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Signs of a Flynn effect in rodents? Secular differentiation of the manifold of general cognitive ability in laboratory mice (Mus musculus) and Norwegian rats (Rattus norvegicus) over a century—Results from two cross-temporal meta-analyses
Abstract
Substantial improvements in factors such as microbiological quality have been noted in laboratory rodent (mouse [Mus musculus] and rat [Rattus norvegicus]) populations over the last 140 years, since domestication of laboratory strains started. These environmental improvements may have caused Flynn effect-like cognitive changes to occur in these populations, perhaps if these improvements enhanced cognitive plasticity and, consequently, learning potential. While lack of relevant data precludes cross-temporal comparison of cognitive performance means of laboratory rodent populations, it is possible to estimate changes in the proportion of cognitive performance variance attributable to general cognitive ability (GCA) over time. This “differentiation effect” has been found to occur along with the Flynn effect in human populations, suggesting that environmental factors, possibly mediated by their effects on life history speed, may weaken the manifold of GCA across time, allowing for greater cultivation of specialized abilities. Meta-analysis of the literature on mouse and rat cognition yielded 25 mouse studies from which 28 GCA effect sizes could be estimated, and 10 rat studies from which 11 effect sizes could be estimated. Cross-temporal meta-analysis yielded evidence of significant “differentiation effects” spanning approximately a century in both mice and rats, which were independent of age, sex, factor estimation technique, and task number in the case of the mice, and both factor estimation technique and task number in the case of the rats. These trends were also independent of the random effect of strain in both cases. While this is suggestive of the presence of the Flynn effect in captive populations of non-human animals, there are still factors that might be confounding these results. This meta-analysis should be followed up with experimental investigation.
... Cross-culturally, shifts in cognitive intelligence (as measured by IQ) have been observed since 1932 (Rundquist 1936), through a phenomenon that was later dubbed the Flynn effect in tribute to the researcher who introduced the concept more than a generation ago (Flynn 1984(Flynn , 1987(Flynn , 2007(Flynn , 2012. These changes in population-wide intelligence range in their magnitude depending on the country (gains in some nations average 7 or 8 points per decade), with the United States reporting a growth of 3 IQ points every ten years, a pattern that has been constant for about a century (Flynn 1984(Flynn , 1987(Flynn , 2007Pietschnig and Voracek 2015;Trahan et al. 2014), with the effect potentially observed in animal populations as well (Woodley et al. 2022). Simply put, the Flynn effect suggests that younger generations score higher on IQ tests than those prior (Dworak et al. 2023). ...
The recent release of the WAIS-5, a decade and a half after its predecessor, the WAIS-IV, raises immediate questions about the Flynn effect (FE). Does the traditional FE of points per decade in the U.S. for children and adults, identified for the Full Scale IQs of all Wechsler scales and for other global IQ scores as well, persist into the 2020s? The WAIS-5 Technical and Interpretive Manual provides two counterbalanced validity studies that address the Flynn effect directly—N = 186 adolescents and adults (16–90 years, mean age = 47.8) tested on the WAIS-IV and WAIS-5; and N = 98 16-year-olds tested on the WISC-V and WAIS-5. The FE is incorporated into the diagnostic criteria for intellectual disabilities by the American Association on Intellectual and Developmental Disabilities (AAIDD), by DSM-5-TR, and in capital punishment cases. The unexpected result of the two counterbalanced studies was a reduction in the Flynn effect from the expected value of 3 IQ points to 1.2 points. These findings raise interesting questions regarding whether the three point adjustment to FSIQs should be continued for intellectual disability diagnosis and whether the federal courts should rethink its guidelines for capital punishment cases and other instances of high stakes decision-making. Limitations include a lack of generalization to children, the impact of the practice effects, and a small sample size.
This chapter reviews the literature on processing speed, documenting the relationship between senescence and slowness. By nature, processing speed is networked. Most often, its measurement relies on sensory input, which is then “processed” by being reacted to in some way, and then that processing has to be demonstrated somehow, whether by writing or speaking or reacting. For example, when completing coding tasks, which are copying tasks used in general intelligence testing to measure processing speed, examinees look to a key at the top of the page to see the symbol associated with a certain number. Each number has its own symbol. They then reference that key when copying missing symbols in the space below under their corresponding number. This requires visual input perceived by the eye’s photoreceptors to travel down the length of the optic nerve, cross at the optic chiasm along its way toward the occipital lobe located in the back of the brain where it will be consciously processed. Moreover, performance is then reliant on white matter tracts traversing the parietal and temporal lobes and “the left middle frontal gyrus” (Turken et al., 2008). And this says nothing yet about the motor capacity requisite to record a response. Thus, this chapter on processing showcases the importance of myelinated tracts in integrating disparate regions and the consequences of their age-related degradation. This chapter also reviews some of the history of processing speed testing as an elementary cognitive task with a relationship to general intelligence. With this history and background, it is hoped that readers will understand the twofold importance of processing: There is first the straightforward need to rapidly take in vast quantities of information, making corresponding judgments in real time. Beyond this, and what makes processing and reaction rate ever more important, is its correlation to general intelligence.
This chapter considers the demands of the presidency alongside those of the legislative and judicial branch, highlighting the trade-off between deliberation and decision. The judicial branch is comprised of nine justices that can contemplate constitutionality, document their reasoning, and dissent from one another, while the legislative branch contains the Senate and House of Representatives, two large, deliberative bodies divided by party and constituencies. By contrast, there is one Chief Executive. During America’s constitutional convention, this arrangement prevailed against alternative models with more than one executive, and, in doing so, followed traditions of having a single monarch on the throne and a supreme general in the field. Implicitly, and sometimes explicitly justified, wisdom seems to dictate that executive responsibilities are best vested in a single person even at the sacrifice of aggregate wisdom. External debate unfolds slowly, whereas internal deliberation can happen almost instantaneously. Even assuming agreement, articulating ideas among nine, one hundred, or four hundred and thirty-five individual minds is cumbersome. Rapidity creates efficiency. In peace, cognitive efficiency allows executives to judiciously dispatch innumerable decisions; in war, cognitive efficiency allows executives to rapidly act on tactics, strategy, and geopolitical considerations. But if a brain is to deliberate and act efficiently, it must be robust, both in its components and connections. As prior chapters have shown, age brings general atrophy represented most basically by declining brain mass. At the same time, white matter tracts, responsible for integrating disparate brain regions that can be inches away within or across the brain’s hemispheres, decline in terms of integrity and efficiency. To the extent that age brings white matter degradation to the connectome, the efficiency of a single brain is diminished. The aged executive retains the potential for myopia, hubris, dogma, vice, and other liabilities that exist inside men’s minds unchecked by the perspective of others in counsel but loses its compensatory speed and efficiency.
