortices. A variety of multisensory phenomena have now been reported in which sensory-specific brain responses and perceptual judgments concerning one sense can be affected by relations with other senses. We survey recent progress in this multisensory field, foregrounding human studies against the background of invasive animal work and highlighting possible underlying mechanisms. These include rapid feedforward integration, possible thalamic influences, and/or feedback from multisensory regions to sensory-specific brain areas. Multisensory interplay is more prevalent than classic modular approaches assumed, and new methods are now available to determine the underlying circuits.     

Touch Interacts with Vision during Binocular Rivalry with a Tight Orientation Tuning

Multisensory integration is a common feature of the mammalian brain that allows it to deal more efficiently with the ambiguity of sensory input by combining complementary signals from several sensory sources. Growing evidence suggests that multisensory interactions can occur as early as primary sensory cortices. Here we present incompatible visual signals (orthogonal gratings) to each eye to create visual competition between monocular inputs in primary visual cortex where binocular combination would normally take place. The incompatibility prevents binocular fusion and triggers an ambiguous perceptual response in which the two images are perceived one at a time in an irregular alternation. One key function of multisensory integration is to minimize perceptual ambiguity by exploiting cross-sensory congruence. We show that a haptic signal matching one of the visual alternatives helps disambiguate visual perception during binocular rivalry by both prolonging the dominance period of the congruent visual stimulus and by shortening its suppression period. Importantly, this interaction is strictly tuned for orientation, with a mismatch as small as 7.5° between visual and haptic orientations sufficient to annul the interaction. These results indicate important conclusions: first, that vision and touch interact at early levels of visual processing where interocular conflicts are first detected and orientation tunings are narrow, and second, that haptic input can influence visual signals outside of visual awareness, bringing a stimulus made invisible by binocular rivalry suppression back to awareness sooner than would occur without congruent haptic input.     

When correlation implies causation in multisensory integration

Inferring which signals have a common underlying cause, and hence should be integrated, represents a primary challenge for a perceptual system dealing with multiple sensory inputs [1-3]. This challenge is often referred to as the correspondence problem or causal inference. Previous research has demonstrated that spatiotemporal cues, along with prior knowledge, are exploited by the human brain to solve this problem [4-9]. Here we explore the role of correlation between the fine temporal structure of auditory and visual signals in causal inference. Specifically, we investigated whether correlated signals are inferred to originate from the same distal event and hence are integrated optimally [10]. In a localization task with visual, auditory, and combined audiovisual targets, the improvement in precision for combined relative to unimodal targets was statistically optimal only when audiovisual signals were correlated. This result demonstrates that humans use the similarity in the temporal structure of multisensory signals to solve the correspondence problem, hence inferring causation from correlation.     

Multimodal integration for the representation of space in the posterior parietal cortex.

The posterior parietal cortex has long been considered an ''association'' area that combines information from different sensory modalities to form a cognitive representation of space. However, until recently little has been known about the neural mechanisms responsible for this important cognitive process. Recent experiments from the author''s laboratory indicate that visual, somatosensory, auditory and vestibular signals are combined in areas LIP and 7a of the posterior parietal cortex. The integration of these signals can represent the locations of stimuli with respect to the observer and within the environment. Area MSTd combines visual motion signals, similar to those generated during an observer''s movement through the environment, with eye-movement and vestibular signals. This integration appears to play a role in specifying the path on which the observer is moving. All three cortical areas combine different modalities into common spatial frames by using a gain-field mechanism. The spatial representations in areas LIP and 7a appear to be important for specifying the locations of targets for actions such as eye movements or reaching; the spatial representation within area MSTd appears to be important for navigation and the perceptual stability of motion signals.     

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.     

Multisensory Causal Inference in the Brain

At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions.     

Audiovisual integration of letters in the human brain

Letters of the alphabet have auditory (phonemic) and visual (graphemic) qualities. To investigate the neural representations of such audiovisual objects, we recorded neuromagnetic cortical responses to auditorily, visually, and audiovisually presented single letters. The auditory and visual brain activations first converged around 225 ms after stimulus onset and then interacted predominantly in the right temporo-occipito-parietal junction (280345 ms) and the left (380-540 ms) and right (450-535 ms) superior temporal sulci. These multisensory brain areas, playing a role in audiovisual integration of phonemes and graphemes, participate in the neural network supporting the supramodal concept of a "letter." The dynamics of these functions bring new insight into the interplay between sensory and association cortices during object recognition.     

Integration of touch and sound in auditory cortex

To form a coherent percept of the environment, our brain combines information from different senses. Such multisensory integration occurs in higher association cortices; but supposedly, it also occurs in early sensory areas. Confirming the latter hypothesis, we unequivocally demonstrate supra-additive integration of touch and sound stimulation at the second stage of the auditory cortex. Using high-resolution fMRI of the macaque monkey, we quantified the integration of auditory broad-band noise and tactile stimulation of hand and foot in anaesthetized animals. Integration was found posterior to and along the lateral side of the primary auditory cortex in the caudal auditory belt. Integration was stronger for temporally coincident stimuli and obeyed the principle of inverse effectiveness: greater enhancement for less effective stimuli. These findings demonstrates that multisensory integration occurs early and close to primary sensory areas and--because it occurs in anaesthetized animals--suggests that this integration is mediated by preattentive bottom-up mechanisms.     

