Although some of the previously proposed reasons our brains are special may have been debunked, there are still many ways in which we are different. They lie in our genes and our ability to adapt to our surroundings. Two other recently published studies add new insight to the debate. Unique genetic signatures At the genetic level, humans are similar to other animals.
We share more than 90 percent of our DNA with our closest relatives , including chimpanzees, bonobos and gorillas. Mice and humans also share many of the same genes —which is why scientists use them as a model to study many human diseases. Studies in recent years, however, have revealed that the way in which genes, the segments of DNA that code for specific proteins, are expressed can be quite different among humans and other animals.
One reason scientists can now unravel these more nuanced differences between the human brain and those of other species is the rise of more robust data collection techniques. For example, scientists at the Allen Institute for Brain Science have developed detailed atlases of the expression patterns of thousands of genes in various species, including those of adult mice and human brains. In a study published last week in Nature Neuroscience researchers used these enormous data sets to look for the patterns of gene expression that are shared within the human population.
They were able to identify 32 unique signatures within 20, genes that appear to be shared across brain regions in six individuals see a map here. This unique genetic code may help explain what gives rise to our distinctly human traits. When the researchers compared humans with mice, they found that whereas the genes associated with neurons were well preserved among species, those associated with glial cells—nonneuronal cells with a wide variety of functions—were not.
This finding may have another important implication—the capacity for plasticity; researchers have found the glia play an important role in shaping the brain. From monkey to human Plasticity may be what underlies the specific differences in our brain that lead to our unique cognitive abilities. He presents four evolved mechanisms of human thought that give us access to a wide range of information and the ability to find creative solutions to new problems based on access to this information.
The challenge is to identify which systems animals and human share, which are unique, and how these systems interact and interface with one another. Recently, scientists have found that some animals think in ways that were once considered unique to humans: For example, some animals have episodic memory, or non-linguistic mathematical ability, or the capacity to navigate using landmarks.
However, despite these apparent similarities, a cognitive gulf remains between humans and animals. Hauser presents four distinguishing ingredients of human cognition, and shows how these capacities make human thought unique. These four novel components of human thought are the ability to combine and recombine different types of information and knowledge in order to gain new understanding; to apply the same "rule" or solution to one problem to a different and new situation; to create and easily understand symbolic representations of computation and sensory input; and to detach modes of thought from raw sensory and perceptual input.
Earlier scientists viewed the ability to use tools as a unique capacity of humans, but it has since been shown that many animals, such as chimpanzees, also use simple tools. Differences do arise, however, in how humans use tools as compared to other animals.
While animal tools have one function, no other animals combine materials to create a tool with multiple functions. In fact, Hauser says, this ability to combine materials and thought processes is one of the key computations that distinguish human thought. The Frenchman Alfred Binet, who held a degree in law, constructed problems designed to determine whether children who did not meet certain school requirements suffered from mental retardation or from behavioral disturbances.
They were asked in what respect a fly, an ant, a butterfly and a flea are alike, and they had to reproduce drawings from memory. From adolescence on, however, the average mental age scores increasingly converge, and because of the linear increase in chronological age, the IQ would decline—a trend that obviously does not match reality. World War I pushed the development of non-verbal intelligence tests, which were used to select young male immigrants with poor English language skills for military service.
The high psychometric quality of the intelligence tests constructed in different parts of the world by scientists in the early decades of the twentieth century have influenced research ever since. This is a narrow definition, but it is the only point of departure for a rigorous discussion of the tests.
It would be better if the psychologists could have used some other and more technical term, since the ordinary connotation of intelligence is much broader. The damage is done, however, and no harm need result if we but remember that measurable intelligence is simply what the tests of intelligence test, until further scientific observation allows us to extend the definition.
It is not merely book learning, a narrow academic skill, or test-taking smarts. This definition is in line with the substantial correlations between intelligence test scores and academic success, 52 whereas correlations with measures of outside-school success, such as income or professional status, are lower but still significant. Two groups of people born in and took a test of mental ability at school when they were 11 years old.
The same data set also demonstrated a substantial long-term impact of intelligence on various factors of life success, among them career aspects, health, and longevity.
Intelligence tests scores have proven to be objective, reliable, and valid measures for predicting learning outcome and more general life success. At the same time, the numerous data sets on intelligence tests that were created all over the world also contributed to a better understanding of the underlying structure of cognitive abilities.
Although a factor g could be extracted in almost all data sets, correlations between subtests varied considerably, suggesting individual differences beyond general cognitive capabilities. Modality factors verbal, numerical, or visual spatial have been observed, showing increased correlations between tests based on the same modality, but requiring different mental operations.
On the other hand, increased correlations were also observed between tests based on different modalities, but similar mental operations e.
The hierarchical structure of intelligence, with factor g on the top and specific factors beneath, was quite obvious from the very beginning of running statistical analyses with intelligence items. Nonetheless, it appeared a major challenge for intelligence researchers to agree on a taxonomy of abilities on the second and subsequent levels.
In , John Carroll published his synthesis of hundreds of published data sets on the structure of intelligence after decades of research. Eighty narrower abilities, such as spatial scanning, oral production fluency, and sound discrimination, are located in the bottom layer. Factor g could be a comprehensive characteristic of the brain that makes information processing generally more or less efficient top-down-approach.
Existing data sets, however, are also compatible with a model of intelligence according to which the human brain is comprised of a large number of single abilities that have to be sampled for mental work bottom-up approach.