Attention, memory, and processing speed have thus far been shown to decline with age. That general intelligence also declines with age is a foregone conclusion, simply based on its strong correlations with attention, memory, and processing speed. Nevertheless, in the preceding chapter, we used psychometric theory, intelligence test data, and comparisons to nonhuman animals to establish connections between what may otherwise be perceived as unrelated abilities. The present chapter continues to develop two important points, both introduced in the prior chapter, but neither of which were sufficiently elaborated. First, we review a representative sample of decline within neurobiological systems supporting general intelligence. We show, in more detail than before, that the brain’s systems are senescing—its circulatory system, its axonal connections, and its neurochemical supports. Seeing the broad aging of these systems equally explains the global nature of intelligence and the corresponding global nature of its age-related decline. Second, this chapter reviews cross-sectional and longitudinal data, further documenting age-related intellectual decline and additionally showing that age-related cognitive decline, rather than being particular to select cognitive functions, is global decline in general intelligence.
Born in 1767, John Quincy Adams served his single term in his early fifties. Rather than retiring, Adams spent his remaining years in the House of Representatives, from 1831 to his death in 1848. The opposite trajectory is increasingly common, with presidential office holding coming in one’s twilight years. Nevertheless, the decade between 50 and 60 is likely the ideal decade to serve, as it witnesses the sufficient increase in crystallized knowledge before fall off in fluid intelligence becomes pronounced. Half of all presidents have been within this decade at the time of inauguration. The septuagenarian president is effectively a modern innovation. Harrison was elected at 68 but died just months into his first term. Beyond that, the past has only a handful of presidents that were elected in their sixties but served into their seventies. Only as of late are we electing seventy-year-old presidents. Reagan can be included, as he was just weeks away from seventy upon beginning his first term and effectively spent the whole of his service in his seventies. Trump and Biden were both seventy upon inauguration. The founders placed a minimum age for the presidential office, barring those under thirty-five. Ever wise, this rule was meant to prevent youthful inexperience, and perhaps also brash impulsivity, from gaining presidential powers. The opposite danger was not as prominent or pertinent, though it is increasing. Even as we review a range of articles advocating for a constitutional amendment, we neither endorse this nor any other point of policy.
As the brain ages, so do memory functions. However, there are myriad forms of memory, not all of which are affected equally by age. Declarative memory, the memory of explicit semantic facts, is perhaps most resistant to aging. We can thereby turn to older individuals, relying on their ability to access a lifetime of learning in their counsels. Nevertheless, preserved declarative memory distracts from declining networked memory. By networked memory, we mean working memory and memory formation, which defy notions of massive modularity and instead rely more obviously on neural networks. These neural networks composed of white matter tracts are disproportionally affected by aging. For example, working memory is a complex form of processing memory akin to a computer’s random-access memory; it is not located in a single region but instead invokes both frontal and posterior brain regions that must interface through a network. Not incidentally, working memory is, relative to other forms of memory, highly g-loaded, meaning that it is positively correlated with general intelligence. The same is true of processing speed and executive functioning, and seemingly this is so because all are reliant on network efficiency which is in turn reliant on white matter integrity. These are effectively all higher-level cognitive processes, and all show a precipitous age-related arc of decline.
As this chapter’s title states, we review literature on three related forms of cognition: arousal, attention, and executive functioning. These can be understood hierarchically. Physiological arousal, or alertness, is a basic precondition for attention, while attention is a precondition for executive functioning, which entails the allocation and deployment of attention. More than simple arousal, which is a requirement of any productive activity, and attention, which is a requirement of any cognitive demanding activity, we focus on executive functioning because executive functioning is specifically important to managerial and executive responsibilities. Arousal and attention are necessary for an employee to accomplish a task, but high-level executive functioning is necessary for a manager to place employees in congruent roles, monitor performance, switch course based on outcomes, decide where to allocate resources, and outline goals. We review psychological and neurological data marking age-induced decline across these three interrelated cognitive abilities.
Three consecutive chapters considered three cognitive functions. In each instance, abilities were paired with the underlying neurobiological evidence of decline. From our separate treatment of attention, memory, and processing, the unified nature of this decline is not obvious. For the sake of illustration and in the face of complexity, we have thus far followed the modularity thesis, a common approach to viewing the brain as a conglomeration of compartmentalized regions with distinctly identifiable functions. However, even where individual abilities are directly supported by discrete brain regions, disaggregation is difficult. Any complex brain function is reliant on modules being networked through white matter pathways. Beyond this, complex reasoning, executive functioning, and working memory more fully defy modularity, arising from emergent properties of integrated networks. Supporting these networked modules and emergent abilities is an extraordinarily elaborate complexity of circulatory architecture, neuroendocrine chemicals, and axonal projections. To better appreciate this more unified understanding of cognitive decline, we present literature on general intelligence, first showing its psychometric unity and next showing its global decline. By the chapter’s end, one can move from seeing the brain as comprised of compartmentalized islands of ability to seeing a tight integration among brain regions as giving rise to a more unified general intelligence, which then declines along with its supporting neurobiological networks.
Waning attention, executive functioning, memory, and processing speed are merely representatives of general fluid intelligence, the decline of which coincides with neuroendocrine, cerebrovascular, and connectome senescence. Before turning to the consequences of cognitive aging on executive competence, we provide this final chapter within this second part to the end of explaining why cognitive aging begins so early in the human lifespan. This chapter broadly explains why fluid intelligence declines alongside many physical fitness indicators, from respiratory function and cardiac efficiency to strength and speed, and many fertility indicators, from menopause and erectile dysfunction to testosterone and estrogen levels. After drawing parallels between fluid intelligence, physical fitness, and fertility, we conclude with a summary statement showing cognitive decline to be part of a generalized decline in all bodily systems, explicable in the light of life history theory, antagonistic pleiotropy, the disposable soma hypothesis, and other biological theories of senescence.
Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N ∼ 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery—Revised (WJ–R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion “accelerated” data (e.g., J. J. McArdle & R. Q. Bell, 2000; J. J. McArdle & R. W. Woodcock, 1997). Nonlinear mixed-effects growth models based on a dual exponential rate yield a reasonable fit to all life span cognitive data. These results suggest that most broad cognitive functions fit a generalized curve that rises and falls. Novel multilevel models directly comparing growth curves show that broad fluid reasoning (Gf) and acculturated crystallized knowledge (Gc) have different growth patterns. In all comparisons, any model of cognitive age changes with only a single g factor yields an overly simplistic view of growth and change over age.
An open question in comparative psychology is whether the source of correlations among different measures of ability (the g factor) is shared between species, or is distinct. This is examined using data on the performance of three monkey species (tufted capuchins, black-handed spider monkeys, and long-tailed macaques) on 16 cognitive ability measures. The differences between species pairs across measures are not generally strongly related to the degree to which the g factor loads on each subtest. Iteratively removing the subtests with the lowest coefficients of variance (CV), and recorrelating the species differences with subtest g-loadings was found to increase the association between the two. Across iterations, subtest pooled CV strongly and positively predicts the increase in the degree to which g-loadings are predictive of species difference in two comparisons, but is a weaker predictor in the comparison between tufted capuchins and black-handed spider monkeys. These associations were not related to phylogenetic distance but were very strongly related to species differences in the means of certain ecological factors. g-scores computed for each species on the basis of the three subtests with the highest CV values exhibited very-high magnitude (>.9) associations with species-level G-scores. G is simply the species-level equivalent of g, resulting from covariation among cognitive ability measures at the level of species differences. Finally, as with previous studies, subtests that show the greatest species differences seem to draw on executive functioning, and attention, suggesting that these may be a phylogenetically conserved source of g across many animal taxa. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Cognition is vital for carrying out behaviours required for survival and reproduction. Cognitive performance varies between species, but also between individuals within populations. While variation is a prerequisite for natural selection, selection does not act on traits in isolation. The extent to which cognitive traits covary with other aspects of phenotype (e.g. personality traits) may be important in shaping evolutionary dynamics. Here we adopted a multivariate approach to test spatial learning in male Poecilia reticulata and asked whether differences in cognitive performance are associated with (repeatable) differences in stress response behaviour. Functional links between cognitive traits and ‘stress coping style’ have been hypothesized. Furthermore, individual level studies of cognitive performance typically rely on multiple testing paradigms that may themselves be a stressor. There is a risk that variation in stress responsiveness is itself a cause of apparent, but artefactual variance in cognitive ability. Fish repeatedly experienced two spatial learning tasks (maze layouts) and an acute stress response test (open field trial). We found repeatable differences between individuals in performance within and across maze layouts. On average, performance improved with experience in the first maze, consistent with spatial learning, but not in the second. Individuals varied in the trajectory of mean performance with trial number in both mazes, suggesting they differ in ‘learning rate’. Acute stress response behaviour was repeatable but predicted neither average time to solve the maze nor learning rate. We found no support for between-individual correlation between acute stress response and cognitive performance. However, we highlight the possibility that cumulative, chronic stress effects may nevertheless cause declines in performance for some individuals (leading to lack of improvement in mean time to solve the second maze). If so, this may represent a pervasive but difficult challenge for our ability to robustly estimate learning rates in studies of animal cognition.
This paper discusses the co-occurrence model and its associated research program, and it argues that the model provides the best supported theory of secular changes in cognitive ability. The co-occurrence model offers a better solution to Cattell's paradox (relative to the alternatives in the literature), and it is able to accommodate Flynn's four major paradoxes also. A review of empirical work conducted in order to test the model's predictions demonstrates that many populations in which selection favors lower intelligence have experienced a decline in g or some cognitive ability variable that correlates with g, at the same time that average phenotypic IQ has increased. Moreover, since the co-occurrence model makes predictions about variables that are not directly concerned with cognitive ability testing, its research program can be extended to other domains of research.
Objective: In the framework of a larger project aiming to test putative cognitive enhancer drugs in a system with improved translational validity, we established a rodent test battery, where different, clinically relevant cognitive domains were investigated in the same animal population. The aim of the current study was to check whether performances in the different tasks representing different cognitive functions are assay-specific or may originate in an underlying general learning ability factor. Methods: In the experiments 36 Long-Evans and 36 Lister Hooded rats were used. The test battery covered the following cognitive domains: attention and impulsivity (measured in the 5-choice serial reaction time task), spatial memory (Morris water-maze), social cognition (cooperation task), cognitive flexibility (attentional set shifting test), recognition memory (novel object recognition) and episodic memory (water-maze based assay). The outcome variables were analyzed by correlation analysis and principal component analysis (PCA). The datasets consisted of variables measuring learning speed and performance in the paradigms. From the raw variables composite variables were created for each assay, then from these variables a composite score was calculated describing the overall performance of each individual in the test battery. Results: Correlations were only found among the raw variables characterizing the same assay but not among variables belonging to different tests or among the composite variables. The PCAs did not reduce the dimensionality of the raw or composite datasets. Graphical analysis showed variable performance of the animals in the applied tests. Conclusions: The results suggests the assay outcomes (learning performance) in the system are based on independent cognitive domains.
Problem solving is important for survival, allowing animals to access novel food resources or escape from predators. It was originally thought to rely on an animal’s intelligence; however, studies examining the relationship between individual cognitive ability and problem solving performance show mixed results, and studies are often restricted to only one cognitive and one problem solving task. We investigated the relationship between general cognitive ability and problem solving across multiple tasks in the fawn-footed mosaic-tailed rat Melomys cervinipes. We measured general cognitive ability across different domains (memory in an odour learning association task, recognition in a novel object recognition task, size discrimination using different sized pieces of food, and learning across multiple presentations of a food-baited activity board). We also measured problem solving across different contexts (food-baited puzzle boxes in home cage, obstruction task, and food-baited activity board in a novel arena). Mosaic-tailed rats showed a general cognitive ability, with average problem solving latency, memory ability, and learning in the tile task being correlated. As such, individuals that were able to remember an association and learned to solve the tile task solved the problems faster than individuals that could not remember or learn. Our results suggest that problem solving in mosaic-tailed rats likely relies on some forms of simple cognition, particularly memory, but could also depend on other traits, such as an individual’s persistence.