A Synchrony-Dependent Influence of Sounds on Activity in Visual Cortex Measured Using Functional Near-Infrared Spectroscopy (fNIRS)

Evidence from human neuroimaging and animal electrophysiological studies suggests that signals from different sensory modalities interact early in cortical processing, including in primary sensory cortices. The present study aimed to test whether functional near-infrared spectroscopy (fNIRS), an emerging, non-invasive neuroimaging technique, is capable of measuring such multisensory interactions. Specifically, we tested for a modulatory influence of sounds on activity in visual cortex, while varying the temporal synchrony between trains of transient auditory and visual events. Related fMRI studies have consistently reported enhanced activation in response to synchronous compared to asynchronous audiovisual stimulation. Unexpectedly, we found that synchronous sounds significantly reduced the fNIRS response from visual cortex, compared both to asynchronous sounds and to a visual-only baseline. It is possible that this suppressive effect of synchronous sounds reflects the use of an efficacious visual stimulus, chosen for consistency with previous fNIRS studies. Discrepant results may also be explained by differences between studies in how attention was deployed to the auditory and visual modalities. The presence and relative timing of sounds did not significantly affect performance in a simultaneously conducted behavioral task, although the data were suggestive of a positive relationship between the strength of the fNIRS response from visual cortex and the accuracy of visual target detection. Overall, the present findings indicate that fNIRS is capable of measuring multisensory cortical interactions. In multisensory research, fNIRS can offer complementary information to the more established neuroimaging modalities, and may prove advantageous for testing in naturalistic environments and with infant and clinical populations.     

Audio-Tactile Integration and the Influence of Musical Training

Perception of our environment is a multisensory experience; information from different sensory systems like the auditory, visual and tactile is constantly integrated. Complex tasks that require high temporal and spatial precision of multisensory integration put strong demands on the underlying networks but it is largely unknown how task experience shapes multisensory processing. Long-term musical training is an excellent model for brain plasticity because it shapes the human brain at functional and structural levels, affecting a network of brain areas. In the present study we used magnetoencephalography (MEG) to investigate how audio-tactile perception is integrated in the human brain and if musicians show enhancement of the corresponding activation compared to non-musicians. Using a paradigm that allowed the investigation of combined and separate auditory and tactile processing, we found a multisensory incongruency response, generated in frontal, cingulate and cerebellar regions, an auditory mismatch response generated mainly in the auditory cortex and a tactile mismatch response generated in frontal and cerebellar regions. The influence of musical training was seen in the audio-tactile as well as in the auditory condition, indicating enhanced higher-order processing in musicians, while the sources of the tactile MMN were not influenced by long-term musical training. Consistent with the predictive coding model, more basic, bottom-up sensory processing was relatively stable and less affected by expertise, whereas areas for top-down models of multisensory expectancies were modulated by training.     

Reshaping sensory representations by task-specific brain states: Toward cortical circuit mechanisms

Perception is internally constructed by integrating brain states with external sensory inputs, a process depending on the topdown modulation of sensory representations. A wealth of earlier studies described task-dependent modulations of sensory cortex corroborating perceptual and behavioral phenomena. But only with recent technological advancements, the underlying circuit-level mechanisms began to be unveiled. We review recent progress along this line of research. It begins to be appreciated that topdown signals can encode various types of task-related information, ranging from task engagement, and category knowledge to decision execution; these signals are transferred via feedback pathways originating from distinct association cortices and interact with sensory cortical circuits. These plausible mechanisms support a broad range of computations from predictive coding to inference making, ultimately form dynamic percepts and endow behavioral flexibility.     

Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations

Perceptual decisions can be made when sensory input affords an inference about what generated that input. Here, we report findings from two independent perceptual experiments conducted during functional magnetic resonance imaging (fMRI) with a sparse event-related design. The first experiment, in the visual modality, involved forced-choice discrimination of coherence in random dot kinematograms that contained either subliminal or periliminal motion coherence. The second experiment, in the auditory domain, involved free response detection of (non-semantic) near-threshold acoustic stimuli. We analysed fluctuations in ongoing neural activity, as indexed by fMRI, and found that neuronal activity in sensory areas (extrastriate visual and early auditory cortex) biases perceptual decisions towards correct inference and not towards a specific percept. Hits (detection of near-threshold stimuli) were preceded by significantly higher activity than both misses of identical stimuli or false alarms, in which percepts arise in the absence of appropriate sensory input. In accord with predictive coding models and the free-energy principle, this observation suggests that cortical activity in sensory brain areas reflects the precision of prediction errors and not just the sensory evidence or prediction errors per se.     

