Lifespan development Assignment | Top Essay Writing

In this journal,
Reflect on the factors (cognitive development, language development, and intelligence), that you have learned about this week.
Identify what you believe to be the most important variables that are associated with each of the following areas:
memory development
language development
infant intelligence development
cognitive development in adolescence
Evaluate your personal or vicarious experiences and/or weekly sources, utilizing citations, to support your beliefs about why, what you included, is of such importance.
Your journal this week should be 400 to 500 words and have an introduction and conclusion.


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As we have seen, our attention increases from infancy into adulthood, and by late adulthood it begins to decline. Does the same pattern hold true for memory? Memory is defined as the retention of information over a period of time. Without our memory, not many things in our life would make sense. Could you imagine waking up every day and not knowing what you have to do that day, or who the people around you are, or the roles that they play in your life? Memory is vital.

Memory Storage

Short-term memory, without further processing or rehearsal, lasts no more than 30 seconds. In contrast, long-term memory is relatively unlimited.

Long term memory is a relatively permanent and unlimited store of information. Memories of childhood, favorite teachers and classes, past vacations—these are all long term memory. Short term memory, on the other hand, is the retention of information for 15–30 seconds. Memorizing a phone number for a few seconds until you can grab a phone and dial the digits is an example of short term memory, which has a very limited capacity. Much of the research on memory focuses on how humans encode information in their brains, how they retain or store it, and, finally, how they retrieve it. If any of these three parts of the process fails, individuals may not be able to retrieve the information that they have stored.

Memories can be inaccurate for a number of reasons (Sabbagh, 2009). Unlike a video recorder or computer memory, people construct and then reconstruct their own memories (Gaesser, Sacchetti, Addis, & Schachter, 2011). According to schema theory, people mold memories to fit information that already exists in their minds. This process is fostered by schemas, which are mental frameworks that organize concepts and information (Gaesser et al., 2011). For example, Sophia, who is 27, is telling her co-workers at lunch about how her boyfriend had proposed to her over the weekend. She tells everyone that he took her out to her favorite restaurant, they ordered lobster for dinner, he had even ordered fancy champagne, and he had the ring placed in her dessert. After the proposal, they went on a romantic carriage ride around the city. The next day, Sophia overhears one of her co-workers retelling the story, and she notices that she is saying Sophia got engaged on the carriage ride and then went out to dinner to celebrate. She knows that her co-worker is not intentionally lying about this story; she is just reconstructing it as she remembers it. Many childhood memories are often reconstructed through family pictures or stories. For example, Eileen, who is now 35, recalls many of the details of her third birthday party. When she tells the story to others, many think she is making it up because there is no way she could remember that many details about something that happened 32 years ago. Eileen has based her story on not only the pictures that were taken that day but also on her parents’ retelling of the story.


How much memory do infants have? Do they have the same memory capacity as a 3-year-old child? What are their first memories? In a study of memory in infants, Rovee-Collier (2007) placed an infant in a crib with an elaborate mobile hanging over her head. She then took a ribbon and tied the baby’s leg to the mobile so that when the infant kicked, the mobile moved around. She then had the infant come back a few weeks later and placed her back in the crib with the elaborate mobile hanging over her head, except this time, she did not tie the ribbon around her leg. However, the infant still kicked her leg to get the mobile to move even though this time the mobile would not respond. Rovee-Collier also noticed that if she put the infant in the crib and anything was changed, even slightly, the infant would not kick her legs. However, if she put the ribbon back on the child’s leg, she would immediately start kicking to move the mobile. After conducting this experiment multiple times, Rovee-Collier found that infants even as young as 2.5 months have a memory that is full of incredible detail.

The infants in the Rovee-Coller study only had to remember what to do for a few weeks. However, studies have found that infants who are only 2 to 6 months old are able to remember some things until they are 1½ to 2 years old (Rovee-Collier, 2007; Rovee-Collier & Barr, 2010). However, another researcher, Jean Mandler, believes that the infants that participated in Rovee-Collier’s study were only displaying implicit memory. Implicit memory is defined as memory with unconscious recollection; it is merely a memory of skill and routine procedures that are performed automatically. Explicit memory, on the other hand, refers to the conscious memory of facts and experiences.

