Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences

More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence

The relationship between mental speed and mental abilities

Mental speed is one often proposed candidate property of information processing affecting different cognitive abilities that may underlie individual differences in general intelligence. The aim of the present work was to expand the measurement of mental speed in individual differences research beyond the measurement of response times using state-of-the-art psychological and physiological methods. Based on two multivariate studies, this thesis demonstrates the benefits of a process-oriented approach to measuring mental speed. Moreover, the results show that on a behavioral level, mental speed can be considered as a one-dimensional trait, whereas one has to distinguish between the speed of early visual processing and higher-order processing on a physiological level. Finally, structural equation modeling revealed that on a behavioral level, general intelligence was associated with generally faster response times, whereas on a neurophysiological level, general intelligence was differentially related to earlier and later ERP latencies and was most strongly associated with ERP latencies reflecting the speed of higher-order processing. Taken together, ERP latencies explained about 85 percent of the variance in general intelligence, whereas response times explained only 19 percent. This result suggests that ERP latencies may provide a purer measurement of mental speed and that response times may be contaminated by additional processes such as motor planning and execution that are largely unrelated to general intelligence

Disentangling the Effects of Processing Speed on the Association between Age Differences and Fluid Intelligence

Several studies have demonstrated that individual differences in processing speed fully mediate the association between age and intelligence, whereas the association between processing speed and intelligence cannot be explained by age differences. Because measures of processing speed reflect a plethora of cognitive and motivational processes, it cannot be determined which specific processes give rise to this mediation effect. This makes it hard to decide whether these processes should be conceived of as a cause or an indicator of cognitive aging. In the present study, we addressed this question by using a neurocognitive psychometrics approach to decompose the association between age differences and fluid intelligence. Reanalyzing data from two previously published datasets containing 223 participants between 18 and 61 years, we investigated whether individual differences in diffusion model parameters and in ERP latencies associated with higher-order attentional processing explained the association between age differences and fluid intelligence. We demonstrate that individual differences in the speed of non-decisional processes such as encoding, response preparation, and response execution, and individual differences in latencies of ERP components associated with higher-order cognitive processes explained the negative association between age differences and fluid intelligence. Because both parameters jointly accounted for the association between age differences and fluid intelligence, age-related differences in both parameters may reflect age-related differences in anterior brain regions associated with response planning that are prone to be affected by age-related changes. Conversely, age differences did not account for the association between processing speed and fluid intelligence. Our results suggest that the relationship between age differences and fluid intelligence is multifactorially determined

Don't waste your time measuring intelligence: Further evidence for the validity of a three-minute speeded reasoning test

The rise of large-scale collaborative panel studies has generated a need for fast, reliable, and valid assessments of cognitive abilities. In these studies, a detailed characterization of participants' cognitive abilities is often unnecessary, leading to the selection of tests based on convenience, duration, and feasibility. This often results in the use of abbreviated measures or proxies, potentially compromising their reliability and validity. Here we evaluate the mini-q (Baudson & Preckel, 2016), a three-minute speeded reasoning test, as a brief assessment of general cognitive abilities. The mini-q exhibited excellent reliability (0.96–0.99) and a substantial correlation with general cognitive abilities measured with a comprehensive test battery (r = 0.57; age-corrected r = 0.50), supporting its potential as a brief screening of cognitive abilities. Working memory capacity accounted for the majority (54%) of the association between test performance and general cognitive abilities, whereas individual differences in processing speed did not contribute to this relationship. Our results support the notion that the mini-q can be used as a brief, reliable, and valid assessment of general cognitive abilities. We therefore developed a computer-based version, ensuring its adaptability for large-scale panel studies. The paper- and computer-based versions demonstrated scalar measurement invariance and can therefore be used interchangeably. We provide norm data for young (18 to 30 years) and middle-aged (31 to 60 years) adults and provide recommendations for incorporating the mini-q in panel studies. Additionally, we address potential challenges stemming from language diversity, wide age ranges, and online testing in such studies

Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR

Fluid Intelligence Is (Much) More than Working Memory Capacity: An Experimental Analysis

