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Includes various theoretical approaches explaining memory development across the life span. Memory Development: Universal Changes and Individual Differences is of interest to researchers, undergraduates and graduate students in developmental psychology, educational psychology and technology, and experimental psychology. Attachment Theory and Psychoanalysis.

Peter Fonagy. Desire, Self, Mind, and the Psychotherapies. Coleman R. Theory of Mind. Scott A. Beyond Decoding.

Individual Differences

Richard K. Working Memory, Thought, and Action. Alan Baddeley. Reading in a Second Language. William Grabe. Socioemotional Development in the Toddler Years. Celia A. Applied Child Study. Anthony D. Self-Regulated Learning and Academic Achievement. Barry J. Metacognition, Strategy Use, and Instruction. Harriet Salatas Waters.

ISBN 13: 9780805801484

Cognitive Development and Working Memory. Pierre Barrouillet. Social Cognition. Fritz Strack. Richard M. Cognitive Development. Marc H. Usha Goswami. Foundations for Tracing Intuition. Motivation, Emotion, and Cognition. David Yun Dai. Language Development Over the Lifespan. Kees de Bot. Advances in Relational Frame Theory. Simon Dymond. Implicit and Explicit Mental Processes. Kim Kirsner. The Taxonomy of Metacognition. Pina Tarricone. Self-related Cognitions in Anxiety and Motivation.

Reading Comprehension Difficulties. Cesare Cornoldi. The Self and Perspective Taking. Louise McHugh. The Development of Commonsense Psychology. Chris Moore. Measuring Up. John Sabatini. Fredric N. Dr Terence Butler. Personality Development. Michiel Westenberg. The Psychology of Facial Expression. James A. Social Psychology and the Unconscious. John A. Understanding Priming Effects in Social Psychology. Daniel C.

Foundations of Reading Acquisition and Dyslexia. Benita A. Principles of Memory. Handbook of Metamemory and Memory. John Dunlosky. Margret M. Joint Attention. Axel Seemann. Ruminative Thoughts. Robert S. Sergio Morra. The Nature of Learning Disabilities. Kenneth A. Attachment Theory. Susan Goldberg. Attention, Representation, and Human Performance. Slim Masmoudi. Interpersonal Sensitivity. Judith A. To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50, years.

However, because of their cognitive potential, humans have changed the world tremendously in the past centuries. They have invented technical devices, institutions that regulate cooperation and competition, and symbol systems, such as script and mathematics, that serve as reasoning tools. The exceptional learning ability of humans allows newborns to adapt to the world they are born into; however, there are tremendous individual differences in learning ability among humans that become obvious in school at the latest.

Cognitive psychology has developed models of memory and information processing that attempt to explain how humans learn general perspective , while the variation among individuals differential perspective has been the focus of psychometric intelligence research. Although both lines of research have been proceeding independently, they increasingly converge, as both investigate the concepts of working memory and knowledge construction.


This review begins with presenting state-of-the-art research on human information processing and its potential in academic learning. Then, a brief overview of the history of psychometric intelligence research is combined with presenting recent work on the role of intelligence in modern societies and on the nature-nurture debate. Finally, promising approaches to integrating the general and differential perspective will be discussed in the conclusion of this review. In psychology textbooks, learning is commonly understood as the long-term change in mental representations and behavior as a result of experience.

Rather, learning is associated with changes in mental representations that can manifest themselves in behavioral changes. Mental and behavioral changes that result from learning must be differentiated from changes that originate from internal processes, such as maturation or illness. Learning rather occurs as an interaction with the environment and is initiated to adapt personal needs to the external world.

From an evolutionary perspective, 2 living beings are born into a world in which they are continuously expected to accomplish tasks e. The brains of all types of living beings are equipped with instincts that facilitate coping with the demands of the environment to which their species has been adapted. However, because environments are variable, brains have to be flexible enough to optimize their adaptation by building new associations between various stimuli or between stimuli and responses. In the case of classical conditioning, one stimulus signals the occurrence of another stimulus and thereby allows for the anticipation of a positive or negative consequence.

