COVID-19 and Memory: A Novel Contamination Effect in Memory

The contamination effect on recognition memory: adding evidence of an adaptive mnemonic tuning

Previous research has revealed enhanced free recall for neutral items previously associated with disease-causing agents, compared to when they are associated with neutral information; this has been termed the contamination effect. However, it remains unknown whether this effect extends to recognition memory and, if so, on what processes it would rely (i.e., recollection or familiarity). This is a relevant question to establish the generality and enrich our knowledge about the effect on the various processes in which memory operates. We employed a Yes/No recognition task with Remember/Know judgments to assess the recognition experience. Online American (Experiment 1) and in-person Portuguese (Experiment 2) samples were used. Furthermore, in Experiment 2, participants responded to a Health Status questionnaire and the Fear of COVID-19 scale, allowing us to explore the relation of the effect with these individual variables. In both experiments, the results revealed that objects were significantly better recognised after having been previously associated with sick faces than with healthy faces. Moreover, participants assigned a higher proportion of Remember-judgments to contaminated (vs. non-contaminated) objects, suggesting these were retained with more contextual information. Exploratory analyses revealed that participants' illness recency correlated positively with the proportion of Remember-judgments. The robustness of this effect is supported by its replication among participants from two different countries, employing both online and on-site procedures. Notably, the to-be-recognised objects were the same across conditions, preventing possible item-selection concerns. Overall, this study expands our understanding of the impact of contamination on memory, emphasising its role in disease avoidance.

A diagnostic model for COVID-19 based on proteomics analysis

BACKGROUND AND OBJECTIVE

Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease.

METHODS

The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision.

RESULTS

The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems.

CONCLUSION

The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

Adaptive Education: Learning and Remembering with a Stone-Age Brain

Educators generally accept that basic learning and memory processes are a product of evolution, guided by natural selection. Less well accepted is the idea that ancestral selection pressures continue to shape modern memory functioning. In this article, I review evidence suggesting that attention to nature's criterion-the enhancement of fitness-is needed to explain fully how and why people remember. Thinking functionally about memory, and adopting an evolutionary perspective in the laboratory, has led to recent discoveries with clear implications for learning in the classroom. For example, our memory systems appear to be tuned to animacy (the distinction between living and nonliving things) which, in turn, can play a role in enhancing foreign language acquisition. Effective learning management systems need to align with students' prior knowledge, skill, and interest levels, but also with the inherent content biases or "tunings" that are representative of all people.