Some researchers have found that babies do not have explicit memory until the second half of the first year, and then memory improves threefold during the second year of life (Bauer, Larkina, & Deocampo, 2011). In one longitudinal study, infants were assessed several times during the second year of life (Bauer et al., 2000). Older infants showed more accurate memory and required fewer prompts to demonstrate their memory than younger infants. Researchers have documented that 6-month-olds can remember information up to 24 hours, but by 20 months of age infants can remember information they encountered 12 months earlier. In general, we find that most young infants’ conscious memories are delicate and fade rather quickly until they are at least 2 years old.


During childhood, there are significant advances in short and long term memory as well as the use of memory strategies. A common assessment of short term memory involves listing several numbers in quick succession, then asking the individual to recall them. Children between the ages of 2 and 3 are only able to remember about 2 numbers; however, 7-year-olds are able to remember 5–7 numbers, and children between the ages of 8 and 13 are able to remember around 8–9 numbers (Dempster, 1981).

During childhood, we also have what is called a working memory. A working memory is defined as the structure of memory that can accommodate information for a short period of time. Working memory is often more active and powerful in modifying information than short term memory. A child who is working on a puzzle that he has done previously will use his working memory rather than his short term memory to put the puzzle together.

We also know that working memory is tied to many aspects of a child’s development (Baddeley, 2012). In fact, research has recently examined the importance of working memory in children’s cognitive and language skills. One study found that working memory and attention control predicted growth in emergent literacy and number skills in young children in low-income families (Welsh et al., 2010). Another study found that working memory capacity of a 9- and 10-year-old children predicted foreign language comprehension two years later at 11 or 12 years of age (Andersson, 2010).

Once children are in school, many of them will begin to use memory strategies to remember things that they need to know or will be tested on. There are several different memory strategies that we all use; some are better suited for short term memory and some are better suited for long term memory. For short term memory, people may use rehearsal, which is the repetition of information. For example, if you are trying to remember which 3 items you need to buy at the grocery store, you may repeat those items over and over in your head until you have gotten all three of your items in your basket. However, if you are looking to store something in your long term memory, you are more likely to use organization or elaboration. Both of these strategies make the information personal, making it more likely that you will store the information in your long term memory. Organization involves grouping items together to make them easier to remember. For example, when a child is being tested on the material in the chapter of a book, she will likely study and remember the information in the same order it was presented in the text. If the Civil War was presented first, followed by the World Wars, then the Viet Nam War, this is how she will remember them. During middle childhood, children are more likely to use elaboration, which involves creating relationships or shared meaning between two or more pieces of information that do not belong in the same category. For example, how do you remember the colors in the rainbow? For many of us, we associate the letters ROY G BIV with each color.

The Fuzzy Trace Theory, proposed by Charles Brainerd and Valerie Reyna (1993, 2004), purports that memory is represented by two types of memory representation: verbatim memory trace and gist. Verbatim memory trace consists of the exact details of the situation, whereas the gist refers to the general idea of the information. When the gist memory is used, fuzzy traces are built up. While all people, no matter what age, use gist memory, young children tend to store and retrieve verbatim traces. However, at some point during middle childhood, children begin to use the gist more and more and, according to this theory, using the gist of your memory contributes to the improved memory and reasoning of older children because fuzzy traces are more enduring and less likely to be forgotten than verbatim traces (Reyna & Rivers, 2008).


As we begin to age, our memory begins to change as well. However, not all memory changes occur at the same time or in the same way. Generally, working memory is the first type to show a decline during late adult years (Ornstein & Light, 2010). One explanation may be that older adults are not able to block out irrelevant stimuli and information from entering their working memory, causing them to be more distracted (Healey et al., 2010). For example, young adults may be able to listen to a speaker even while the people next to them are having a conversation, whereas an older adult might struggle to block out the side conversation in order to focus on the speaker.

Explicit memory, a type of long term memory, can be broken down into three separate categories: episodic memory, autobiographical memory, and semantic. Episodic memory is the retention of information about the where and the when of life’s happenings. For example, can you remember what you were doing or where you were when you heard about the attack on the World Trade Center on September 11, 2001? This is your episodic memory. Autobiographical memory is the personal recollection of events and facts. For example, you may remember a very special birthday, or the first time you went on vacation, or the details of your wedding day. Semantic memory is a personal knowledge about the world. It includes a person’s field of expertise (such as a historian who knows all about the Civil War); general academic knowledge (such as the capitals of the American states); and everyday knowledge, which could include things like the meaning of a word (such as copious) or a famous individual (John F. Kennedy).