In this case, factor g can be considered a statistical correlate that is an emerging synergy of narrow abilities. From studies with identical and fraternal twins, we know that genetic differences can explain a considerable amount of variance in IQ. On the other hand, IQ-correlations between raised-together same-sex fraternal twins are rarely higher than.
Given that the shared environment for regular siblings is lower than for fraternal twins, this result qualifies the impact of environmental factors on intelligence. The amount of genetic variance is judged in statistical analyses based on the difference between the intra-pair correlations for identical and fraternal twins.
The search for the genes responsible for the expression of cognitive capabilities has not yet had much success, despite the money and effort invested in human genome projects.
It is entirely plausible that intelligence is formed by a very large number of genes, each with a small effect, spread out across the entire genome.
Moreover, these genes seem to interact in very complicated ways with each other as well as with environmental cues. Reaction norms depict the range of phenotypes a genotype can produce depending on the environment. Other physiological characteristics, such as height, have a high degree of heritability and a large reaction norm. Whether an individual reaches the height made possible by the genome depends on the nutrition during childhood and adolescence.
In a wealthy country with uniform access to food, average height will be larger than in a poor country with many malnourished inhabitants. However, within both countries, people vary in height. In contrast, in the poor country, some were sufficiently nourished and, therefore, reached the height expressed by their genome, while others were malnourished and, therefore, remained smaller than their genes would have allowed under more favorable conditions. For height, the reaction norm is quite large because gene expression depends on nutrition during childhood and adolescence.
This explains the well-documented tendency for people who have grown up in developed countries to become progressively taller in the past decades. People who have found their niche can perfect their competencies by deliberate learning. In the first decades of developing intelligence tests, researchers were naive to the validity of non-verbal intelligence; so-called culture-free or culture-fair tests, based on visual-spatial material such as mirror images, mazes or series and matrices of geometric figures, were supposed to be suitable for studying people of different social and cultural levels.
Approximately 10 years of institutionalized education is necessary for the intelligence of individuals to approach its maximum potential. Generally, the amount of variance in intelligence test scores explained by genes is higher the more society members have access to school education, health care, and sufficient nutrition.
There is strong evidence for a decrease in the heritability of intelligence for children from families with lower socioeconomic status SES. For example, lower SES fraternal twins resembled each other more than higher SES ones, indicating a stronger impact of shared environment under the former condition. Although it may be counterintuitive at first, this suggests that a high heritability rate of intelligence in a society is an indicator of economic and educational equity.
Additionally, this means that countries that ensure access to nutrition, health care, and high quality education independent of social background enable their members to develop their intelligence according to their genetic potential. This was confirmed by a meta-analysis on interactions between SES and heritability rate. While studies run in the United States showed a positive correlation between SES and heritability rate, studies from Western Europe countries and Australia with a higher degree of economic and social equality did not.
In the first part of this paper, cognitive processes were discussed that, in principle, enable human beings to develop the academic competencies that are particularly advantageous in our world today.
In the second part, intelligence test scores were shown to be valid indicators of academic and professional success, and differences in IQ were shown to have sound genetic sources. Over many decades, research on cognitive processes and psychometric intelligence has been developing largely independently of one another, but in the meantime, they have converged.
Tests that were developed to provide evidence for the different components of human cognition revealed large individual differences and were substantially correlated with intelligence tests. Tests of memory function were correlated with tests of factor g. Sensory memory tests have shown that the exposure duration required for reliably identifying a simple stimulus inspection time is negatively correlatedwith intelligence.
In these studies, working memory functions are measured by speed tasks that require goal-oriented active monitoring of incoming information or reactions under interfering and distracting conditions. Neural efficiency has been identified as a major neural characteristic of intelligence; more intelligent individuals show less brain activation measured by electroencephalogram or functional magnetic resonance imaging when completing intelligence test items 75 , 76 as well as working memory items.
Most importantly, they could predict psychometric intelligence in 8-year-old children. These results clearly suggest that a portion of individual differences can be traced back to differences in domain-general cognitive competencies. However, psychometric research also shows that individual differences do exist beyond factor g on a more specific level. Differences in numerical, language, and spatial abilities are well established. Longitudinal studies starting in infancy suggest that sources of these differences may be traced back to variations in core knowledge.
Non-symbolic numerical competencies in infancy have an impact on mathematical achievement. Endowed with general and specific cognitive resources, human beings growing up in modern societies are exposed to informal and formal learning environments that foster the acquisition of procedural as well as declarative knowledge in areas that are part of the school curriculum.
Being endowed with genes that support efficient working memory functions and that provide the basis for usable core knowledge allows for the exploitation of learning opportunities provided by the environment. This facilitates the acquisition of knowledge that is broad as well as deep enough to be prepared for mastering the, as of yet, unknown demands of the future.
As discussed in the first part of this paper, some content areas—first and foremost from STEM fields—are characterized by abstract concepts mainly based on defining features, which are themselves integrated into a broader network of other abstract concepts and procedures.
Only individuals who clearly score above average on intelligence tests can excel in these areas. Education is not the great equalizer, but rather generates individual differences rooted in genes. Omrod, J. Human Learning Pearson, Cosmides, L. Evolutionary psychology: New perspectives on cognition and motivation. Article PubMed Google Scholar. Spelke, E. MIT Press, Tomasello, M. The diverse origins of the human gene pool. Atkinson, R. Baddeley, A.
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