Testing locus coeruleus-norepinephrine accounts of working memory, attention control, and fluid intelligence

Abstract

The current set of studies examined the relationship among working memory capacity, attention control, fluid intelligence, and pupillary correlates of tonic arousal regulation and phasic responsiveness in a combined sample of more than 1,000 participants in two different age ranges (young adults and adolescents). Each study was designed to test predictions made by two recent theories regarding the role of the locus coeruleus-norepinephrine (LC-NE) system in determining individual differences in cognitive ability. The first theory, proposed by Unsworth and Robison (2017a), posits two important individual differences: the moment-to-moment regulation of tonic arousal, and the phasic responsiveness of the system to goal-relevant stimuli. The second theory, proposed by Tsukahara and Engle (2021a), argues that people with higher cognitive abilities have greater functional connectivity between the LC-NE system and cortical networks at rest. These two theories are not mutually exclusive, but they make different predictions. Overall, we found no evidence consistent with a resting-state theory. However, phasic responsiveness was consistently correlated with working memory capacity, attention control, and fluid intelligence, supporting a prediction made by Unsworth and Robison (2017a). Tonic arousal regulation was not correlated with working memory or fluid intelligence and was inconsistently correlated with attention control, which offers only partial support for Unsworth and Robison's (2017a) second prediction.

Keywords: Arousal; Attention; Intelligence; Pupillometry; Working memory.

Fig. 1

Task-evoked pupillary responses for the color (A), orientation (B), and letter (C) visual arrays tasks by trial type in Study 1

Fig. 2

Scatterplots of correlations among factors in Study 1

Fig. 3

Average task evoked responses for the psychomotor vigilance task (A), Sustained Attention to Response Task (B), and antisaccade task in Study 2 (C)

Fig. 4

Scatterplots of correlations among factors in Study 2

Fig. 5

Structural equation model predicting attention control from phasic responsiveness, tonic arousal regulation, motivation, and alertness in Study 2. Collectively, the four predictors accounted for 48% of the variance in attention control. Only phasic responsiveness had a significant direct path on its own. Solid lines indicate significant paths at p < 0.05. Dashed lines indicate non-significant paths

Fig. 6

Grand-averaged task-evoked pupillary responses (TEPRs) from the Psychomotor vigilance task (A), Arrow flanker task (B), Stroop task (C), and Sustained Attention to Response task (D) in Study 3

Fig. 7

Scatterplots of attention control, working memory capacity, and fluid intelligence with arousal regulation, phasic responsiveness, and baseline pupil diameter factor scores in Study 3

Fig. 8

Confirmatory factor analyses with session-specific factors for both phasic responsiveness and tonic arousal regulation (A), a trait-level factor for phasic responsiveness and session-specific factors for arousal regulation (B), and trait-level factors for both phasic responsiveness and arousal regulation (C) in Study 3

Fig. 9

Structural regression models in which phasic responsiveness, motivation, and alertness were set as predictors of Working memory capacity (A), Attention control (B), and Fluid intelligence (C) in Study 3. In all three models, phasic responsiveness accounted for a significant portion of variance in the respective cognitive ability even after controlling for self-reported motivation and alertness. Solid lines indicate significant paths at p < 0.05; dashed lines indicate nonsignificant paths

Fig. 10

Scatterplots of attention control, working memory capacity, and fluid intelligence with arousal regulation, phasic responsiveness, and baseline pupil diameter factor scores in Study 4