Neural codes in early sensory areas maximize fitness

It has generally been presumed that sensory information encoded by a nervous system should be as accurate as its biological limitations allow. However, perhaps counter intuitively, accurate representations of sensory signals do not necessarily maximize the organism's chances of survival. To test this hypothesis, we developed a unified normative framework for fitness-maximizing encoding by combining theoretical insights from neuroscience, computer science, and economics. Initially, we applied predictions of this model to neural responses from large monopolar cells (LMCs) in the blowfly retina. We found that neural codes that maximize reward expectation---and not accurate sensory representations---account for retinal LMC activity. We also conducted experiments in humans and find that early sensory areas flexibly adopt neural codes that promote fitness maximization in a retinotopically-specific manner, which impacted decision behavior. Thus, our results provide evidence that fitness-maximizing rules imposed by the environment are applied at the earliest stages of sensory processing.

Escherichia coli robustly expresses ATP synthase at growth rate maximizing concentrations

Improved protein expression is an important evolutionary adaptation of bacteria. A key question is whether evolution has led to optimal protein expression that maximizes immediate growth rate (short-term fitness) across conditions. Alternatively, fitter genetic variants could display suboptimal short-term fitness, because they cannot do better or because they strive for long-term fitness maximization by, for instance, anticipating future conditions. To answer this question, we focus on the ATP-producing enzyme F1F0 H+-ATPase, which is an abundant enzyme and ubiquitously expressed across conditions. We tested the optimality of H+-ATPase expression in Escherichia coli across 27 different nutrient conditions. In all tested conditions, wild-type E. coli expresses its H+- ATPase remarkably close to optimal concentrations that maximize immediate growth rate. This work indicates that bacteria can achieve robust optimal protein expression for immediate growth- rate.

Applying evolutionary methods in economics: progress or pitfall?

The Darwinian theory of evolution has arguably become an important building block for experimental and theoretical economists. According to Burnham (J Econ Behav Org 90:S113–S127, 2013), it is possible to formulate novel hypotheses and predictions about human preferences, on the basis of what patterns of behavior would have been adaptive in the ancestral environment. After clarifying two theoretical concepts, the Adaptively Relevant Environment and fitness maximization, I argue that multiple scientifically plausible hypotheses about human preferences are compatible with evolutionary models that target behavior. Moreover, I propose a refinement of Burnham’s method based on theoretical resources provided by the indirect evolutionary approach. Economists apply or build evolutionary models of their own that target particular features of human psychology and cognition. Such models may reduce the number of plausible hypotheses to allow for rigorous scientific testing in laboratory or field experiments.

Wright’s Adaptive Landscape, Fisher’s Fundamental Theorem

Fitness maximization, or optimization, is a controversial idea in evolutionary biology. One classical formulation of this idea is that natural selection will tend to push a population up a peak in an adaptive landscape, as Sewall Wright first proposed. However, the hill-climbing property only obtains under particular conditions, and even then the ascent is not usually by the steepest route; this shows why it is misleading to assimilate the process of natural selection to a process of goal-directed choice. A different formulation of the idea of fitness-maximization is R. A. Fisher’s ‘fundamental theorem of natural selection’. However, the theorem points only to a weak sense in which selection is an optimizing process, for it requires that ‘environmental constancy’ be understood in a highly specific way. It does not vindicate the claim that natural selection has an intrinsic tendency to produce adaptation.

What a difference a day makes—female behaviour is less predictable near ovulation

‘Animal personalities’ have been shown to exist in many species. Yet, fluctuations in the stability of these inter-individual behavioural differences are not well understood. Against this background, we wondered whether behavioural consistency over time is affected by the reproductive cycle. Female mice were tested twice at an interval of eight weeks in four paradigms assessing social interest as well as anxiety-like behaviour and exploratory locomotion. Twenty-two individuals were tested repeatedly near ovulation, whereas another twenty-two were tested repeatedly in the non-receptive phase. While we found no major behavioural effects at the group level, the reproductive state indeed had profound effects on behavioural stability over time: social interest as well as anxiety-like behaviour proved to be significantly less predictable near ovulation. It is generally believed that phenotypic plasticity is limited due to the costs it brings about. In this context, our data indicate that females accept higher costs in phases directly related to fitness maximization.