Assessing the Effect of Instructor’s Emotional Intelligence (EI) on the Students’ Satisfaction Index (SSI): Meta-Analysis of University Students

The present study aims to examine the relationship of instructors’ emotional intelligence (EI) with the satisfaction index of their corresponding students. For this purpose, data were collected from 650 full-time students and 6 male instructors from a major Middle Eastern University. Emotional intelligence of the instructors was measured with the help of average of students’ responses with the weightage of each assessing parameter, i.e., self-awareness, self-management, social awareness, and relationship management which also reflected the students’ satisfaction index (SSI). Moreover, authenticity of the data was confirmed with the help of Cronbach’s alpha, and the analysis of data was carried out using descriptive statistics, correlation, and box plots. The students’ satisfaction index is calculated by correlating various parameters such as comfort, skill, learning, and motivation in order to identify the most critical parameter. For identifying the most critical parameter, box plots are used. Final results reveal a strong correlation of instructor’s EI with student satisfaction index (r = 0.951, p < 0.005 , F >> Fcritical). Findings of the study can be beneficial to highlight the importance of students’ satisfaction index (SSI) which is correlated with instructor’s EI.

Responses to song playback differ in sleeping versus anesthetized songbirds

Vocal learning in songbirds is mediated by a highly localized system of interconnected forebrain regions, including recurrent loops that traverse the cortex, basal ganglia, and thalamus. This brain-behavior system provides a powerful model for elucidating mechanisms of vocal learning, with implications for learning speech in human infants, as well as for advancing our understanding of skill learning in general. A long history of experiments in this area has tested neural responses to playback of different song stimuli in anesthetized birds at different stages of vocal development. These studies have demonstrated selectivity for different song types that provide neural signatures of learning. In contrast to the ease of obtaining responses to song playback in anesthetized birds, song-evoked responses in awake birds are greatly reduced or absent, indicating that behavioral state is an important determinant of neural responsivity. Song-evoked responses can be elicited in sleeping as well as anesthetized zebra finches, and the selectivity of responses to song playback in adult birds tends to be highly similar between anesthetized and sleeping states, encouraging the idea that anesthesia and sleep are highly similar. In contrast to that idea, we report evidence that cortical responses to song playback in juvenile zebra finches (Taeniopygia guttata) differ greatly between sleep and urethane anesthesia. This finding indicates that behavioral states differ in sleep versus anesthesia and raises questions about relationships between developmental changes in sleep activity, selectivity for different song types, and the neural substrate for vocal learning.

Intrinsic somatosensory feedback supports motor control and learning to operate artificial body parts

Objective Considerable resources are being invested to enhance the control and usability of artificial limbs through the delivery of unnatural forms of somatosensory feedback. Here, we investigated whether intrinsic somatosensory information from the body part(s) remotely controlling an artificial limb can be leveraged by the motor system to support control and skill learning. Approach In a placebo-controlled design, we used local anaesthetic to attenuate somatosensory inputs to the big toes while participants learned to operate through pressure sensors a toe-controlled and hand-worn robotic extra finger. Motor learning outcomes were compared against a control group who received sham anaesthetic and quantified in three different task scenarios: while operating in isolation from, in synchronous coordination, and collaboration with, the biological fingers. Main results Both groups were able to learn to operate the robotic extra finger, presumably due to abundance of visual feedback and other relevant sensory cues. Importantly, the availability of displaced somatosensory cues from the distal bodily controllers facilitated the acquisition of isolated robotic finger movements, the retention and transfer of synchronous hand-robot coordination skills, and performance under cognitive load. Motor performance was not impaired by toes anaesthesia when tasks involved close collaboration with the biological fingers, indicating that the motor system can close the sensory feedback gap by dynamically integrating task-intrinsic somatosensory signals from multiple, and even distal, body- parts. Significance Together, our findings demonstrate that there are multiple natural avenues to provide intrinsic surrogate somatosensory information to support motor control of an artificial body part, beyond artificial stimulation.

THE FREQUENCY OF USE OF EEG AND NEUROFEEDBACK IN SPORT, SYSTEMATIC REVIEW

Increasing athlete performance is an eternal challenge in the world of sports. The success of the training work performed can be checked by performance diagnostics. Proper brain processing is essential for skill learning and the implementation of effective motor performance. It was important for brain mapping technology to improve the capabilities of imaging devices in order to measure cognitive-motor performance in the field. The primary purpose of this review was to summarize the frequency of applications of EEG and its associated neurofeedback in sport. Examine the differences and characteristics of protocols. Assess whether there is this uniform, standardized protocol for each sport and how often it is used among both elite and amateur athletes. Electroencephalography was initially used most in sports in which the stable setting was followed by only minimal movement. These include sport shooting, archery and golf and baseball. Later, it was possible to analyze more complex movements with EEG, such as cycling. One of the most commonly used techniques is neurofeedback training, but despite some research on the topic, the arena of neurotechnology in sports psychology still exists in its rudimentary form and is constrained by a plethora of technological problems.

Songbird subthalamic neurons project to dopaminergic midbrain and exhibit singing-related activity

Skill learning requires motor output to be evaluated against internal performance benchmarks. In songbirds, ventral tegmental area (VTA) dopamine neurons (DA) signal performance errors important for learning, but it remains unclear which brain regions project to VTA and how these inputs may contribute to DA error signaling. Here we find that the songbird subthalamic nucleus (STN) projects to VTA and that STN micro-stimulation can excite VTA neurons. We also discover that STN receives inputs from motor cortical, auditory cortical and ventral pallidal brain regions previously implicated in song evaluation. In the first neural recordings from songbird STN, we discover that the activity of most STN neurons is associated with body movements and not singing, but a small fraction of neurons exhibits precise song timing and performance error signals. Our results place the STN in a pathway important for song learning, but not song production, and expand the territories of songbird brain potentially associated with song learning.

Explainable Artificial Intelligence (XAI) in Biomedicine: Making AI Decisions Trustworthy for Physicians and Patients

The use of artificial intelligence (AI) systems in biomedical and clinical settings can disrupt the traditional doctor–patient relationship, which is based on trust and transparency in medical advice and therapeutic decisions. When the diagnosis or selection of a therapy is no longer made solely by the physician, but to a significant extent by a machine using algorithms, decisions become nontransparent. Skill learning is the most common application of machine learning algorithms in clinical decision making. These are a class of very general algorithms (artificial neural networks, classifiers, etc.), which are tuned based on examples to optimize the classification of new, unseen cases. It is pointless to ask for an explanation for a decision. A detailed understanding of the mathematical details of an AI algorithm may be possible for experts in statistics or computer science. However, when it comes to the fate of human beings, this “developer’s explanation” is not sufficient. The concept of explainable AI (XAI) as a solution to this problem is attracting increasing scientific and regulatory interest. This review focuses on the requirement that XAIs must be able to explain in detail the decisions made by the AI to the experts in the field.