How Adaptive Learning Affects Evolution: Reviewing Theory on the Baldwin Effect

We review models of the Baldwin effect, i.e., the hypothesis that adaptive learning (i.e., learning to improve fitness) accelerates genetic evolution of the phenotype. Numerous theoretical studies scrutinized the hypothesis that a non-evolving ability of adaptive learning accelerates evolution of genetically determined behavior. However, their results are conflicting in that some studies predict an accelerating effect of learning on evolution, whereas others show a decelerating effect. We begin by describing the arguments underlying the hypothesis on the Baldwin effect and identify the core argument: adaptive learning influences the rate of evolution because it changes relative fitness of phenotypes. Then we analyze the theoretical studies of the Baldwin effect with respect to their model of adaptive learning and discuss how their contrasting results can be explained from differences in (1) the ways in which the effect of adaptive learning on the phenotype is modeled, (2) the assumptions underlying the function used to quantify fitness and (3) the time scale at which the evolutionary rate is measured. We finish by reviewing the specific assumptions used by the theoretical studies of the Baldwin effect and discuss the evolutionary implications for cases where these assumptions do not hold.     

Parental sex discrimination and intralocus sexual conflict

Intralocus sexual conflict occurs when populations segregate for alleles with opposing fitness consequences in the two sexes. This form of selection is known to be capable of maintaining genetic and fitness variation in nature, the extent of which is sensitive to the underlying genetics. We present a one-locus model of a haploid maternal effect that has sexually antagonistic consequences for offspring. The evolutionary dynamics of these maternal effects are distinct from those of haploid direct effects under sexual antagonism because the relevant genes are expressed only in females. Despite this, we find the same opportunity for sexually antagonistic polymorphism at the maternal effect locus as at a direct effect locus. Thus, sexually antagonistic maternal effects may underlie some natural genetic variation. The model we present permits alternative interpretations of how the genes are expressed and how the fitness variation is assigned, which invites a theoretical comparison to models of both imprinted genes and sex allocation.     

Learning and colonization of new niches: a first step toward speciation

Learning processes potentially play a role in speciation but are often ignored in speciation models. Learning may, for instance, play a role when a new niche is being colonized, because the learning of niche features may cause niche-specific assortative mating and a tendency to produce young in this niche. Several animal species learn about their environmental features that may be important in finding or attracting mates. We use a gene-culture coevolutionary model to look into the effect of such learning on the colonization of new niches and on the genetic divergence between groups using different niches, which are steps necessary in achieving speciation. We assume that density is regulated separately in each of the two niches and that the viability of an individual depends on its genotype as well as on which niche it exploits. Our results show that genetic adaptation to the new niche is enhanced by a high female fecundity and a low viability selection against heterozygotes. Furthermore, when initial colonization (without genetic adaptation) fails, genetic divergence is more difficult when the mating preference is stronger. In contrast, when colonization without genetic adaptation is successful, a stronger mating preference makes genetic divergence easier. An increase in the number of egg-laying mistakes by females can have a positive or negative effect on the success of genetic adaptation depending on other parameters. We show that genetic divergence can be prevented by a niche shift, which can occur only if viabilities in the two niches are asymmetrical.     

Speciation: more likely through a genetic or through a learned habitat preference?

A problem in understanding sympatric speciation is establishing how reproductive isolation can arise when there is disruptive selection on an ecological trait. One of the solutions that has been proposed is that a habitat preference evolves, and that mates are chosen within the preferred habitat. We present a model where the habitat preference can evolve either by means of a genetic mechanism or by means of learning. Employing an adaptive-dynamical analysis, we show that evolution proceeds either to a single population of specialists with a genetic preference for their optimal habitat, or to a population of generalists without a habitat preference. The generalist population subsequently experiences disruptive selection. Learning promotes speciation because it increases the intensity of disruptive selection. An individual-based version of the model shows that, when loci are completely unlinked and learning confers little cost, the presence of disruptive selection most probably leads to speciation via the simultaneous evolution of a learned habitat preference. For high costs of learning, speciation is most likely to occur via the evolution of a genetic habitat preference. However, the latter only happens when the effect of mutations is large, or when there is linkage between genes coding for the different traits.     

