Competition and forgetting during context-based episodic memory retrieval

Recent memory models highlight the importance of contextual information for remembering episodic events. A consequence of binding event memories with their context is that contextually related memories can interfere with the retrieval of targeted memories, leading to retrieval induced forgetting (RIF) of the competing memories (Anderson et al., 2000). A model built to explain this effect describes a nonmonotonic “U shaped” relationship between memory activation and changes in memory strength (Norman et al., 2007). Specifically, competing memories that activate to a moderate degree (vs. low or high activation) are more likely to be weakened and forgotten. However, the factors governing whether and how memories will activate and compete during retrieval are not well understood. Here, we test the hypothesis that events experienced closer in time will be more likely to compete during memory retrieval, leading to RIF.Psycholog

Antimalarial drug discovery - the path towards eradication

Malaria is a disease that still affects a significant proportion of the global human population. Whilst advances have been made in lowering the numbers of cases and deaths, it is clear that a strategy based solely on disease control year on year, without reducing transmission and ultimately eradicating the parasite, is unsustainable. This article highlights the current mainstay treatments alongside a selection of emerging new clinical molecules from the portfolio of Medicines for Malaria Venture (MMV) and our partners. In each case, the key highlights from each research phase are described to demonstrate how these new potential medicines were discovered. Given the increased focus of the community on eradicating the disease, the strategy for next generation combination medicines that will provide such potential is explaine

Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review

Purpose: Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients’ perspectives on the use of an array of AI technologies in the health care setting for design and deployment. Methods: This scoping review followed Arksey and O'Malley’s framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles. Results: Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use. Conclusions: Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients’ needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care. (J Patient Cent Res Rev. 2024;11:51-62.

To be or not to be relevant: Comparing short- and long-term consequences across working memory prioritization procedures

Priority-based allocation of attentional resources has shown robust effects in working memory (WM) with both cue-based and reward-based prioritization. However, direct comparisons between these effects in WM are needed. Additionally, the consequences of WM prioritization for remembering in the long term remain unclear for both prioritization procedures. Here, we tested and compared the immediate and long-term memory (LTM) effects of cue-based versus reward-based retrospective prioritization of WM content. Participants encoded four memory items and were then indicated to prioritize one of the items through the presentation of either a retro-cue or a reward pattern. We then tested their immediate and delayed memory. The results of the first experiment showed better memory for prioritized than for unprioritized information in WM and LTM, but the WM effect was driven solely by the retro-cue, making it difficult to interpret any reward-based effects in LTM. In the second experiment, using a more explicit and meaningful reward-based manipulation, the results showed a prioritization benefit in WM for both prioritization procedures. In LTM, however, the prioritization effect was predominantly driven by the retro-cue manipulation. Taken together, we found that (1) the way in which attention is directed in WM impacts the size of the prioritization benefit in WM, (2) WM prioritization generally results in a prioritization effect in LTM, and (3) that the effect in LTM is more robust for cue-based prioritization. Exploratory analyses indicated that the LTM effect of cue-based prioritization reflected a cost in performance for noncued items rather than a benefit for cued items

From Lab-Testing to Web-Testing in Cognitive Research: Who You Test is More Important Than How You Test

The transition to web-testing, although promising, entails many new concerns. Webtesting is harder to monitor, so researchers need to ensure that the quality of the data collected is comparable to the quality of data typically achieved by lab-testing. Our study yields a novel contribution to this issue, by being the first to distinguish between the impact of web-testing and the impact of sourcing individuals from different participant pools, including crowdsourcing platforms. We presented a fairly general working memory task to 196 MTurk participants, 300 Prolific participants, and 255 students from the University of Geneva, allowing for a comparison of data quality across different participant pools. Among university students, 215 were web-tested, and 40 were lab-tested, allowing for a comparison of testing modalities within the same participant pool. Data quality was measured by assessing multiple data characteristics (i.e., reaction time, accuracy, anomalous values) and the presence of two behavioral benchmark effects. Our results revealed that who you test (i.e., participant pool) is more important than how you test (i.e., testing modality). Concerning how you test, our results showed that web-testing incurs a small, yet acceptable loss of data quality compared to lab-testing. Concerning who you test, Prolific participants were almost indistinguishable from web-tested students, but MTurk participants differed drastically from the other pools. Our results therefore encourage the use of web-testing in the domain of cognitive psychology, even when using complex paradigms. Nevertheless, these results urge for caution regarding how researchers select web-based participant pools when conducting online research