Dataset Information

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception.

ABSTRACT: Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains.

SUBMITTER: Blank H

PROVIDER: S-EPMC5112801 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception.

Blank Helen H Davis Matthew H MH

PLoS biology 20161115 11

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conver ...[more]

PMID: 27846209

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Project description:Prior research has shown that the perception of degraded speech is influenced by within sentence meaning and recruits one or more components of a frontal-temporal-parietal network. The goal of the current study is to examine whether the overall conceptual meaning of a sentence, made up of one set of words, influences the perception of a second acoustically degraded sentence, made up of a different set of words. Using functional magnetic resonance imaging (fMRI), we presented an acoustically clear sentence followed by an acoustically degraded sentence and manipulated the semantic relationship between them: Related in meaning (but consisting of different content words), Unrelated in meaning, or Same. Results showed that listeners' word recognition accuracy for the acoustically degraded sentences was significantly higher when the target sentence was preceded by a conceptually related compared to a conceptually unrelated sentence. Sensitivity to conceptual relationships was associated with enhanced activity in middle and inferior frontal, temporal, and parietal areas. In addition, the left middle frontal gyrus (LMFG), left inferior frontal gyrus (LIFG), and left middle temporal gyrus (LMTG) showed activity that correlated with individual performance on the Related condition. The superior temporal gyrus (STG) showed increased activation in the Same condition suggesting that it is sensitive to perceptual similarity rather than the integration of meaning between the sentence pairs. A fronto-temporo-parietal network appears to consolidate information sources across multiple levels of language (acoustic, lexical, syntactic, semantic) to build, and ultimately integrate conceptual information across sentences and facilitate the perception of a degraded speech signal. However, the nature of the sources of information that are available differentially recruit specific regions and modulate their activity within this network. Implications of these findings for the functional architecture of the network are considered.

Dataset Information

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception.

Publications

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception.

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