Fluid intelligence and the locus coeruleus-norepinephrine system

Abstract

The last decade has seen significant progress identifying genetic and brain differences related to intelligence. However, there remain considerable gaps in our understanding of how cognitive mechanisms that underpin intelligence map onto various brain functions. In this article, we argue that the locus coeruleus-norepinephrine system is essential for understanding the biological basis of intelligence. We review evidence suggesting that the locus coeruleus-norepinephrine system plays a central role at all levels of brain function, from metabolic processes to the organization of large-scale brain networks. We connect this evidence with our executive attention view of working-memory capacity and fluid intelligence and present analyses on baseline pupil size, an indicator of locus coeruleus activity. Using a latent variable approach, our analyses showed that a common executive attention factor predicted baseline pupil size. Additionally, the executive attention function of disengagement--not maintenance--uniquely predicted baseline pupil size. These findings suggest that the ability to control attention may be important for understanding how cognitive mechanisms of fluid intelligence map onto the locus coeruleus-norepinephrine system. We discuss how further research is needed to better understand the relationships between fluid intelligence, the locus coeruleus-norepinephrine system, and functionally organized brain networks.

Keywords: brain basis of intelligence; cognitive ability; individual differences; locus coeruleus; pupil size.

Fig. 1.

A depiction of the locus coeruleus–norepinephrine projection system throughout the brain. Created with BioRender.com.

Fig. 2.

Baseline pupil size as a function of mental effort and repeated measurement. (Left) Baseline pupil size was about 1 mm larger for high working-memory capacity subjects (n = 20) than for low working-memory capacity subjects (n = 20). Pupil size was measured in a simple memory span task during a 30-s interval after the last presented memory item and before recall. Memory load set sizes of four, six, and eight were administered. Pupil size increased with larger set sizes, suggesting an increase in mental effort. However, pupil size for low working-memory capacity subjects at the largest set size was still smaller than pupil size at baseline for high working-memory capacity subjects. This suggests that differences in mental effort at baseline cannot explain why high working-memory capacity subjects have a larger baseline pupil size. (Right) Baseline pupil size was larger for high working-memory capacity subject (n = 57) than for low working-memory capacity subjects (n = 53), replicating our previous findings. Baseline pupil size was repeatedly measured across multiple testing sessions during a working-memory training study. Subjects came in for 23 sessions over the course of 3.5 to 16.5 wk. Although baseline pupil size decreased over the testing sessions, high working-memory capacity subjects still had a larger baseline pupil size at sessions 12 and 23 compared to low working-memory capacity subjects. Therefore, working-memory capacity differences in baseline pupil size are highly reliable over time and repeated testing. Additionally, baseline pupil size correlated strongly across the three testing sessions (average r = 0.79). This suggests that baseline pupil size captures reliable trait-level characteristics and not simply state-level variables, like arousal and mental effort. These figures were adapted from experiment 1 (Left) and experiment 2 (Right) of Tsukahara et al. (1).

Fig. 3.

Relationship of baseline pupil size to working-memory capacity and fluid intelligence. (Upper) Baseline pupil size correlated with fluid intelligence, r = 0.35, P < 0.05, n =337. Error bar represents the SE of measurement. (Lower) Fluid intelligence and working-memory capacity were highly correlated, r = 0.68. After accounting for this shared variance, only fluid intelligence uniquely predicted baseline pupil size (n = 337). The Lower figure was adapted from experiment 3 of Tsukahara et al. (1).

Fig. 4.

Baseline pupil size as a function of lighting conditions. (Upper) Baseline pupil size as a function of room lighting, background color on the monitor, and monitor brightness settings; each factor was independently manipulated for a total of eight conditions (n = 201). In the two brightest lighting conditions (room lights on/off, white background, and bright monitor settings), the mean and variability of baseline pupil size values were severely restricted, such that the mean approached the physiological minimum pupil size. Error bars represent the within-subject SE of measurement. (Lower) Hierarchical linear modeling results showing that fluid intelligence predicted baseline pupil size in all the lighting conditions except for the two brightest conditions (n = 201).

Fig. 5.

Structural equation model with a common executive attention factor and unique working-memory capacity and fluid intelligence factors predicting baseline pupil size from study 1 (Upper) and study 2 (Lower) of Tsukahara and Engle (2). Dotted lines represent nonsignificant regression paths and factor loadings, P < 0.05.

Fig. 6.

Structural equation model with maintenance and disengagement factors predicting baseline pupil size from Study 1 (Upper) and Study 2 (Lower) of Tsukahara and Engle (2). Dotted lines represent nonsignificant regression paths and factor loadings, P < 0.05.