Short Term Memory

ong before infants say their first word, they can make fine distinctions among the sounds of language (Sachs, 2009). In Patricia Kuhl’s (2011) experiment, phonemes (the basic units of language) from various world languages are piped through a speaker for an infant to hear. A box with a bear in it is placed where the infant can see it. A string of identical syllables is played and then the syllables are changed (from babababa to lalalala). If the infant turns her head when the syllables change, the box lights up and the bear begins to dance around, rewarding the infant for noticing the change. This research has showed that from birth to about 6 months of age, infants have the ability to recognize when sounds change (most of the time) regardless of the language that the syllables are coming from. Kuhl (2011) calls these children “citizens of the world.” However, over the next 6 months infants get even better at recognizing the changes in sounds from the language that they hear spoken at home, and they gradually lose the ability to recognize phonemic differences that are not a part of their own language.

Children develop language according to an invariant sequence of steps or stages. Infants begin with what we call pre-linguistic vocalizations, which are a form of communication. These vocalizations do not represent an object or an action; they are merely forms that an infant uses to communicate to those around them. The first type of pre-linguistic development is crying. While this may not seem like communication, ask any parent, teacher, or other caregiver how their infants communicate and you will hear a resounding answer of “crying.” For new infants, only a few weeks old, crying is the only way that they can communicate their needs. To an untrained ear, all cries may sound alike, but if you ask someone who has spent a lot of time around one infant, that caregiver would tell you that there are different types of cry, each indicating whether the infant is hungry, tired, or experiencing other discomfort. Table 6.1 provides a summary of these stages.

Language and Cognition

Language development and cognitive development go hand in hand (Waxman &Lidz, 2006). For example, a preschool child may be able to discriminate objects on the basis of distinct features, such as size, movement, sounds that they make, and color. At the same time, their language is also developing and they are adding new words that represent broader categories, such as animals or mammals. Many researchers investigate the question of which comes first. Does the child first develop these concepts and then acquire the language to describe them—or do the child’s new language capabilities lead to the development of these new concepts?

Infant Intelligence


Assessing an infant’s intelligence requires the use of nonverbal techniques.

To measure an infant’s intelligence the same way that we measure an adult’s intelligence would be nearly impossible because infants lack the language skills to communicate their answers. However, there are some ways in which we can assess infants and their abilities.


Some research has found that habituation tasks have the potential for measuring an infant’s intelligence. For example, if a baby is shown a toy or a picture over and over, how many times does that child have to be shown that item to no longer show any interest in it? The speed at which habituation or recognition of the item occurs may be related to both a baby’s neurological and cognitive development. That is, neurological and cognitive processes may be related to the characteristics that psychologists refer to as intelligence. As a result, the variations among an individual’s rate of habituation within the first few months of life may predict later intelligence scores. In fact, several studies done on this very idea have found that to be true: The earlier the infant becomes habituated to the object or recognizes the object, the higher their IQ later in life (Cuevas & Bell, 2013; Domsch, Lahaus, & Thomas, 2010).

Fagan Test of Infant Intelligence

If recent research has indicated that habituation tasks have some insight into an infant’s intelligence, are they the best way for psychologists to measure infant intelligence? Some believe that they are. Joseph Fagan, who is a psychologist, developed a standardized measure for the habituation rate: the Fagan Test of Infant Intelligence (Fagan &Detterman, 1992). He believed that a test of the habituation rate, referred to as novelty preference and visual recognition, is appropriate for infants who perform poorly on conventional tests such as the Bayley Scale on Infant Development (which we will discuss in the next section). For example, an infant who suffers from cerebral palsy may be unable to complete many of the tasks in other assessments; however, she may be fully capable of viewing visual stimuli and exhibiting habituation to them. The Fagan Test of Infant Intelligence is a useful measure of cognitive function among special populations (Fagan &Detterman, 1992; Gaultney& Gingras, 2005; McCorry& Hepper, 2010).

