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Domain-General Learning and Domain-Specific Learning Mechanisms
It is evident that humans can acquire knowledge and learn about their environment. With regards to when this occurs, philosophers such as Plato take the nativistic approach and argue that humans are born innately with knowledge of the world while others such as John Locke take the empiricist approach and argue that knowledge is gained through their life experiences. However, there have been fewer articles in the literature focusing on how humans acquire knowledge and learn throughout their lifetimes. Domain-general learning and domain-specific learning mechanisms provide insight into this dilemma; the former illustrates that learning is achieved using mechanisms that function across a wide range of knowledge areas, and the latter demonstrates that learning is achieved using mechanisms that only function for specific knowledge areas. For example, working memory would be a domain-general learning mechanism because humans take in several different types of input from the environment and apply it to appropriate contexts and specialized numerical representations would be a domain-specific learning mechanism because humans would only take advantage of this when numbers need to be processed. Based on these definitions, there is a possibility that some types of knowledge are acquired specifically using domain-general learning mechanisms while other types of knowledge are acquired specifically using domain-specific learning mechanisms. However, it is assumed that an understanding of the association between these two mechanisms is crucial to formulate further hypotheses and conduct research as to how thorough knowledge acquisition takes place in humans.

Importance of Domain-General and Domain-Specific Learning Mechanisms


Rakison and Yermolayeva (2011) showed that domain-general learning was something interesting and important to talk about. These researchers have suggested that U-shaped learning curves are a product of domain-general learning. These learning curves consist of three different stages; children start off learning tasks relatively easily, then performance declines with age, and improves again later in life. This trend has been shown in various entities such as motor and social learning, behavioural studies involving the learning of faces, languages, and gesture, as well as event-related potential (ERP) studies in infants, all of which have been previously associated with domain-general learning mechanisms. Since the same U-shaped learning curve existed across all these domains, researchers have advocated that this type of curve was a result of domain-general learning mechanisms.

Rakison and Yermolayeva (2011) also gave reasons as to why a domain-specific mechanism would not give rise to a U-shaped learning curve. They suggested that domain-specific mechanisms require little to no experience to operate at a highly functional level, and therefore, it was extremely unlikely that a severe decline in performance would result after something had already been learned. Also, because domain-specific mechanisms were formulated innately, the researchers advised that performance on learning tasks would improve monotonically once these mechanisms were triggered in infants shortly after birth.

Hong and Milgram (2010) challenged the work done by Rakison and Yermolayeva (2011). For decades, creative thinking ability was considered as a domain-general ability; individuals who scored high on a test of creative thinking ability were able to generate ideas in a wide variety of domains. However, other researchers preached that this ability was mainly domain-specific, arguing that the domains were learned through different specific theoretical and operational definitions. Hong and Milgram (2010) hypothesized that creative thinking ability was a result of an interaction between domain-general and domain-specific learning mechanisms and performed three studies to test their hypothesis. Creative thinking ability was split into general and specific creative thinking with the idea being that these mechanisms helped to enhance their respective types of creative thinking ability. Also, the hypothesis was tested to see if it was consistent across gender, education, different learning abilities, and ethnicity.

Gender, education, different learning abilities, and ethnicity had varying effects on general and specific creative thinking with a stronger association overall with specific creative thinking. However, both types of creative thinking ability were still associated with these variables, further suggesting that both domain-general and domain-specific learning mechanisms interact with creative thinking ability. This lead to the notion that an understanding of certain human abilities like creative thinking ability also required an understanding of the association between domain-general and domain-specific learning mechanisms, which provided further insight into the problem of how humans acquire knowledge and learn from the environment.

Mathematics
Chan and Ho (2010) continued to have an influence on current research regarding domain-general and domain-specific learning mechanisms. These researchers examined four domain-specific skills (arithmetic procedural skills, number retrieval, place value concept, and number sense) and two domain-general processing skills (working memory and processing speed) to see how these skills may have been accounted for in mathematical learning difficulties seen in Chinese children. Two age groups were tested (7-8 and 9-11 years of age) and split into children with mathematics difficulties and age-matched typically achieving control children.

Deficits in arithmetic procedural skills include the inability to form strategies to solve complex and simple math problems, deficits in number retrieval include the inability to recall shortcuts on how to solve certain math problems that have been previously learned and used, deficits in place value concept include forgetting to carry the digits over when adding or subtracting, and deficits in number sense include an inability to generalize what numbers mean and apply them to real-life contexts. A deficit in working memory would suggest a faulty temporary processing system which affects how math problems are solved, and a deficit in processing speed would indicate that more time is necessary to solve even the simplest of math problems.

Children with mathematics difficulties in both age groups did worse than the age-matched control children on all of the domain-specific and domain-general skills. More importantly, the bigger differences existed in the domain-specific skills, and the researchers suggested that that was because the domain-specific skills dealt more with number skills that had direct associations with arithmetic and less with general processing skills. Specifically, number retrieval and place value understanding were the two most common domain-specific skills that the Chinese children with mathematics difficulties lacked.

