Brains Structures and Their Influence on Cognition and Learning (Essay Sample)

Brains Structures and Their Influence on Cognition and Learning Student’s Name Institutional Affiliation Brains Structures and Their Influence on Cognition and Learning A student's brain has many intricate structures that underlie every fact and skill he acquires. As a teacher, you should picture yourself as a neuroscientist, navigating your students' neural pathways to foster learning and development. There is a name for this type of education: neuro-learning. Before tackling this major project, familiarize yourself with the brain's anatomy and how each region contributes to learning and memory. Higher education has been linked to better cognitive performance, and larger regional brain volumes in several studies spanning the fields of epidemiology, imaging, neuropsychology, and pathology. The cognitive reserve (CR) theory has long been used to account for the beneficial effects of education. More specifically, cognition is used to explain how people with higher levels of education do not manifest noticeable clinical symptoms. Only in terms of processing speed did grey matter volumes act as a mediator between education and cognition. The purpose of this article is to explain how a sentence is viewed over time, including the anatomical and functional brain functions underlying the learning process. Specifically, several studies have examined the link between markers of brain function and task-based markers of cognitive capacity (such as speed processing, and working memory) in persons who are otherwise healthy but aging. Although structures of the brain provide the platform for brain function, the relationship between brain structure metrics and intelligence is poorly understood. Such novel neuro-architectonic approaches include connectivity-based parcellation, which divides brain regions based on their connectivity areas to other brain parts, and advanced objective cytoarchitectonic analysis, which divides the cortex into different regions. Aerobic exercise and brain anatomy have been assessed by several MRI techniques. Structure-based MRI of the brain allows for accurate measurements of cortical and subcortical volumes, as well as cortical and subcortical surface areas. Herting et al. (2017) found a correlation between higher aerobic fitness, more significant hippocampus sizes, and rostral middle frontal volumes. Furthermore, there was a favorable correlation between aerobic fitness and the size of the bilateral lingual gyri. Ross et al. (2015) showed a correlation between aerobic fitness and increased volume in the orbitofrontal cortex in 63 obese and 43 nonobese participants aged 15 to 21. These associations between brain regions have been observed in studies involving people of varying ages (Lopez-Vicente et al., 2017), but the samples have been relatively small. Studies of pre-adolescent children (ages 9 to 10) and adults over 55 have revealed similar changes in the hippocampus. Overall, the data from the known research point to different profiles of anatomical connectivity between the cortex, the subcortex, and the white matter in teenagers with higher aerobic fitness. Many studies have looked into the links between exercise and brain function over the past two decades. Much research suggests that regular exercise can delay or prevent cognitive decline and dementia and enhance brain-behavior connections across the lifespan. Still, other studies have found no advantage to aerobic exercise over active control training regarding cognition. Physical activity, on the other hand, has been found to improve numerous types of cognitive function, including "controlled processing," "processing speed," "executive control," and "visuospatial ability," among others. The effects of physical activity have been examined, and so have the effects of different learning processes on brain areas including the hippocampus and the prefrontal cortex. Cognitive activities that rely on processing in those specific parts may also benefit from the obvious structural changes owing to activity, indicating one technique for researching brain-behavior correlations that arise from the logic of the underlying processes. This notion has been met with mixed support in the exercise literature. There may be no link between exercise and modifications to grey matter in the brain, according to some research. On the other hand, some researchers find that aerobic exercise improves both hippocampus grey matter architecture and the ability to recall recent events in one's memory (Lynn & Bassett, 2019). However, evidence for this link in brain regions other than the hippocampus is scant; only two cross-sectional studies have found the link between exercise, executive functions, and the volume of grey matter in the prefrontal cortex, the structure of the white matter in the frontal lobes, and the cardiovascular responses to exercise. Source: Friederici, A. D. (2011). The Brain Basis of Language Processing: From Structure to Function., 91(4), 1357–1392. Jonasson et al. (2017) examined how aerobic exercise affects brain architecture and mental performance. They found that compared to participants randomized to stretching and toning control training, inactive older persons randomly assigned to aerobic exercise significantly improved cognitive ability, as measured by a cognitive score. The intervention had no apparent effect on brain structures, but aerobic fitness did predict cortical thickness at the baseline but not with the passage of time. Memory retrieval is a primary area of study in structural brain models. The mechanisms that produce the information measured by memory tests have received less attention due to this emphasis on memory structures that govern retrieval. The goal of this functional model, in contrast to many structural models of memory, is to help teachers better understand the generative nature of comprehension, which in turn can lead to uses for generative teaching in schools and other training environments where it has the potential to significantly improve students' ability to learn (Friederici, 2011). According to research, the first of the brain's three functional systems is responsible for arousal and attention. The cortex and driven behavior influence these processes via the ascending reticular activating circuits, in addition to metabolic and external sensory cues. That is to say, the attentional and motivational processes of the brain, and thus the stimuli we pay attention to, as well as the degree to which we devote our effort to that stimulus and their relevance, are influenced by the learner's goals and intents, which are mediated by the frontal lobes of the cortex. The second of the brain's three functional units, the information processing, analysis, and storage circuitry, is responsible for decoding and integrating data from the senses. The frontal lobes send signals to this area, which limits the range of possible responses and interpretations by areas (arousal and attention unit). The human brain's linguistic, spatial, propositional, analytic, as well as holistic generative learning and comprehension mechanisms are all located in this region. Studies in neuropsychology have highlighted the generative aspect of learning and have influenced fields as diverse as cognitive psychology, intelligence modeling, and educational psychology (Oschwald et al., 2019). Studies of hemispheric processes reveal, for example, that meaning is formed in a...