Research examining the utility of the Fagan Test of Infant Intelligence on children without any issues or delays has produced inconsistent results. For example, an infant’s scores on the Fagan test have been shown to be related to specific cognitive functions, such as language comprehension, as captured by tests of intelligence that are administered at a later stage in the child’s life (Andersson, 1996; Domsch, Lahaus, & Thomas, 2010; Thompson, Fagan, &Fulker, 1991). However, other studies have shown no such relationship with later measures of those same variables, such as language (Cardon &Faulker, 1991; Tasbihsazan, Nettelbeck, & Kirby, 2003). Therefore, we cannot say for certain whether or not habituation rate can be used as a standardized measure of intelligence for infants.

Bayley Scales of Infant Development

The best known and most widely used scale for infants is the Bayley Scales of Infant Development, which attempt to measure how children think about, react to, and learn about the world around them (Bayley, 1969, 2006). The language portion of the Bayley Scales consists of two parts: receptive language skills, which reflect how well a child recognizes sounds and how much a child understands spoken words and directions, and expressive language skills, which reflect how well a child is able to use a variety of spoken words. The scales also assess motor skills, which also consist of two parts: fine motor skills, which reflect how well a child can use his or her hands and fingers to make things happen, and gross motor skills, which reflect how well a child can move his or her body. In addition, the scales look at social-emotional milestones and adaptive behavior.

Daniel Hurst/iStock/Thinkstock

Even mundane activities like visiting the grocery store can be learning opportunities for infants.

The scales measure development by identifying milestones that are normally achieved by certain ages. For example, a 3-month-old infant is challenged to reach for a dangling ring, a 9-month-old is observed attempting to put cubes in a cup, and at 17 months old, children are observed while they build a tower of three cubes. When looking at cognitive development, a child who is 8 months old is expected to find a hidden toy under a cloth to exhibit object permanence. The purpose of the scales is to identify young children with developmental delays and to provide information for intervention planning (Bayley, 2006). This tool is not used as a predictive instrument for forecasting later IQ or school performance.

Many parents today spend a good deal of time trying to teach their children skills that will enhance their IQs. Any number of commercial products prey on parents’ fears that their children may not measure up, and these products are found on many toy store shelves. While it is clear that these products do not decrease a child’s potential IQ, there is no evidence that they enhance them either. Parents may find that it is better to spend their time reading to their infants, playing with them, and taking them out on stimulating excursions. A grocery store can provide ample opportunities for learning when parents talk about shapes, colors, temperatures, and types of foods with their children.

Web Field Trip: iPads for Infants

Technology is omnipresent; even babies use it! The following article takes a look at this growing trend:

What do you think about the use of iPads for infants? Do you think it could contribute to intelligence? Why or why not? Make sure that you use at least one scholarly reference to support your opinion.

Stability and Change in Intelligence From Childhood Through Adolescence

Stability and change in intelligence throughout childhood and adolescence seem to vary dramatically across children. While some children’s IQs show a lot of growth, others show little. For example, a very early study (Honzik, MacFarlane, & Allen, 1948) conducted looked at the correlation between IQs at a number of different ages. Using the Stanford-Binet test, the study found that there was a strong relationship among scores at ages 6, 8, 9, and 10 years of age. The correlation between IQ at the age of 8 and IQ at the age of 10 was 0.88; the correlation between IQ at the age of 9 and at the age of 10 was 0.90 (Honzik et al., 1948). This means that there was very little change in a person’s intelligence during these years.

While the stability of intelligence has mostly been based on measuring groups of children, some researchers study individual children longitudinally to determine whether or not there is stability in IQ as they each grow up. For example, one study looked at 140 children between the ages of 2½ and 17 and found that average ranges of IQs were more than 28 points. The scores of one out of three children changed as much as 40 points. The scales that were used were the Mental Development Index (MDI) of the Bayley Scales of Infant Development at 12 and 24 months of age, the Stanford-Binet Intelligence Scale at 4 years of age (the 3-year score was used if the 4-year score was missing, which occurred for 27% of the sample), and the Wechsler Intelligence Scale for Children (Revised) at 6 years of age (the Wechsler Preschool and Primary Scale for Intelligence at 5 years was used if the score at age 6 was missing, which occurred for 36% of the sample) (McCall, Applebaum, &Hogarty, 1973).