Simple Summary
When working with laboratory animals, scientists need to ensure that animals are not suffering from natural infections. Therefore, hygienic standards have been developed over the last 100+ years. The key element of hygienic standardization is the monitoring of infectious agents that can compromise the animal’s health, are dangerous for the personnel or interfere with the research. However, scientists became aware that by eliminating such unwanted infectious agents the overall diversity of all microbes in research animals, the so-called microbiome, has also been reduced. Moreover, it became clear that the microbiome composition has an enormous impact on how research models react, e.g., to treatments. This might hinder the translation of findings in preclinical research to the clinical situation. Therefore, new concepts in hygienic standardization need to be developed that take animal welfare as well as scientific value into account. In this review, we will give an overview of how classical monitoring has been evolved and demonstrate concepts regarding how to handle the microbiome as a yet unknown variable in animal-based research in order to enhance the validity of research findings.
Abstract
The health monitoring of laboratory rodents is essential for ensuring animal health and standardization in biomedical research. Progress in housing, gnotobiotic derivation, and hygienic monitoring programs led to enormous improvement of the microbiological quality of laboratory animals. While traditional health monitoring and pathogen detection methods still serve as powerful tools for the diagnostics of common animal diseases, molecular methods develop rapidly and not only improve test sensitivities but also allow high throughput analyses of various sample types. Concurrently, to the progress in pathogen detection and elimination, the research community becomes increasingly aware of the striking influence of microbiome compositions in laboratory animals, affecting disease phenotypes and the scientific value of research data. As repeated re-derivation cycles and strict barrier husbandry of laboratory rodents resulted in a limited diversity of the animals’ gut microbiome, future monitoring approaches will have to reform—aiming at enhancing the validity of animal experiments. This review will recapitulate common health monitoring concepts and, moreover, outline strategies and measures on coping with microbiome variation in order to increase reproducibility, replicability and generalizability.
The study of intelligence in humans has been ongoing for over 100 years, including the underlying structure, predictive validity, related cognitive measures, and source of differences. One of the key findings in intelligence research is the uniform positive correlations among cognitive tasks. This has been replicated with every cognitive test battery in humans. Nevertheless, many other aspects of intelligence research have revealed contradictory lines of evidence. Recently, cognitive test batteries have been developed for animals to examine similarities to humans in cognitive structure. The results are inconsistent, but there is evidence for some similarities. This article reviews the way intelligence and related cognitive abilities are assessed in humans and animals and suggests a different way of devising test batteries for maximizing between-species comparisons.
General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (r = 0.185; 95% CI: 0.087–0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.
Although genetically heterogeneous laboratory mice express individual differences in general cognitive ability (c.f., “intelligence”), it is unknown whether these differences are translated into behaviors that would promote survival. Here, genetically heterogeneous laboratory CD-1 mice were administered a series of cognitive tests from which their aggregate general cognitive ability was estimated. Subsequently, all animals were tested on nine (unlearned) tasks designed to assess behaviors that could contribute to survival in the wild. These tests included nest building (in the home and a novel environment), exploration, several indices of food finding, retrieval, and preference, and predator avoidance. Like general cognitive ability, a principal component analysis of these measures of survival-related behaviors (survival-readiness) yielded a general factor that accounted for ∼25% of the variance of mice across all of the tasks. An aggregate metric of general cognitive ability predicted an aggregate metric of general survival-readiness (r = 0.64), suggesting that more intelligent animals would be more suited for survival in natural environments. The nature of the pattern of correlations between general cognitive ability and performance on individual tests of survival-readiness (where tests conducted in previously unexplored contexts were more closely related to general cognitive ability) suggests the possibility that heightened attention (which is taxed in a novel environment) may be the common mediator of both of these classes of abilities, although other potential mediators are discussed. In total, these results suggest that performance on tasks that are explicitly intended to assess the likelihood of survival can be impacted by cognitive abilities.
Spearman’s g is the name for the shared variance across a set of intercorrelating cognitive tasks. For some—but not all—theorists, g is defined as general intelligence. While g is robustly observed in Western populations, it is questionable whether g is manifested in cognitive data from other cultural groups. To test whether g is a cross-cultural phenomenon, we searched for correlation matrices or data files containing cognitive variables collected from individuals in non-Western, nonindustrialized nations. We subjected these data to exploratory factor analysis (EFA) using promax rotation and 2 modern methods of selecting the number of factors. Samples that produced more than 1 factor were then subjected to a second-order EFA using the same procedures and a Schmid-Leiman solution. Across 97 samples from 31 countries totaling 52,340 individuals, we found that a single factor emerged unambiguously from 71 samples (73.2%) and that 23 of the remaining 26 samples (88.5%) produced a single second-order factor. The first factor in the initial EFA explained an average of 45.9% of observed variable variance (SD = 12.9%), which is similar to what is seen in Western samples. One sample that produced multiple second-order factors only did so with 1 method of selecting the number of factors in the initial EFA; the alternate method of selecting the number of factors produced a single higher-order factor. Factor extraction in a higher-order EFA was not possible in 2 samples. These results show that g appears in many cultures and is likely a universal phenomenon in humans.
We used contemporary psychometric theory of intelligence and confirmatory factor analysis to reanalyze data obtained on samples of nonhuman primates administered the Primate Cognition Test Battery. Our main goals were to interpret stability of the Primate Cognition Test Battery tasks and factors over time and to determine whether the cognitive factors that emerge from confirmatory factor analysis for apes can be interpreted from the perspective of a major theory of human intelligence, namely, the Cattell–Horn–Carroll model. We also analyzed data for 2½-year-old children on Wechsler’s preschool test to afford a comparison between ape and child cognitive factors. Results indicated that multiple cognitive abilities provide the best factor solutions for both apes and children, and that the ape factors can be meaningfully interpreted from Cattell–Horn–Carroll theory.
General cognitive ability can be highly heritable in some species, but at the same time, is very malleable. This apparent paradox could potentially be explained by gene–environment interactions and correlations that remain hidden due to experimental limitations on human research and blind spots in animal research. Here, we shed light on this issue by combining the design of a sibling study with an environmental intervention administered to laboratory mice. The analysis included 58 litters of four full-sibling genetically heterogeneous CD-1 male mice, for a total of 232 mice. We separated the mice into two subsets of siblings: a control group (maintained in standard laboratory conditions) and an environmental-enrichment group (which had access to continuous physical exercise and daily exposure to novel environments). We found that general cognitive ability in mice has substantial heritability (24% for all mice) and is also malleable. The mice that experienced the enriched environment had a mean intelligence score that was 0.44 standard deviations higher than their siblings in the control group (equivalent to gains of 6.6 IQ points in humans). We also found that the estimate of heritability changed between groups (55% in the control group compared with non-significant 15% in the enrichment group), analogous to findings in humans across socio-economic status. Unexpectedly, no evidence of gene–environment interaction was detected, and so the change in heritability might be best explained by higher environmental variance in the enrichment group. Our findings, as well as the ‘sibling intervention procedure’ for mice, may be valuable to future research on the heritability, mechanisms and evolution of cognition.
