Doctor of Philosophy(PhD) / The main aim of this thesis was to examine whether learning processes occur in fluid intelligence (Gf) tasks, whether it is essential for them to occur for induction to take place and whether they contribute to individual differences in performance. In mainstream differential research, Gf is conceptualised as a factor important in induction tasks that are considered novel and context-free (Cattell, 1963, 1987). Thus, performance has typically been assumed to be uninfluenced by previous acquisitions of knowledge structures. Sources of individual differences in Gf task performance have been attributed to working memory capacity (WMC), particularly individual differences in the ability to combat proactive interference. In contrast, the cognitive reasoning literature associates induction with the use of prior conceptual knowledge. A middle-ground position is that Gf tasks may require learning to occur across the task, which would draw upon WMC. That is, individual differences in Gf task performance may be due to knowledge learnt across the task, rather than knowledge brought to the task. Gf items have traditionally been presented in easy-to-hard order but easier items may unintentionally provide learning opportunity for harder items. This would contradict both classic and modern test theories which make the assumption that items within a task are independent of each other. The learning hypothesis was explored in the current work along with the issue of whether it is possible to reliably solve complex Gf items without some relevant, prior knowledge. Also, the distinction between within-item induction and across-item learning was investigated, along with the relationship between across-item learning and proactive interference. An experimental-differential approach was used to manipulate learning opportunity within Gf tasks in four experiments. The first experiment examined whether learning takes place in Raven’s Advanced Progressive Matrices (Raven, 1962) and if so, to what extent this learning is a source of individual differences. Specifically, whether rule learning within the task is necessary for abstraction to take place and whether those of higher Gf ability learn faster than those of lower Gf ability. The next three experiments examined the distinction between knowledge that may be brought to the task, learning that occurs across multiple items in the task and induction within a single item that may be independent of any prior knowledge including knowledge learnt across the task. The effect of proactive interference as a consequence of learning and knowledge was also investigated. The experiments examined which of these are relevant to general performance (i.e., common to everyone) and which contribute to individual differences. Learning-opportunity was manipulated in a task from the cognitive reasoning literature – the Modified Sweller and Gee (MSG) Task. Traditional Series Completion tasks were used as Gf markers and data analyses employed Hierarchical Linear Modelling (HLM). The advantage of the MSG Task is that it has qualities typical of Gf tasks but unlike conventional Gf tasks, it is able to assess within-item induction in isolation from any potential influences from across-item learning. This is because it involves multiple attempts within each item with feedback, allowing single items to be administered reliably. When across-item learning opportunity is absent, the MSG Task is able to provide an estimate of participants’ within-item induction success through the number of attempts they need within a single item. The amount participants learn across items can be approximated by comparing performance on items preceded by learning opportunity (i.e., easier items with similar rule-types), with items not preceded by learning opportunity. Lastly, the effects of proactive interference can be evaluated by comparing performance on items preceded by interference (i.e., items with different rule-types) with those that are not preceded by interference. Overall, it was found that with no learning opportunity leading up to novel items (to provide relevant prior knowledge), solution was nearly impossible for all participants. When learning opportunity was provided, all participants were able to greatly improve their performance but those of higher Gf improved more. It was concluded that while Gf tasks appear visually novel, they must contain a combination of familiar elements in earlier items (which make use of knowledge that participants bring to the task) and novel elements in later items (which require the use of knowledge that must be learnt from earlier items); and those of higher Gf perform better on Gf tasks, at least partly because they are able to benefit more from the learning opportunity provided by earlier items. It was found that proactive interference affects all participants when they learn from prior items. However, insufficient evidence was found to suggest that the ability to combat proactive interference contributes to individual differences in performance.

An unresolved issue exists in the study of mental ability: which aspect of cognition is responsible for the emergence of psychometric “g” (Spearman, 1904, 1927), a general factor that predicts performance in all kinds of cognitive tasks and many important life outcomes? On the basis of themes present in the literature on fluid intelligence (Chapter 1), this thesis explores the relative contributions of processing, storage, and task modelling demands to the recruitment of g in task performance. Six experiments are presented which employed two computer-based tasks. The tasks were designed such that the level of demand for processing and storage was separated and manipulated in order to establish their relationship to scores on a standard test of g. Task manipulations were implemented in the context of varying the number of distinct verbal chunks in which task instructions were presented, whilst controlling for the presented amount of operative task-relevant information. The findings showed that the recruitment of g in task performance was strengthened by the presence (versus the absence) of a requirement to inhibit a prepotent response tendency (Experiments 1, 2, and 3), and by the presence of requirements to inhibit a prepared response and to maintain and update information in working memory (Experiments 4 and 5). However, these effects were observed only when task instructions were presented as four (and not as two) distinct rules. Additional findings showed that reconceptualisation of task requirements—that is, flexibly imposing order on a complex set of instructions thus reducing the number of distinct verbal chunks—was dependent on performance on the test of Spearman’s g (Experiments 3 through 6). These findings are deemed consistent with a task conceptualisation theory of g, with real-time execution demand, particularly inhibition, posing as a risk factor for the recruitment of g only when task requirements are maintained in mind as a relatively large number of chunks or rules.

