Examining interpersonal metacognitive monitoring in artificial grammar learning.

This study investigates the observers' ability to monitor the ongoing cognitive processes of a partner who is implicitly learning an artificial grammar. Our hypothesis posits that learners experience metacognitive feelings as they attempt to apply their implicit knowledge, and that observers are capable of detecting and interpreting these feelings as cues of the learner's cognitive state. For instance, learners might encounter affective signals linked to cognitive conflicts and errors at different processing stages, which observers can construe as manifestations of the learner's cognitive dissonance. The research involved 126 participants organized into dyads, with one participant acting as a learner, and the other as an observer. The observer's task was to judge whether the learner agrees with the information presented (consonance judgment) and was limited to reading the learner's nonverbal signals to avoid explicit mindreading. The findings suggest that observers possess mindreading abilities, enabling them to detect both learners' confidence and accuracy in stimuli classification. This extends our understanding of non-verbal mindreading capabilities and indicates that observers can effectively interpret early implicit metacognitive information, even in the absence of explicit self-evaluation from the learners. This research offers significant insights into how individuals interpret others' mental states during implicit learning tasks, particularly in the context of utilizing early affective cues within the Artificial Grammar Learning paradigm.

Opportunities for combining data of Estonian and Russian monitoring to reflect on water quality in large transboundary Lake Peipsi.

Lake Peipsi, one of the world's largest lakes, is shared between Estonia and Russia. The water quality in different parts of the lake has so far been assessed independently. Here we explore opportunities for combining data of Estonian and Russian monitoring. For that, we 1) analysed the compatibility of data for some water quality variables; 2) estimated the potential effects of the differences in sampling frequency; 3) provided a few regression models to calculate the missing data for months not sampled by the Russian side. Data of the concurrent Estonian and Russian sampling indicated a good compatibility. Estonian data analysis suggested that water quality assessment results are sensitive to sampling frequency. For example, total phosphorus (TP) in the largest basin showed a long-term decreasing trend in three month data that disappeared when data for other months were added. Disregarding some months may lead to under- or overestimation of certain factors with no consistency in the response of different basins. Hence, data of the whole ice-free period are recommended for an adequate water quality assessment. Furthermore, we demonstrated that monthly values of the water quality variables of the same year are autocorrelated. Based on this, we filled the gaps in the long-term data and compiled a dataset for the whole lake that enables its most comprehensive use in water quality assessment and management. Long-term data revealed no water quality improvement of Lake Peipsi. Further reduction of the external nutrient load is needed. Eutrophication is sustained by high internal phosphorus load.