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BackgroundWorking memory (WM) is imperative for effective selective attention. Distractibility is greater under conditions of high (vs. low) concurrent working memory load (WML), and in individuals with low (vs. high) working memory capacity (WMC). In the current experiments, we recorded the flanker task performance of individuals with high and low WMC during low and high WML, to investigate the combined effect of WML and WMC on selective attention.Methodology/Principal FindingsIn Experiment 1, distractibility from a distractor at a fixed distance from the target was greater when either WML was high or WMC was low, but surprisingly smaller when both WML was high and WMC low. Thus we observed an inverted-U relationship between reductions in WM resources and distractibility. In Experiment 2, we mapped the distribution of spatial attention as a function of WMC and WML, by recording distractibility across several target-to-distractor distances. The pattern of distractor effects across the target-to-distractor distances demonstrated that the distribution of the attentional window becomes dispersed as WM resources are limited. The attentional window was more spread out under high compared to low WML, and for low compared to high WMC individuals, and even more so when the two factors co-occurred (i.e., under high WML in low WMC individuals). The inverted-U pattern of distractibility effects in Experiment 1, replicated in Experiment 2, can thus be explained by differences in the spread of the attentional window as a function of WM resource availability.Conclusions/SignificanceThe current findings show that limitations in WM resources, due to either WML or individual differences in WMC, affect the spatial distribution of attention. The difference in attentional constraining between high and low WMC individuals demonstrated in the current experiments helps characterise the nature of previously established associations between WMC and controlled attention.

Higher education students' perception of and level of engagement with the feedback they receive has gained increasing attention in the literature to identify areas which require educators' attention. However, predictors of students' perception and engagement have yet to be identified. To address this, a survey measuring students' views and practices regarding feedback was completed. Characteristics of the individual student (gender, age and whether English is their first language) the learning environment (student lives on or off campus) and course related variables (year of study of the course and whether the second subject was the same or a different discipline) were analysed to assess whether these variables predict students' perception and engagement with feedback. Multivariate analysis established that the only significant predictor variable was year of study of the course thus identifying a key predictor whilst ruling out numerous potential predictors. As the only significant predictor of students' relationship with feedback was their year of study, further analyses were conducted to establish which specific areas of perception of and engagement with feedback change as a function of this.