Literature reports that test anxiety may have an influence on aptitude test performance for some racial groups and therefore serves as a source of bias (Zeidner, 1998). Testing organisations have also found that individuals from African groups perform poorly on measures of fluid intelligence, putting them at a disadvantage when these scores are used for selection and training purposes. The current study examines a model defining the relationship between test anxiety, attentional resources and fluid intelligence in the following manner: an increase in test anxiety will result in a decrease of attentional resources as well as a decrease in fluid intelligence. With a decrease in attentional resources we will see a negative influence on fluid intelligence and test performance for different racial groups. Twenty-five African individuals and twenty-five individuals from Caucasian racial groups have set the stage to answer the question if certain groups experience higher test anxiety and thus perform poorly on fluid intelligence measures. Significant relationships were found, within and between groups, for attentional resources and fluid intelligence. Meanwhile, other factors, such as test anxiety, were not strongly associated with fluid intelligence performance. Future research into reasons why certain racial groups display lower overall attention in testing situations is suggested in order to ensure that tests for selection and training and aptitude tests are fair to all racial groups. / Psychology / M.A. Soc. Sc.(Psychology)

Although much educational research has investigated the relative effectiveness of different educational interventions and policies, little is known about the absolute net benefits of K-12 schooling independent of growth due to chronological age and out-of-school experience. The nearly universal policy of age tracking in schools makes this a difficult topic to investigate. However, a quasi-experimental regression discontinuity design can be used to separate observed test score differences between grades into independent age and schooling components, yielding an estimate of the net effects of school exposure at each grade level. In this study, a multilevel version of this design was applied to scores on 22 common ability and achievement tests from two major standardized test batteries. The ability battery contained 9 measures of Verbal, Quantitative, and Figural reasoning. The achievement battery contained 13 measures in the areas of Language, Mathematics, Reading, Social Studies, Science, and Sources of Information. The analysis was based on sample of over 20,000 students selected from a longitudinal database collected by a large U.S. parochial school system. The theory of fluid (Gf) and crystallized (Gc) intelligence predicts that these tests will show systematically different levels of sensitivity to schooling. Indeed, the achievement (Gc) tests were found to be three times more sensitive to schooling than they were to aging (one-year effect sizes of .41 versus .15), whereas the ability (Gf) tests were equally influenced by age (.18) and schooling (.19). Nonetheless, the schooling effect on most Gf tests was substantial, especially when the compounding over a typical school career is considered. This replicates the results of previous investigations of age and schooling using regression discontinuity methods and once again contradicts common interpretations of fluid ability. Different measures of a construct often exhibited varying levels of school sensitivity. Those tests that were less sensitive to schooling generally required reading, reasoning, transfer, synthesis, or translation; posed a wider range of questions; and/or presented problems in an unfamiliar format. Quantitative reasoning tests showed more sensitivity to schooling than figural reasoning tests, while verbal reasoning tests occupied a middle ground between the two. Schooling had the most impact on basic arithmetic skills and mathematical concepts, and a significantly weaker impact on the solution of math word problems. School-related gains on isolated language skills were much larger than gains on solving grammar problems in context. The weakest schooling impact overall was on reading comprehension where effects were no larger than those on verbal ability measures. An interesting dichotomy was found between spelling and paper folding (a measure of figural and spatial reasoning). Spelling skills showed robust schooling effects but a consistently negative age slope, a puzzling result which indicates that younger students in each group outperformed older students. Paper folding showed the opposite pattern, a large age effect and a small but consistently negative schooling effect. Results serve to rebut skepticism about both the impact of schooling on test scores and the validity of distinctions between ability and achievement. It is argued that the regression discontinuity design has great potential in the measurement of school effectiveness, while also offering a source of validity evidence for test developers and test users. Implications for theories of cognitive ability and future research on schooling effects are discussed.

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Although the independent roles of working memory capacity (WMC) and knowledge in problem solving have been thoroughly researched, there is significantly less work that has explored how WMC and knowledge interact during problem solving. The present study investigated how the quality of knowledge representations contribute to rule transfer in a problem-solving context and how WMC might contribute to the subsequent failure or success in transferring the relevant information. Participants were trained on individual figural analogies rules and then asked to rate how similar they thought the rules were to determine how stimulispecific or abstract their rule representations were. Their rule representation score, along with other measures (WMC and fluid intelligence measures) were used to predict accuracy on a set of test items, of which half included only the trained rules, and the other half were comprised of entirely new rules. Results indicated that the training did improve performance on the test items and that WMC largely explained the ability to transfer rules. Although the rule representations scores did not predict accuracy on the trained items, the results suggest that rule representations may be important for inductive reasoning or pattern recognition, rather than explaining transfer. Furthermore, rule representations uniquely explained performance on the figural analogies task, even after accounting for WMC and fluid intelligence. Altogether, these results indicate that WMC plays a large role in knowledge transfer, even when transferring to a more complex problem-solving context, and that rule representations may be important for novel problem solving.

