Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample

Attending school is a multifaceted experience. Students are not only exposed to new knowledge but are also immersed in a structured environment in which they need to respond flexibly in accordance with changing task goals, keep relevant information in mind, and constantly tackle novel problems. To quantify the cumulative effect of this experience, we examined retrospectively and prospectively, the relationships between educational attainment and both cognitive performance and learning. We analyzed data from 196,388 subscribers to an online cognitive training program. These subscribers, ages 15–60, had completed eight behavioral assessments of executive functioning and reasoning at least once. Controlling for multiple demographic and engagement variables, we found that higher levels of education predicted better performance across the full age range, and modulated performance in some cognitive domains more than others (e.g., reasoning vs. processing speed). Differences were moderate for Bachelor’s degree vs. High School (d = 0.51), and large between Ph.D. vs. Some High School (d = 0.80). Further, the ages of peak cognitive performance for each educational category closely followed the typical range of ages at graduation. This result is consistent with a cumulative effect of recent educational experiences, as well as a decrement in performance as completion of schooling becomes more distant. To begin to characterize the directionality of the relationship between educational attainment and cognitive performance, we conducted a prospective longitudinal analysis. For a subset of 69,202 subscribers who had completed 100 days of cognitive training, we tested whether the degree of novel learning was associated with their level of education. Higher educational attainment predicted bigger gains, but the differences were small (d = 0.04–0.37). Altogether, these results point to the long-lasting trace of an effect of prior cognitive challenges but suggest that new learning opportunities can reduce performance gaps related to one’s educational history.

Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior

Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.

Inferring affect from fMRI data

Neuroimaging findings are often interpreted in terms of affective experience, but researchers disagree about the advisability or even possibility of such inferences, and few frameworks explicitly link these levels of analysis. Here, we suggest that the spatial and temporal resolution of functional magnetic resonance imaging (fMRI) data could support inferences about affective states. Specifically, we propose that fMRI nucleus accumbens (NAcc) activity is associated with positive arousal, whereas a combination of anterior insula activity and NAcc activity is associated with negative arousal. This framework implies quantifiable and testable inferences about affect from fMRI data, which may ultimately inform predictions about approach and avoidance behavior. We consider potential limits on neurally inferred affect before highlighting theoretical and practical benefits.

Affective traits link to reliable neural markers of incentive anticipation

While theorists have speculated that different affective traits are linked to reliable brain activity during anticipation of gains and losses, few have directly tested this prediction. We examined these associations in a community sample of healthy human adults (n=52) as they played a Monetary Incentive Delay task while undergoing functional magnetic resonance imaging (FMRI). Factor analysis of personality measures revealed that subjects independently varied in trait Positive Arousal and trait Negative Arousal. In a subsample (n=14) retested over 2.5years later, left nucleus accumbens (NAcc) activity during anticipation of large gains (+$5.00) and right anterior insula activity during anticipation of large losses (-$5.00) showed significant test-retest reliability (intraclass correlations>0.50, p's<0.01). In the full sample (n=52), trait Positive Arousal correlated with individual differences in left NAcc activity during anticipation of large gains, while trait Negative Arousal correlated with individual differences in right anterior insula activity during anticipation of large losses. Associations of affective traits with neural activity were not attributable to the influence of other potential confounds (including sex, age, wealth, and motion). Together, these results demonstrate selective links between distinct affective traits and reliably-elicited activity in neural circuits associated with anticipation of gain versus loss. The findings thus reveal neural markers for affective dimensions of healthy personality, and potentially for related psychiatric symptoms.