Archive for the ‘Neuroscience’ Category
What is going on in our brains when we are in our creative mode? Besides being in a flow state, what parts of the brain are we calling on to help us imagine and create? There are three major networks in use when creating: the default mode network (also known as the Imagination Network), the executive attention network, and the salience network. The default mode network is the part of the brain responsible for our “virtual reality” — when we daydream or imagine alternative scenarios of the past and possibilities of the future; the movies in our minds. The executive attention network comes into play when you’re hyper-focused. It’s best at problem-solving and concentrating. The salience network filters internal and external events and decides what’s important and what can best solve a task.
When these forces are combined, we are free-associating with the imagination network, focusing that imagination with the executive attention network, and both determining what is a good idea and staying with it using the salience network. If we are full of contradictions, going from focused to daydreaming, introspective to outwardly aware, weaving through dark and light, perhaps it is because our brains are particularly good at switching from one network to another.
Read also: The Real Neuroscience of Creativity
Curiosity is a basic element of our cognition, but its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity. Another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.
Students asking questions and then exploring the answers. That’s something any good teacher lives for. And at the heart of it all is curiosity. But why? What, exactly, is curiosity and how does it work? A study published in the October issue of the journal Neuron, suggests that the brain’s chemistry changes when we become curious, helping us better learn and retain information.
“There’s this basic circuit in the brain that energizes people to go out and get things that are intrinsically rewarding,” Ranganath explains. This circuit lights up when we’re curious. When the circuit is activated, our brains release a chemical called dopamine which gives us a high. “The dopamine also seems to play a role in enhancing the connections between cells that are involved in learning.” Indeed, when the researchers later tested participants on what they learned, those who were more curious were more likely to remember the right answers.
Read also: How Curiosity drives Learning
Studies show that children from low-income families have smaller brains and lower cognitive abilities. The stress of growing up poor can hurt a child’s brain development starting before birth, research suggests — and even very small differences in income can have major effects on the brain. Researchers have long suspected that children’s behaviour and cognitive abilities are linked to their socioeconomic status, particularly for those who are very poor. The reasons have never been clear, although stressful home environments, poor nutrition, exposure to industrial chemicals such as lead and lack of access to good education are often cited as possible factors.
In the largest study of its kind, published on 30 March 2015 in Nature Neuroscience, “Family income, parental education and brain structure in children and adolescents”, a team led by neuroscientists Kimberly Noble from Columbia University in New York City and Elizabeth Sowell from Children’s Hospital Los Angeles, California, looked into the biological underpinnings of these effects. They imaged the brains of 1,099 children, adolescents and young adults in several US cities. Because people with lower incomes are more likely to be from minority ethnic groups, the team mapped each child’s genetic ancestry and then adjusted the calculations so that the effects of poverty would not be skewed by the small differences in brain structure between ethnic groups. Even at this early age, the researchers found, infants in the lower socioeconomic brackets had smaller brains than their wealthier counterparts.
For several decades, myths about the brain — neuromyths — have persisted in schools and colleges, often being used to justify ineffective approaches to teaching. Many of these myths are biased distortions of scientific fact. Cultural conditions, such as differences in terminology and language, have contributed to a ‘gap’ between neuroscience and education that has shielded these distortions from scrutiny. In recent years, scientific communications across this gap have increased, although the messages are often distorted by the same conditions and biases as those responsible for neuromyths. In the future, the establishment of a new field of inquiry that is dedicated to bridging neuroscience and education may help to inform and to improve these communications.
This book presents a unique synthesis of the current neuroscience of cognition by one of the world’s authorities in the field. The guiding principle to this synthesis is the tenet that the entirety of our knowledge is encoded by relations, and thus by connections, in neuronal networks of our cerebral cortex. Cognitive networks develop by experience on a base of widely dispersed modular cell assemblies representing elementary sensations and movements. As they develop cognitive networks organize themselves hierarchically by order of complexity or abstraction of their content. Because networks intersect profusely, a neuronal assembly anywhere in the cortex can be part of many networks, and therefore many items of knowledge. All cognitive functions consist of neural transactions within and between cognitive networks. After reviewing the neurobiology and architecture of cortical networks (also named cognits), the author undertakes a systematic study of cortical dynamics in each of the major cognitive functions–perception, memory, attention, language, and intelligence. In this study, he makes use of a large body of evidence from a variety of methodologies, in the brain of the human as well as the nonhuman primate. The outcome of his interdisciplinary endeavor is the emergence of a structural and dynamic order in the cerebral cortex that, though still sketchy and fragmentary, mirrors with remarkable fidelity the order in the human mind.
Most accounts of human cognitive architectures have focused on computational accounts of cognition while making little contact with the study of anatomical structures and physiological processes. A renewed convergence between neurobiology and cognition is well under way. A promising area arises from the overlap between systems/cognitive neuroscience on the one side and the discipline of network science on the other. Neuroscience increasingly adopts network tools and concepts to describe the operation of collections of brain regions. Beyond just providing illustrative metaphors, network science offers a theoretical framework for approaching brain structure and function as a multi-scale system composed of networks of neurons, circuits, nuclei, cortical areas, and systems of areas. This paper views large-scale networks at the level of areas and systems, mostly on the basis of data from human neuroimaging, and how this view of network structure and function has begun to illuminate our understanding of the biological basis of cognitive architectures.
Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. The present results support these claims and the neural efficiency hypothesis.
I’ve suggested that traditional ethical convictions in our culture have been grounded in a belief (often tacit) in an immaterial soul that somehow uses the brain, but reserves for itself the powers of moral reasoning, decision making, and an appreciation of meaning and purpose. The cognitive neuroscience revolution challenges that belief, and increasingly forces us to recognize that all mental life is a product of the evolved, genetically influenced structure of the brain. This challenge has also been seen to threaten sacred moral values, but I would argue (and like to think that Gazzaniga agrees) that in fact that is not a logical consequence. On the contrary, I think a better understanding of what makes us tick, and of our place in nature, can clarify those values. This understanding shows that political equality does not require sameness, but rather policies that treat people as individuals with rights; that moral progress does not require that the mind is free of selfish motives, only that it has other motives to counteract them; that responsibility does not require that behavior is uncaused, only that it responds to contingencies of credit and blame; and that finding meaning in life does not require that the process that shaped the brain have a purpose, only that the brain itself have a purpose.