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The definition and study of human intelligence is a subject that has attracted its fair share of controversy over the years. This is largely because there’s no consensus in how intelligence is defined. For example, while some scholars attribute practical problem solving skills, verbal ability and social competence as measures of intelligence, others include adaptability to new problems and situations, capacity for knowledge and creativity as key indicators. Then there are recent sociologists such as Daniel Goleman, who have revolutionized the concept of intelligence by including an ‘emotional’ dimension to the already accepted ‘cognitive’ dimension. Hence, the study of human intelligence is presently a flourishing field of scientific inquiry with a broad range of perspectives and approaches leading to its understanding. It is in this context that the role of working memory in the functioning of intelligence should be investigated.

Working memory (WM), alongside other components of general cognitive architecture such as length comparison, reading comprehension and abstract reasoning, has been identified as strong candidates for individual differences in intellectual abilities. Casually, working memory can be described as a ‘mental jotting pad’. How spacious it is will determine an individual’s learning ability. Theoretically, Working memory is defined as “the work space of the mind, a system for accessing goal-relevant information as needed to support complex cognition. This theoretical definition is supported by widespread observations.” (Broadway & Engle, 2010, p.563) In other words, working memory is key for understanding a broad range of phenomena that finds application in many applied disciplines of psychology such as clinical, educational, social and neuropsychological fields. It is also noted to be important for frontal lobe function (executive ability). But working memory is far from a simple ‘work space of the mind’, in that, it is comprised of “multiple component processes for maintaining, accessing, manipulating, and coordinating information.” (Broadway & Engle, 2010, p.563) Hence, scholars have attempted to isolate hypothetical working memory processes that are behind observed links between WM capacity and complex cognition. Present theories differ in their depth and focus, but there is a growing agreement among researchers that executive, attention-related processes are crucial to these links.

In a research experiment conducted by the scholar team of Broadway & Engle (2010), the findings show correlation between working memory and general fluid intelligence. Their results indicate that the span of running memory is a strong indicator of working memory space and is also equated with general fluid intelligence. However, it may not be as directly responsive to details of administration as has been so far thought. The results “add to growing evidence that running memory span tasks function similarly to complex-span tasks as measures of working memory capacity that are strongly predictive of general fluid intelligence.” (Broadway & Engle, 2010, p.563)

The research team of Fukuda, Vogel, et.al., further add evidence to the link between working memory and intelligence. They assert that working memory (WM) plays a core role in most large-scale models of cognition. Additionally, the incentive for further research on WM is due to its strong correlations with measures of broader intelligence such as SAT (Scholastic Aptitude Test) scores and fluid intelligence. Continuing and reinforcing on the theme studied by Broadway and Engle, Fukunda, et.al. write that the connection between working memory capacity and fluid intelligence has now been established across an array of experimental paradigms. One of these experimental approaches has shown this connection

“estimated using complex span measures that were designed to tap into both storage capacity and processing aspects of WM ability. Moreover, although several studies have emphasized the importance of the processing component in complex span tasks for the link with fluid intelligence, subsequent research has shown that even tasks that measure pure storage-in the absence of secondary processing loads-exhibit clear correlations with fluid intelligence. Therefore, pure storage capacity alone is linked with the broader construct of fluid intelligence.” (Fukuda, et.al, 2010, p.673)

Scientific evidence connecting storage capacity in WM and fluid intelligence is a significant stage in comprehending basic determinants of intelligence. Specifically, the simple tasks incorporated in such experiments lead to obvious conclusions about basic cognitive processes that are behind fluid intelligence. These conclusions are also consistent with data from complex span procedures which assess mental abilities such as dual task coordination, resistance to interference, and access to secondary memory. (Fukuda, et.al, 2010, p.673) Further, the work by Fukuda and team reveal new insights into the role played by WM. For example, by employing a simple change detection procedure, they were able to obtain significant support for a

“two-factor model of WM capacity, in which the number and resolution of the representations in WM are determined by distinct aspects of memory ability. This two-factor model enabled a straightforward test of which aspects of WM capacity mediate its link with fluid intelligence. The data were very clear. The number of representations that could be held in WM showed a robust correlation with fluid intelligence (r = .66), whereas mnemonic resolution showed no trace of a reliable link with fluid intelligence (r = -.05). Thus, the relationship between storage capacity in WM and fluid intelligence appears to be mediated solely by the maximum number of items that can be simultaneously stored in WM, rather than by the resolution or precision of those representations.” (Fukuda, et.al, 2010, p.673)

In a similar research project undertaken by Salthouse and Pink (2008), more insights into the relationship between working memory and intelligence are obtained. Not only did they find correlations between WM and fluid intelligence, but also between WM and general intelligence. Employing widely used assessment tools in cognitive psychology, the researchers were able to present participants with a range of tasks to measure WM, with some ambiguity as to what constructs the tasks represent. One task is to mentally rearrange items in a sequence. Another task involved assessing verbal fluency, generation of random numerals, and task switching efficiency. WAIS-III scale first proposed by Wechsler in 1997 is another robust method of arriving at WM index. The scale is based on participant performance in basic forward/backward digit span, verbal/arithmetic problems and letter-number sequencing tasks. Indeed, some of the tests and tasks used for working memory measurement resemble those used for measuring Intelligence Quotient and higher order cognition. Hence, it is unreasonable to claim that “the WM construct is theoretically more tractable or less opaque than are intelligence constructs, given the fact that it is operationalized in so many different ways that appear to have little conceptual integration.” (Salthouse & Pink, 2008, p.364 )

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