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Executive function (EF) is wrought with confusion around how it is conceptualized and measured. The roots of EF trace back to unusual neurological case studies beginning in the 19th century and war injuries in the early 20th century. Since then, EF has taken on quite a number of meanings and operationalizations. This presentation will compare a variety of current perspectives on EF, review key issues relevant to its understanding, and offer (at least one) path forward, to allow for greater coherence in this complex field. 

Objective Online surveys are a common method of data collection. The use of “attention-check” questions are an effective method of identifying careless responding in surveys (Liu & Wronski, 2018; Meade & Craig 2012; Ward & Meade, 2023), which occurs in 10-12% of undergraduate samples (Meade & Craig, 2012). Instructed response type attention checks are straightforward and the most recommended (Meade & Craig, 2012; Ward & Meade, 2023). This study evaluated the effect of instructed response attention check questions on the measurement of math ability and non-cognitive factors commonly related to math (self-efficacy and math anxiety). We evaluated both level differences as well as whether check questions alter the relationship of non-cognitive factors to math. We expected that incorrect responding to check questions would lower math performance but were unable to make hypotheses about level of self-report non-cognitive factors. We predicted that incorrect responding to check questions would moderate the relationship between both math anxiety and self-efficacy to math performance. Participants and Methods Participants were 424 undergraduates (age 20.4, SD=2.7) at a large southwestern university. The sample was majority female (74%) but diverse socioeconomically and in race/ethnicity. The non-cognitive measures were researcher developed Math Anxiety (MA) and Math Self-Efficacy (MSE; Betz & Hackett, 1993) scales, with items selected directly targeting the use/manipulation of math in everyday life; both showed good reliability (α=.95). The two math scales were also researcher developed; one was a pure symbolic computational measure (EM-A) and the other consisted of word problems in an everyday context (EM-B). These measures had good reliability (α=.80 and α=.73). The four check questions were embedded in the surveys and two groupings were formed – one consisting of those who provided the correct answer for all items versus those who did not, and a second consisting of those who got all correct or only one answer incorrect versus those with more items incorrect. Correlational, ANOVA, and ANCOVA models were utilized. Results Descriptively, check questions were skewed – 75% participants answered all check questions correctly, and 8% missed only one. Relations of both MA and MSE with EM-A and EM-B were modest though significant (|r|=.22 to .37) and in the expected directions (all p<.001). Check questions were related to level of all tasks (p<.001), with incorrect responses resulting in lower math performance, lower MSE, and higher MA. Check questions did not moderate the relation of MA or MSE to either math performance, with some suggestion that MA was more strongly related to EM-B in those who missed check questions, though only when failing several. Conclusions Check questions showed a clear relation to both self-report and math performance measures. However, check questions did not alter the relation of MA or MSE to math performance in general. These results affirm extant relations of key self-perceptions to math using novel measures and highlight the need to evaluate the validity of self-report measures, even outside of objective performance indicators. Future work could examine the effect of attention checks in domains other than math and investigate other types of attention checks. 

Objective Math and reading are related at the disability level and along the continuum of skill (Cirino, 2022). Cognitive correlates of math and reading in children are well-known separately, with a recent focus on the reason for their overlap. However, less is known about these issues in community college (CC) students despite more than half of post-secondary education occurring at this level. Here we assess cognitive predictors of math and reading (language, working memory, processing speed, nonverbal reasoning, attention) in CC students, outcome overlap, and the extent that predictors account for overlap. We expect all predictors to relate to achievement, with language and working memory as the strongest predictors, and accounting for the most overlap. We also expect more overlap and stronger prediction for complex outcomes (reading comprehension and math applications) relative to foundational skills (word reading and computations). Participants and Methods Participants were 94 CC students enrolled in their first math class. Approximately half the students were taking developmental coursework. Participants were administered four KTEA-3 measures: Letter-Word Reading, Reading Comprehension, Math Computation, and Math Concepts and Application. Language consisted of Vocabulary (K-BIT-2), and Elision and Rapid Naming subtests of the CTOPP-2. Working memory was assessed with two complex span measures (Symmetry Span and Reading Span). Processing speed was measured with the WAIS-IV, and nonverbal reasoning with the K-BIT-2. Attention was assessed via a researcher-designed continuous performance task and a self-rating scale. Multiple regression assessed cognitive prediction for each achievement measure; and partial correlation evaluated overlap. Results For computations, all predictors accounted for R2=53% variance; nonverbal reasoning and elision were unique predictors (p<.05). For math applications, R2=58%, with unique prediction for nonverbal reasoning, vocabulary, elision, and symmetry span. For word reading, R2=50%, with unique prediction for vocabulary, elision, and reading span. Finally, for reading comprehension, R2=47%, with unique prediction for vocabulary and nonverbal reasoning. Regarding overlap, computations and word reading correlated r=.50, and math applications and reading comprehension r=.57, which is higher than a recent meta-analysis (Unal et al., 2023). Language was the strongest contributor of overlap; these variables reduced the correlation for foundational achievement by 50%, and for complex achievement, by 32%. Other domains accounted for little overlap, despite significant zero-order correlations. Substantive results were generally similar when covariates were considered. Conclusions Individual prediction was dominated by language, nonverbal reasoning, and working memory variables. Math and reading performances were strongly related, and language was the strongest predictor of this overlap, which is only partially consistent with extant literature but adds context and generalization for CC students. Attention and processing speed were only weakly related to performance, which may reflect the overlearned nature of these skills at this level. Future work might need to include more malleable factors (e.g., motivation), as well as broader views of achievement (e.g., course grades).