This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.
Significance
Using administrative register data with information on family relationships and cognitive ability for three decades of Norwegian male birth cohorts, we show that the increase, turning point, and decline of the Flynn effect can be recovered from within-family variation in intelligence scores. This establishes that the large changes in average cohort intelligence reflect environmental factors and not changing composition of parents, which in turn rules out several prominent hypotheses for retrograde Flynn effects.
In both humans and mice, performance on tests of intelligence or general cognitive ability (GCA) is related to dopamine D1 receptor-mediated activity in the prelimbic cortex, and levels of DRD1 mRNA predict the GCA of mice. Here we assessed the turnover rate of D1 receptors as well as the expression level of the D1 chaperone protein (DRiP78) in the medial PPC (mPFC) of mice to determine whether rate of receptor turnover was associated with variations in the GCA of genetically heterogeneous mice. Following assessment of GCA (aggregate performance on four diverse learning tests) mice were administered an irreversible dopamine receptor antagonist (EEDQ), after which the density of new D1 receptors were quantified. GCA was positively correlated with both the rate of D1 receptor recovery and levels of DRiP78. Additionally, the density of D1 receptors was observed to increase within 60 min (or less) in response to intense demands on working memory, suggesting that a pool of immature receptors was available to accommodate high cognitive loads. These results provide evidence that innate general cognitive abilities are related to D1 receptor turnover rates in the prefrontal cortex, and that an intracellular pool of immature D1 receptors are available to accommodate cognitive demands.
Meta‐analysis and meta‐regression are statistical methods for synthesizing and modelling the results of different studies, and are critical research synthesis tools in ecology and evolutionary biology (E&E). However, many E&E researchers carry out meta‐analyses using software that is limited in its statistical functionality and is not easily updatable. It is likely that these software limitations have slowed the uptake of new methods in E&E and limited the scope and quality of inferences from research syntheses.
We developed OpenMEE: Open Meta‐analyst for Ecology and Evolution to address the need for advanced, easy‐to‐use software for meta‐analysis and meta‐regression. OpenMEE has a cross‐platform, easy‐to‐use graphical user interface (GUI) that gives E&E researchers access to the diverse and advanced statistical functionalities offered in R , without requiring knowledge of R programming.
OpenMEE offers a suite of advanced meta‐analysis and meta‐regression methods for synthesizing continuous and categorical data, including meta‐regression with multiple covariates and their interactions, phylogenetic analyses, and simple missing data imputation. OpenMEE also supports data importing and exporting, exploratory data analysis, graphing of data, and summary table generation.
As intuitive, open‐source, free software for advanced methods in meta‐analysis, OpenMEE meets the current and pressing needs of the E&E community for teaching meta‐analysis and conducting high‐quality syntheses. Because OpenMEE 's statistical components are written in R , new methods and packages can be rapidly incorporated into the software. To fully realize the potential of OpenMEE , we encourage community development with an aim to advance the capabilities of meta‐analyses in E&E.
Rodents (especially Mus musculus and Rattus norvegicus) have been the most widely used models in biomedical research for many years. A notable shift has taken place over the last two decades, with mice taking a more and more prominent role in biomedical science compared to rats. This shift was primarily instigated by the availability of a much larger genetic toolbox for mice, particularly embryonic-stem-cell-based targeting technology for gene disruption. With the recent emergence of tools for altering the rat genome, notably genome-editing technologies, the technological gap between the two organisms is closing, and it is becoming more important to consider the physiological, anatomical, biochemical and pharmacological differences between rats and mice when choosing the right model system for a specific biological question. The aim of this short review and accompanying poster is to highlight some of the most important differences, and to discuss their impact on studies of human diseases, with a special focus on neuropsychiatric disorders.
The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this puzzle, and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents and primates). However, the interpretation of a psychometric g-factor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength.
The Flynn effect (rising intelligence test performance in the general population over time and generations) varies
enigmatically across countries and intelligence domains; its substantive meaning and causes remain elusive. This first
formal meta-analysis on the topic revealed worldwide IQ gains across more than one century (1909–2013), based
on 271 independent samples, totaling almost 4 million participants, from 31 countries. Key findings include that IQ
gains vary according to domain (estimated 0.41, 0.30, 0.28, and 0.21 IQ points annually for fluid, spatial, full-scale,
and crystallized IQ test performance, respectively), are stronger for adults than children, and have decreased in
more recent decades. Altogether, these findings narrow down proposed theories and candidate factors presumably
accounting for the Flynn effect. Factors associated with life history speed seem mainly responsible for the Flynn effect’s
general trajectory, whereas favorable social multiplier effects and effects related to economic prosperity appear to be
responsible for observed differences of the Flynn effect across intelligence domains.
Despite the increasing importance of the ability-level differentiation hypothesis, no study has been conducted to clarify the role played by sex regarding this issue. A battery of cognitive tests was administered to a sample of 10247 participants (6,068 males and 4,179 females). Results show a differentiation effect in males (around 2%) but not in females. Therefore, the ability-level differentiation hypothesis is substantiated for males only.
Many theories about the role of the environment in raising IQ have been put forward. There has not been an equal effort, however, in experimentally testing these theories. In this paper, we test whether the role of the environment in raising IQ is bidirectional/reciprocal. We meta-analyze the evidence for the fadeout effect of IQ, determining whether interventions that raise IQ have sustained effects after they end. We analyze 7584 participants across 39 randomized controlled trials, using a mixed-effects analysis with growth curve modeling. We confirm that after an intervention raises intelligence the effects fade away. We further show this is because children in the experimental group lose their IQ advantage and not because those in the control groups catch up. These findings are inconsistent with a bidirectional/reciprocal model of interaction. We discuss explanations for the fadeout effect and posit a unidirectional-reactive model for the role of the environment in the development of intelligence.
