This review provides an introduction to two eyetracking measures that can be used to study cognitive development and plasticity: pupil dilation and spontaneous blink rate. We begin by outlining the rich history of gaze analysis, which can reveal the current focus of attention as well as cognitive strategies. We then turn to the two lesser-utilized ocular measures. Pupil dilation is modulated by the brain's locus coeruleus-norepinephrine system, which controls physiological arousal and attention, and has been used as a measure of subjective task difficulty, mental effort, and neural gain. Spontaneous eyeblink rate correlates with levels of dopamine in the central nervous system, and can reveal processes underlying learning and goal-directed behavior. Taken together, gaze, pupil dilation, and blink rate are three non-invasive and complementary measures of cognition with high temporal resolution and well-understood neural foundations. Here we review the neural foundations of pupil dilation and blink rate, provide examples of their usage, describe analytic methods and methodological considerations, and discuss their potential for research on learning, cognitive development, and plasticity.
Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level-dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-dependent brain plasticity in humans. Here, we evaluate the hypothesis that resting-state functional connectivity reflects the repeated history of co-activation between brain regions. To this end, we review resting-state fMRI studies in the sensory, motor, and cognitive learning literature. This body of research provides evidence that the brain's resting-state functional architecture displays dynamic properties in young adulthood.
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.
Reasoning, our ability to solve novel problems, has been shown to improve as a result of learning experiences. However, the underlying mechanisms of change in this high-level cognitive ability are unclear. We hypothesized that possible mechanisms include improvements in the encoding, maintenance, and/or integration of relations among mental representations – i.e., relational thinking. Here, we developed several eye gaze metrics to pinpoint learning mechanisms that underpin improved reasoning performance. We collected behavioral and eyetracking data from young adults who participated in a Law School Admission Test preparation course involving word-based reasoning problems or reading comprehension. The Reasoning group improved more than the Comprehension group on a composite measure of four visuospatial reasoning assessments. Both groups improved similarly on an eyetracking paradigm involving transitive inference problems, exhibiting faster response times while maintaining high accuracy levels; nevertheless, the Reasoning group exhibited a larger change than the Comprehension group on an ocular metric of relational thinking. Across the full sample, individual differences in response time reductions were associated with increased efficiency of relational thinking. Accounting for changes in visual search and a more specific measure of relational integration improved the prediction accuracy of the model, but changes in these two processes alone did not adequately explain behavioral improvements. These findings provide evidence of transfer of learning across different kinds of reasoning problems after completing a brief but intensive course. More broadly, the high temporal precision and rich derivable parameters of eyetracking make it a powerful approach for probing learning mechanisms.
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