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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.
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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.
As for all expectations, expected precision maximises Bayesian model evidence (see Appendix).
In biological formulations of the free-energy principle, current thinking is that dopamine might encode the precision of prior expectations [39], [48].
In our model of hierarchal Bayesian inference, we have emphasized how the interaction between the 'nature and precision' of prior expectations and the nature and precision of prediction errors caused by sensory data generates what we perceive or whether and how we move.
For various estimating (N) of malware tests, the expectation precision of malware discovery runs up to 98.7% with N = 100.
In the Bayesian brain, attention means the process of optimizing synaptic gain to represent the relative precisions of prior expectations and sensory information during inference (Feldman and Friston, 2010) and provides a formal basis for the Jamesian view of attention.
These parameters encode the subject's prior expectations about precision, hazard rates, and their sensitivity to monetary cues, respectively.
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.
THE distinction between home cooking and a fine restaurant meal is less a matter of style than an expectation of precision and attention to detail.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com