Visual Sequence Learning in Infancy


Shafto and colleagues (2012) investigated visual sequence learning and its connection to language development in 8.5 month old infants. A novel visual sequence learning task that relied on reaction time was used to assess how well infants learned a simple repeating three-item spatiotemporal sequence, a three-item sequence was used rather than a two-item sequence because it was more complex, and therefore more likely to map onto cognitive processes of interest such as language acquisition. A significant correlation was found between visual sequence learning and vocabulary comprehension at the time of the test. The visual sequence learning task involved the use of visual-motor skills, while vocabulary comprehension used audition. The other correlation between visual sequence learning and gestural ability five months after performing the visual sequence learning task was also of importance because they both involved the use of visual-motor skills. This pattern of results suggested that both sequence learning and language learning involved a combination of domain-general and domain-specific components.

Importantly, this same combination of domain-general and domain-specificity appeared to also characterize language. Both reading and listening tasks involved a common phonological network of brain regions such as the inferior frontal gyrus, whereas visual and auditory unimodal and association areas were preferentially active during reading and listening tasks, respectively. This combination of domain-generality and domain-specificity in sequence learning and language may therefore explain the correlation between visual sequence learning task performance and gesture comprehension score. To some extent, visual sequence learning relied on the same domain-general learning mechanisms used for language processing; it is associated with global measures of language development, regardless of the domain. On the other hand, because visual sequence learning also involved domain-specific components for learning visual-motor sequential patterns, visual sequence learning appeared to be useful for predicting aspects of visual-motor communication later in development, specifically, the comprehension of gestures. This was the first piece of evidence showing both a domain-general and domain-specific association between sequence learning and language development.

Laboratory Mice
Domain-general and domain-specific learning mechanisms have also expanded to other areas of research outside the realm of cognitive science. For example, Kolata and colleagues (2008) looked at how these mechanisms influence laboratory mice learning. Most animal studies have focused primarily on single domains of learning such as Pavlovian conditioning and did not explore aspects of learning common across all the possible learning domains. These researchers argue that human research has a bigger constrain on ethics and therefore, laboratory mice would be more suitable to test.

Mice possessed a domain-general learning factor that was similar to general intelligence in humans. The laboratory mice were tested on five primary learning tasks and a subset of these mice were also tested in two additional learning tasks. These learning tasks were designed to test different sensory and motor information-processing and motivational systems and as much as 44% of the variance in performance across these learning tasks was explained by this single domain-general factor of learning. Specifically, the authors assumed that one aspect of working memory (selective attention) was most strongly predictive of general learning abilities, suggesting that these mice used a hierarchical structure to preferentially learn aspects of domain-general learning mechanisms. The mice also showed a domain-specific factor of learning, but the exact nature of it was not fully known. The researchers imposed that three of the learning tasks relied heavily only on the hippocampus. However, the researchers suggested that lesion studies needed to take place in order to fully understand the nature of this domain-specific factor of learning.

Limitations
Researchers have attempted to model the acquisition of the English word “one” in order to identify necessary constraints for successful acquisition. . Previous modeling efforts have succeeded by only using a domain-general learning algorithm, but these researchers have shown that this algorithm is faulty which limited its use. It has been argued that a successful learner must rely on both domain-general and domain-specific learning mechanisms when learning the English word “one.”

When learning, the person must have a way of updating their knowledge on the basis of the selected data, they must have a way of representing the data to be learned, and they must identify which parts of the data is important for learning. The researchers suggested that these are examples of domain-specific constraints and hypothesized that learning the English word “one” can only be done using a combination of a domain-general learning algorithm along with these domain-specific constraints that filtered all of the given data. Researchers also argued that filtering the data discarded potentially informative data but, more importantly, also helped the learner to generalize to the correct subset of data needed to learn "one". Furthermore, past research has been heavily centered on a dichotomy where language learning was either brought upon by domain-general learning mechanisms or it was not. This research claimed that this dichotomy is false; it stated that learners took both domain-general and domain-specific learning mechanisms into account when learning the English word “one”.

Educational Practice
Sternberg (2008) applied his domain-general theory of successful intelligence to educational practice. This theory formed the basis for the first generalized approach to successful intelligence. Successful intelligence is defined as the use of an integrated set of abilities that are needed to attain success in life within a person’s sociocultural context. . People are successfully intelligent if strengths were capitalized upon and weaknesses were corrected or compensated for. Successfully intelligent people adapted to, shaped, and selected environments through finding a balance in their personal analytical, creative, and practical abilities.

All three kinds of abilities significantly predicted course performance. This suggested that students who only had analytical abilities for example, would only flourish if the teaching style was primarily analytical (memory/fact based) as well. This theory of successful intelligence suggested that tests should include all three abilities to generate fair assessments for all students. Another factor to take into account was socioeconomic status; kids who came from upper-class families also tended to have above average analytical skills and kids who came from lower-class families also tended to have above average creative and practical skills primarily because of the need to survive in harsh environments. This further promoted the importance of generating unbiased testing strategies so everybody can have an equal chance to succeed. Most interestingly, Sternberg’s theory of successful intelligence was only an approximation of how intelligence was associated with education. The researcher merely wanted to advocate a need to develop theories that can be applied to all aspects of education in all subject matter and at all grade levels that catered to everyone.