Springing from memory and imagination, mind wandering is a mental state occupying as much as half of our waking life, involving a shift of attention away from the external environment and toward task-unrelated concerns. Although mind wandering may play an important role in planning and creativity, it is also widely associated with negative mood and degraded performance on measures of vigilance, working memory, fluid intelligence, and reading comprehension. The intrinsically subjective and spontaneous nature of mind wandering has made it difficult to investigate with direct experimental manipulations. Researchers have used various approaches to do so indirectly, by altering related factors such as mood, motivation, the amount of time spent on a task, or cognitive load. However, these factors may influence various cognitive processes besides mind wandering. Moreover, these approaches do not directly implicate underlying neural mechanisms of mind wandering. In contrast, Axelrod et al. demonstrate that mind wandering can be increased by direct experimental manipulation of brain activity using transcranial direct current stimulation (tDCS) to the prefrontal cortex (PFC). The article by Axelrod et al. thus marks a new era for research into mind wandering and previews some of the insights that continued methodological advances will likely make possible.
Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.
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
Child prodigies are defined as those individuals who reach a professional level of achievement in a culturally relevant domain before the age of 10 or adolescence. Although child prodigies are often the object of historical wonder and modern day awe, because of the difficulty involved with assembling a large sample of prodigies, until recently, little was known about the source of their achievements. Recent studies have begun to tackle this enigma, and a few traits have surfaced as key underpinnings of prodigiousness across domains: an average or higher IQ, extraordinary working memory, and a heightened attention to detail. The present study investigated whether the prodigies’ cognitive profiles differed according to their area of specialty. Using the Stanford Binet 5th ed. intelligence test the investigator assessed the cognitive profiles of 18 child prodigies across the domains of art, music, and math. The results suggest that prodigies in each domain have distinct cognitive profiles. While all of the child prodigies had exceptional memories, the music and math prodigies scored significantly higher on working memory than the art prodigies. The math prodigies displayed the highest levels of general intelligence and extraordinary visual spatial skills. The art prodigies displayed a surprising deficit in visual spatial skills, obtaining scores much lower than both the math prodigies and music prodigies. The differences in the prodigies’ cognitive underpinnings across domains may have implications for the general population.
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.
Sex/gender differences in the brain are of high social interest because their presence is typically assumed to prove that humans belong to two distinct categories not only in terms of their genitalia, and thus justify differential treatment of males and females. Here we show that, although there are sex/gender differences in brain and behavior, humans and human brains are comprised of unique “mosaics” of features, some more common in females compared with males, some more common in males compared with females, and some common in both females and males. Our results demonstrate that regardless of the cause of observed sex/gender differences in brain and behavior (nature or nurture), human brains cannot be categorized into two distinct classes: male brain/female brain.
The lack of internal consistency in human brain and gender characteristics undermines the dimorphic view of human brain and behavior and calls for a shift in our conceptualization of the relations between sex and the brain. Specifically, we should shift from thinking of brains as falling into two classes, one typical of males and the other typical of females, to appreciating the variability of the human brain mosaic. Scientifically, this paradigm shift entails replacing the currently dominant practice of looking for and listing sex/gender differences with analysis methods that take into account the huge variability in the human brain (rather than treat it as noise), as well as individual differences in the specific composition of the brain mosaic. At the social level, adopting a view that acknowledges human variability and diversity has important implications for social debates on longstanding issues such as the desirability of single-sex education and the meaning of sex/gender as a social category.
The paper deals with the interrelations between the philosophy, sociology and historiography of science in Thomas Kuhn’s theory of scientific development. First, the historiography of science provides the basis for both the philosophy and sociology of science in the sense that the fundamental questions of both disciplines depend on the principles of the form of historiography employed. Second, the fusion of the sociology and philosophy of science, as advocated by Kuhn, is discussed. This fusion consists essentially in a replacement of methodological rules by cognitive values that influence the decisions of scientific communities. As a consequence, the question of the rationality of theory choice arises, both with respect to the actual decisions and to the possible justification of cognitive values and their change.