Biases in Visual,Auditory, and Audiovisual Perception of Space

Localization of objects and events in the environment is critical for survival, as many perceptual and motor tasks rely on estimation of spatial location. Therefore, it seems reasonable to assume that spatial localizations should generally be accurate. Curiously, some previous studies have reported biases in visual and auditory localizations, but these studies have used small sample sizes and the results have been mixed. Therefore, it is not clear (1) if the reported biases in localization responses are real (or due to outliers, sampling bias, or other factors), and (2) whether these putative biases reflect a bias in sensory representations of space or a priori expectations (which may be due to the experimental setup, instructions, or distribution of stimuli). Here, to address these questions, a dataset of unprecedented size (obtained from 384 observers) was analyzed to examine presence, direction, and magnitude of sensory biases, and quantitative computational modeling was used to probe the underlying mechanism(s) driving these effects. Data revealed that, on average, observers were biased towards the center when localizing visual stimuli, and biased towards the periphery when localizing auditory stimuli. Moreover, quantitative analysis using a Bayesian Causal Inference framework suggests that while pre-existing spatial biases for central locations exert some influence, biases in the sensory representations of both visual and auditory space are necessary to fully explain the behavioral data. How are these opposing visual and auditory biases reconciled in conditions in which both auditory and visual stimuli are produced by a single event? Potentially, the bias in one modality could dominate, or the biases could interact/cancel out. The data revealed that when integration occurred in these conditions, the visual bias dominated, but the magnitude of this bias was reduced compared to unisensory conditions. Therefore, multisensory integration not only improves the precision of perceptual estimates, but also the accuracy.     

听觉皮层信号处理

听觉系统和视觉系统的不同之处在于：听觉系统在外周感受器和听皮层间具有更长的皮层下通路和更多的突触联系。该特殊结构反应了听觉系统从复杂听觉环境中提取与行为相关信号的机制与其他感觉系统不同。听皮层神经信号处理包括两种重要的转换机制,声音信号的非同构转换以及从声音感受到知觉层面的转换。听觉皮层神经编码机制同时也受到听觉反馈和语言或发声过程中发声信号的调控。听觉神经科学家和生物医学工程师所面临的挑战便是如何去理解大脑中这些转换的编码机制。我将会用我实验室最近的一些发现来阐述听觉信号是如何在原听皮层中进行处理的,并讨论其对于言语和音乐在大脑中的处理机制以及设计神经替代装置诸如电子耳蜗的意义。我们使用了结合神经电生理技术和量化工程学的方法来研究这些问题。     

Neural Mechanisms by Which Attention Modulates the Comparison of Remembered and Perceptual Representations

Attention is important for effectively comparing incoming perceptual information with the contents of visual short-term memory (VSTM), such that any differences can be detected. However, how attentional mechanisms operate upon these comparison processes remains largely unknown. Here we investigate the underlying neural mechanisms by which attention modulates the comparisons between VSTM and perceptual representations using functional magnetic resonance imaging (fMRI). Participants performed a cued change detection task. Spatial cues were presented to orient their attention either to the location of an item in VSTM prior to its comparison (retro-cues), or simultaneously (simultaneous-cues) with the probe array. A no-cue condition was also included. When attention cannot be effectively deployed in advance (i.e. following the simultaneous-cues), we observed a distributed and extensive activation pattern in the prefrontal and parietal cortices in support of successful change detection. This was not the case when participants can deploy their attention in advance (i.e. following the retro-cues). The region-of-interest analyses confirmed that neural responses for successful change detection versus correct rejection in the visual and parietal regions were significantly different for simultaneous-cues compared to retro-cues. Importantly, we found enhanced functional connectivity between prefrontal and parietal cortices when detecting changes on the simultaneous-cue trials. Moreover, we demonstrated a close relationship between this functional connectivity and d′ scores. Together, our findings elucidate the attentional and neural mechanisms by which items held in VSTM are compared with incoming perceptual information.     

Variability of perceptual multistability: from brain state to individual trait

Few phenomena are as suitable as perceptual multistability to demonstrate that the brain constructively interprets sensory input. Several studies have outlined the neural circuitry involved in generating perceptual inference but only more recently has the individual variability of this inferential process been appreciated. Studies of the interaction of evoked and ongoing neural activity show that inference itself is not merely a stimulus-triggered process but is related to the context of the current brain state into which the processing of external stimulation is embedded. As brain states fluctuate, so does perception of a given sensory input. In multistability, perceptual fluctuation rates are consistent for a given individual but vary considerably between individuals. There has been some evidence for a genetic basis for these individual differences and recent morphometric studies of parietal lobe regions have identified neuroanatomical substrates for individual variability in spontaneous switching behaviour. Moreover, disrupting the function of these latter regions by transcranial magnetic stimulation yields systematic interference effects on switching behaviour, further arguing for a causal role of these regions in perceptual inference. Together, these studies have advanced our understanding of the biological mechanisms by which the brain constructs the contents of consciousness from sensory input.     

Resting State Networks' Corticotopy: The Dual Intertwined Rings Architecture

How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called “the dual intertwined rings architecture”) that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or “corticotopy”). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This “dual intertwined architecture” suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi-temporal integration (i.e., relates past, present, and future representations at different temporal scales).