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As for all expectations, expected precision maximises Bayesian model evidence (see Appendix).
Of particular interest here is the trait or prior expectation about precision, namely α.
The corresponding state or "posterior" expectations about precision reflect trial-by-trial changes in confidence about waiting for a high offer, that are nuanced by these "prior" beliefs.
To meet high expectations concerning precision and accuracy of reference materials, preparation of matrix-free reference materials using thermal decomposition-gas chromatography-mass spectrometry (TD-GC-MS) was proposed in this study.
In predictive coding, prior beliefs and sensory data are represented as probability distributions, with a mean value (expectation) and a precision (inverse variance).
Hohwy explores the idea that mechanisms for optimizing precision expectations map onto those that account for attention, and argues that attentional phenomena such as change blindness can be explained within the PC paradigm.
Although the derived PDFs were found to be irreducible integrals, they are easily evaluated via numerical integration and can be used to obtain expectation values, population precisions and confidence intervals.
The hierarchical Bayesian account is straightforward: an overly precise expectation of 'no sensation' is not fundamentally different to an overly precise expectation of 'pain'; the pathologically elevated precision of the expectation of 'no sensation' can override any bottom-up (relatively imprecise nocioceptive) sensory data.
In fact, any expectation of high accuracy and precision is unfair.
This provides a formal account of the interaction between attention and expectation, where attention optimizes the precision of various processing channels given appropriate cues for expectations.
As such, the expectations for an élite athletes' precision have only increased.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com