In the case of operant conditioning, behavior is modified by its consequence. Human beings constantly react and adapt to their environment by learning through conditioning, frequently unconsciously. However, there is more to human learning than conditioning, which to the best of our knowledge, makes us different from other species. All living beings must learn how to obtain access to food in their environment, but only human beings cook and have invented numerous ways to store and conserve their food.

While many animals run faster than humans and are better climbers, the construction and use of vehicles or ladders is unique to humans. There is occasional evidence of tool use among non-human species passed on to the next generation, 3 , 4 but this does not compare to the tools humans have developed that have helped them to change the world. The transition from using stonewedges for hunting to inventing wheels, cars, and iPhones within a time period of a few thousand years is a testament to the unique mental flexibility of human beings given that, to the best of our knowledge, the genes that guide human brain development have not undergone remarkable changes during the last 50, years.

What is so special about human information processing? Answers to this question are usually related to the unique resource of consciousness and symbolic reasoning abilities that are, first and foremost, practiced in language. Working from here, a remarkable number of insights on human cognition have been compiled in the past decades, which now allow for a more comprehensive view of human learning. Learning manifests itself in knowledge representations processed in memory.

The encoding, storage, and retrieval of information have been modeled in the multi-store model of human memory depicted in Fig. To allow goal-directed behavior and selective attention, only a fractional amount of this information passes into the working memory, which is responsible for temporarily maintaining and manipulating information during cognitive activity.

It is the gatekeeper to long-term memory, which is assumed to have an unlimited capacity. Here, information acquired through experience and learning can be stored in different modalities as well as in symbol systems e. The multi-store model of human information processing is not a one-way street, and long-term memory is not to be considered a storage room or a hard-disk where information remains unaltered once it has been deposited. A more appropriate model of long-term memory is a self-organizing network, in which verbal terms, images, or procedures are represented as interlinked nodes with varying associative strength.

Very strong incoming stimuli e. For the most part, however, working memory filters out irrelevant and distracting information to ensure that the necessary goals will be achieved undisturbed. This means that working memory is continuously selecting incoming information, aligning it with knowledge retrieved from long-term memory, and preparing responses to accomplishing requirements demanded by the environment or self-set goals. Inappropriate and unsuitable information intruding from sensory as well as from long-term memory has to be inhibited, while appropriate and suitable information from both sources has to be updated.

In case of intentional learning, working memory guards more against irrelevant information than in the case of mind wandering. Less inhibitory control makes unplanned and unintended learning possible i. These working memory activities are permanently changing the knowledge represented in long-term memory by adding new nodes and by altering the associative strength between them. The different formats knowledge can be represented in are listed in Fig.

In cognitive psychology, learning is associated with modifications of knowledge representations that allow for better use of available working memory resources. Procedural knowledge knowing how enables actions and is based on a production-rule system. As a consequence of repeated practice, the associations between these production rules are strengthened and will eventually result in a coordinated series of actions that can activate each other automatically with a minimum or no amount of working memory resources.

This learning process not only allows for carrying out the tasks that the procedural knowledge is tailored to perform more efficiently, but also frees working memory resources that can be used for processing additional information in parallel. Meaningful learning requires the construction of declarative knowledge knowing that , which is represented in symbol systems language, script, mathematical, or visual-spatial representations. Learning leads to the regrouping of declarative knowledge, for instance by chunking multiple unrelated pieces of knowledge into a few meaningful units. Individuals who have stored both dates and can retrieve them from long-term memory are able to chunk 14 single units into two units, thereby freeing working memory resources.

Memory artists, who can reproduce dozens of orally presented numbers have built a very complex knowledge base that allows for the chunking of incoming information. Learning also manifests itself in the extension of declarative knowledge using concept formation and inferential reasoning.

Often, concepts are hierarchically related with superordinate e.

Human learning and information processing

This provides the basis for creating meaningful knowledge by deductive reasoning. If the only thing a person knows about a wombat is that it is an animal, she can nonetheless infer that it needs food and oxygen. Depending on individual learning histories, conceptual representations can contain great variations.