Quantitative integration of genetic factors in the learning and production of canary song

Learned bird song is influenced by inherited predispositions. The canary is a model system for the interaction of genes and learning on behaviour, especially because some strains have undergone artificial selection for song. In this study, roller canaries (bred for low-pitched songs) and border canaries (whose song is higher pitched, similar to the wild-type) were interbred and backcrossed to produce 58 males that sorted into seven genetically distinct groups. All males were tutored with the same set of songs, which included both low- and high-pitched syllables. Individuals were consistent within genetic groups but differed between groups in the proportion of low- versus high-pitched syllables they learned and sang. Both sex-linked and autosomal factors affected song learning and song production, in an additive manner. Dominant Z-chromosome factors facilitated high-pitched syllable learning and production, whereas the sex-linked alleles associated with the switch to low-pitched syllables under artificial selection were largely recessive. With respect to autosomal effects, the most surprising result is that males in the same genetic group had almost identical repertoires. This result challenges two common preconceptions: that genetic changes at different loci lead to distinct phenotypic changes, and that genetic predispositions affect learning in simple and general ways. Rather, different combinations of genetic changes can be associated with the same phenotypic effect; and predispositions can be remarkably specific, such as a tendency to learn and sing one song element rather than another.     

Learning-induced changes in the neural circuits underlying motor sequence execution

As the old adage goes: practice makes perfect. Yet, the neural mechanisms by which rote repetition transforms a halting behavior into a fluid, effortless, and “automatic” action are not well understood. Here we consider the possibility that well-practiced motor sequences, which initially rely on higher-level decision-making circuits, become wholly specified in lower-level control circuits. We review studies informing this idea, discuss the constraints on such shift in control, and suggest approaches to pinpoint circuit-level changes associated with motor sequence learning.     

Con-A-induced suppressor activity of lymphocytes distinguished by the presence or absence of the Fc receptor.

In the present work we show that concanavalin A (Con A)-activated cells are competent to suppress polyclonal antibody responses induced by different polyclonal B-cell activators. Such an effect does not seem to be mediated by cytotoxic killer cells nor by overactivation, suggesting, therefore, that true suppressor cells are responsible for the phenomenon. The dose of Con A required for induction of these suppressor cells was found to exceed 2 μg/ml. Irradiation of the suppressor cell population abrogated their inhibitory capacity and no evidence of genetic restriction between effector and responding cells was found. Suppression affects cell proliferation, which suggests that, in our system, suppressor cells act directly on antibody-forming cell precursors by halting their proliferation and/or production of antibody.     

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization.     

Effects of inbreeding on aversive learning in Drosophila

Inbreeding adversely affects life history traits as well as various other fitness‐related traits, but its effect on cognitive traits remains largely unexplored, despite their importance to fitness of many animals under natural conditions. We studied the effects of inbreeding on aversive learning (avoidance of an odour previously associated with mechanical shock) in multiple inbred lines of Drosophila melanogaster derived from a natural population through up to 12 generations of sib mating. Whereas the strongly inbred lines after 12 generations of inbreeding (0.75 < F < 0.93) consistently showed reduced egg‐to‐adult viability (on average by 28%), the reduction in learning performance varied among assays (average = 18% reduction), being most pronounced for intermediate conditioning intensity. Furthermore, moderately inbred lines (F = 0.38) showed no detectable decline in learning performance, but still had reduced egg‐to‐adult viability, which indicates that overall inbreeding effects on learning are mild. Learning performance varied among strongly inbred lines, indicating the presence of segregating variance for learning in the base population. However, the learning performance of some inbred lines matched that of outbred flies, supporting the dominance rather than the overdominance model of inbreeding depression for this trait. Across the inbred lines, learning performance was positively correlated with the egg‐to‐adult viability. This positive genetic correlation contradicts a trade‐off observed in previous selection experiments and suggests that much of the genetic variation for learning is owing to pleiotropic effects of genes affecting functions related to survival. These results suggest that genetic variation that affects learning specifically (rather than pleiotropically through general physiological condition) is either low or mostly due to alleles with additive (semi‐dominant) effects.     

Reinforcement and sex linkage

We present a general model for the effect of sex linkage on the evolution of reinforcement of mating preferences on an island. We find that the level of reinforcement can vary up to 80% depending on the mode of inheritance of the female preference and male trait. When reinforcement is driven mainly by selection in the male trait and intrinsic hybrid incompatibilities are weak, sex-linked preferences and autosomal male traits are the most conducive to reinforcement, whereas autosomal preferences and X-linked traits are the least. Surprisingly, the effect of mode of inheritance on reinforcement is poorly predicted by its effect on the genetic correlation between the male trait and female preference. Sex-linkage of genetic incompatibility loci increases reinforcement, though this is not due solely to the occurrence of Haldane's rule. We find that reinforcement can lead to complete reproductive isolation in some cases but not others and that the mode of inheritance can determine which outcome occurs.     

