A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distributions. An alternative view is that the brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property takes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that the inference strategies employed by humans may deviate from Bayes-optimal integration when the computational demands are high

Genome packaging in influenza A virus

The negative-sense RNA genome of influenza A virus is composed of eight segments, which encode 12 proteins between them. At the final stage of viral assembly, these genomic virion (v)RNAs are incorporated into the virion as it buds from the apical plasma membrane of the cell. Genome segmentation confers evolutionary advantages on the virus, but also poses a problem during virion assembly as at least one copy of each of the eight segments is required to produce a fully infectious virus particle. Historically, arguments have been presented in favour of a specific packaging mechanism that ensures incorporation of a full genome complement, as well as for an alternative model in which segments are chosen at random but packaged in sufficient numbers to ensure that a reasonable proportion of virions are viable. The question has seen a resurgence of interest in recent years leading to a consensus that the vast majority of virions contain no more than eight segments and that a specific mechanism does indeed function to select one copy of each vRNA. This review summarizes work leading to this conclusion. In addition, we describe recent progress in identifying the specific packaging signals and discuss likely mechanisms by which these RNA elements might operate

Rhythm quantization for transcription

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