It is better than you think: fluid intelligence across the lifespan

The growth and decline of fluid intelligence is associated with brain structural changes. For example, development of fluid IQ is associated with cortex thickness during the critical period between 6 to 12 years old. On the other end of the lifespan, poor performance in cognitive functioning is attributed to a decrease of frontal gray matter density in elderly populations. In particular, there is a sharp decline in fluid IQ scores after 65 years of age. There is substantial evidence that working memory and fluid intelligence (Gf) share neural substrates, such as the prefrontal and parietal cortices. However, very little research has examined whether the pattern of growth and decline in working memory mirrors that of fluid intelligence. For example, does the decline of working memory skills in elderly populations mirror fluid intelligence? Is the rate of working memory decline similar to the rate of growth

ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology

We predicted residual fluid intelligence scores from T1-weighted MRI data available as part of the ABCD NP Challenge 2019, using morphological similarity of grey-matter regions across the cortex. Individual structural covariance networks (SCN) were abstracted into graph-theory metrics averaged over nodes across the brain and in data-driven communities/modules. Metrics included degree, path length, clustering coefficient, centrality, rich club coefficient, and small-worldness. These features derived from the training set were used to build various regression models for predicting residual fluid intelligence scores, with performance evaluated both using cross-validation within the training set and using the held-out validation set. Our predictions on the test set were generated with a support vector regression model trained on the training set. We found minimal improvement over predicting a zero residual fluid intelligence score across the sample population, implying that structural covariance networks calculated from T1-weighted MR imaging data provide little information about residual fluid intelligence.Comment: 8 pages plus references, 3 figures, 2 tables. Submission to the ABCD Neurocognitive Prediction Challenge at MICCAI 201

Task rules, working memory, and fluid intelligence

Many varieties of working memory have been linked to fluid intelligence. In Duncan et al. (Journal of Experimental Psychology:General 137:131–148, 2008), we described limited working memory for new task rules: When rules are complex, some may fail in their control of behavior, though they are often still available for explicit recall. Unlike other kinds of working memory, load is determined in this case not by real-time performance demands, but by the total complexity of the task instructions. Here, we show that the correlation with fluid intelligence is stronger for this aspect of working memory than for several other, more traditional varieties—including simple and complex spans and a test of visual short-term memory. Any task, we propose, requires construction of a mental control program that aids in segregating and assembling multiple task parts and their controlling rules. Fluid intelligence is linked closely to the efficiency of constructing such programs, especially when behavior is complex and novel

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

The relationship between higher-order cognition and personality.

A latent variable approach was used to (1) examine the relationship between working memory capacity and fluid intelligence, (2) compare the relationship between fluid intelligence and two measures of working memory capacity (complex span and n-back), (3) identify higher-order personality factors and (4) determine the relationship between higher-order personality factors, working memory capacity and fluid intelligence. Confirmatory factor analysis followed by structural equation modeling described the complex span and n-back as highly correlated yet distinct constructs. Consistent with previous research, both measures correlated highly with fluid intelligence. Four higher-order personality factors best modeled the structure of personality. Moreover, these four factors had differential relationship to cognitive constructs. The current research provides a deeper understanding of the relationship between working memory capacity and fluid intelligence, including discrepancies considering the magnitude of the relationship between two types of working memory measures and fluid intelligence, and finally, the influence of a diverse personality structure on working memory capacity and fluid intelligence. Importantly, the study examined these relationships on a broad scale using multiple tasks at a latent level contributing to better understanding of the nature of working memory capacity - fluid intelligence relationship and the influence of personality on higher-order cognition.Ph.D.Committee Chair: Engle, Randall, W

Assembly and use of new task rules in fronto-parietal cortex

Severe capacity limits, closely associated with fluid intelligence, arise in learning and use of new task rules. We used fMRI to investigate these limits in a series of multirule tasks involving different stimuli, rules, and response keys. Data were analyzed both during presentation of instructions and during later task execution. Between tasks, we manipulated the number of rules specified in task instructions, and within tasks, we manipulated the number of rules operative in each trial block. Replicating previous results, rule failures were strongly predicted by fluid intelligence and increased with the number of operative rules. In fMRI data, analyses of the instruction period showed that the bilateral inferior frontal sulcus, intraparietal sulcus, and presupplementary motor area were phasically active with presentation of each new rule. In a broader range of frontal and parietal regions, baseline activity gradually increased as successive rules were instructed. During task performance, we observed contrasting fronto-parietal patterns of sustained (block-related) and transient (trial-related) activity. Block, but not trial, activity showed effects of task complexity. We suggest that, as a new task is learned, a fronto-parietal representation of relevant rules and facts is assembled for future control of behavior. Capacity limits in learning and executing new rules, and their association with fluid intelligence, may be mediated by this load-sensitive fronto-parietal network

Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation

While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized.Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher.Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts

Show your hands — Are you really clever? Reasoning, gesture production, and intelligence

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This study investigates the relationship of reasoning and gesture production in individuals differing in fluid and crystallized intelligence. It combines mea-sures of speed and accuracy of processing geometric analogies with analyses of spontaneous hand gestures that accompanied young adults’ subsequent ex-planations of how they solved the geometric analogy task. Individuals with superior fluid intelligence processed the analogies more efficiently than par-ticipants with average fluid intelligence. Additionally, they accompanied their subsequent explanations with more gestures expressing movement in non- egocentric perspective. Furthermore, gesturing (but not speaking) about the most relevant aspect of the task was related to higher fluid intelligence. Within the gestures-as-simulated action framework, the results suggest that i ndividuals with superior fluid intelligence engage more in mental simulation during vi-sual imagery than those with average fluid intelligence. The findings stress the relationship between gesture production and general cognition, such as fluid intelligence, rather than its relationship to language. The role of gesture pro-duction in thinking and learning processes is discussed.Peer Reviewe