Which of the following describe an individuals intellectual capabilities and are closely linked to how a person makes decisions and processes information?

Intellectual Function

A useful definition ofintellectual function is the capacity to think in the abstract, reason, problem-solve, and comprehend. The concept of intelligence has had many definitions and theoretical models, including Spearman's unitary concept of “the g-factor,” the “verbal and nonverbal” theories (e.g., Binet, Thorndike), the 2-factor theory from Catell (crystallized vs fluid intelligence), Luria's simultaneous and successive processing model, and more recent models that view intelligence as a global construct composed of more-specific cognitive functions (e.g., auditory and visual-perceptual processing, spatial abilities, processing speed, working memory).

The expression of intellect is mediated by many factors, including language development, sensorimotor abilities, genetics, heredity, environment, and neurodevelopmental function. When an individual's measured intelligence is >2 standard deviations below the mean (a standard score of <70 on most IQ tests) and accompanied by significant weaknesses in adaptive skills, the diagnosis ofintellectual disability may be warranted (seeChapter 53).

Functionally, some common characteristics distinguish children with deficient intellectual functioning from those with average or above-average abilities. Typically, those at the lowest end of the spectrum (e.g., profound or severe intellectual deficiencies) are incapable of independent function and require a highly structured environment with constant aid and supervision. At the other end of the spectrum are those with unusually well-developed intellect (“gifted”). Although this level of intellectual functioning offers many opportunities, it can also be associated with functional challenges related to socialization and learning and communication style. Individuals whose intellect falls in the below-average range (sometimes referred to as the “borderline” or “slow learner” range) tend to experience greater difficulty processing and managing information that is abstract, making connections between concepts and ideas, and generalizing information (e.g., may be able to comprehend a concept in one setting but are unable to carry it over and apply it in different situation). In general, these individuals tend to do better when information is presented in more concrete and explicit terms, and when working with rote information (e.g., memorizing specific material). Stronger intellect has been associated with better-developed concept formation, critical thinking, problem solving, understanding and formulation of rules, brainstorming and creativity, andmetacognition (ability to “think about thinking”).

Cognitive abilities

Cognitive ability is one of the most extensively studied topics within the field of behavioral genetics (McGue & Bouchard, 1998). Cognitive ability, sometimes referred to as general intelligence (g), is essential for human adaptation and survival. It includes the capacity to “reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience” (Plomin, 1999). Beyond memorization or imitation, intelligence supports the ability to comprehend situations, figure out what is needed, and plan a course of action. Cognitive ability is closely associated with educational attainment, occupation, and health outcomes (Plomin & Von Stumm, 2018). The question of how genetics and environment contribute to cognitive ability in adolescence becomes a central issue as we consider the life choices and pathways that become available to young people during this sensitive period of life.

Early studies of cognitive ability with MZTs and DZTs as well as adoption studies report a heritability of about 0.50 (McGue & Bouchard, 1998). Heritability estimates increase with age, with studies reporting estimates over 0.80 for MZTs in adolescence and adulthood (Finkel, Pedersen, McGue, & McClearn, 1995; McGue, Bouchard, Iacono, & Lykken, 1993). One implication is that the genetic contribution to cognitive ability may change as children mature. For example, genetic factors that support sensorimotor investigation and categorization in infancy may play a different role as they support spatial learning and problem-solving in middle childhood and adolescence (Plomin, 1999). Another implication is that genetic capacities play an increasing role in the choices young people make, the people they choose to interact with, the pursuit of educational and occupational goals, and the kinds of activities they find stimulating. The idea of genetic-environment correlation suggests that the person’s cognitive abilities shape the nature of their social, leisure, health, and occupational environments. The concept of genetic × environment interaction suggests that depending on one’s sensitivity to environmental conditions, specific features of the environment may accentuate or suppress one’s genetic potential.

Despite the relatively high heritability of cognitive ability and its stability over the life span, it is substantially < 1.0. In other words, environments play an important role in the emergence and flourishing of cognitive abilities, both the shared and nonshared environments of the prenatal period, infancy, and childhood, and the correlated and evoked environments that become increasingly nonshared in adolescence and adulthood. As implied by the concept of the norm of reaction, there is evidence that genetic contributions to cognitive ability are maximized in high resource environments, and suppressed in low resource environments. When resources are plentiful, adolescents have more opportunities to allow their genetic potential to guide their choices and for environmental resources to stimulate, evoke, or enhance their genetic capacities. When environments are impoverished, there is less opportunity for cognitive stimulation, fewer choices, fewer opportunities to express individual talents, and more constraints on behavior just to survive (Tucker-Drob, Briley, & Harden, 2013).

