Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people’s confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality.     

A modified particle swarm optimization algorithm for parameter estimation of a biological system

Background

Mathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.

Methods

In this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.

Results

Our proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.

Conclusion

The results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.

     

Nucleotide sequence analysis of the lemur beta-globin gene family: evidence for major rate fluctuations in globin polypeptide evolution

Lemur beta-related globin genes have been isolated and sequenced. Orthology of prosimian and human epsilon-, gamma-, and beta-related globin genes was established by dot-matrix analysis. All of these lemur globin genes potentially encode functional beta-related globin polypeptides, though precisely when the gamma-globin gene is expressed remains unknown. The organization of the 18-kb brown lemur beta-globin gene cluster (5' epsilon-gamma-[psi eta-delta]-beta 3') is consistent with its evolution by contraction via unequal crossing-over from the putative ancestral mammalian beta-globin gene cluster (5' epsilon-gamma- eta-delta-beta 3'). The dwarf lemur nonadult globin genes are arranged as in the brown lemur. Similar levels of synonymous (silent) nucleotide substitutions and noncoding DNA sequence differences have accumulated between species in all of these genes, suggesting a uniform rate of noncoding DNA divergence throughout primate beta-globin gene clusters. These differences are comparable with those observed in the nonfunctional psi eta pseudogene and have therefore accumulated at the presumably maximal neutral rate. In contrast, nonsynonymous (replacement) nucleotide substitutions show a significant heterogeneity in distribution for both the same gene in different lineages and different genes in the same lineage. These major fluctuations in replacement but not silent substitution rates cannot be attributed to changes in mutation rate, suggesting that changes in the rate of globin polypeptide evolution in primates is not governed solely by variable mutation rates.