When combining information across different senses humans need to flexibly select cues of a common origin whilst avoiding distraction from irrelevant inputs. The brain could solve this challenge using a hierarchical principle, by deriving rapidly a fused sensory estimate for computational expediency and, later and if required, filtering out irrelevant signals based on the inferred sensory cause(s). Analysing time-and source-resolved human magnetoencephalographic data we unveil a systematic spatio-temporal cascade of the relevant computations, starting with early segregated unisensory representations, continuing with sensory fusion in parietal-temporal regions and culminating as causal inference in the frontal lobe. Our results reconcile previous computational accounts of multisensory perception by showing that prefrontal cortex guides flexible integrative behaviour based on candidate representations established in sensory and association cortices, thereby framing multisensory integration in the generalised context of adaptive behaviour.

Behavioral data obtained with perceptual decision making experiments are typically analyzed with the drift-diffusion model. This parsimonious model accumulates noisy pieces of evidence toward a decision bound to explain the accuracy and reaction times of subjects. Recently, Bayesian models have been proposed to explain how the brain extracts information from noisy input as typically presented in perceptual decision making tasks. It has long been known that the drift-diffusion model is tightly linked with such functional Bayesian models but the precise relationship of the two mechanisms was never made explicit. Using a Bayesian model, we derived the equations which relate parameter values between these models. In practice we show that this equivalence is useful when fitting multi-subject data. We further show that the Bayesian model suggests different decision variables which all predict equal responses and discuss how these may be discriminated based on neural correlates of accumulated evidence. In addition, we discuss extensions to the Bayesian model which would be difficult to derive for the drift-diffusion model. We suggest that these and other extensions may be highly useful for deriving new experiments which test novel hypotheses.

Perception adapts to mismatching multisensory information, both when different cues appear simultaneously and when they appear sequentially. While both multisensory integration and adaptive trial-by-trial recalibration are central for behavior, it remains unknown whether they are mechanistically linked and arise from a common neural substrate. To relate the neural underpinnings of sensory integration and recalibration, we measured whole-brain magnetoencephalography while human participants performed an audio-visual ventriloquist task. Using single-trial multivariate analysis, we localized the perceptually-relevant encoding of multisensory information within and between trials. While we found neural signatures of multisensory integration within temporal and parietal regions, only medial superior parietal activity encoded past and current sensory information and mediated the perceptual recalibration within and between trials. These results highlight a common neural substrate of sensory integration and perceptual recalibration, and reveal a role of medial parietal regions in linking present and previous multisensory evidence to guide adaptive behavior.

Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision. Here we show that there are experimental tasks for which one can analyse the exact spatio-temporal details of a dynamic sensory noise and better understand variability in human perceptual decisions. Using a new experimental visual tracking task and a novel Bayesian decision making model, we found that the spatio-temporal noise fluctuations in the input of single trials explain a significant part of the observed responses. Our results show that modelling the precise internal representations of human participants helps predict when perceptual decisions go wrong. Furthermore, by modelling precisely the stimuli at the single-trial level, we were able to identify the underlying mechanism of perceptual decision making in more detail than standard models.

Our senses often receive conflicting multisensory information, which our brain reconciles by adaptive recalibration. A classic example is the ventriloquism aftereffect, which emerges following both cumulative (long-term) and trial-wise exposure to spatially discrepant multisensory stimuli. Despite the importance of such adaptive mechanisms for interacting

12The perceptual use of multisensory information apparently changes with age. Yet it remains unclear 13 whether previously reported age-effects arise from changes in the sensory computations by which 14 information is combined, from a reduced sensory precision with age, or changes in the belief that 15 different sensory-motor cues are causally linked. To address this question we analysed how healthy 16 young and older adults integrate audio-visual information within (ventriloquist-effect) and between trials 17 (ventriloquist after-effect) using models of Bayesian causal inference. Despite a reduced precision of 18 sensory representations in the elderly, both groups exhibited comparable ventriloquist biases that were 19 reproduced by largely the same sensory computations. While the after-effect bias was also comparable 20 between groups, modelling showed that this was driven by previous sensory information in younger but 21 by the previous response in older participants. This suggests a transition from a sensory-to a behavior-22 driven influence of past experience on subsequent choices with age, possibly related to the reduced 23 sensory precision or memory capacity with age. 24 25 26

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Deep brain stimulation on the subthalamic nucleus has been used to relieve Parkinsonian motor symptoms. However, the underlying physiological mechanism has not been fully understood. Beta-band cortico-muscular coherence increases when healthy humans perform isometric contraction. We hypothesized that this might be a measure of symptomatic improvement in motor performance after subthalamic nucleus deep brain stimulation. Here, we measured the beta-band cortico-muscular coherence with magnetoencephalography from three Parkinson's disease patients. We then compared the coherence values for stimulator on-state and off-state. We found that when the stimulator is on, the beta cortico-muscular coherence elevates significantly for the tremorous hand compared with that when the stimulator is off. This suggests that deep brain stimulation resulted in better cortico-muscular coordination.