Education/leisure activities/early-life cognitive abilities

In 1990, a study performed in Shanghai demonstrated an association between a lower educational attainment and dementia risk (Zhang et al., 1990). Subsequently, several other studies have demonstrated an association between low educational attainment and increased dementia risk (Qiu et al., 2001;Stern et al., 1994).

In addition to education, participation in certain leisure activities, including reading, dancing, playing board games, and playing musical instruments, is associated with a decreased dementia risk (Verghese et al., 2003).

These studies and others have led to the development of the cognitive reserve hypothesis. Which attempts to explain why those withcertain life experiences, including higher educational attainment and increased leisure activity participation, are more resistant to neurodegenerative changes (Stern, 2012).

Early-life cognitive abilities also may play an important role in dementia risk. In the nun study, autobiographical essays from nuns at a mean age of 22 were evaluated for idea density and grammatical complexity. Those with low idea density and grammatical complexity had lower cognitive scores later in life, and, in a small sample of nuns who came to autopsy, those with low early-life linguistic ability had AD pathology while those with linguistic talent did not have AD pathology (Snowdon et al., 1996). Similarly in 1932, participants in the 1921 Scottish birth cohort took a test of intelligence at age 11. Lower mental ability at age 11 was associated with an increased risk of dementia (Whalley et al., 2000).

Predicting Cognitive Ability

Cognitive abilities build upon motor accomplishments, so individual differences in infant motor milestone attainment might plausibly predict later cognitive abilities. Indeed, Joseph Campos and colleagues have argued that self-produced locomotion in the form of crawling has positive consequences for various cognitive skills. For example, self-produced locomotion can enhance perspective-taking skills. These ideas have historical parallels. Bayley and Shirley both considered whether the age of first walking was predictive of preschool mental ability, and both reported that it was; later walking was associated with lower ability scores. However, the strength of the relationship, though statistically significant, was not large, and critics subsequently argued that the relation was due primarily to the influence of a small number of cases where development was greatly delayed. There is little doubt that extreme delays in infant motor development are predictive of poorer later outcomes; the more contentious issue is whether variation in the normal range of motor development predicts later outcomes.

More recently medical researchers considered the potency of individual milestone attainment for predicting later developmental deviations or delays. Like Bayley and Shirley they found a small-to-moderate negative correlation between age of attainment and scores on later intelligence tests. However, the size the relation was too small for clinical diagnostic use (i.e., for predicting individual outcomes).

Another relevant literature has to do with the predictive value of infant development tests for predicting later cognitive abilities. As noted earlier, physical milestones are an important part of infant development tests, so the predictive success of infant development tests bears on whether or not milestone variability has any predictive utility. Generally, scores on infant tests were predictive, but the relationships were too small to allow for predicting later individual outcomes. These findings, together with those discussed above, consistently suggest that individual differences in milestone attainment have some relation to later individual differences cognitive ability. Although there is a relationship, it is too small to allow for individual prediction.

The Corpus Callosum and Anterior Commissure Transfer Thoughts, Memories, Training, and Other Information Between the Two Cerebral Hemispheres

Fibers in thecorpus callosum provide abundant bidirectional neural connections between most of the cortical areas of the two cerebral hemispheres, except for the anterior portions of the temporal lobes; these temporal areas, including especially theamygdala, are interconnected by fibers that pass through theanterior commissure.

One of the functions of the corpus callosum and the anterior commissure is to make information stored in the cortex of one hemisphere available to corresponding cortical areas of the opposite hemisphere. The following important examples illustrate such cooperation between the two hemispheres.

Cutting the corpus callosum blocks transfer of information from Wernicke’s area of the dominant hemisphere to the motor cortex on the opposite side of the brain. Therefore, the intellectual functions of Wernicke’s area, located in the left hemisphere, lose control over the right motor cortex that initiates voluntary motor functions of the left hand and arm, even though the usual subconscious movements of the left hand and arm are normal.

Cutting the corpus callosum prevents transfer of somatic and visual information from the right hemisphere into Wernicke’s area in the left dominant hemisphere. Therefore, somatic and visual information from the left side of the body frequently fails to reach this general interpretative area of the brain and thus cannot be used for decision making.