Previous research examining the impact of training and practice effects on modifying performance of fluid intelligence tasks (Gf) and crystallized intelligence tasks (Gc) were extended to include self-rated performance attributions among non-depressed and depressed older persons. The following general questions were addressed. How does level of depression affect performance on Gf and Gc measures and performance attributions? How does level of depression and degree of benefit from either training or practice relate to changes in attributional styles? The framework used for predicting shifts in attributional styles was the reformulated learned helplessness model. Three hundred twenty-five community-dwelling older persons completed the Gf/Gc Sampler, Beck Depression Inventory, and Attributions for Success/Failure Questionnaire at pretest, posttest (one week), and follow-up (one month). Between the pretest and posttest sessions, subjects participated in one of three experimental conditions; (a) cognitive (induction) training, (b) stress inoculation training, and (c) no-contact control groups. The results from univariate and multivariate analysis of covariance procedures provided partial support for the hypotheses. At pretest, both non-depressed and depressed older persons had internal attributional styles, although based on differential performance outcomes. The depressed persons were found to have more failure experiences as a result of their significantly poorer performance on Gf tasks, versus the non-depressed. Specific Gf training effects were documented regarding attributional shifts for the non-depressed, while there were no changes on their attributional style due to practice on either Gf or Gc tasks. In contrast, only differential practice effects were documented for depressed subjects across Gf and Gc tasks. The importance of assessing personality dimensions in older persons and their xelationship to training and practice effects were discussed, in addition to limitations of the study and suggestions for future research.

Matrices reasoning tests, which contain missing pieces in matrices that participants attempt to figure out, are one of the most popular types of tests to measure general intelligence. This thesis introduces several methods to develop matrices items, and presents them in different test forms to assess general intelligence. Part 1 introduces the development of a matrices test with reference to Carpenter’s five rules of Raven’s Progressive Matrices. The test items developed were administered together with the Standard Ravens’ Progressive Matrices (SPM). Results based on confirmatory factor analysis and inter-item correlation demonstrate good construct validity and reliability. Item characteristics are explored with Item-Response Theory (IRT) analyses. Part 2 introduces the development of a large item bank with multiple alternatives for each SPM item, with reference to the item components of the original SPM. Results showed satisfactory test validity and reliability when using the alternative items in a test. Findings also support the hypothesis that the combination of item components accounts for item difficulty. The work lays the foundation for the future development of computer adaptive versions of Raven’s Progressive Matrices. Part 3 introduces the development of an automatic matrix item generator and illustrates the results of the analyses of the items generated using the distribution-of-three rule. Psychometric properties of the items generated are explored to support the validity of the generator. Figural complexity, features, and the frequency at which certain rules were used are discussed to account for the difficulty of the items. Results of further analyses to explore the underlying factors of the difficulty of the generated items are presented and discussed. Results showed that the suggested factors explain a substantial amount of the variance of item difficulty, but are insufficient to predict the item difficulty. Adaptive on-the-fly item generation is yet to be possible for the test at this stage. Overall, the methods for creating matrices reasoning tests introduced in the dissertation provide a useful reference for research on abstract reasoning and fluid intelligence measurements. Implications for other areas of psychometric research are also discussed.

Information and Communication Technologies (ICT) are changing the way people learn, do business, build relationships, and manage their lives. ICT allow easy and continuous access to open source intelligence (OSINT) that acts as force multipliers, enabling civilians to find new and more effective ways to participate in civil society and address disempowering strategies implemented by governments around the world to maintain stability. ICT and OSINT cultivate fluid intelligence and adaptive governance and can act as a catalyst to cultivate these capacities to transform conflict. The research question sought to determine whether fluid intelligence (cognitive ability to adapt and innovate) and adaptive governance (leadership and systems that work together with the governed to create favorable outcomes) are correlated with stability in gulf monarchies in the Middle East. This thesis examined the Kingdom of Saudi Arabia, the State of Qatar, the United Arab Emirates (UAE), the Kingdom of Bahrain, the State of Kuwait, and the Sultanate of Oman using a complex adaptive systems analytic framework that drew upon the theories of adaptive governance and fluid and crystallized intelligence. Group grievance often indicates levels of stability in civilian populations. This study revealed a strong correlation between adaptive regimes with fluid populations and stability. Populations high in fluid intelligence in adaptive monarchical regimes had lower group grievance, but populations high in fluid intelligence in non-adaptive monarchical regimes had higher group grievance.