From cells to computers: computing with membranes (P systems)

The aim of this paper is to introduce to the reader the main ideas of computing with membranes, a recent branch of (theoretical) molecular computing. In short, in a cell-like system, multisets of objects evolve according to given rules in the compartments defined by a membrane structure and compute natural numbers as the result of halting sequences of transitions. The model is parallel, nondeterministic. Many variants have already been considered and many problems about them were investigated. We present here some of these variants, focusing on two central classes of results: (1) characterizations of the recursively enumerable sets of numbers and (2) possibilities to solve NP-complete problems in polynomial--even linear--time (of course, by making use of an exponential space). The results are given without proofs. An almost complete bibliography of the domain, at the middle of October 2000, is also provided.     

The evolutionary language game: an orthogonal approach

Evolutionary game dynamics have been proposed as a mathematical framework for the cultural evolution of language and more specifically the evolution of vocabulary. This article discusses a model that is mutually exclusive in its underlying principals with some previously suggested models. The model describes how individuals in a population culturally acquire a vocabulary by actively participating in the acquisition process instead of passively observing and communicate through peer-to-peer interactions instead of vertical parent-offspring relations. Concretely, a notion of social/cultural learning called the naming game is first abstracted using learning theory. This abstraction defines the required cultural transmission mechanism for an evolutionary process. Second, the derived transmission system is expressed in terms of the well-known selection-mutation model defined in the context of evolutionary dynamics. In this way, the analogy between social learning and evolution at the level of meaning-word associations is made explicit. Although only horizontal and oblique transmission structures will be considered, extensions to vertical structures over different genetic generations can easily be incorporated. We provide a number of simplified experiments to clarify our reasoning.     

How placebo responses are formed: a learning perspective

Despite growing scientific interest in the placebo effect and increasing understanding of neurobiological mechanisms, theoretical conceptualization of the placebo effect remains poorly developed. Substantial mechanistic research on this phenomenon has proceeded with little guidance by any systematic theoretical paradigm. This review seeks to present a theoretical perspective on the formation of placebo responses. We focus on information processing, and argue that different kinds of learning along with individuals' genetic make-up evolved as the proximate cause for triggering behavioural and neural mechanisms that enable the formation of individual expectations and placebo responses. Conceptualizing the placebo effect in terms of learning offers the opportunity for facilitating scientific investigation with a significant impact on medical care.     

Recombination and the evolution of coordinated phenotypic expression in a frequency-dependent game

A long standing question in evolutionary biology concerns the maintenance of adaptive combinations of traits in the presence of recombination. This problem may be solved if positive epistasis selects for reducing the rate of recombination between such traits, but this requires sufficiently strong epistasis. Here we use a model that we developed previously to analyze a frequency-dependent strategy game in asexual populations, to study how adaptive combinations of traits may be maintained in the presence of recombination when epistasis is too weak to select for genetic linkage. Previously, in the asexual case, our model demonstrated the evolution of adaptive associations between social foraging strategies and learning rules. We verify that these adaptive associations, which are represented by different two-locus haplotypes, can easily be broken by genetic recombination. We also confirm that a modifier allele that reduces the rate of recombination fails to evolve (due to weak epistasis). However, we find that under the same conditions of weak epistasis, there is an alternative mechanism that allows an association between traits to evolve. This is based on a genetic switch that responds to the presence of one social foraging allele by activating one of the two alternative learning alleles that are carried by all individuals. We suggest that such coordinated phenotypic expression by genetic switches offers a general and robust mechanism for the evolution of adaptive combinations of traits in the presence of recombination.     

Autosomal Modifiers of the Bobbed Phenotype Are a Major Component of the Rdna Magnification Paradox in DROSOPHILA MELANOGASTER

rDNA magnification in Drosophila melanogaster is defined experimentally as the ability of bb/Ybb- males to produce exceptional progeny that are wild type with respect to rDNA associated phenotypes. Here, we show that some of these bobbed-plus progeny result not from genetic reversion at the bb locus but rather from variants at two or more autosomal loci that ameliorate the bobbed phenotype of rDNA deficient males in Drosophila. In doing so we resolve several aspects of a long-standing paradox concerning the phenomenon of rDNA magnification. This problem arose from the use of two genetic assays, which were presumed to be identical, but paradoxically, produced conflicting data on both the kinetics of reversion and the stability of magnified bb+ chromosomes. We resolve this problem by demonstrating that in one assay bobbed-plus progeny arise primarily by genetic reversion at the bobbed locus, whereas in the other assay bobbed-plus progeny arise both by reversion and by an epistatic effect of autosomal modifiers on the bobbed phenotype. We further show that such modifiers can facilitate the appearance of phenotypically bobbed-plus progeny even under conditions where genetic reversion is blocked by magnification defective mutants. Finally, we present a speculative model relating the action of these modifiers to the large increases in rDNA content observed in males undergoing magnification.     

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

The term “effect” in additive genetic effect suggests a causal meaning. However, inferences of such quantities for selection purposes are typically viewed and conducted as a prediction task. Predictive ability as tested by cross-validation is currently the most acceptable criterion for comparing models and evaluating new methodologies. Nevertheless, it does not directly indicate if predictors reflect causal effects. Such evaluations would require causal inference methods that are not typical in genomic prediction for selection. This suggests that the usual approach to infer genetic effects contradicts the label of the quantity inferred. Here we investigate if genomic predictors for selection should be treated as standard predictors or if they must reflect a causal effect to be useful, requiring causal inference methods. Conducting the analysis as a prediction or as a causal inference task affects, for example, how covariates of the regression model are chosen, which may heavily affect the magnitude of genomic predictors and therefore selection decisions. We demonstrate that selection requires learning causal genetic effects. However, genomic predictors from some models might capture noncausal signal, providing good predictive ability but poorly representing true genetic effects. Simulated examples are used to show that aiming for predictive ability may lead to poor modeling decisions, while causal inference approaches may guide the construction of regression models that better infer the target genetic effect even when they underperform in cross-validation tests. In conclusion, genomic selection models should be constructed to aim primarily for identifiability of causal genetic effects, not for predictive ability.     

A model for the emergence of adaptive subsystems

We investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have ‘fixed’ and ‘flexible’ alleles. The former express themselves as synaptic connections that remain unchanged during ontogeny and the latter as synapses that can be adjusted through a learning algorithm. Evolution is modelled using genetic algorithms and the changing environment is represented by two optimal synaptic patterns that alternate a fixed number of times during the ‘life’ of the individuals. The amplitude of the change is related to the Hamming distance between the two optimal patterns and the rate of change to the frequency with which both exchange roles. This model is an extension of that of Hinton and Nowlan in which the fitness is given by a probabilistic measure of the Hamming distance to the optimum. We find that two types of evolutionary pathways are possible depending upon how difficult (costly) it is to cope with the changes of the environment. In one case the population loses the learning ability, and the individuals inherit fixed synapses that are optimal in only one of the environmental states. In the other case a flexible subsystem emerges that allows the individuals to adapt to the changes of the environment. The model helps us to understand how an adaptive subsystem can emerge as the result of the tradeoff between the exploitation of a congenital structure and the exploration of the adaptive capabilities practised by learning.     

A new interpretation of thalamocortical circuitry

Almost all the information that is needed to specify thalamocortical and neocortical wiring derives from patterned electrical activity induced by the environment. Wiring accuracy must be limited by the anatomical specificity of the cascade of events triggered by neural activity and culminating in synaptogenesis. We present a simple model of learning in the presence of plasticity errors. One way to achieve learning specificity is to build better synapses. We discuss an alternative, circuit-based, approach that only allows plasticity at connections that support highly selective correlations. This circuit resembles some of the more puzzling aspects of thalamocorticothalamic circuitry.     

Self-organized criticality and emergent oscillations in models of termite architecture with crowding

The termite architecture model of O'Toole et'al. (1999) is extended to incorporate arbitrary halting time-scales. It is shown that this also means that the assumption of synchronous building must be relaxed. Numerical simulations show that ordered nest architecture emerges under a wide range of time-scales but also that there is an optimal region of halting times. This optimal region is explained by the emergence of synchronized periods of termite activity. The correlation length of the building distribution is shown to diverge providing strong evidence that the model is self-organized critical.