Finally, people whose corpus callosum is completely sectioned have two separate conscious portions of the brain. For example, in a teenage boy with a sectioned corpus callosum, only the left half of his brain could understand both the written word and the spoken word because the left side was the dominant hemisphere. Conversely, the right side of the brain could understand the written word but not the spoken word. Furthermore, the right cortex could elicit a motor action response to the written word without the left cortex ever knowing why the response was performed. The effect was quite different when an emotional response was evoked in the right side of the brain: in this case, a subconscious emotional response occurred in the left side of the brain as well. This response undoubtedly occurred because the areas of the two sides of the brain for emotions, the anterior temporal cortices and adjacent areas, were still communicating with each other through the anterior commissure that was not sectioned. For example, when the command “kiss” was written for the right half of his brain to see, the boy immediately and with full emotion said, “No way!” This response required function of Wernicke’s area and the motor areas for speech in the left hemisphere because these left-sided areas were necessary to speak the words “No way!” When asked why he said this, however, the boy could not explain it.

Individual core characteristics and innovation

An individual's cognitive ability provides the foundation for his or her innovative capabilities. Such cognitive abilities include intelligence, perseverance, creative thinking ability, and even pattern recognition. Cognitive ability refers to the functioning usually considered to be a person's mental faculties. In general, the higher an individual's cognitive abilities, the more able that person is to develop innovations and implement innovations from other sources. Leonardo da Vinci and Michaelangelo are perhaps the exemplars of strong cognitive abilities being linked to great innovations.

People with certain personality types have also been found to be more innovative. Those with a more creative personality tend to be more innovative as well. Characteristics that predispose one to innovation include openness to new ideas, perseverance, self-confidence, tolerance of ambiguity, independence, and originality. There are also personality traits that reduce a person's propensity for innovation. These include authoritarianism and being rules oriented. Personality, like cognitive ability, is thought to be a relatively stable aspect of a person, and thus not very amenable to alteration. While there are ways to improve both aspects, intervention techniques are usually aimed at other individual level characteristics.

Cognitive abilities

Cognitive ability associations, particularly intelligence and executive function task performance, have been examined as correlates of temporal discounting preferences in adult samples. Shamosh and Gray (2008) conducted a meta-analysis of the relationship between temporal discounting and intelligence. They found that participants with higher intelligence tended to display lower delay discounting or less sensitivity to delay periods. Their reported effect size, based on a random effects model with a weighted mean, was r = −  0.23, p < .001, which would be considered in the small range. Shamosh et al. (2008) also reported a significant positive correlation between temporal discounting choices and working memory performance in a sample of university students. We also found a significant positive correlation between both intelligence and executive function task performance with the interest rate score on our temporal discounting task in undergraduate students (Basile & Toplak, 2015). In our CART adult battery, we did not find a significant correlation between our Rational Temporal Discounting subtest and cognitive ability measures (Stanovich et al., 2016). Overall, studies that have reported significant correlations display relatively small effect sizes, suggesting that there is considerable opportunity for dissociation between these measures.

In terms of cognitive ability correlations with temporal discounting in developmental samples, Steinberg et al. (2009) demonstrated that indifference points were positively related to intelligence in their sample of participants 10–30 years of age. Similarly, correlations with executive function tasks have also been examined, however these correlations have been somewhat mixed in developmental samples. Steinberg et al. (2009) reported a marginally significant association between temporal discounting and executive function task performance on the Tower of London and Stroop tasks after controlling for intelligence. Other developmental studies have reported no significant associations with measures of inhibition and working memory (Prencipe et al., 2011). Delay of gratification paradigms also seem to display a parallel trend. It has been found that delay ability in preschoolers significantly predicted SAT scores in adolescents (Mischel et al., 1988). Toplak et al. (2016) reported that the preference for a larger delayed reward across all of the indices examined were positively correlated with age, intelligence, and executive function task performance, including the interest rate rational thinking score.

Cognitive Functioning

Cognitive abilities are among the most heritable dimensions of behavior, with genetic factors consistently accounting for about 50% of the variability in studies of childhood, adolescence, and young adulthood. Studies of later life have indicated higher levels of heritability for general cognitive abilities (see Table 1) than are typically observed in younger populations. For example, assessments from the SATSA indicated that 80% of the variability for twins 60 years of age (average age) was due to genetic differences. It was originally speculated that the higher heritability estimates could be related to specific characteristics of the Swedish sample, but these results have been replicated in studies using middle-age and older subjects in United States and Norwegian samples.