Attention is a component of the working memory system, and as such, is responsible for protecting task-relevant information from interference. Cognitive performance (particularly outside of the laboratory) is often plagued by interference, and the source of this interference, either external or internal, might influence the expression of individual differences in attentional ability. By definition, external attention (also described as “selective attention”) protects working memory against sensorial distractors of all kinds, while internal attention (also called “inhibition”) protects working memory against emotional impulses, irrelevant information from memory, and automatically-generated responses. At present, it is unclear if these two types of attention are expressed independently in non-human animals, and how they might differentially impact performance on other cognitive processes, such as learning. By using a diverse battery of four attention tests (with varying levels of internal and external sources of interference), here we aimed both to explore this issue, and to obtain a robust and general (less task-specific) measure of attention in mice. Exploratory factor analyses revealed two factors (external and internal attention) that in total, accounted for 73% of the variance in attentional performance. Confirmatory factor analyses found an excellent fit with the data of the model of attention that assumed an external and internal distinction (with a resulting correlation of 0.43). In contrast, a model of attention that assumed one source of variance (i.e., “general attention”) exhibited a poor fit with the data. Regarding the relationship between attention and learning, higher resistance against external sources of interference promoted better new learning, but tended to impair performance when cognitive flexibility was required, such as during the reversal of a previously instantiated response. The present results suggest that there can be (at least) two types of attention that contribute to the common variance in attentional performance in mice, and that external and internal attentions might have opposing influences on the rate at which animals learn.
The Dickens and Flynn model of the Flynn effect (generational increases in mean IQ) assigns an important role to genotype-environment covariance (GE-cov). We quantify GE-cov in a longitudinal simplex model by modeling it as phenotype to environment (Ph->E) transmission in twin data. The model fits as well as the standard genetic simplex model, which assumes uncorrelated genetic and environmental influences. We use the results to explore numerically the possible role of GE-cov in amplifying increases in environmental means. Given the estimated Ph->E transmission parameters, GE-cov resulted in an amplification (in std units) of a factor 1.57 (full scale IQ) to 1.7 (performance IQ). The results lend credence to the role of GE-cov in the Flynn effect.
The Flynn effect refers to the observed rise in IQ scores over time, which results in norms obsolescence. Although the Flynn effect is widely accepted, most efforts to estimate it have relied upon "scorecard" approaches that make estimates of its magnitude and error of measurement controversial and prevent determination of factors that moderate the Flynn effect across different IQ tests. We conducted a meta-analysis to determine the magnitude of the Flynn effect with a higher degree of precision, to determine the error of measurement, and to assess the impact of several moderator variables on the mean effect size. Across 285 studies (N = 14,031) since 1951 with administrations of 2 intelligence tests with different normative bases, the meta-analytic mean was 2.31, 95% CI [1.99, 2.64], standard score points per decade. The mean effect size for 53 comparisons (N = 3,951, excluding 3 atypical studies that inflate the estimates) involving modern (since 1972) Stanford-Binet and Wechsler IQ tests (2.93, 95% CI [2.3, 3.5], IQ points per decade) was comparable to previous estimates of about 3 points per decade but was not consistent with the hypothesis that the Flynn effect is diminishing. For modern tests, study sample (larger increases for validation research samples vs. test standardization samples) and order of administration explained unique variance in the Flynn effect, but age and ability level were not significant moderators. These results supported previous estimates of the Flynn effect and its robustness across different age groups, measures, samples, and levels of performance. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Human performance on diverse tests of intellect are impacted by a "general" regulatory factor that accounts for up to 50% of the variance between individuals on intelligence tests. Neurobiological determinants of general cognitive abilities are essentially unknown, owing in part to the paucity of animal research wherein neurobiological analyses are possible. We report a methodology with which we have assessed individual differences in the general learning abilities of laboratory mice. Abilities of mice on tests of associative fear conditioning, operant avoidance, path integration, discrimination, and spatial navigation were assessed. Tasks were designed so that each made unique sensory, motor, motivational, and information processing demands on the animals. A sample of 56 genetically diverse outbred mice (CD-1) was used to assess individuals' acquisition on each task. Indicative of a common source of variance, positive correlations were found between individuals' performance on all tasks. When tested on multiple test batteries, the overall performance ranks of individuals were found to be highly reliable and were "normally" distributed. Factor analysis of learning performance variables determined that a single factor accounted for 38% of the total variance across animals. Animals' levels of native activity and body weights accounted for little of the variability in learning, although animals' propensity for exploration loaded strongly (and was positively correlated) with learning abilities. These results indicate that diverse learning abilities of laboratory mice are influenced by a common source of variance and, moreover, that the general learning abilities of individual mice can be specified relative to a sample of peers. Matzel,L.D.,Han YR,Grossman H,Karnik MS,Patel D,Scott N,Specht SM,Gandhi CC.
Following WW2, various researchers found and reported secular gains in IQ, but it was not until additional reports appeared in the 1980s that researchers began to look for the cause or causes. It was quickly apparent that the gains were not limited to any group or nation, but the manifestation of the gains was different depending on time and place. For every discovery, there was a different or opposite result in a different data set. Gains have been large, small, variable, and even negative. Some researchers have found that the gains were on g, while more have found no g loading. Abstract test formats, such as the Raven have often shown the greatest gains, but gains have also appeared in tests of crystallized intelligence. Some data has shown greater gains for the lower half of the intelligence distribution, while others have shown greater gains in the top half, and others have shown equal gains at all levels. Hypotheses for the causes have included environmental factors, genetic effects, reduced fertility, and methodological dependence. Two models are discussed.
Species-level data on five cognitive ability measures from 69 primate species are used in conjunction with comparative phylogenetic methods to test for the existence of primary and secondary modules. The former are ‘hard wired’, and solve phylogenetically recurrent problems, whereas the latter are a function of domain general problem-solving mechanisms being applied to solving narrower problems, which yields the ability to spontaneously solve those problems once the solutions are learned. It is found that these abilities exhibit affinities for different macroevolutionary patterns relative to ‘Big G', and positive associations with dietary breadth and brain size. The analyses were also conducted using each ability residualised for G. It was found that the Ornstein-Uhlenbeck (OU) model best captured the macroevolution of residual tactical deception, and White Noise (WN) best fit the remainder. Residual tactical deception positively associates with brain volume, whereas the extractive foraging and innovation residuals negatively associate with this and the innovation residual negatively associates with social group size. The affinity of residual tactical deception for the OU model indicates that it may be a primary module under adaptive optimization selection. The predominance of WN in characterizing the macroevolution of the remaining residuals indicates that they may be secondary modules, under the influence of developmental and ecological (rather than phylogenetic) factors. Negative associations involving brain size (in two cases) and social group size (in one) suggest that the optimal conditions for cultivating these modules exist when these parameters are low.
