Estimating Level of Engagement from Ocular Landmarks

E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders

Chemikal weathening of clay-rich sandstone matrix - control and case studies

The research is focused on two aspects of the sandstone decay: mineralogical compositions of neo-formed phases as the result of enviromental conditions in reginal scale and reaction of the clay matrix with sulphate-rich acid waters

Fluorescence, photosynthesis, and stress: How are they coupled?

Tuitional text for participants in a practical course in biology of the project "Open science"

Annual variation of the steady-state chlorophyll fluorescence emission of evergreen plants in temperate zone

Remotely sensed passive chlorophyll fluorescence emission has a potential to become one of the major global-scale reporter signals on vegetation performance and stress. In contrast to the actively probed parameters such as maximal (FM') or minimal (F-0') emission, the steady-state chlorophyll fluorescence, Chl-F-S, (FM' > Chl-FS > F-0') has not been adequately studied. Using fluorescence imaging of leaves, we explored the modulation of Chl-F-S by actinic irradiance and by temperature in laboratory, as well as the changes that occurred in three coniferous and broadleaf plant species grown in field. The experiments revealed that Chl-F-S is largely insensitive to the incident irradiance once this is above early morning or late evening levels. The characteristic, pre-noon measured Chl-F-S correlated positively with the CO2 assimilation rate when measured in field during the year. It was low and stable in the cold winter months and steeply increased with the spring onset. The high values of the characteristic Chl-F-S persisted throughout the vegetation season and rapidly decreased in the fall. The seasonal Chl-F-S transitions coincided with the last spring frosts or the first fall frosts that persisted for several consecutive nights. The transitions were marked by an elevated variability of the Chl-F-S signal. We propose that the signal variability occurring during the transition periods can be used to detect from satellites the beginning and the end of the photosynthetic activity in evergreen canopies of the temperate zone

Confirmatory reinforcement learning changes with age during adolescence.

Funder: Jacobs Foundation; Id: http://dx.doi.org/10.13039/501100003986Funder: Agence Nationale de la Recherche; Id: http://dx.doi.org/10.13039/501100001665Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11-32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages. RESEARCH HIGHLIGHTS: Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments. People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence. Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices. Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias