Genetic experiments with animal learning: a critical review. Behavioral bioology 7 (1972)
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BibTeX
@MISC{Wahlsten72geneticexperiments,
author = {Douglas Wahlsten},
title = {Genetic experiments with animal learning: a critical review. Behavioral bioology 7},
year = {1972}
}
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Abstract
Abstract: The basic patterns of inheritance of learning ability in animals have been delineated. Summaries of strain differences in learning rate, responses to selective breeding for learning, heritabilities of learning phenotypes, and heterosis and overdominance are presented. In addition, the patterns of inheritance are shown to vary with the early environment. The causes of genetic differences have received much attention, but much of the research is inconclusive. Both general learning ability and task-specific abilities are important, but their relative importance is not known for most learning tasks. Strain differences have been found to vary widely in response to variations in stimulus parameters, motivational levels, temporal spacing of trials, and pharmacological manipulations. However, in only a few cases have strain differences in learning actually been shown to be attributable to differences in sensory capacities, motivation, memory or activity levels. The physiological bases for differences are totally unknown. The pathways of gene action on learning also await discovery. Although some researchers have claimed to study the adaptive value of learning, their exclusive utilization of laboratory populations precludes meaningful interpretation of their results. Several methodological shortcomings of various experiments are considered, and important areas for future research are suggested. Article: Learning is a phenotype which has engaged the interests of numerous researchers seeking genetic bases for behavioral differences. In fact, much of the earliest research identifiable as behavior genetics dealt with some aspect of learning in animals A central motive for compiling the present review is the author's opinion that the increase in genetic sophistication has not been paralleled by a similar growth in the sophistication of measures of learning. In many studies it appears that learning was selected as a phenotype of convenience and general interest. Similarly, the questions about learning investigated have tended to be simplistic and of little interest to those concerned with the nature of the learning process itself. Many recent studies have raised issues that were presented in the earliest research and therefore have contributed little to progress in the area. This is an unfortunate situation in view of the potential power of genetic techniques to answer important questions about learning. It is hoped that the organization of the present review around major questions in the area, instead of around techniques or species, will clarify some of the issues and indicate promising directions for future research. THE NULL HYPOTHESIS Logically, although not chronologically, the first issue to be raised is whether genes affect learning at all. To the student of animal behavior in 1971, it seems a little unbelievable that informed scientists ever seriously questioned the involvement of genotype in the learning process, given that genetic effects upon physical and chemical characteristics were so widely known. Nevertheless, this was a very lively issue until quite recently, and it spawned numerous experiments which purported to demonstrate that animals known to have different genotypes also had different scores on a particular learning task. Even today such experiments continue to be performed and subsequently are considered worthy of promulgation. Strain Comparisons The first step in examining genetic differences in learning is, of course, to obtain some animals which are known by other criteria to possess different genotypes. This is most easily done by procuring standardized strains which have been inbred for at least 20 generations, using brother-by-sister matings to ensure that less than 2% of the loci are likely to be unfixed. Similar comparisons of noninbred animals are also pertinent, although the various haphazard breeding schemes and diverse origins of the parent populations used to maintain the lines preclude the possibility of guaranteeing samples with uniform gene frequencies in successive generations or even different shipments from the same supplier and thereby prevent finer analyses of observed differences. Several strain comparisons of performance on learning tasks are summarized in Artificial Selection In a heterogeneous population composed of very many genotypes, virtually one per individual, artificial selection for high and low learning scores is a very strong test of genetic involvement in learning. All of the early selection studies used rats in the exceedingly complex mazes in vogue at the time, and they all had two purposes: to produce lines of rats with high and low error scores and to fix these lines for the loci relevant to learning by the process of inbreeding. The first goal was to show that genes affected learning ability, and the second was presumably to allow subsequent analyses of the genetic mechanisms involved. Since these early efforts, psychologists have become aware that the two goals of selection, high-and lowscoring genotypes and genetic fixation by inbreeding, are diametrically opposed. Selection operates on genetic variance, which is progressively reduced by inbreeding. This is not to say that no response to selection will occur if inbreeding is practiced, but the rate of divergence and the asymptotic separation of the two selected lines will certainly be reduced. In addition, inbreeding can lead to sterility of many matings and even loss of the selected lines altogether. Realizing this, Bignami (1965) selected for high and low scores on avoidance in a shuttlebox without using any full-sib pairs; he also tried, but lost, a line selected with concurrent inbreeding. A large response to selection was observed in the very first selected generation, and even larger separation of lines was obtained by the fifth generation. The parent population averaged 104.9 avoidances in 250 trials, while by F 5 the high line had a mean of 170.6 avoidances compared to 50.9 for the low line. No difficulties with sterility were reported for either of the lines. It is evident that success in selectively breeding for high and low learning rates in laboratory rats and mice is commonplace. Taken together with the numerous strain comparisons mentioned above as well as more sophisticated genetic experiments to be presented below, these results allow the null hypothesis that genotype does not affect learning to be firmly rejected for the populations studied. RELATIVE MAGNITUDE OF GENETIC VARIATION Once the statistical significance of learning differences between animals of the various genotypes has been firmly established, the question arises concerning the relative importance of genetic variation as a source of variation in learning ability. If large numbers of subjects from numerous strains must be tested to establish the validity of the phenomenon, then the importance of genetic variation is questionable. On the other hand, if a substantial portion of the total variation in learning scores within a population of animals can be traced to genetic origins, then students of learning must give serious attention to the genetic structure of their experimental populations. The question of relative importance can be stated quite simply: What proportion of the total variance in a learning phenotype in a population can be attributed to genetic differences among individuals? In the case of strain comparisons with a one-way analysis of variance design, this question can be answered by calculating the strength of effect (ω 2 ). In experiments involving breeding, it is customary to posit a linear model for genetic effects and then partition variances appropriately. If an individual's score or phenotype (P) is partitioned into components of genetic (G) and environmental (E) origin, and if G and E do not interact, then P = G + E, and the variances are such that Vp = V G + V E (see Strength of Effect The coefficient ω 2 estimates the proportion of total variance in an experiment which can be attributed to differences between strains. When highly inbred strains are employed, between-strain variation should reflect primarily genetic variation, while within-strain variation should represent differences in postfertilization environment as well as error in measuring the behavior itself. Several estimates of ω 2 for strain comparisons are presented in The wide range of estimated ω 2 values indicates that no simple statement can be made. It should be noted, however, that many values greater than 30% were obtained, which signifies a very substantial effect as judged by results from other areas of behavioral research. Coefficient of Genetic Determination Heritability Of the several methods available for calculating heritability (h 2 ), realized response to selection for learning appears to be the most efficient No researcher can obtain today a population known to have the same genetic properties as any of those previous ones, because the breeding schemes employed by most animal suppliers are generally haphazard and are certainly not uniform for different suppliers of the same outbred strains. Also, in all studies, except those of Other methods for estimating h 2 (see Although the proper interpretation of these measures of ω 2 , C.G.D., and h 2 is not readily apparent, some limitations on their generality are obvious. The inherent genetic variation of a population influences greatly the results, since reduction of V G through inbreeding or of V A through selection would lead to the observation of low h 2 . Similarly, environmental attributes can influence the V E component. Intuitively, rearing under uniform conditions is expected to yield the largest possible proportion of genetic variance, because V E should be small. However, recent evidence reported by Another factor must be the reliability of the learning measure itself. If the environmental component, "E," is partitioned into E due to pretesting environment and e from noise in the measuring instrument, it follows that Vp = V G + V E + V e . V e will be small for tests with high test-retest reliability (r tt ) or when many repeated measures on the same animals are administered. The data presented by Bovet, Bovet-Nitti, and Oliverio (1969, p. 140) show that individual scores in shuttle avoidance are very stable from day to day when 100 trials are administered; in turn they find large strain differences (ω 2 = .95, The magnitude of ω 2 and h 2 may also be influenced by the