As the world moves towards more rapid development of artificial intelligence, what exactly do we know about human intelligence? How is intelligence differentiated from achievement?
When we consider that there are many schools of thought on what really constitutes human intelligence, the schools of thought differ with respects to whether any particular theory is capable of being tested empirically with a sound basis for results and conclusions. When a theory of intelligence is the basis for an intelligence quotient test (IQ), that theory is generally deemed to be more credible. But what are we really testing?
What is Intelligence?
Theories of intelligence have been around for roughly 100 years or as long as psychology as an empirical social science evolved from philosophy and the scientific method. Back in 1904, an early theory of intelligence posited by Charles Spearman, identified a general intelligence metric or measure ‘g’ that supported a functional and thus all-encompassing, single numerical measure of the correlated variables that constitute intelligence (Brody, N. (1992). The premise by Spearman was to develop a common measure of intelligence that translates to consistency of ratings between different cognitive skills. To put it plainly, if a person demonstrates a high measure of intelligence (g) in one cognitive area, Spearman’s theory assumes that the person would also demonstrate a similar high measure in another cognitive area. Hence, the g factor is not thought to be domain specific or systematic.
However, Spearman’s theory has some weaknesses in that it does not comprehensively identify all the correlates of intelligence but merely the ones that were attributable to and thus, demonstrated a positive correlation to the identified g factor. In other words, Spearman assumed only positive correlations between variables impacted by the g factor. Hence, his theory would use factor analysis to analyze the level of correlations between g and the cognitive skills under analysis. However, such analysis would assume that no inter-correlations exist between cognitive skills. For example, a person who demonstrates high verbal skills but low mathematical skills would be inconsistent with Spearman’s model. Spearman’s g factor might indicate a positive correlation with the verbal skills but could not explain the high/low difference between these 2 cognitive skills.
Raymond Cattell posited a more hierarchal theory of intelligence broke the g factor into 2 sub-hierarchies of fluid intelligence (Gf) and crystallized intelligence (Gc). Cattell theorized that individuals develop early stage fluid intelligence which includes speed or efficiency of thought processing, or what is typically now referred to as “executive processing”. This fluid intelligence Gf develops until a person reaches the age of 20 at which point Gf declines and Gc “crystallized intelligence” becomes more prominent in a person’s cognitive toolbox. Gc is less plastic in terms of the acquisition of new skills but it is stronger in the retrieval and usage of stored memories in more combinatory cognitive tasks. (Brody, E. B., & Brody, N. 1976). Cattell’s theory influenced the theorist Carroll who posited a 3 layered enhancement of Cattell’s theory by adding a multiple sub-factored measure of g back into the mix as a general intelligence factor. (Carroll, J.B. 1993) Carroll came to this theory of a multiple factored g metric by examining positive inter-correlations between over 450 cognitive variables.
Other theorists (R.J. Sternberg and H. Gardner) have posited more of structural systemic theories of intelligence where multiple factors of intelligence are explicitly defined and differentiated. Their reasoning is that a person can and does often demonstrate significant differences in between these domains of intelligence. Gardiner theory for example identified 7 domains of intellect that can exist separately (Roberts, R. D., & Lipnevich, A. A. 2012); Gardner asserted that humans have independent intelligent domains in the areas of linguistics, spatial reasoning, musical ability, athletic prowess, empathic/interpersonal skills, and psychological aptitude/intrapersonal skills. However, in specifying that these areas operate via independent brain operations, his theory contradicts current trends in neuroscience testing that shows inter-relatedness between areas such as for example, spatial reasoning, math and musical abilities
Other weaknesses in these systemic theories is that they are not subject to empirical testing without a valid identifiable metric or rate of intelligence subject to testing while excluding of the other domains. This is why no current test of Intelligence Quotient (IQ) is currently modeled after any systemic theories.
So in light of the above information, what exactly is intelligence by today’s standards? Some definitions are needed here. In Plato’s “Euthyphro” dialogue, he defines things as based upon their irreducibility. For our discussion, this translates to definitions by exclusions of attributes of things until such things are reduced to their raw essence or form. Human intelligence can thus be theorized as a rate or measurement that is distinct from raw data or constructed skills. Raw data are irreducible concrete or abstract objects. We can posit therefore that intelligence is the rate of organizing/assembling raw data objects and abstract data perceived via the senses into compound information that can be used for further cognitive constructive skills and objects. We can determine this intelligence rate by examining its construction and its compound utility. For example, when we speak of verbal skills, spatial reasoning, or logic, we are already discussing compound cognitive objects. These skills are not intelligence in a “rate” form but rather they constitute the traditional means to measure intelligence rate using the most commonly identifiable and universal constructed skills. However, it may be really the rate of their construction and the magnitude of their compound usage that is the key in measuring one’s rate or intelligence. After all, when we measure any sort of rate, we evaluate its changes or difference by evaluating its output or results. Using an analogy, we can only determine if a runner runs at a fast rate by evaluating this rate by the distance covered within the time elapsed. The same can be said of intelligence rate. We can only evaluate intelligence rate by the cognitive objects constructed within a given time constraint.
Intelligence Rate or Quotient is thus, scored based upon what is observable, testable and measurable. Thus, the verbal skills, spatial reasoning, cognitive processing speed/executive function, and/or working memory that are part of the aforementioned g factor are how intelligence is still measured. The most popular IQ tests in use (Woodcock/Johnson, Weschler, Stanford-Binet, & Kaufman Assessment tests) typically measure and assess these g factor skills (Roberts, R. D., & Lipnevich, A. A. 2012)
What is Achievement?
Achievement is defined as “the level of attainment or proficiency in relation to a standard measure of performance, or, of success in bringing about a desired end.” (ERIC 2016). Thus, for this discussion, achievement is essentially the difference between points of intelligence and/or the acquisition of knowledge.
Educators and psychologists become concerned with achievement in the quest to develop means to enhance or increase the level, the quality and the retention of learning achievement gains. Achievement has both intrinsic influences of which intelligence is one of the primary drivers and extrinsic influences such as education methods, goal setting, influence of peer group achievement levels, parental education and influence.
What’s the difference between Intelligence and Achievement?
Intelligence is a human attribute of biological origin and can be influenced by early stage environmental factors which can either enhance or stagnate the development of regions of the brain associated with intelligence. Achievement on the other hand, is primarily a factor of the environment but can also be significantly impacted by the optimization of one’s intelligence across the lifespan.
Studies assert that the combination of intelligence with self-efficacy yields greater achievement however the impact of environmental factors (as early as the conception stage throughout the lifespan) can and often do impact gains in achievement.
Who achieves greater academic/professional success? Is it a factor of intelligence or behaviors?
While higher achievement/learning outcomes is typically in a positive correlation with higher levels of intelligence, achievement gains in lower performing groups can also be influenced by earlier exposures to literacy, instances of teacher expectation bias as well as a greater availability of education resources. Achievement is typically reported to be lower among students living in poverty due primary to the unavailability or poor availability to teachers and effective schools. Achievement is also a factor of parental expectations, and peer achievement levels.
Environmental Influence on Achievement #1 – Teacher expectation bias and boundary goals
Additionally teacher expectation bias and boundary goals (Corker, K. S., & Donnellan, M. B. 2012) have been found to influence student achievement and outcomes bi-directionally. Teacher expectation bias can influence students in an affective or quasi-empathic manner in that students who perceive that teachers have a positive or negative achievement expectation were shown to adjust their efforts to accommodate the expectation (Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E. 2010). For example, if a teacher exhibits lower expectations from students, evidence suggests that the students will underperform accordingly. (Corker, K. S., & Donnellan, M. B. 2012) assert that student “boundary goals” also factor into motivating and thus, effecting student achievement. They suggest that students have an intrinsic boundary achievement goal objective that can and often does hinder optimal achievement. They further suggest that a teacher should focus on prompting students to demand more from themselves or set their boundary goals higher in order to achieve greater academic gains.
(De Boer, H., Bosker, R. J., & van der Werf, M. P. C. 2010) performed a similar study on expectation bias and their evidence suggests that “positive expectation bias increased later achievement more than negative bias decreased achievement”. However, prior achievement somewhat mediated this correlation. The researchers in this case also cited prior case studies showing even further correlation between what they indicated as “stigmatized groups” such as African-Americans however, their classification of what constitutes “stigmatized” was not disclosed and nor was the researchers position as to why race might be a factor in expectation bias. Therefore, it is not clear as to what extent race factors into expectation bias and its correlation to under/over achievement.
Teacher expectation bias has shown little direct correlation to effecting changes in intelligence g factor or with respects to structural systemic intelligence factors as expectation bias and goal setting relates to motivational correlates, not intelligence variables. We can’t thus, prompt a student to perform at a higher level of competency solely on the basis of setting expectations higher or by prompting them to raise their boundary goals.
Biological Influence on Intelligence /Achievement #2 – Working Memory & Attention Control
Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E. (2010) asserted that a child’s early stage working memory and attention control (WMAC) appears to be in an implicit bi-directional relationship with the domain specific intelligences of spatial /mathematical reasoning abilities and the emergence of reading & verbal skills. Working or short term memory is theorized to facilitate retrieval and efficient use of longer term memory constructs.
The focus or control of attention enables the filtration of processing of unnecessary cognitive information thereby allowing humans to focus on more important and thus intended information.
With respects to achievement, WMAC impacts student readiness for more advanced math and verbal subjects. Per the (Welsh, et al 2010 study), “emergent literacy skills at the pre-kindergarten level successfully predicted… over the course of the longitude study…, both math and reading achievement as well as self-regulatory skills” Literacy exposure to children within early stage development has also been linked to increases in both intellectual aptitude as well as greater achievement gains with implications for retention of greater academic and career success throughout the lifespan.
Intelligence and achievement are different breeds but with some correlates in bi-directional relationships. More work needs to be done with efforts towards the derivation of a general theory of learning that expands upon both the intelligence factor g as well as the aforementioned systemic structural intelligence theories.
Brody, E. B., & Brody, N. (1976). Intelligence: Nature, determinants, and consequences. New York: Academic Press.
Brody, N. (1992). Intelligence (2nd ed.). San Diego: Academic Press. A comprehensive review of contemporary research on intelligence.
Carroll, J.B. (1993), Human cognitive abilities: A survey of factor-analytic studies, Cambridge University Press, New York, NY, USA
Corker, K. S., & Donnellan, M. B. (2012). Setting Lower Limits High: The Role of Boundary Goals in Achievement Motivation. Journal Of Educational Psychology, 104(1), 138-149. doi:10.1037/a0026228
De Boer, H., Bosker, R. J., & van der Werf, M. P. C. (2010). Sustainability of teacher expectation bias effects on long-term student performance. Journal of Educational Psychology, 102(1), 168-179.
Ekinci, B. (2014). The Relationships Among Sternberg’s Triarchic Abilities, Gardner’s Multiple Intelligences, And Academic Achievement. Social Behavior & Personality: An International Journal, 42(4), 625-633. doi:10.2224/sbp.2014.42.4.625
ERIC (2016) definition of achievement. Retrieved from http://eric.ed.gov/?ti=Achievement
Roberts, R. D., & Lipnevich, A. A. (2012). From general intelligence to multiple intelligences: Meanings, models, and measures. In K. R. Harris, S. Graham, T. Urdan, S. Graham, J. M. Royer, M. Zeidner, … M. Zeidner (Eds.) , APA educational psychology handbook, Vol 2: Individual differences and cultural and contextual factors (pp. 33-57). Washington, DC, US: American Psychological Association. doi:10.1037/13274-002
Welsh, J. A., Nix, R. L., Blair, C., Bierman, K. L., & Nelson, K. E. (2010). The development of cognitive skills and gains in academic school readiness for children from low-income families. Journal of Educational Psychology, 102(1), 43-53.