Intelligence in Adulthood

Does intelligence decline in adulthood? For decades, researchers had been giving the same answer over and over again: intelligence peaks at the age of 18, stays fairly steady until the mid-20s, and then gradually declines until the end of life. However, today many psychologists view changes in intelligence across the lifespan as more complicated than that, and they have come to a different and more complex conclusion.

Much of the conclusion that was drawn about intelligence starting to diminish starting in the mid-20s was based on cross-sectional research. Cross-sectional studies test people of different ages at the same point in time. In other words, a study asks eight groups (of 10-, 20-, 30-, 40-, 50-, 60-, 70-, and 80-year-olds) the same sample of questions at the same time. These cross-sectional studies found that older adults did not score as well on intelligence tests as younger adults did. However, consider the drawbacks of cross-sectional research in this context. Think about how different life is today from life 60 years ago. Not nearly as many people went to college after they graduated from high school back then, so the younger adults who partake in this cross-sectional research have a lot more formal education than the subjects in later adulthood have. Consider health issues, as well. We know from Chapter 4 that as we get older, our physical health declines. Perhaps someone who is 20 can more easily sit at a desk for several hours to take a test—and more easily see and hear the test questions—than someone who is 70. Because these types of things are not controlled for in intelligence tests, it can look like intelligence declines in late adulthood even if it does not.

Because there were criticisms of using cross-sectional studies to measure intelligence, researchers began to use longitudinal studies. A longitudinal study examines the same people periodically throughout their lifespans. That is, the same group of individuals is tested when they are 20, again when they are 30, and so on. Longitudinal studies on intelligence have found a different developmental pattern of intelligence: Adults show more stable (and even increasing) intelligence test scores until their mid-30s and, in some cases, up into their 50s, after which time their scores begin to decline (Bayley & Oden, 1955; they used the Concept Mastery test, which consists of synonym-antonym sets and analogies drawn from a wide variety of arts, sciences, and general life activities.)

However, just as there are drawbacks to cross-sectional research, there are also drawbacks to longitudinal studies of intelligence. For example, a person taking the same intelligence test several times throughout his life may begin to perform better because he becomes more familiar with the test and comfortable with the testing situation. Further, some subjects may remember some test questions and improve their scores based on memory. Thus, different results in stability and variability of intelligence may be explained by the different methodologies of cross-sectional and longitudinal studies.

Fluid and Crystallized Intelligence

Cognitive psychologist John Horn emphasizes that some abilities increase throughout the lifespan while others decrease (Horn, 2007; Horn & Donaldson, 1980). Horn theorizes that crystallized intelligence, which is the store of information, skills, and strategies acquired through education and prior experiences, continues to increase throughout the lifespan. On the other hand, he suggests that fluid intelligence, an intelligence that reflects information processing capabilities, reasoning, and memory, begins to decline in middle adulthood. There are IQ tests that examine both crystallized and fluid intelligence. For example, the Wechsler Adult Intelligence Scale (WAIS) measures fluid intelligence on the performance scale and crystallized intelligence on the verbal scale. The overall score is based on a combination of these two scales.


Some people will argue that wisdom is another important aspect of cognitive function as we grow older. In fact, Baltes (2003) defined wisdom as expert knowledge about the practical aspects of life. This knowledge facilitates judgment. He believed that practical knowledge involves exceptional insight into human development and has implications for how people cope with the difficult problems that they encounter. Wisdom focuses on life’s pragmatic concerns and human conditions (Staudinger & Gluck, 2011b).

Baltes and his colleagues (Baltes&Kunzmann, 2004; Baltes, Lindenberger, & Staudinger, 2006; Baltes& Smith, 2008) have uncovered the following trends in their research of wisdom:

  1. High levels of wisdom are not seen very often and only a small percentage of adults show wisdom even in late adulthood;
  2. Factors other than age are a vital part of having wisdom (for example, on-the-job experience);
  3. People who have real wisdom are often more concerned about others’ well-being than their own;
  4. Personality traits such as openness to experience, generosity, and creativity are better predictors of wisdom than cognitive factors such as intelligence.
  5. Observations of Home Environmental Qualities
  6. The Home Observation for Measurement of the Environment (HOME) is a checklist for gathering information about the quality of children’s home lives through observation and parental interview (Caldwell & Bradley, 1994). This scale can be used to assess the home environment from infancy all the way to middle childhood. Table 7.1 presents the factors that HOME measures in infancy, toddlerhood, early childhood, and middle childhood.
  7. Table 7.1: Features of high quality home life in infancy and toddlerhood, early childhood, and middle childhood: The HOME subscales
Infancy and toddlerhood Early childhood Middle childhood
1. Emotional and verbal responsiveness of the parent 1. Parental pride, affection, and warmth 1. Parental emotional and verbal responsiveness
2. Parental acceptance of the child 2. Avoidance of physical punishment 2. Emotionally positive parent–child relationship
3. Parental involvement with the child 3. Language stimulation 3. Parental encouragement of social maturity
4. Organization of the physical environment 4. Stimulation of academic behavior 4. Provisions for active stimulation
5. Provisions of appropriate play 5. Stimulation through toys, games, and reading materials 5. Growth-fostering material and experiences
6. Opportunities for variety in daily stimulation 6. Parental modeling and encouragement of social maturity 6. Family participation in developmentally stimulating experiences
7. Opportunities for variety in daily stimulations 7. Parent involvement in child rearing
8. Physical environment is safe, clean, and conducive to development 8. Physical environment is safe, clean, and conducive to development
  1. Source: Bradley, R. H., Mundfrom, D. J., Casey, P. H., & Barrett, K. (1994). A factor analytic study of the infant-toddler and early childhood versions of the HOME inventory administered to white, black, and Hispanic American parents of children born preterm. Child Development, 65, 880–888. Copyright © John Wiley and Sons.
  2. Evidence from the HOME confirms what scholars had been consistently finding for decades: stimulation provided by parents is moderately linked to mental development. Regardless of SES and ethnicity, an organized, stimulating physical environment, along with parental encouragement, involvement, and affection, predict better language skills and higher IQs in toddlerhood and in early childhood (Duckwork, Quinn, &Tsukayama, 2012; Epsy, Molfese, &DiLalla, 2001; Fuligni, Han, & Brooks-Gunn, 2004; Tamis-LeMonda, Shannon, Cabrera, & Lambs, 2004). However, the HOME-IQ does seem to decline in middle childhood, perhaps because older children are spending more time out of the home environment and in the school setting or in the homes of peers. Nonetheless, two of the middle childhood features are very strong predictors of academic achievement: (1) provision for active stimulation and (2) family participation in developmentally stimulating experiences (Bradley, Caldwell, & Rock, 1988).
  3. Kraig Scarbinsky/Photodisc/Thinkstock
  4. Family participation in developmentally stimulating activities is often associated with academic achievement.
  5. Keep in mind that in all of the studies that showed that the family environment did make a difference, all of the children were being raised by their biological parents. As you may recall from our discussion in Chapter 2, parents who are genetically more intelligent may provide better home experiences as well as bear children who are genetically more intelligent. The HOME-IQ correlation is stronger for biological than for adopted children, which suggests that parent–child genetic similarities may elevate the relationship (Saudino& Plomin, 1997).
  6. However, heredity does not account for the entire association between the home environment and mental test scores. It is also important to consider the family living conditions; both the HOME scores and the affluence of the surrounding neighborhood continue to predict children’s IQ beyond the contribution of parental IQs and parental education levels (Marcus Jenkins, Woolley, Hooper, & De Bellis, 2013).
  7. Family Beliefs About Intellectual Success
  8. Parental support for achievement is greater in higher SES families in which both parents and child IQs are higher, making it difficult to tease out the impact of family beliefs on a child’s performance. In a study of more than 1,300 Caucasian American and African American families with school-aged children, parental expectations for educational attainment predicted parents’ involvement in their children’s school activities, supervision of homework, and, two years later, children’s reading and math achievement (Zahn, 2005). Another study that examined Asian American, Hispanic, and Caucasian American families found that with each group, the more education the parents had, the higher their expectations were for their fourth- and fifth-graders (Okagaki&Frensch, 1998). So it seems that parents’ expectations were not merely responsive to their child’s prior achievements, but they also predicted school performance (Davis-Kean, 2005)
  9. The Influence of Heredity and Environment
  10. The nature-versus-nurture debate once again comes to the forefront in intelligence discussions (Martinez, 2010). Intelligence researchers continue to struggle with how genetics and environment impact intelligence independently and concurrently.
  11. Genetic Influences
  12. Heritability is defined as the portion of the variance for any given condition in a population that is directly attributable to genetic factors. Heritability is expressed in terms of a heritability index, which is reported as a correlation coefficient. Thus, a heritability index of 1.0 indicates a perfect relationship between two conditions, a heritability index of 0.75 indicates a strong relationship between two conditions, and a heritability index of 0.20 indicates a weak relationship between two conditions.

