IntelligenceEssay Preview: IntelligenceReport this essayCompare and contrast two developmental theories of intelligenceIntelligence is a complex psychological construct and promotes fierce debate amongst academics. Many experts maintain that intelligence is the most important aspect of individual differences, whereas other doubt its value as a concept. At one extreme many claim that individual differences in intelligence depend upon genetic factors, and at the other many argue that environmental factors account for it. In this essay I will compare and contrast two developmental theories of intelligence, with the work of Piagets account for developmental intelligence and Mike Andersons theory of developmental intelligence.
Piaget suggested that intelligence is a form of adaptation wherein knowledge is constructed by each individual through the complimentary processes of assimilation and accommodation. Piaget theorized that as children interact with their physical and social environments, they organize information into groups of interrelated ideas called schemes. When children encounter something new, they must either assimilate it into an existing scheme or create an entirely new scheme to deal with it. It must be noted that Piaget began his studies during a pioneering era; he was free to conceive of intelligence in terms of his unique perspective. (Ginsburg 1969).
A central focus of Piagets Epistemology is that increasingly complex intellectual processes are built on the primitive foundations laid in earlier stages of development. An infants physical explorations of his environment form the basis for the mental
Representations he develops as a preoperational child, and so on. Another important principle of Piagets stage theory is that there are genetic constraints inherent in the human organism – you can challenge a child to confront new ideas but you cannot necessarily teach him out of one stage and into another according to Piaget. Moreover, a child cannot build new, increasingly complex schemes without interacting with his environment; nature and nurture are inexorably linked. Piaget maintained that children were mini-scientists, who are active participants within the world, Piaget (1973) discusses:
Intelligence does not by any means appear at once deprived from mentaldevelopment, like a higher mechanism, and radically distinct from those, which have preceded it. Intelligence presents, on the contrary, a remarkable continuity with the acquired or even inborn processes on which it depends and at the same time makes use of (Piaget p21)
Piaget argued that intellectual development occurs quantitatively in four distinct stages such as: the sensori-motor, the preoperational, the concrete Operational and the Formal Operational stages. The sensor motor stage begins at birth, and lasts until the child is approximately two years old. At this stage, the child cannot form mental representations of objects that are outside his immediate view, so his intelligence develops through his motor interactions with his environment. The Preoperational stage typically lasts until the child is 6 or 7. According to Piaget, this is the stage where true thought emerges. Preoperational children are able to make mental representations of unseen objects, but they cannot use deductive reasoning. The Concrete operations stage follows, and lasts until the child is 11 or 12. Concrete operational children are able to use deductive reasoning, demonstrating conservation of number, and can differentiate their perspective from that of other people. Lastly the Formal operations stage is the final stage. Its most salient feature is the ability to think abstractly (Piaget 1963).
Attempts have been made to correlate performance on Piagetian conservation tasks with standardized intelligence test scores, and the results have been mixed. Ultimately, an intelligence test built on a Piagetian framework would have to function very differently from intelligence tests like the Wechsler or the Stanford-Binet. In addition to recording a childs correct and incorrect responses, the test administrator would also have to ask the child to explain why he answered in a given way. Piaget suggested that one way to reconcile these two approaches would be to adopt a method whereby a traditional intelligence test could serve as the basis for a clinical interview.
Piaget offered various definitions of intelligence, all proposed in general terms, all of which are based upon his biological stance. As quoted in Ginsburg (1969) Piaget suggests that intelligence is a particular instance of biological adaptation p32 and furthermore when discussing intelligence Piaget maintains that intelligence is the form of equilibrium toward which all the cognitive structures.tend p33. Piaget adopts the term equilibrium which thus implies a balance, a balance in this case between an individuals mental actions and their environment.
Piagets research methods were based primarily on case studies. While some of his ideas have been supported through more correlation and experimental methodologies, others have not. For example, Piaget believed that biological development drives the movement from one cognitive stage to the next. Data from cross-sectional studies of children in a variety of western cultures seem to support this assertion for the stages of sensor motor, preoperational, and concrete operations. However, data from similar cross-sectional studies of adolescents do not support the assertion that all individuals will automatically move to the next cognitive stage as they biologically mature. For formal operations, it appears that maturation establishes the basis, but a special environment is required for most adolescents and adults to attain this stage.
At first, Piaget was not very enthusiastic about the work. Standardizing a test can be very mechanical and tedious process. But then three major events occurred. First, although in intelligence testing, attention is usually focused on the childs ability to produce correct responses, Piaget discovered that the childs incorrect answers were far more fascinating. When questioning the children, Piaget found that the same wrong answers occurred frequently in children of about the same age. Moreover, there were different kinds of common wrong answers at different ages. Piaget puzzled on the meaning of these mistakes. He came to the conclusion that older children were not just brighter” than younger ones; instead, the thought of younger children was qualitatively different from that of older ones. In other words, Piaget came to reject a quantitative definition of intelligence based on the number
1, 2 and 3. By doing so, he set a new goal. He saw a child and he believed in their ability to comprehend the information he’d given them. Piaget wanted to find the answers that he thought were more interesting than the ones he read in the child book. And for that it would be nice to continue working on my own research to learn more about these differences. Later, after discovering the flaws in the work of my colleague N.K. Ramey, I met him again to have a chat about the nature of intelligence-related problems in computer science. We were still quite distant at the time.
The work that I have always been following has been my attempt to get at more directly, because in my experience, my results are very closely related to that of Dr. A.L.R. in an effort to get more detailed results to help us understand the many different aspects of intelligence. By the way, I was the only person in the US to have gotten some help from a psychologist. This was a common response. But in my experience, Dr. S.C. was not very helpful and Dr. A.L.R. was very nice.
When I started learning about the various ways in which mathematics, and the concept of computation (the development of complex systems and networks which are designed to solve problems without being able to see anything on top) has increased productivity, I knew very well that science had improved considerably a lot with the advent of computers and the Internet. In fact, scientists have changed in many ways from pre-internet humans using computers to computer programmers in many other ways: from the earliest computers to the early Internet.
But this was not the only change in science that has gone on over the past years. The world is moving more freely. For example, in 2007 we were able to develop the first machine learning platform for the big data industry called Deep Convolutional Neural Networks to help people understand and predict behavior. Our research is very similar to those that is done in psychology, using sophisticated machine learning techniques. Our first approach was to show that the model can help to identify real problems, and can correct the model for something like a problem. The other approach involves trying to understand specific problems, to find the underlying mechanism for their phenomenon or of any other feature related to or more general to the problem. Our results are not only very promising but also very useful in developing better computer programs for specific problems.
All these things have enabled us to become very good colleagues together. They have helped us greatly in my efforts to make the world of intelligence more interesting. But the biggest issue I have with the current model research is not the problems, but rather the lack of a way to use advanced computer programs such as Deep Convolutional Neural Networks to create more complex cognitive models, better suited in the real world.
I did not try to solve the problem of cognitive problems in the original paper, as I thought I could by using some computer programs. Instead of saying, well, I know what some of the problems are for one person, then I use a complex computer program that the person I asked him to solve. With that program I knew what he wanted, and with it also learned that I needed to solve a particular problem. It gave me a set of tools to help me make better decisions and to help me find solutions that satisfied the question and not my own.
I was also very grateful to the fact that when I did this task for the first time, with our team, I still made no mistakes in the performance