Empirical evidence suggests a great positive association between measures of fluid intelligence and working memory capacity, which implied to some researchers that fluid intelligence is little more than working memory. Because this conclusion is mostly based on correlation analysis, a causal relationship between fluid intelligence and working memory has not yet been established. The aim of the present study was therefore to provide an experimental analysis of this relationship. In a first study, 60 participants worked on items of the Advanced Progressive Matrices (APM) while simultaneously engaging in one of four secondary tasks to load specific components of the working memory system. There was a diminishing effect of loading the central executive on the APM performance, which could explain 15% of the variance in the APM score. In a second study, we used the same experimental manipulations but replaced the dependent variable with complex working memory span tasks from three different domains. There was also a diminishing effect of the experimental manipulation on span task performance, which could now explain 40% of the variance. These findings suggest a causal effect of working memory functioning on fluid intelligence test performance, but they also imply that factors other than working memory functioning must contribute to fluid intelligence

Thermoresponsive poly(2-oxazoline) block copolymers exhibiting two cloud points: complex multistep assembly behavior

Aqueous solutions of poly(2-oxazoline) block copolymers consisting of a 2-ethyl-2-oxazoline block and a block consisting of a random copolymer of 2-ethyl-2-oxazoline and 2-n-propyl-2-oxazoline (PEtOx-block-P(EtOx-stat-PropOx)) have been studied by dynamic light scattering (DLS), static light scattering (SLS), and turbidimetry. Even at temperatures significantly below the lower critical solution temperature (LCST), polymer unimers are found to coexist with a few large aggregates with an open structure. When heated, the systems exhibit an intricate transmittance behavior whereby the samples becomes visually clear again after an initial cloud point and then exhibit a second cloud point at even higher temperatures. The DLS data indicate that the aggregates formed around the first cloud point restructure and fragment into smaller micelle-like structures ascribed to further dehydration of the more hydrophobic PPropOx containing block, causing the samples to become optically clear again. The observed fragmentation is confirmed by the SLS experiments. At even higher temperatures, both blocks become hydrophobic, causing the formation of large, compact aggregates, resulting in a second cloud point

Intestinal BMP-9 locally upregulates FGF19 and is down-regulated in obese patients with diabetes

believed to be mainly produced in the liver. The serum levels of BMP-9 were reported to be reduced in newly diagnosed diabetic patients and BMP-9 overexpression ameliorated steatosis in the high fat diet-induced obesity mouse model. Furthermore, injection of BMP-9 in mice enhanced expression of fibroblast growth factor (FGF)21. However, whether BMP-9 also regulates the expression of the related FGF19 is not clear. Because both FGF21 and 19 were described to protect the liver from steatosis, we have further investigated the role of BMP-9 in this context. We first analyzed BMP-9 levels in the serum of streptozotocin (STZ)-induced diabetic rats (a model of type I diabetes) and confirmed that BMP-9 serum levels decrease during diabetes. Microarray analyses of RNA samples from hepatic and intestinal tissue from BMP-9 KO- and wild-type mice (C57/Bl6 background) pointed to basal expression of BMP-9 in both organs and revealed a down-regulation of hepatic Fgf21 and intestinal Fgf19 in the KO mice. Next, we analyzed BMP-9 levels in a cohort of obese patients with or without diabetes. Serum BMP-9 levels did not correlate with diabetes, but hepatic BMP-9 mRNA expression negatively correlated with steatosis in those patients that did not yet develop diabetes. Likewise, hepatic BMP-9 expression also negatively correlated with serum LPS levels. In situ hybridization analyses confirmed intestinal BMP-9 expression. Intestinal (but not hepatic) BMP-9 mRNA levels were decreased with diabetes and positively correlated with intestinal E-Cadherin expression. In vitro studies using organoids demonstrated that BMP-9 directly induces FGF19 in gut but not hepatocyte organoids, whereas no evidence of a direct induction of hepatic FGF21 by BMP-9 was found. Consistent with the in vitro data, a correlation between intestinal BMP-9 and FGF19 mRNA expression was seen in the patients’ samples. In summary, our data confirm that BMP-9 is involved in diabetes development in humans and in the control of the FGF-axis. More importantly, our data imply that not only hepatic but also intestinal BMP-9 associates with diabetes and steatosis development and controls FGF19 expression. The data support the conclusion that increased levels of BMP-9 would most likely be beneficial under pre-steatotic conditions, making supplementation of BMP-9 an interesting new approach for future therapies aiming at prevention of the development of a metabolic syndrome and liver steatosis

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience