Rapid acquisition of near optimal stopping using Bayesian by-products

Prior to any decision, animals (including humans) must stop deliberating. Both accumulating evidence for too little or too long can be costly. In contrast to accounts of decision making, accounts of stopping do not typically claim that animals use Bayesian posteriors. Considering a generic perceptual decision making task we show that, under approximation, only two variables are relevant to the question of when to stop evidence accumulation; time and the maximum posterior probability. We explored the rate at which stopping rules are learned using deep neural networks as model learners. A network which only used time and the maximum posterior probability learned faster than any other network considered. Therefore, such an approach may be highly adaptive, and animals may be able to reuse the same neural machinery they use for decisions for stopping. These results suggest that Bayesian inference may be even more important for animals than previously thought.

A new species of Aphyocharax Günther, 1868 (Characiformes, Characidae) from the Maracaçumé river basin, eastern Amazon

A new species of Aphyocharax is described from the Maracaçumé river basin, eastern Amazon, based on morphological and molecular data. The new species differs from all its congeners, mainly by possessing the upper caudal-fin lobe longer than the lower one in mature males, and other characters related to teeth counts, colour pattern, and body depth at dorsal-fin origin. In addition, the new species is corroborated by a haplotype phylogenetic analyses based on the Cytochrome B (Cytb) mitochondrial gene, where its haplotypes are grouped into an exclusive lineage, supported by maximum posterior probability value, a species delimitation method termed the Wiens and Penkrot analysis (WP).

An adaptive window object tracking algorithm based on variable resolution

This paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.