Delayed juvenile behavioural development and prolonged dependence are adaptations to desert life in the grey Falcon

Rapid learning in the young of most endothermic animals can be expected to be favoured by natural selection because early independence reduces the period of vulnerability. Cases of comparatively slow juvenile development continue, therefore, to attract scientific attention. In most species of birds, including raptors, the young depend on their parents for some time after fledging for the provisioning of food and for protection whilst they learn to become nutritionally and otherwise independent. Among raptors, post-fledging dependence periods that exceed six months are exclusive to the largest species and these have reproductive cycles that exceed 12 months. By contrast, young of the medium-sized grey falcon Falco hypoleucos have been reported in close company with their parents up to 12 months after fledging, i.e., at a time when the adults are expected to breed again. We investigated the occurrence and characteristics of prolonged adult-juvenile association relative to other falcons and similar-sized raptors. We found that the behavioural development of grey falcon young is extremely delayed, and that they even depend nutritionally on their parents for up to 12 months after fledging. We suggest that these two distinctive features are, ultimately, adaptations of the grey falcon to its extreme environment, Australia’s arid and semi-arid zone, one of the hottest environments in the world.

A Rapid Learning Health System to Support Implementation of Early Intervention Services for Psychosis in Quebec, Canada: Study Protocol

Background: Given the strong evidence for their effectiveness, early intervention services for psychosis (EIS) are being widely implemented. However, heterogeneity in the implementation of essential components, remains an ongoing challenge. Rapid learning health systems (RLHS), that embed data collection in clinical settings for real-time learning and continual quality improvement, can address this challenge. We therefore implemented a RLHS in 11 EIS in Quebec, Canada. This project aims to determine the feasibility and acceptability of implementing a RLHS in EIS, and to assess its impact on compliance with standards for essential EIS components. Methods: Following literature recommendations, the implementation of this RLHS involves six iterative phases: external and internal scan, design, implementation, evaluation, adjustment, and dissemination. Multiple stakeholder groups (service users, families, clinicians, researchers, decision makers, provincial EIS association) are involved in all phases. Meaningful indicators of EIS quality (e.g., satisfaction, timeliness of response to referrals) were selected based on literature review, provincial guidelines, and stakeholder consensus on indicators prioritisation. A digital infrastructure was designed and deployed that comprises (a) a user-friendly interface for routinely collecting data from programs (b) a digital terminal and mobile app to collect feedback from service users and families regarding care received, health, and quality of life (c) data analytic, visualization and reporting functionalities to provide participating programs with real-time feedback on their performance over time, and in relation to standards and to other programs, along with tailored recommendations. Community of practice activities are being conducted that leverage insights from data to build capacity among programs to continually progress towards aligning their practice with standards/best practices. Guided by the RE-AIM framework, we are collecting quantitative and qualitative data on the Reach, Effectiveness Adoption, Implementation and Maintenance of our RLHS. These RE-AIM data will be analyzed to evaluate our RLHS’s impacts. Discussion: This project will yield valuable insights about how a RLHS can be implemented by EIS, along with preliminary evidence for its acceptability, feasibility and impacts on program-level outcomes. Its findings will refine our RLHS further and advance approaches that bring data, stakeholder voices and collaborative learning to improve outcomes and service quality in psychosis. Trial registration: NA

STEAM model in teaching biology

The STEAM educational model is based on the idea of teaching students 5 specific fields of Science ( Teshology) , Enerineering ( Energineering), Art (Art), Mathematics ( Marth) in a joint and integrated way. The teaching of biology at school is based on regular demonstrations of experience. However, it is difficuit for students with figurative memory to leam abstract, pictureless processes. My observations today give grounds to say that the teaching of Biology with the help of STEAM will become more difficuit in order to solve these problems and achieve the active use of internet resources in the teaching process. Learning biology through STEAM wil play an important role in education and prepare children for future challenges and opportunities/ It will develop the skills of rapid learning that are needed in educational institutions and today. Key words: biology subject, STEAM, teaching model, education technology

Same or Different? Perceptual Learning for Connected Speech Induced by Brief and Longer Experiences

Perceptual learning, defined as long-lasting changes in the ability to extract information from the environment, occurs following either brief exposure or prolonged practice. Whether these two types of experience yield qualitatively distinct patterns of learning is not clear. We used a time-compressed speech task to assess perceptual learning following either rapid exposure or additional training. We report that both experiences yielded robust and long-lasting learning. Individual differences in rapid learning explained unique variance in performance in independent speech tasks (natural-fast speech and speech-in-noise) with no additional contribution for training-induced learning (Experiment 1). Finally, it seems that similar factors influence the specificity of the two types of learning (Experiment 1 and 2). We suggest that rapid learning is key for understanding the role of perceptual learning in speech recognition under adverse conditions while longer learning could serve to strengthen and stabilize learning.

Auditory Cortical Changes Precede Brainstem Changes During Rapid Implicit Learning: Evidence From Human EEG

The auditory system is sensitive to stimulus regularities such as frequently occurring sounds and sound combinations. Evidence of regularity detection can be seen in how neurons across the auditory network, from brainstem to cortex, respond to the statistical properties of the soundscape, and in the rapid learning of recurring patterns in their environment by children and adults. Although rapid auditory learning is presumed to involve functional changes to the auditory network, the chronology and directionality of changes are not well understood. To study the mechanisms by which this learning occurs, auditory brainstem and cortical activity was simultaneously recorded via electroencephalogram (EEG) while young adults listened to novel sound streams containing recurring patterns. Neurophysiological responses were compared between easier and harder learning conditions. Collectively, the behavioral and neurophysiological findings suggest that cortical and subcortical structures each provide distinct contributions to auditory pattern learning, but that cortical sensitivity to stimulus patterns likely precedes subcortical sensitivity.

Knowledge Consolidation based Class Incremental Online Learning with Limited Data

We propose a novel approach for class incremental online learning in a limited data setting. This problem setting is challenging because of the following constraints: (1) Classes are given incrementally, which necessitates a class incremental learning approach; (2) Data for each class is given in an online fashion, i.e., each training example is seen only once during training; (3) Each class has very few training examples; and (4) We do not use or assume access to any replay/memory to store data from previous classes. Therefore, in this setting, we have to handle twofold problems of catastrophic forgetting and overfitting. In our approach, we learn robust representations that are generalizable across tasks without suffering from the problems of catastrophic forgetting and overfitting to accommodate future classes with limited samples. Our proposed method leverages the meta-learning framework with knowledge consolidation. The meta-learning framework helps the model for rapid learning when samples appear in an online fashion. Simultaneously, knowledge consolidation helps to learn a robust representation against forgetting under online updates to facilitate future learning. Our approach significantly outperforms other methods on several benchmarks.

Mice in a labyrinth exhibit rapid learning, sudden insight, and efficient exploration

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.