Table 1. Estimates of heritability, shared environment, and non-shared environment for a variety of cognitive abilities across studies focusing on later lifea

Research on specific cognitive abilities (e.g., verbal, perceptual speed, spatial orientation, memory) also implicates substantial genetic involvement, albeit less than what is reported for general abilities. Across multiple studies, the heritabilities range from 0.0 to 0.86, with the lowest estimates for measures of memory and the highest estimates for verbal ability and perceptual speed. A perusal of Table 1, however, indicates that there is much variability in these results. It has been hypothesized that the genetic influences on the cognitive domain are more general than specific. An interesting analysis from the SATSA looked at the relationship between the factor loading of the specific scale on the principal component and estimates of heritability, and found that the factor loadings were correlated with the heritability of the tests. The authors speculated that the more a trait taps into general cognitive ability, the more heritable it is.

Interestingly, much of the remaining variance in cognitive functioning is due to non-shared environment, although there are notable exceptions here as well. For example, findings from verbal ability tests consistently illustrate the importance of environmental factors in contributing to familial similarity, with estimates for shared environment ranging from 0.14 to 0.30. The Seattle Longitudinal Study explicitly assessed the extent to which aspects of the early and current family environment (measured with the Family Environment Scale) contributed to familial similarity (between siblings and between parents and offspring) for cognitive abilities. Results indicated that early family environment, both shared and uniquely experienced, impacted familial similarity in adult cognitive functioning, especially in siblings.

Another area of later life that has received much attention is cognitive decline, and multiple studies have measured different aspects of cognitive status (e.g., the Mini-Mental Status Exam [MMSE]). One sample in which cognitive decline has been extensively studied is the NHLBI, which showed heritability estimates of 0.22 for the Iowa Screening Battery, 0.38 for the MMSE, and 0.76 for Digit Symbol. A subsample of 44 of the male twin pairs was followed over a 5-year period to assess changes in cognitive function, based on the digit symbol measure. Although the sample was small, the results indicated that digit symbol substitution was heritable (0.80 and 0.88 at times 1 and 2, respectively). The prevalence of decline (defined as one or more SD changes) was similar in both MZ (35%) and DZ (39%) twins, but the concordance rates for decline were not. For identical twins, the concordance rate was 45%, whereas in fraternal twins the rate was 8%; thus, at least a portion of the rate of change in cognition (indexed by a measure of perceptual speed) is influenced by hereditary factors.

The MTSADA researchers looked at the relationship between memory and cognitive functioning, lifestyle, and personality factors. They were interested in the general observation that there are large individual differences in memory ability among older individuals. In addition, older individuals who are high in verbal ability maintain a high level of intellectual activity, have larger working memory capacity, maintain a high level of general or physical activity, and manifest little test anxiety. The researchers specifically focused on the etiology of the relationship between measures of memory capacity and measures of social class (occupation, education, and vocabulary), processing speed (reaction time, digit symbol), intellectual activity, and physical activity. The results indicated that genetic influences on memory are largely mediated by processing speed and social class, whereas environmental influences on memory are mediated to some extent by physical activity. Thus, the authors suggested that interventions for a decline in memory functioning might best be targeted at lifestyle variables such as physical activity.

Conclusions

Cognitive abilities of subjects who smoked cannabis are reliable only if assessed after a prolonged time of abstinence. Eventual long-term impairment observed after this period could reflect the consequence of a complex premorbid gene—early environmental predisposition. This predisposition can influence the contact with the substance, and a particular pattern of cannabis use, moderated by other environmental factors. This could be true even for clinical samples since, according to the neurodevelopmental theory of schizophrenia (Murray & Lewis, 1987), the neurocognitive impairment in psychosis remains stable after the onset of the illness (Bora & Murray, 2014). Moreover, cannabis use could be a trigger for a neurodevelopmental predisposition.

So, is cannabis safe? The answer is no. Since we do not know which special premorbid cocktail was prepared for us, cannabis could act like a “Russian roulette” placed against the brain: the more you play, the higher is the risk you are taking but, sometimes, a single game could be enough.

