In sum, it would be useful to know whether maximizing covariance, or maximizing information about velocity, would lead to the same conclusions as maximizing correlation.
This particular approach to information is critical to the claim that the present theory addresses the fundamental computational goal of minimizing uncertainty (or maximizing information) about the world.
We demonstrate that these search strategies as well as the transition between them can be quantitatively explained by a maximally informative search strategy, where the searcher seeks to continuously maximize information about the target.
In motor neuroscience, rectification of the surface EMG is a common pre-processing step prior to calculating CMC, intended to maximize information about action potential timing, whilst suppressing information relating to motor unit action potential (MUAP) shape.
The computational goal of the nervous system is to minimize uncertainty (maximize information) about the state of the world (or more specifically, an aspect of the world that could be referred to as "future reward").
Additionally, paired read methods have been used in an attempt to maximize information about splicing and other long-range phenomena in RNA Seq experiments, but the ligation and amplification steps in such methods can introduce errors as evidenced by a relatively large proportion of paired reads appearing as chimeras arising from distinct genes (5 9%, [20]), most of which are artifactual.
Animals might counteract this problem with predictive coding, using foraging strategies that maximize information about the food source location at a sufficient time in the future (Polani, 2009; Tishby and Polani, 2011; Bialek, 2013).
In contingency tables, we used single items from the SYRAAP's benefits and barriers subscales to maximize information about these potentially modifiable constructs; the gamma measure of association was used to explore the strength of association between variables.
The authors studied the foraging behavior of the C. elegans worm and found that the transitions from a local (search intensive) to a global (less turns, larger area) search strategy can be explained by a drift diffusion model that effectively maximizes information about the location of the food source.
Finally, researchers might consider strategies for maximizing information delivery.
Najemnik and Geisler argued that humans could choose fixations that maximize information gained about the target's location; this strategy [ 31] allows humans to select features from the scene optimally such as an "ideal searcher".
