Critically discuss the Role of Working memory in Intelligence - Essay Example

?Critically discuss the Role of Working memory in Intelligence: The definition and study of human intelligence is a 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 ) While the role played by working memory in the manifestation of intelligence is universally agreed, the same cannot be said of visual working memory (VWM). There is ongoing debate among scholars in this regard, with evidence both for and against the capacity of VWM to reflect “a central limit that is a bottleneck in higher order cognition”. (Cusack, et.al., 2009, p.641) One frequently used method to measure VWM is ‘change detection’. In this experiment, a memo consisting of numerous coloured squares are shown for a small duration. This is done in order to reduce chunking or the piecemeal transfer of items into phonological working memory. Following this, a delay of approximately 1 second is introduced to make sure that sensory representations responsible for iconic memory are perished. This is followed by a probe display. The participant will then be asked to compare the color and position of items in the probe with those in the sample. Using this method, VWM is shown to be an indicator of fluid intelligence and academic performance in both children and adults. (Cusack, et.al., 2009, p.641) Apart from systematic/scientific methods of finding the role played by WM in the expression of intelligence, there is also the evidence given by educators. Academics in the UK contend that the underperformance of pupils in their school exams could be due to deficient working memory rather than poor intelligence. When a research team from Durham University surveyed more than 3,000 school children, it found that one in ten students across all grade levels suffered from inadequate working memory that undermines their academic performance. But what is troublesome is the fact that teachers are seldom privy to this memory deficiency problem, and instead attribute inattention or lack of intelligence for the students’ failure to learn. This is where school administrators need to be careful. They should work with children with poor working memory and device suitable intervention programs. Since working memory disposition tends to be inheritable, the education and career prospects of these disadvantaged children can depend on timely intervention. In this context, the Working Memory Rating Scale (WMRS) is a handy tool for educators. For example, the WMRS will help teachers to recognize children who potentially have a working memory deficiency without even subjecting them to a test. Since the WMRS is designed in a checklist format, teachers can assess working memory problems by the scores indicated by the checklist. For example, a high score is a strong indicator of WM problems. They can then proceed to give practical recommendations for these pupils, which include “repetition of instructions, talking in simple short sentences and breaking down tasks into smaller chunks of information.” (McKay, 2008, p.9) References Broadway, J. M., & Engle, R. W. (2010). Validating Running Memory Span: Measurement of Working memory Capacity and Links with Fluid Intelligence. Behavior Research Methods, 42(2), 563+. Cusack, R., Lehmann, M., Veldsman, M., & Mitchell, D. J. (2009). Encoding Strategy and Not Visual Working memory Capacity Correlates with Intelligence. Psychonomic Bulletin & Review, 16(4), 641+. Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, Not Quality: the Relationship between Fluid Intelligence and Working memory Capacity. Psychonomic Bulletin & Review, 17(5), 673+. McKay, Neil. A Question of Memory; Poor Recall 'Affects Learning Ability'. (2008, February 28). The Journal (Newcastle, England), p. 9. Salthouse, T. A., & Pink, J. E. (2008). Why Is Working memory Related to Fluid Intelligence?. Psychonomic Bulletin & Review, 15(2), 364+. Unsworth, N. (2009). Variation in Working memory Capacity, Fluid Intelligence, and Episodic Recall: a Latent Variable Examination of Differences in the Dynamics of Free Recall. Memory & Cognition, 37(6), 837+. Read More