Whereas social support contributes to individual vitality and academic performance, the theoretical process through which social support promotes performance, and for whom it is most beneficial in this respect, remain open questions. We developed a conceptual model in which social support influences academic performance by promoting vitality, particularly among individuals who are low on social self-efficacy (SSE). Social support has a positive effect on university students' academic performance, which is largely explained by its relationship with reported vitality among students with relatively low levels of SSE but not among students with higher SSE. We discuss the theoretical and practical implications for the existing literature on social support and vitality in academic settings.

When examining rapid instructed task learning behaviorally, one out of two paradigms is usually used, the Inducer-Diagnostic (I-D) and the NEXT paradigm. Even though both paradigms are supposed to examine the same phenomenon of Automatic Effect of Instructions (AEI), there are some meaningful differences between them, notably in the size of the AEI. In the current work, we examined, in two pre-registered studies, the potential reasons for these differences in AEI size. Study 1 examined the influence of the data-analytic approach by comparing two existing relatively large data-sets, one from each paradigm (Braem et al., in Mem Cogn 47:1582–1591, 2019; Meiran et al., in Neuropsychologia 90:180–189, 2016). Study 2 focused on the influence of instruction type (concrete, as in NEXT, and abstract, as in I-D) and choice complexity of the task in which AEI-interference is assessed. We did that while using variants of the NEXT paradigm, some with modifications that approximated it to the I-D paradigm. Results from Study 1 indicate that the data-analytic approach partially explains the differences between the paradigms in terms of AEI size. Still, the paradigms remained different with respect to individual differences and with respect to AEI size in the first step following the instructions. Results from Study 2 indicate that Instruction type and the choice complexity in the phase in which AEI is assessed do not influence AEI size, or at least not in the expected direction. Theoretical and study-design implications are discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s00426-021-01596-1.

The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0-5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.