Dual-Process Models and Implications for Decision Making in Older Adults

Cognitive ability is one of the possible contributors to choice set-size performance as well as preference, and dual-process models have been postulated to characterize the role of cognitive ability in decision making. When making decisions, theorists proposed that information processing involves two types of procedures (e.g., Epstein, 1994; Kahneman, 2003): System 1, which refers to an affective/experiential system; and System 2, which refers to a more deliberative/analytical system. System 1 can be thought of as automatic, effortless, rigid, heuristic-based, affective, and implicit. It is the kind of decision that can be made almost unconsciously, such as stereotyping. In contrast, System 2 is described as effortful, conscious, analytical, slow, flexible, and more resource intensive. It requires attention and concentration, such as computing and comparing probabilities (Kahneman, 2011; Stanovich & West, 2000). The two systems can work simultaneously, and affective information can also influence deliberative thinking. However, System 2 can become depleted and less efficient with effort. Given the nature of health-related decision making, it is reasonable to assume that such decisions would involve both deliberate and affective components.

A number of researchers have capitalized on dual-process models to better understand life-span changes in decision-making abilities (Peters, Hess, Västfjäll, & Auman, 2007; Peters & Bruine de Bruin, 2012; see also Hess, in this volume). Overall, there is a general consensus by those studying aging and decision making that older adults will perform worse on tasks that are more heavily dependent on System 2 processes relative to those dependent on System 1, based on the findings that aging is associated with normative decline in specific cognitive abilities typically associated with System 2 (Peters & Bruine de Bruin, 2012). For example, changes in working memory and processing speed would more directly impact System 2 type deliberative processes than System 1 type processes (Evans, 2003).

Indeed, there are now ample data to argue that age effects on choice performance and strategies are most likely associated with System 2 type processes (e.g., Hanoch, Wood, & Rice, 2007), especially when the decision-making tasks are cognitively demanding or lack supportive environments for decisions (Finucane, Mertz, Slovic, & Schmidt, 2005; Yoon, Cole, & Lee, 2009). Declines in cognitive abilities make it more difficult for older adults to navigate a complex decision-making environment that requires concentration. For example, older adults are slower in terms of processing speed, which is associated with decreased performance on other cognitive tasks (Salthouse, 1996). Also, although it remains to be investigated in more depth, the tendency for older adults to seek less information in decision-making tasks might be related to decreased working memory capacity (for a review, see Mather, 2006). These findings from the cognitive aging literature imply that aging is associated with declines in fluid abilities, such as speed of processing, working memory, and executive functioning (Schaie & Willis, 2002), precisely the abilities that characterize System 2 processing and functioning. Whether older adults are cognizant of these changes and thus are more likely to actively prefer less demanding choice environments is an open empirical question. Regardless of preference, however, their performance in different choice environments may very well decline if these environments tax System 2 types of processes.

There is support for dual-process theories in the area of medical decision-making and aging. Because older adults tend to use more health-related services, more work was done in the health domain versus other areas of decision-making abilities. Hibbard, Slovic, Peters, Finucane, and Tusler (2001) have long been interested in older adults’ abilities to understand health-related (e.g., insurance) information. In one study, they evaluated older and younger adults’ comprehension of health and financial information about health insurance. Their results indicated that older adults are more likely to make mistakes compared to younger adults. Finucane et al. (2005), in a related investigation, focused on the association between age and decision quality by varying the complexity of tasks in a number of related domains: health, financial, and dietary. Their data showed that as the task became more complex, the number of errors increased as well, with older adults experiencing even greater difficulties than their younger participants. As such, one would predict that as the number of choices increases, older adults would be less likely to make optimal decisions compared with younger counterparts.

More evidence supports the relationship between cognitive resources and decision making in aging. Based on a series of studies, Johnson (1990, 1993) had amassed sufficient evidence to show that, when deciding about cars or apartments, older adults tend to evaluate less information, reexamine information more often, need longer time to review information, and use more simplified search strategies. Mata and colleagues (Mata, von Helversen, & Rieskamp, 2010; Mata, Schooler, & Rieskamp, 2007) have provided similar results, using somewhat different tasks. In their investigations, they were interested in the relationship between aging and the ability to utilize adaptive decision strategies in a number of different environmental structures. In line with Johnson’s earlier work, Mata and colleagues found that older adults frequently use less information and require more time to evaluate it in their decision making. Furthermore, older adults often utilized simpler decision strategies due to, according to the authors, declines in cognitive abilities. A meta-analysis by Mata and Nunes (2010) provides further indication that older adults tend to use more heuristic-based decision strategies, as they often search and use less information in their decision-making process. However, other studies (Hess, Queen, & Ennis, 2013; Queen, Hess, Ennis, Dowd, & Grühn, 2013) found smaller differences in search strategies and highlight the importance of individual difference factors like education and search environment in strategy selection across the life span. Taken together, these findings appear to indicate that older adults are more likely than younger adults to adopt simpler strategies in their searches.