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Knowledge and Meaning. On the New Illiteracy from a Hermeneutical Perspective. Many life situations and especially communication situations tell us that we should not mistake an ordinary piece of information for knowledge proper or for a meaning proper. These three types of intentionalities – to inform, to know and to understand – are very different, although they continuously interfere. Man’s specific capacity of understanding appears to us now more important than others. In the absence of a good exercise of understanding, information can remain either strange, or indifferent to us. Man can become informed or even a connoisseur, but still, an alien to those elevated landmarks – moral or human, cultural – which make possible a good self-orientation. This fact is directly connected to what is called nowadays “functional illiteracy”. Actually, this concerns particularly the capacity of understanding something said or done. For this reason, this can be considered a case of illiteracy of hermeneutical nature. The pedagogical solution for such a phenomenon involves continuously practising the competence of understanding. Thus, it would be absolutely normal to focus on understanding, and not on information and, further on, on self-understanding and understanding the other, instead of focusing on an ordinary phenomenon. Consequently, cultivating the senses and the mind, for a better orientation in the world of life, is more important than the technical efficiency of the learning process.

Background The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are the most commonly used scales to detect mild cognitive impairment (MCI) in population-based epidemiologic studies. However, their comparison on which is best suited to assess cognition is scarce in samples from multiple regions of China. Methods We conducted a cross-sectional analysis of 4923 adults aged ≥55 years from the Community-based Cohort Study on Nervous System Diseases. Objective cognition was assessed by Chinese versions of MMSE and MoCA, and total score and subscores of cognitive domains were calculated for each. Education-specific cutoffs of total score were used to diagnose MCI. Demographic and health-related characteristics were collected by questionnaires. Correlation and agreement for MCI between MMSE and MoCA were analyzed; group differences in cognition were evaluated; and multiple logistic regression model was used to clarify risk factors for MCI. Results The overall MCI prevalence was 28.6% for MMSE and 36.2% for MoCA. MMSE had good correlation with MoCA (Spearman correlation coefficient = 0.8374, p  < 0.0001) and moderate agreement for detecting MCI with Kappa value of 0.5973 ( p  < 0.0001). Ceiling effect for MCI was less frequent using MoCA versus MMSE according to the distribution of total score. Percentage of relative standard deviation, the measure of inter-individual variance, for MoCA (26.9%) was greater than for MMSE (19.0%) overall ( p  < 0.0001). Increasing age (MMSE: OR = 2.073 for ≥75 years; MoCA: OR = 1.869 for≥75 years), female (OR = 1.280 for MMSE; OR = 1.163 for MoCA), living in county town (OR = 1.386 and 1.862 for MMSE and MoCA, respectively) or village (OR = 2.579 and 2.721 for MMSE and MoCA, respectively), smoking (OR = 1.373 and 1.288 for MMSE and MoCA, respectively), hypertension (MMSE: OR = 1.278; MoCA: OR = 1.208) and depression (MMSE: OR = 1.465; MoCA: OR = 1.350) were independently associated with greater likelihood of MCI compared to corresponding reference group in both scales (all p  < 0.05). Conclusions MoCA is a better measure of cognitive function due to lack of ceiling effect and with good detection of cognitive heterogeneity. MCI prevalence is higher using MoCA compared to MMSE. Both tools identify concordantly modifiable factors for MCI, which provide important evidence for establishing intervention measures.

Brain–computer interfaces (BCIs) are a form of technology that read a user’s neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as “BCI illiteracy,” and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.

Brain-computer interfaces (BCIs) facilitate direct interaction between the human brain and computers, enabling individuals to control external devices through cognitive processes. Despite its potential, the problem of BCI illiteracy remains one of the major challenges due to inter-subject EEG variability, which hinders many users from effectively utilizing BCI systems. In this study, we propose a subject-to-subject semantic style transfer network (SSSTN) at the feature-level to address the BCI illiteracy problem in electroencephalogram (EEG)-based motor imagery (MI) classification tasks. Our approach uses the continuous wavelet transform method to convert high-dimensional EEG data into images as input data. The SSSTN 1) trains a classifier for each subject, 2) transfers the distribution of class discrimination styles from the source subject (the best-performing subject for the classifier, i.e., BCI expert) to each subject of the target domain (the remaining subjects except the source subject, specifically BCI illiterates) through the proposed style loss, and applies a modified content loss to preserve the class-relevant semantic information of the target domain, and 3) finally merges the classifier predictions of both source and target subject using an ensemble technique. We evaluate the proposed method on the BCI Competition IV-2a and IV-2b datasets and demonstrate improved classification performance over existing methods, especially for BCI illiterate users. The ablation experiments and t-SNE visualizations further highlight the effectiveness of the proposed method in achieving meaningful feature-level semantic style transfer.

Nutrition and food literacy are two important concepts that are often used interchangeably, but they are not synonymous. Nutrition refers to the study of how food affects the body, while food literacy refers to the knowledge, skills, and attitudes necessary to make informed decisions about food and its impact on health. Despite the growing awareness of the importance of food literacy, food illiteracy remains a global issue, affecting people of all ages, backgrounds, and socioeconomic status. Food illiteracy has serious health implications as it contributes to health inequities, particularly among vulnerable populations. In addition, food literacy is a complex and multidisciplinary field, and there are numerous challenges to health communication that must be addressed to effectively promote food literacy and improve health outcomes. Addressing food illiteracy and the challenges to health communication is essential to promote health equity and improve health outcomes for all populations.

Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users’ mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.