Search for: All records

Recent work suggests combining physical activity with cognitive tasks may have been critical to human evolution and may be beneficial to human brain health today. These combined tasks are key elements of foraging, a lifestyle employed by human ancestors for over 2 My. However, it is unclear whether cognitive engagement during foraging-like tasks impacts endurance, and therefore foraging performance, and whether cognitive adaptations may mitigate these effects. We tested the hypothesis that cognitive engagement during endurance walking increases perceived physical effort without influencing physiological responses, and that enhanced cognition mitigates these effects. Thirty healthy adults (n_female= 17; aged 18 to 53) underwent nonlocomotor cognitive testing and completed two separate randomized endurance tests: one without (Ex) and one with simultaneous executive function tasks (ExCog). For each condition, participants walked on a treadmill for up to 30-min while physiological responses were recorded, and perception of effort was assessed every 2-min using Borg’s rating of perceived exertion (RPE) scale. During the ExCog condition, RPE was significantly greater (P= 0.005), while energy expenditure was significantly lower (P= 0.008) compared to the Ex condition. Additionally, we observed significant interactions between cognitive abilities and endurance performance—for example, individuals with greater visuospatial abilities experienced a smaller increase in perceived effort (RPE) in the ExCog condition compared to the Ex condition (FDRP= 0.039). These results indicate that cognitive demands and cognitive abilities associated with foraging distinctly influence endurance, suggesting that evolutionary shifts in human cognitive capacities may have relaxed constraints on endurance foraging performance. 

Psychological test scores are commonly used in high-stakes settings to classify individuals. While measurement invariance across groups is necessary for valid and meaningful inferences of group differences, full measurement invariance rarely holds in practice. The classification accuracy analysis framework aims to quantify the degree and practical impact of noninvariance. However, how to best navigate the next steps remains unclear, and methods devised to account for noninvariance at the group level may be insufficient when the goal is classification. Furthermore, deleting a biased item may improve fairness but negatively affect performance, and replacing the test can be costly. We propose item-level effect size indices that allow test users to make more informed decisions by quantifying the impact of deleting (or retaining) an item on test performance and fairness, provide an illustrative example, and introduce unbiasr, an R package implementing the proposed methods.