General cognitive ability (or general intelligence; g) is a latent variable that describes performance across a broad array of cognitive skills. This general influence on cognitive ability varies between individuals and shares a similar structure in both humans and mice. Evidence suggests that much of the variation in general intelligence is related to the efficacy of the working memory system. We have previously observed that one component of the working memory system, selective attention, disproportionately accounts for the relationship between working memory and general intelligence in genetically heterogeneous mice. In the three studies reported here, we test a hypothesis that emerges from human behavioral studies which suggests that attentional disengagement, a sub-component of selective attention, critically mediates its relationship with g. Studies 1 and 2 both assess the factor loadings (on a principal component analysis) of the performance of mice on an array of learning tasks as well as tasks designed to make explicit demands on attentional disengagement. We find that attentional disengagement tasks load more highly than measures of cognitive performance that place less explicit demands on disengagement and that performance in these disengagement tasks is strongly predictive of the general cognitive performance of individual animals. In Study 3 we observed that groups of mice (young and old) with known differences in general cognitive abilities differ more on a discrimination task that requires attentional disengagement than on a simple discrimination task with fewer demands on disengagement. In total, these results provide support for the hypothesis that attentional disengagement is strongly related to general intelligence, and that variations in this ability may contribute to both individual differences in intelligence as well as age-related cognitive declines.
Most quantifiable traits exhibit some degree of heritability. The heritability of physical traits is often high, but
the heritability of some personality traits and intelligence can also be highly heritable. Importantly, estimates of
heritability can change dramatically depending on such variables as the age or the environmental history of the
sample from which the estimate is obtained. Interpretation of these changing estimates is complicated in studies
of humans, where (based on correlational observations) environmental variables are hard to directly control or
specify. Using laboratory mice, here we could control specific environmental variables. We assessed 58 groups of
four full sibling male CD-1 genetically heterogeneous mice (n=232). Using a standard full-sibling analysis,
physical characteristics (body weight and brain weight) were highly heritable (h of body weight=0.66 on a 0–1
scale), while behaviors indicative of a personality trait (exploration/boldness) and learning abilities (in a passive
avoidance and egocentric maze task) were weakly-to-moderately heritable. Half of the siblings from each set of
four were housed in an “enriched” environment, which provided extensive and varied opportunities for exploration.
This enrichment treatment promoted improvements in learning and a shift toward a more bold personality
type. Relative to animals in control (“impoverished” environments), the history of enrichment had
significant impact on estimates of heritability. In particular, the heritability of behaviors related to the personality
trait (exploration/boldness) more than doubled, and a similar increase was observed for learning (in the
passive avoidance task). Physical traits (brain and body weight), however, were insensitive to environmental
history (where in both environments, animals received the same diet). These results indicate that heritable traits
can be responsive to variations in the environment, and moreover, that estimates of heritability of learning and
personality traits are strongly influenced by environments that modulate those traits.
Since the Industrial Revolution, human societies have experienced high and sustained rates of economic growth. Recent explanations of this sudden and massive change in economic history have held that modern growth results from an acceleration of innovation. But it is unclear why the rate of innovation drastically accelerated in England in the 18 th century. An important factor might be the alteration of individual preferences with regard to innovation due to the unprecedented living standards of the English during that period, for two reasons. First, recent developments in economic history challenge the standard Malthusian view according to which living standards were stagnant until the Industrial Revolution. Pre-industrial England enjoyed a level of affluence that was unprecedented in history. Second, Life History Theory, a branch of evolutionary biology, has demonstrated that the human brain is designed to respond adaptively to variations in resources in the local environment. In particular, a more favorable environment (high resources, low mortality) triggers the expression of future-oriented preferences. In this paper, I argue that some of these psychological traits – a lower level of time discounting, a higher level of optimism, decreased materialistic orientation, and a higher level of trust in others – are likely to increase the rate of innovation. I review the evidence regarding the impact of affluence on preferences in contemporary as well as past populations, and conclude that the impact of affluence on neuro-cognitive systems may partly explain the modern acceleration of technological innovations and the associated economic growth.
The cognitive ability differentiation hypothesis, which is also termed Spearman's Law of Diminishing Returns, proposes that cognitive ability tests are less correlated and less g loaded in higher ability populations. In addition, the age differentiation hypothesis proposes that the structure of cognitive ability varies across respondent age. To clarify the literature regarding these expectations, 106 articles containing 408 studies, which were published over a 100-year time span, were analyzed to evaluate the empirical basis for ability as well as age differentiation hypotheses. Meta-analyses provide support for both hypotheses and related expectations. Results demonstrate that the mean correlation and g loadings of cognitive ability tests decrease with increasing ability, yet increase with respondent age. Moreover, these effects have been nearly constant throughout the century of analyzed data. These results are important because we cannot assume an invariant cognitive structure for different ability and age levels. Implications for practice as well as drawbacks are further discussed.
Latent growth curve techniques and longitudinal data are used to examine predictions from the theory of fluid and crystallized intelligence (Gf-Gc theory; J. L. Horn & R. B. Cattell, 1966, 1967). The data examined are from a sample (N ∼ 1,200) measured on the Woodcock-Johnson Psycho-Educational Battery-Revised (WJ-R). The longitudinal structural equation models used are based on latent growth models of age using two-occasion accelerated data (e.g., J. J. McArdle & R. Q. Bell, 2000; J. J. McArdle & R. W. Woodcock, 1997). Nonlinear mixed-effects growth models based on a dual exponential rate yield a reasonable fit to all life span cognitive data. These results suggest that most broad cognitive functions fit a generalized curve that rises and falls. Novel multilevel models directly comparing growth curves show that broad fluid reasoning (Gf) and acculturated crystallized knowledge (Gc) have different growth patterns. In all comparisons, any model of cognitive age changes with only a single g factor yields an overly simplistic view of growth and change over age.
Species-level research on animal behavior is decades old and very well described, but individual differences in cognition has only gained momentum much more recently. Although there have been some studies of individual differences in cognition in primates, the new research has mainly focused on general cognitive ability (g) in mice. Fortunately, the timing is right for combining our understanding of the genetics and neuroscience of intelligence in humans with genetic manipulation models of learning and memory in mice. This will help forge deeper understanding of human intelligence and mental cognitive disorders such as retardation and Alzheimer Disease. In this chapter, we survey the academic literature associated with g in animals, with discussions of links with genetics, cross-species comparisons and neuroscience. We then focus on mice to describe the rapidly-growing genetic manipulation models of learning, memory and cognitive dysfunction. Ultimately, we believe that cognitive test batteries for mice, in combination with exploring the structure of cognition from the individual differences perspective, creates a useful framework for describing the effects of cognition-related genes and extrapolating these up to the human brain and experience.
Generational IQ changes (the Flynn effect) have been shown to be predominantly positive but differentiated according to IQ domains and countries. However, evidence from recent studies points towards a decrease of the Flynn effect globally or even a reversal in some countries. In the present meta-analysis, we show an inverse u-shaped trajectory of IQ test performance changes in a large number of samples (k = 96; N = 13,172) on a well-known test for spatial perception (the three-dimensional cubes test, 3DC) in German-speaking countries over 38 years (1977-2014). Assessment of both item response theory-based measures as well as more standard measures of classical test theory showed initial increases and a subsequent decrease of performance when controlling for age, sample type (general population vs. mixed samples vs. university students) and sex. Our results suggest saturation and diminishing returns of IQ increasing factors (e.g., life history speed) whilst negative associations of IQ changes with psychometric g may have led to the observed IQ score decrease in more recent years.
Data from 14 nations reveal IQ gains ranging from 5 to 25 points in a single generation. Some of the largest gains occur on culturally reduced tests and tests of fluid intelligence. The Norwegian data show that a nation can make significant gains on a culturally reduced test while suffering losses on other tests. The Dutch data prove the existence of unknown environmental factors so potent that they account for 15 of the 20 points gained. The hypothesis that best fits the results is that IQ tests do not measure intelligence but rather a correlate with a weak causal link to intelligence. This hypothesis can also explain differential trends on various mental tests, such as the combination of IQ gains and Scholastic Aptitude Test losses in the United States.
IntroductionIndividual studiesThe summary effectHeterogeneity of effect sizesSummary points
Learning, attentional, and perseverative deficits are characteristic of cognitive aging. In this study, genetically diverse CD-1 mice underwent longitudinal training in a task asserted to tax working memory capacity and its dependence on selective attention. Beginning at 3 mo of age, animals were trained for 12 d to perform in a dual radial-arm maze task that required the mice to remember and operate on two sets of overlapping guidance (spatial) cues. As previously reported, this training resulted in an immediate (at 4 mo of age) improvement in the animals' aggregate performance across a battery of five learning tasks. Subsequently, these animals received an additional 3 d of working memory training at 3-wk intervals for 15 mo (totaling 66 training sessions), and at 18 mo of age were assessed on a selective attention task, a second set of learning tasks, and variations of those tasks that required the animals to modify the previously learned response. Both attentional and learning abilities (on passive avoidance, active avoidance, and reinforced alternation tasks) were impaired in aged animals that had not received working memory training. Likewise, these aged animals exhibited consistent deficits when required to modify a previously instantiated learned response (in reinforced alternation, active avoidance, and spatial water maze). In contrast, these attentional, learning, and perseverative deficits were attenuated in aged animals that had undergone lifelong working memory exercise. These results suggest that general impairments of learning, attention, and cognitive flexibility may be mitigated by a cognitive exercise regimen that requires chronic attentional engagement
Contemporary descriptions of human intelligence hold that this trait influences a broad range of cognitive abilities, including learning, attention, and reasoning. Like humans, individual genetically heterogeneous mice express a "general" cognitive trait that influences performance across a diverse array of learning and attentional tasks, and it has been suggested that this trait is qualitatively and structurally analogous to general intelligence in humans. However, the hallmark of human intelligence is the ability to use various forms of "reasoning" to support solutions to novel problems. Here, we find that genetically heterogeneous mice are capable of solving problems that are nominally indicative of inductive and deductive forms of reasoning, and that individuals' capacity for reasoning covaries with more general learning abilities. Mice were characterized for their general learning ability as determined by their aggregate performance (derived from principal component analysis) across a battery of five diverse learning tasks. These animals were then assessed on prototypic tests indicative of deductive reasoning (inferring the meaning of a novel item by exclusion, i.e., "fast mapping") and inductive reasoning (execution of an efficient search strategy in a binary decision tree). The animals exhibited systematic abilities on each of these nominal reasoning tasks that were predicted by their aggregate performance on the battery of learning tasks. These results suggest that the coregulation of reasoning and general learning performance in genetically heterogeneous mice form a core cognitive trait that is analogous to human intelligence
Black/White differences in mean IQ have been clearly shown to strongly correlate with g loadings, so large group differences on subtests of high cognitive complexity and small group differences on subtests of low cognitive complexity. IQ scores have been increasing over the last half century, a phenomenon known as the Flynn effect. Flynn effect gains are predominantly driven by environmental factors. Might these factors also be responsible for group differences in intelligence? The empirical studies on whether the pattern of Flynn effect gains is the same as the pattern of group differences yield conflicting findings. A psychometric meta-analysis on all studies with seven or more subtests reporting correlations between g loadings and standardized score gains was carried out, based on 5 papers, yielding 11 data points (total N = 16,663). It yielded a true correlation of − .38, and none of the variance between the studies could be attributed to moderators. It appears that the Flynn effect and group differences have different causes. Suggestions for future research are discussed.
The relationship between the Flynn effect and ability differentiation is investigated in a reanalysis of published data on Estonian student cohorts tested in 1933/36, 1997/98 and 2006 on the National Intelligence Test (Must, te Nijenhuis, Must, & van Vianen, 2009). To determine whether there was a relationship we computed the vector correlation between the Flynn effects (d) and the change in the g loading (Δg) between measurement occasions for each of the 10 NIT subtests and for each of the seven cohort comparisons, giving a total N of 70 effect sizes. The association between d and Δg was robustly negative (indicating that the Flynn effects were negatively associated with changes in the g loading of subtests) for all cohort comparisons, with values of r ranging from −.100 to −.461 (N = 10). When all effect sizes were analyzed together, the vector correlation was found to be −.281 (p ⩽ .05, N = 70). This indicates a significant association between the Flynn effect and ability differentiation. Possible causes of this association are discussed.