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However, if RS is best explained by a predictive coding model then we would predict changes in "backward" or top-down (FFA-to-OFA) connectivity during RS.
Evidence that RS to faces is best accounted for by a predictive coding model would have major implications for the interpretation of numerous studies that have used RS to infer the nature of representations within different regions of the face network.
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In this way, we link affect to predictions within a predictive coding framework.
Three features would classify this net as a predictive coding (PC) model.
To account for the error propagation induced by predictive coding, a low-pass temporal filtering is applied to the degradation index maps.
All audio samples are compressed using ADPCM and a linear predictive coding, a method dubbed BRR.
The main coding flow for an ACELP coding technique is to perform a linear predictive coding (LPC) on an input speech signal and then perform an adaptive as well as an algebraic codebook search on an LPC residual signal.
Given sensory data from other sources suggesting the absence of a stimulus, or a prior expectation that no stimulus is present, this noise will be explained as such by the predictive coding scheme and will not be perceived.
Further improvements are possible by means of predictive coding, where previously quantized CSI is employed to predict the current CSI and only the prediction error is quantized [30, 39].
The source redundancy is exploited at the encoder by means of predictive coding.
Here, we sought to determine whether RS of facial identity reflects locally based changes such as neuronal fatigue or is best accounted for by predictive coding models emphasizing the role of top-down modulation.
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