Does a high working memory capacity attenuate the negative impact of trait anxiety on attentional control? Evidence from the antisaccade task
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Bibliographic record
Abstract
AbstractAccording to attentional control theory, high trait anxious individuals experience reduced attentional control as compared to low trait anxious individuals due to the imbalance between goal-directed and stimulus-driven attentional systems. One consequence is that high trait anxious individuals have difficulty resisting distraction, as compared to low trait anxious individuals. A separate line of research on individual differences in working memory capacity (WMC) has shown that individuals with higher WMC have better attentional control and thus are better able to resist distraction. The present study investigated the hypothesis that high WMC compensates for high trait anxiety in a task that evaluates the ability to resist distraction, the antisaccade task. Participants completed the State-Trait Anxiety Inventory to measure trait anxiety and the Operation Span and Reading Span tasks to measure WMC. As hypothesised, individuals who were high trait anxious exhibited increased attentional control on the antisaccade task when they had high WMC. Theoretical implications and directions for future research are discussed.Keywords: Antisaccade taskAttentional controlTrait anxietyWorking memory capacity We thank Brittany Bennett, Anna Goupal, Meagan Just-Mancini, Shannon St. Pier, and Dolores Viteri for their assistance with data collection and coding. We also thank three anonymous reviewers for their excellent feedback and suggestions.This research was supported by grants from the Natural Sciences and Engineering Research Council (NSERC) and Alberta Innovates-Health Solutions (AIHS) to C. R. Sears and a graduate scholarship from the Social Sciences and Humanities Research Council to C. A. Wright.We thank Brittany Bennett, Anna Goupal, Meagan Just-Mancini, Shannon St. Pier, and Dolores Viteri for their assistance with data collection and coding. We also thank three anonymous reviewers for their excellent feedback and suggestions.This research was supported by grants from the Natural Sciences and Engineering Research Council (NSERC) and Alberta Innovates-Health Solutions (AIHS) to C. R. Sears and a graduate scholarship from the Social Sciences and Humanities Research Council to C. A. Wright.Notes1 The target identification component of the task was not expected to be affected by attentional control abilities, but for completeness we report the analyses of the target identification latencies and errors. A mixed-model ANOVA with target identification latencies as the dependent variable produced a main effect of Task, F(1, 68) = 85.55, p < .001, MSE = 179322.22, partial η2 = .56, no effect of Anxiety Group (F < 1), and no Anxiety Group × Task interaction (F < 1). The identical analysis of the percentage of target identification errors also produced a main effect of Task, F(1, 68) = 28.57, p < .001, MSE = 77.14, partial η2 = .30, no effect of Anxiety Group (F < 1), and no Anxiety Group × Task interaction (F < 1).2 We created a composite measure of WMC (using the mean of the standardised OSPAN and RSPAN scores) and used this predictor in the same regression analysis of antisaccade latencies. Using this composite measure, the Anxiety Group × RSPAN/OSPAN interaction was marginally significant (B = −27.98, β = −.190, p = .05). The nature of the interaction was identical to the one observed in the RSPAN regression analysis (the difference between the groups' antisaccade latencies decreased as scores on the RSPAN/OSPAN composite measure increased). This outcome suggests that this interaction is present for both measures of WMC, but that it is weaker for the OSPAN measure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it