difficulty of the task employed. Wahlsten (1971) found that requiring mice to either run (one-way) or jump (jump-out) led to ω 2 values of .34 and .18, respectively, but that a smaller ω 2 of .11 resulted when each subject could either run or jump (optional) to escape or avoid shock (see Another important aspect of heritability is its relation to fitness and the adaptive value of learning ability. This topic will be discussed in another section of the paper. GENETIC CORRELATES OF LEARNING Observation of large genetic variation in learning rates leads directly to questions about the causal bases for these differences, as well as their generality to other kinds of learning. It is worthwhile to determine precisely what mechanisms or components of the learning process are modified in different gentotypes and thereby yield the observed phenotypic differences. If there exists a finite set of mechanisms that results in overt learning, are all of these mechanisms affected by genetic variation, or are certain components of the learning process more likely to be changed than others? Whenever a complex behavior such as learning is the object of study, many genes are expected to be involved in differences between genotypes. Although no one gene may be individually identifiable, it is possible to study relations between polygenic traits with the methods of quantitative genetics. While pleiotropic gene action at any one locus may not be demonstrable, the genetic correlation coefficient measures something analogous to pleiotropy. In order to accomplish this, it is necessary to perform a genetic experiment by crossing individuals that differ in genotype. If an experiment is correctly designed and executed, it is possible to partition the correlation between two phenotypes (r p ) into components attributable to genetic similarities (r g ) and environmental actions (r e ). Actually the more common practice is to partition between additive genetic similarities (r A ) and everything else ("r E "). Several methods have been employed to study the genetic correlates of learning. Since they are not equally useful, it is pertinent to discuss briefly their limitations at the outset. The simplest design applicable to this question entails the measurement of many other characteristics of strains of animals that are already known to differ on at least one learning task. More elegant experiments subject the strains to different experimental manipulations in order to determine whether all strains are affected equally or whether the original differences in learning are to be found under other conditions. However, the nature of gene fixation during inbreeding leads one to believe that the study of inbred strains alone can never reliably detect the causes of learning differences, regardless of the outcome of an experiment. Briefly stated, it is utterly impossible to determine whether two distinct behaviors observed in a single genotype (i.e., an inbred strain) are controlled by identical, overlapping, or entirely independent sets of genes by the sole method of statistical comparisons of several strains. Even if a significant and substantial correlation between two phenotypes occurred, it still could not be confidently stated that a causal genetic relation existed, for they might be similar for reasons other than common genetic mechanisms of action. They might be manifestations of common experience, if the measures come from the same animals. The simple operation of crossing inbred strains to obtain F 1 , F 2 , and backcross generations provides an abundance of information which cannot be obtained by any environmental manipulations of inbred strains alone. Paramount among these benefits is the possibility of examining correlations between several aspects of learning which were observed to covary among the parent strains. When the strains are crossed, the measures of learning or other behaviors in the F 1 and F2 generations may continue or cease to exhibit phenotypic correlations, depending on whether they are genetically related or independent, respectively. James (1941) seems to have been the first to employ this technique to study correlations. He observed correlations between body type and learning of leg-flexion avoidance and Pavlovian salivation training. The outcome of crossing two breeds was clear: In the two polar types ... there seems to be a definite correlation between bodily form and behavior. There is a harmonious relationship among the genetic factors for physical form, glandular conditions, and behavior. When the two polar types are bred together, however, this relation breaks up. A dog may inherit the bodily form of the basset hound, yet behave like the excitable shepherd dog under experimental conditions (p. 613). Whereas a strain study may detect concommitants to learning differences which really are quite unrelated to learning, a proper selection study in which a learning phenotype is the only selection criterion will lead to correlated changes in other phenotypes that are related to learning through the additive action of common genes. By employing large enough populations in the selected lines, spurious correlations resulting from random sampling or genetic drift may be reduced to a very small magnitude. Correlated responses to selection become especially informative in such an experiment because the ones most closely related genetically to the learning genotype should show the most rapid response to selection, while measures that are less closely related should exhibit correspondingly smaller changes. Thus, in principle, the selection experiment can be employed to derive empirically the additive or linear genetic correlates of learning ability. It must be mentioned that most of the above selection studies were not conducted in a manner that allowed computation of r A . Parent populations and selected lines tended to have few animals (see The most useful techniques for the study of genetic correlates entail the study of parents and offspring in a random-breeding population. They allow robust estimates of both r A and r E between phenotypes, and the accuracies of these estimates may be calculated easily. Generality of Learning Differences Since the interest of most researchers centers on learning ability in the broader sense rather than on performance changes during a single training procedure, it is important to determine whether strain differences with one task are also observable with other paradigms and motives. General learning ability in animals may be analogous to the concept of intelligence (g) in humans and in this respect is a measure which should transcend the specific requirements of any one task. Resolution of these seemingly divergent findings has been made possible by the recent work of One feature of the literature on strain variation in avoidance learning appeared to argue against any significant general learning ability. The problem was that some investigators observed certain strains, e.g., C3H or CBA, to learn very slowly, if at all Although the above experiments with inbred mice indicate the importance of general learning ability, research with other species has frequently revealed substantial strain-by-training procedure interactions. The learning abilities on diverse tasks of strains selected for learning rate on a single task are also of interest. More extensive tests have been performed with the descendants of Tryon's lines (Brights are S 1 , Dulls are S 3 ). Certainly, the most eminent study among these was by Thus, research with the Tryon strains has confirmed the findings of the many strain comparisons in that reversals in learning rates may occur when strains are tested on tasks having many differences. The existence of such interactions makes it imperative that the degree of genetic correlation between tasks be quantified as was done by Of course, learning rate is one thing, but a full-blown law of learning is quite something else. Strains could differ widely in acquisition rates on diverse tasks without necessarily invalidating learning principles. A principle can be studied only by experimental manipulation of several independent variables which are believed to influence learning and performance. Since most of the studies reviewed herein were relatively modest in their use of independent variables, it is clear that most researchers were not interested in this particular question. The more extensive experiments generally did not test anything resembling a law of learning. Hence, judgment must be suspended for lack of evidence. Lest there be a sudden upsurge in behavior-genetic analyses of learning principles, researchers should be aware of the current state of flux in the study of learning by the more traditional methods of psychology. Sensory Capacities and Preferences Among the various processes which are necessary to allow learning to be demonstrated, sensory input obviously occupies a position of primacy. Information must enter the brain before it can be evaluated and stored. Genotypes which lead to differential abilities to gather sensory data should differ in learning rates as a result. Research with strains homozygous for retinal degeneration (rd) has revealed that visual input is necessary for solving certain tasks but not for others. Strains such as C3H and CBA that have rodless retinas did very poorly on black-white discrimination (Wimer and Weller, 1969), pattern discrimination Albinism is no stranger to learning research. Wilcock's interpretation is supported by a recent experiment by Other interpretations of the causes of learning deficits resulting from homozygosity for the albino gene have not been convincing. The gene short-ear (se) has been shown to raise the hearing intensity threshold (Bundy, 1951). The above studies indicate that rd and c effects upon learning are indeed trivial when unintended. They leave entirely unexplored the extent of sensory differences between strains, both in terms of relative acuities within a sensory mode and in terms of preferences for one sensory mode over another. Of course, such tests of sensory acuity and preference are time-consuming and require sophisticated learning paradigms. Nonetheless, they could be edifying. Several reports have appeared of differences in sensory processes between the Tryon rats. Tryon (1940) carried out numerous experiments which showed that surgically disrupting the senses had little effect on the behavior of Brights. Motivation The relation between motivation and learning has a long history of theoretical dispute (see discussion by Kimble, 1961, Chap. 13). One central issue concerns the necessity of proper motivation to assure learning at all. Unfortunately, demonstrations of latent learning, sensory preconditioning, and transfer between drive states have not been attempted with genetic experiments.