The Influence of Heredity and Environment

The nature-versus-nurture debate once again comes to the forefront in intelligence discussions (Martinez, 2010). Intelligence researchers continue to struggle with how genetics and environment impact intelligence independently and concurrently.

Genetic Influences

Heritability is defined as the portion of the variance for any given condition in a population that is directly attributable to genetic factors. Heritability is expressed in terms of a heritability index, which is reported as a correlation coefficient. Thus, a heritability index of 1.0 indicates a perfect relationship between two conditions, a heritability index of 0.75 indicates a strong relationship between two conditions, and a heritability index of 0.20 indicates a weak relationship between two conditions.


There is a strong relationship between genetics and intelligence.

In 1996, the American Psychological Association put together a committee of researchers to examine the extent to which intelligence could be explained by genetics. These authors determined that by late adolescence, the relationship between genetics and intelligence is expressed by a heritability index of 0.75. This index suggested a strong relationship between genetics and intelligence (Neisser et al., 1996).

Since the 1996 report was released, numerous intelligence researchers have pointed out several limitations of the heritability index (Kovas et al., 2013; Sternberg, Kaufman, & Grigorenko, 2008). First, the heritability index is only as good as the data that is used to create the correlation coefficient. Second, the majority of data that were used for the heritability index were drawn from intelligence tests that may not be the best indicator of intelligence (Gardner, 2002; Sternberg, 2011a, 2011b). Finally, separating genetic from environmental factors continues to plague researchers across multiple domains; it is inherently difficult to determine to what extent genetics contributes to intelligence and to what extent environment contributes to intelligence independently.

Environmental Influences

The environmental experiences of children and adults have implications for intelligence and intellectual ability (Grigorenko &Takanishi, 2010; Martinez, 2010). For children, schooling is associated with, and certainly influences, intelligence and intelligence testing. In fact, when children experience large gaps in their formal schooling, their IQs often decrease (Cliffordson& Gustafsson, 2008).

One of the primary arguments for the role of schooling (and one’s environment, in general) in intelligence can be seen in the Flynn effect. The Flynn effect is defined as the worldwide increase in intelligence test scores that has occurred over a relatively short period of time (Flynn, 1999, 2007, 2011). For example, IQs appear to be rising at such a rate that a sizable proportion of the population who would have been considered to have average intelligence in the early 1990s would now be considered “below average.” In fact, if a sample of today’s children were to be given the 1932 version of the Stanford-Binet, approximately one fourth of them would earn scores defined as “very superior.” However, the distinction of “very superior” by today’s standards (and according to a normal distribution) should be reserved for just 2–3% of the population.

The Flynn effect (as well as changes in individuals’ test scores) suggests that heredity cannot be the only factor in intellectual abilities. Several other studies have implicated a variety of environmental factors that affect intelligence. For example, prenatal and early postnatal nutrition has been associated with higher intelligence scores (Lynn, 2009). In addition, children whose parents (and especially their mothers) are college educated and/or are reared in families with higher incomes score higher on tests of achievement (Ang, Rodgers, &Wanstrom, 2010; Shriner, Mullis, & Shriner, 2010). Although these environmental factors are associated with higher scores on tests of achievement and intelligence, it can be argued that ultimately, it appears that both heredity and environment influence intelligence (Grigorenko &Takanishi, 2010; Sternberg, 2011a, 2011b).

Web Field Trip: The Flynn Effect

Are we getting smarter with each successive g


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