Fluid intelligence emerges from representing relations

Based on recent findings in cognitive neuroscience and psychology as well as computational models of working memory and reasoning, I argue that fluid intelligence (fluid reasoning) can amount to representing in the mind the key relation(s) for the task at hand. Effective representation of relations allows for enormous flexibility of thinking but depends on the validity and robustness of the dynamic patterns of argument-object (role-filler) bindings, which encode relations in the brain. Such a reconceptualization of the fluid intelligence construct allows for the simplification and purification of its models, tests, and potential brain mechanisms

The relation between fluid intelligence and the general factor as a function of cultural background: a test of Cattell's investment theory

According to Cattell’s (1987) Investment theory individual differences in acquisition of knowledge and skills are partly the result of investment of Fluid Intelligence (Gf) in learning situations demanding insights in complex relations. If this theory holds true Gf will be a factor of General Intelligence (g) because it is involved in all domains of learning. The purpose of the current study was to test the Investment theory, through investigating effects on the relation between Gf and g of differential learning opportunities for different subsets of a population. A second-order model was fitted with confirmatory factor analysis to a battery of 17 tests hypothesized to measure four broad cognitive abilities The model was estimated for three groups with different learning opportunities (N = 2358 Swedes, N = 620 European immigrants, N = 591 non-European immigrants), as well as for the total group. For this group the g Gf relationship was 0.83, while it was close to unity within each of the three subgroups. These results support the Investment theory.Structure of intelligence; Cattell’s Investment theory; fluid Intelligence; general Intelligence

Online Assessment and Game-Based Development of Inductive Reasoning

The aims of the study were (1) to develop a domain-general computer-based assessment tool for inductive reasoning and to empirically test the theoretical models of Klauer and Christou and Papageorgiou; and (2) to develop an online game to foster inductive reasoning through mathematical content and to investigate its effectiveness. The sample was drawn from fifth-grade students for the assessment (N = 267) along with the intervention study (N = 122). The online figurative test consisted of 54 items: nine items were developed for each of the six inductive reasoning processes. The digital game-based training program included 120 learning tasks embedded in mathematical content with differential feedback and instructional support. The test had good psychometric properties regarding reliabilities, means, and standard deviations. Confirmatory factor analyses revealed that the six processes of inductive reasoning and the three latent factors of Similarity, Dissimilarity, and Integration could be empirically confirmed. The training program was effective in general (corrected effect size = .38); however, the process of cross-classification was not developed significantly. Findings could contribute to a more detailed understanding of the structure and the modifiability of inductive reasoning processes and could reveal further insights into the nature of fluid intelligence

Fluid reasoning is equivalent to relation processing

Fluid reasoning (Gf) - the ability to reason abstractly - is typically measured using nonverbal inductive reasoning tests involving the discovery and application of complex rules. We tested whether Gf, as measured by such traditional assessments, can be equivalent to relation processing (a much simpler process of validating whether perceptually available stimuli satisfy the arguments of a single predefined relation - or not). Confirmatory factor analysis showed that the factor capturing variance shared by three relation processing tasks was statistically equivalent to the Gf factor loaded by three hallmark fluid reasoning tests. Moreover, the two factors shared most of their residual variance that could not be explained by working memory. The results imply that many complex operations typically associated with the Gf construct, such as rule discovery, rule integration, and drawing conclusions, may not be essential for Gf. Instead, fluid reasoning ability may be fully reflected in a much simpler ability to effectively validate single, predefined relations