For many academic fields, first and foremost in the STEM area Science, Technology, Engineering, Mathematics , it has been demonstrated that experts and novices who use the same words may have entirely different representations of their meaning. This has been convincingly demonstrated for physics and particularly in the area of mechanics. Younger elementary school children often agree that a pile of rice has weight, but they may also deny that an individual grain of rice has weight at all.

However, representing weight as the property of an object is still not compatible with scientific physics in the Newtonian sense by which weight is conceptualized as a relation between objects. Understanding weight in this sense requires an interrelated network of knowledge, including the concepts of force, gravity, and mass among others.

As a result of classroom instruction, students are expected to acquire procedural and conceptual knowledge of the subjects they were taught. While procedures emerge as a function of repetition and practice, the acquisition of advanced concepts, which are consistent with state of the art science, is less straightforward. Several instructional methods have been developed and evaluated that support students in restructuring and refining their knowledge and thereby promote appropriate conceptual understanding, including self-explanations, 20 contrasting cases, 21 , 22 and metacognitive questions.

The taxonomy acknowledges the distinction between procedural and conceptual knowledge and includes six cognitive processes listed in Fig. What makes humans efficient learners, however, goes beyond general memory functions discussed so far. Similar to other living beings, humans do not enter the world as empty slates 2 but are equipped with so-called core knowledge Fig. Evidence for core knowledge comes from preferential looking experiments with infants who are first habituated to a particular stimulus or scenario.

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Then, the infant is shown a second scenario that differs from the first in a specific manner. If the time he or she looks at this stimulus exceeds the looking-time at the end of the habituation phase of the first stimulus, this suggests that the infant can discriminate between the stimuli. This paradigm helps to determine whether infants detect violations of principles that underlie the physical world, such as the solidity of objects, where an object cannot occupy the same space as another object.

Core knowledge can serve as a starting point for the acquisition of content knowledge that has emerged as a result of cultural development. This has been examined in detail for numerical and mathematical reasoning. Two core systems have been detected in infants. As early as at 6 months of age, infants show an ability for the approximate representations of numerical magnitude, which allow them to discriminate two magnitudes depending on their ratio. Cultural transformations based on invented symbol systems were the key to advanced mathematics.

Central content areas in mathematics curricula of high schools, such as calculus, were only developed less than three centuries ago. Core knowledge about navigation is meant to guide the acquisition of geometry, an area involved in numerous academic fields. Script is a rather recent cultural invention, going back approximately 5, years, whereas the human genome emerged approximately 50, years ago. Nonetheless, today, most 6-year-old children become literate during their 1st years of schooling without experiencing major obstacles. Human beings are endowed with the many skills that contribute to the ability to write and read, such as, first and foremost, language as well as auditory and visual perception and drawing.

These initially independent working resources were coopted when script was invented, and teaching children to write and read at school predominantly means supporting the development of associations among these resources. Part of the core knowledge innate to humans has also been found in animals, for instance numerical knowledge and geometry, but to the best of our knowledge, no other animals have invented mathematics.

Additionally, the unique function of human working memory is the precondition for the integration of initially independent representational systems. However, the full potential of working memory is not in place at birth, but rather matures during childhood and undergoes changes until puberty. To summarize what has been discussed so far, there are two sources for the exceptional learning capacity of humans. The first is the function of working memory as a general-purpose resource that allows for holding several mental representations simultaneously for further manipulation.

The second is the ancient corpus of the modularized core knowledge of space, quantities, and the physical and social world. Working memory allows for the connection of this knowledge to language, numerals, and other symbol systems, which provides the basis for reasoning and the acquisition of knowledge in academic domains, if appropriate learning opportunities are provided.

Both resources are innate to human beings, but they are also sources of individual differences, as will be discussed in the following sections. In the early twentieth century, a pragmatic need for predicting the learning potential of individuals initiated the development of standardized tests. 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.

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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.

What is individual differences - Type Of Individual Differences(

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.

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