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Studies on multisensory perception often focus on simplistic conditions in which one single stimulus is presented per modality. Yet, in everyday life, we usually encounter multiple signals per modality. To understand how multiple signals within and across the senses are combined, we extended the classical audio‐visual spatial ventriloquism paradigm to combine two visual stimuli with one sound. The individual visual stimuli presented in the same trial differed in their relative timing and spatial offsets to the sound, allowing us to contrast their individual and combined influence on sound localization judgements. We find that the ventriloquism bias is not dominated by a single visual stimulus but rather is shaped by the collective multisensory evidence. In particular, the contribution of an individual visual stimulus to the ventriloquism bias depends not only on its own relative spatio‐temporal alignment to the sound but also the spatio‐temporal alignment of the other visual stimulus. We propose that this pattern of multi‐stimulus multisensory integration reflects the evolution of evidence for sensory causal relations during individual trials, calling for the need to extend established models of multisensory causal inference to more naturalistic conditions. Our data also suggest that this pattern of multisensory interactions extends to the ventriloquism aftereffect, a bias in sound localization observed in unisensory judgements following a multisensory stimulus. [ABSTRACT FROM AUTHOR]

Perceptual coherence in the face of discrepant multisensory signals is achieved via the processes of multisensory integration, recalibration and sometimes motor adaptation. These supposedly operate on different time scales, with integration reducing immediate sensory discrepancies and recalibration and motor adaptation reflecting the cumulative influence of their recent history. Importantly, whether discrepant signals are bound during perception is guided by the brains' inference of whether they originate from a common cause. When combined, these two notions lead to the hypothesis that the time scales on which integration and recalibration (or motor adaptation) operate are associated with different time scales of evidence about a common cause underlying two signals. We tested this prediction in a well‐established visuo‐motor paradigm, in which human participants performed visually guided hand movements. The kinematic correlation between hand and cursor movements indicates their common origin, which allowed us to manipulate the common‐cause evidence by titrating this correlation. Specifically, we dissociated hand and cursor signals during individual movements while preserving their correlation across the series of movement endpoints. Following our hypothesis, this manipulation reduced integration compared with a condition in which visual and proprioceptive signals were perfectly correlated. In contrast, recalibration and motor adaption were not affected by this manipulation. This supports the notion that multisensory integration and recalibration deal with sensory discrepancies on different time scales guided by common‐cause evidence: Integration is prompted by local common‐cause evidence and reduces immediate discrepancies, whereas recalibration and motor adaptation are prompted by global common‐cause evidence and reduce persistent discrepancies. [ABSTRACT FROM AUTHOR]

To make accurate perceptual estimates, observers must take the reliability of sensory information into account. Despite many behavioural studies showing that subjects weight individual sensory cues in proportion to their reliabilities, it is still unclear when during a trial neuronal responses are modulated by the reliability of sensory information or when they reflect the perceptual weights attributed to each sensory input. We investigated these questions using a combination of psychophysics, EEG-based neuroimaging and single-trial decoding. Our results show that the weighted integration of sensory information in the brain is a dynamic process; effects of sensory reliability on task-relevant EEG components were evident 84 ms after stimulus onset, while neural correlates of perceptual weights emerged 120 ms after stimulus onset. These neural processes had different underlying sources, arising from sensory and parietal regions, respectively. Together these results reveal the temporal dynamics of perceptual and neural audio-visual integration and support the notion of temporally early and functionally specific multisensory processes in the brain. [ABSTRACT FROM AUTHOR]

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

During sensory processing, cortical areas continuously exchange information in different directions along the hierarchy. The functional role of such interactions, however, has been the subject of various proposals. Here, we investigate the role of bottom-up and top-down interactions in processing stimulus structure and their relation to expected events. Applying multivariate autoregressive methods to local field potentials recorded in alert cats, we quantify directed interactions between primary (A17/18) and higher (A21) visual areas. A trial-by-trial analysis yields the following findings. To assess the role of interareal interactions in processing stimulus structure, we recorded in naïve animals during stimulation with natural movies and pink noise stimuli. The overall interactions decrease compared with baseline for both stimuli. To investigate whether forthcoming events modulate interactions, we recorded in trained animals viewing two stimuli, one of which had been associated with a reward. Several results support such modulations. First, the interactions increase compared with baseline and this increase is not observed in a context where food was not delivered. Second, these stimuli have a differential effect on top-down and bottom-up components. This difference is emphasized during the stimulus presentation and is maximal shortly before the possible reward. Furthermore, a spectral decomposition of the interactions shows that this asymmetry is most dominant in the gamma frequency range. Concluding, these results support the notion that interareal interactions are more related to an expectancy state rather than to processing of stimulus structure. [ABSTRACT FROM AUTHOR]

The local field potential (LFP) is a population measure of neuronal activity complementary to spike trains. Whereas the response properties of the spiking activity in the visual cortex have been characterized extensively, the responses of the LFP have not been well explored. No coherent picture exists about which frequency ranges exhibit feature tuning or show stimulus locked activity. Addressing this, we recorded LFP in the primary visual cortex of alert cats and calculated the tuning indices for orientation, spatial and temporal frequency. Furthermore, we quantified the locking of the power in different LFP frequency bands to the velocity profile of artificial and natural stimuli. We found that the LFP in alert animals is well tuned with similar specificity to orientation, spatial frequency and temporal frequency. Tuning to these features is most prominent in two frequency bands (8–23 Hz and 39–109 Hz). In two complementary frequency bands (23–39 Hz and above 109 Hz) the dynamics of the LFP power is locked tightly to the temporal structure of the stimulus. This locking is furthermore independent of the spatial structure of the stimulus. Together these four frequency bands cover the whole frequency range investigated. In contrast to previous studies, which often reported correlates of visual processing only in a limited frequency range of the LFP, the present results suggest that the entire frequency range of the LFP can be assigned a role in visual processing. [ABSTRACT FROM AUTHOR]

Neurons in primary visual cortex are typically classified as either simple or complex. Whereas simple cells respond strongly to grating and bar stimuli displayed at a certain phase and visual field location, complex cell responses are insensitive to small translations of the stimulus within the receptive field [Hubel & Wiesel (1962) J. Physiol. (Lond.), 160, 106-154; Kjaer et al. (1997) J. Neurophysiol., 78, 3187-3197]. This constancy in the response to variations of the stimuli is commonly called invariance. Hubel and Wiesel's classical model of the primary visual cortex proposes a connectivity scheme which successfully describes simple and complex cell response properties. However, the question as to how this connectivity arises during normal development is left open. Based on their work and inspired by recent physiological findings we suggest a network model capable of learning from natural stimuli and developing receptive field properties which match those of cortical simple and complex cells. Stimuli are drawn from videos obtained by a camera mounted to a cat's head, so they should approximate the natural input to the cat's visual system. The network uses a competitive scheme to learn simple and complex cell response properties. Employing delayed signals to learn connections between simple and complex cells enables the model to utilize temporal properties of the input. We show that the temporal structure of the input gives rise to the emergence and refinement of complex cell receptive fields, whereas removing temporal continuity prevents this processes. This model lends a physiologically based explanation of the development of complex cell invariance response properties.