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Multisensory causal inference in the brain.

Kayser C, Shams L - PLoS Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.

ABSTRACT
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.

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Bayesian models of multisensory integration.Schematic of different causal structures in the environment giving rise to visual and acoustic inputs (e.g., seeing a face and hearing a voice) that may or may not originate from the same speaker. The left panels display the inferred statistical causal structure, with SA, SV, and S denoting sources for acoustic, visual, or multisensory stimuli and XA and XV indicating the respective sensory representations (e.g., location). The right panels display the probability distributions of these sensory representations and the optimal estimate of stimulus attribute (e.g., location) derived from the Bayesian model under different assumptions about the environment. For the sake of simplicity of illustration, it is assumed that the prior probability of the stimulus attribute is uniform (and therefore not shown in the equations and figures). (A) Assuming separate sources (C = 2) leads to independent acoustic and visual estimates of stimulus location, with the optimal value matching the most likely unisensory location. (B) Assuming a common source (C = 1) leads to integration (fusion). The optimal Bayesian estimate is the combination of visual and acoustic estimates, each weighted by its relative reliability (with σA and σV denoting the inverse reliability of each sense). (C) In Bayesian causal inference (assuming a model-averaging decision strategy), the two different hypotheses about the causal structure (e.g., one or two sources) are combined, each weighted by its inferred probability given the visual and acoustic sensations. The optimal stimulus estimate is a mixture of the unisensory and fused estimates.
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pbio.1002075.g001: Bayesian models of multisensory integration.Schematic of different causal structures in the environment giving rise to visual and acoustic inputs (e.g., seeing a face and hearing a voice) that may or may not originate from the same speaker. The left panels display the inferred statistical causal structure, with SA, SV, and S denoting sources for acoustic, visual, or multisensory stimuli and XA and XV indicating the respective sensory representations (e.g., location). The right panels display the probability distributions of these sensory representations and the optimal estimate of stimulus attribute (e.g., location) derived from the Bayesian model under different assumptions about the environment. For the sake of simplicity of illustration, it is assumed that the prior probability of the stimulus attribute is uniform (and therefore not shown in the equations and figures). (A) Assuming separate sources (C = 2) leads to independent acoustic and visual estimates of stimulus location, with the optimal value matching the most likely unisensory location. (B) Assuming a common source (C = 1) leads to integration (fusion). The optimal Bayesian estimate is the combination of visual and acoustic estimates, each weighted by its relative reliability (with σA and σV denoting the inverse reliability of each sense). (C) In Bayesian causal inference (assuming a model-averaging decision strategy), the two different hypotheses about the causal structure (e.g., one or two sources) are combined, each weighted by its inferred probability given the visual and acoustic sensations. The optimal stimulus estimate is a mixture of the unisensory and fused estimates.

Mentions: Bayesian statistics describes sensory representations in probabilistic terms, attributing likelihoods to each possible encoding of a sensory attribute [11]. Moreover, it describes how different variables interact in determining the outcome, such as how prior knowledge affects perceptual estimates or how inputs from two senses combine. As shown in Fig. 1A, when considered independently, each sensory modality can be conceptualized as providing a noisy (probabilistic) estimate of the same attribute. Yet, under the assumption of a common source, Bayesian inference predicts the multisensory estimate arising from the combination of both senses by weighing each input in proportion to its reliability (Fig. 1B).


Multisensory causal inference in the brain.

Kayser C, Shams L - PLoS Biol. (2015)

Bayesian models of multisensory integration.Schematic of different causal structures in the environment giving rise to visual and acoustic inputs (e.g., seeing a face and hearing a voice) that may or may not originate from the same speaker. The left panels display the inferred statistical causal structure, with SA, SV, and S denoting sources for acoustic, visual, or multisensory stimuli and XA and XV indicating the respective sensory representations (e.g., location). The right panels display the probability distributions of these sensory representations and the optimal estimate of stimulus attribute (e.g., location) derived from the Bayesian model under different assumptions about the environment. For the sake of simplicity of illustration, it is assumed that the prior probability of the stimulus attribute is uniform (and therefore not shown in the equations and figures). (A) Assuming separate sources (C = 2) leads to independent acoustic and visual estimates of stimulus location, with the optimal value matching the most likely unisensory location. (B) Assuming a common source (C = 1) leads to integration (fusion). The optimal Bayesian estimate is the combination of visual and acoustic estimates, each weighted by its relative reliability (with σA and σV denoting the inverse reliability of each sense). (C) In Bayesian causal inference (assuming a model-averaging decision strategy), the two different hypotheses about the causal structure (e.g., one or two sources) are combined, each weighted by its inferred probability given the visual and acoustic sensations. The optimal stimulus estimate is a mixture of the unisensory and fused estimates.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4339834&req=5

pbio.1002075.g001: Bayesian models of multisensory integration.Schematic of different causal structures in the environment giving rise to visual and acoustic inputs (e.g., seeing a face and hearing a voice) that may or may not originate from the same speaker. The left panels display the inferred statistical causal structure, with SA, SV, and S denoting sources for acoustic, visual, or multisensory stimuli and XA and XV indicating the respective sensory representations (e.g., location). The right panels display the probability distributions of these sensory representations and the optimal estimate of stimulus attribute (e.g., location) derived from the Bayesian model under different assumptions about the environment. For the sake of simplicity of illustration, it is assumed that the prior probability of the stimulus attribute is uniform (and therefore not shown in the equations and figures). (A) Assuming separate sources (C = 2) leads to independent acoustic and visual estimates of stimulus location, with the optimal value matching the most likely unisensory location. (B) Assuming a common source (C = 1) leads to integration (fusion). The optimal Bayesian estimate is the combination of visual and acoustic estimates, each weighted by its relative reliability (with σA and σV denoting the inverse reliability of each sense). (C) In Bayesian causal inference (assuming a model-averaging decision strategy), the two different hypotheses about the causal structure (e.g., one or two sources) are combined, each weighted by its inferred probability given the visual and acoustic sensations. The optimal stimulus estimate is a mixture of the unisensory and fused estimates.
Mentions: Bayesian statistics describes sensory representations in probabilistic terms, attributing likelihoods to each possible encoding of a sensory attribute [11]. Moreover, it describes how different variables interact in determining the outcome, such as how prior knowledge affects perceptual estimates or how inputs from two senses combine. As shown in Fig. 1A, when considered independently, each sensory modality can be conceptualized as providing a noisy (probabilistic) estimate of the same attribute. Yet, under the assumption of a common source, Bayesian inference predicts the multisensory estimate arising from the combination of both senses by weighing each input in proportion to its reliability (Fig. 1B).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom.

ABSTRACT
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.

Show MeSH