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This is consistent with the interpretations that N1-suppression effects for predicted stimuli are due to a match between prediction and input.
In the second step, the comparison between prediction and input is performed, and only a specific match results in a positive judgment of agency.
To recapitulate what has been described so far, feedforward prediction error signals are determined by the degree of (mis)match between prediction and input, which determines their content, and by several variables that can influence the gain, which determines their magnitude.
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The prediction errors per se are simply the difference between predictions and afferent input.
Apart from this specific case, deviations between prediction and observation appeared to be due to the heterogeneity of input data or caused by the influence of the formulation additives that have been disregarded in the mixture toxicity predictions.
Discrepancies between sensory predictions and sensory input could be used as an internal error signal for recalibrating local motion detectors.
Additionally, functionalities such as the supported prediction methods and input formats have been extended.
This rests on a change-detection mechanism; in which the MMN reflects greater prediction error or mismatch between top-down predictions and current inputs.
Differences between ChIP and input are shown.
Differences between ChIP and input are plotted.
This is achieved by considering a quadratic polynomial relationship between the output and input elements so as to generate the minimum prediction error.
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