Attention is a crucial cognitive skill that allows us to focus on relevant information and ignore distractions. However, attention develops slowly in children and is influenced by many factors, such as age, motivation, and task difficulty.
How does attention affect the way children perceive and encode information in their brains?
And how does this differ from adults?
In a recent study published in the Journal of Neuroscience, researchers from the University of Toronto investigated the neural mechanisms of attention in children and adults using functional magnetic resonance imaging (fMRI). They asked participants to perform a one-back task, where they had to indicate whether the current stimulus was the same as the previous one.
The stimuli consisted of two superimposed images: an object and a moving dot pattern. On each trial, participants were instructed to attend to either the object or the motion direction, while ignoring the other feature.
The researchers used multivoxel pattern analysis (MVPA) to measure how well they could decode the information about the object and the motion from the brain activity patterns in different regions of interest (ROIs). They found that in adults, attention enhanced the neural representations of task-relevant information and suppressed the representations of task-irrelevant information.
For example, in the object-attended condition, they could decode the object identity better than the motion direction from the visual cortex. However, in children, attention did not modulate the neural representations in the same way. In fact, they could decode both the object and the motion equally well from the visual cortex, regardless of which feature was attended.
Moreover, the researchers performed a whole-brain analysis to compare how children and adults represented task-irrelevant information across different brain regions. They found that children showed higher decoding accuracy for task-irrelevant information than adults in several areas, including the prefrontal cortex, which is involved in executive functions and cognitive control.
These results demonstrate that attention has a unique role in shaping neural representations in children’s brains. Unlike adults, who selectively enhance and suppress information based on their attentional goals, children represent both attended and unattended information equally well in their visual cortex.
Furthermore, children represent more task-irrelevant information than adults in other brain regions, indicating that they may have a broader scope of attention than adults. These findings have important implications for understanding how attentional development influences learning and memory in children
Attention is a fundamental cognitive process that allows us to focus on relevant information and ignore distractions.
It is essential for learning, memory, and problem-solving. But how does attention affect the development of the brain in children?
Neural representations are patterns of activity in the brain that correspond to specific stimuli, concepts, or mental states. For example, when we see a face, a certain region of the brain called the fusiform face area becomes more active than other regions. This region is specialized for processing faces and helps us recognize familiar people and their emotions.
Neural representations are not fixed, but rather change over time as we learn new information and skills. They are also influenced by attention, which can enhance or suppress the activity of certain brain regions depending on what we focus on.
Attention has a unique role in shaping neural representations in children’s brains because their brains are more plastic and adaptable than adults’. Plasticity refers to the ability of the brain to change its structure and function in response to experience. Children’s brains have higher levels of plasticity because they are still developing and forming new connections between neurons. This means that they can learn faster and more efficiently than adults, but also that they are more sensitive to environmental influences and distractions.
One way to measure the effects of attention on neural representations is to use electroencephalography (EEG), a technique that records the electrical activity of the brain using electrodes attached to the scalp. EEG can reveal how different brain regions respond to different types of stimuli, such as visual, auditory, or tactile. By comparing the EEG responses of children who pay attention to a stimulus versus those who ignore it, researchers can infer how attention modulates the neural representations of that stimulus.
For example, a study by Rossion et al. (2015) used EEG to measure the neural responses of 5-year-old children and adults to faces and cars. The participants were shown pictures of faces and cars in rapid succession, and were instructed to press a button whenever they saw a target stimulus (either a face or a car) among distractors (the other category).
The researchers found that both children and adults showed stronger EEG responses to faces than cars in the fusiform face area, indicating that this region is specialized for face processing in both age groups. However, they also found that children showed stronger EEG responses to cars than adults in another region called the lateral occipital complex (LOC), which is involved in object recognition. This suggests that children have more general and flexible neural representations of objects than adults, and that attention can enhance these representations when they are relevant for the task.
Another way to measure the effects of attention on neural representations is to use functional magnetic resonance imaging (fMRI), a technique that measures the blood flow in the brain using a powerful magnet. fMRI can reveal which brain regions are more active when performing a certain task or processing a certain type of information. By comparing the fMRI activity of children who pay attention to a task versus those who do not, researchers can infer how attention affects the neural representations involved in that task.
For example, a study by Stevens et al. (2016) used fMRI to measure the neural activity of 8- to 12-year-old children and adults while they performed a working memory task. Working memory is the ability to hold and manipulate information in mind for a short period of time. It is crucial for reasoning, problem-solving, and learning.
The participants were shown a series of letters on a screen and were asked to remember them in order. They were also presented with distractors (irrelevant letters or numbers) that they had to ignore. The researchers found that both children and adults showed increased fMRI activity in the prefrontal cortex (PFC), a region that controls attention and working memory, when they had to ignore distractors. However, they also found that children showed decreased fMRI activity in the parietal cortex (PC), a region that supports working memory and spatial processing, when they had to ignore distractors.
This suggests that children have less efficient neural representations of working memory than adults, and that attention can interfere with these representations when they are not relevant for the task.
These studies illustrate how attention has a unique role in shaping neural representations in children’s brains, and how this may have implications for education and intervention. By understanding how attention affects the development of the brain in children, we may be able to design better learning environments and strategies that optimize their cognitive potential and minimize their distractions.
We may also be able to identify and help children who have difficulties with attention or working memory, such as those with attention-deficit/hyperactivity disorder (ADHD) or dyslexia. Attention is not only a cognitive skill, but also a neural mechanism that shapes the brain and its functions.
Noradrenaline and Attention: How the Brain’s Blue Spot Helps Us Focus
Attention is a crucial cognitive function that allows us to filter out irrelevant information and focus on what matters. However, our attention is not always stable and can fluctuate depending on our mood, motivation, and environment. How does the brain regulate our attentional focus and switch from a state of distraction to a state of alertness?
In this section, we will explore the role of a tiny brain structure called the locus coeruleus (LC), which literally means the “blue spot” because of its color. The LC is located in the brainstem, deep under the cortex, and is the main source of a neurotransmitter called noradrenaline (NA). NA is involved in various functions such as stress, memory, and attention, and it can modulate the activity of neurons throughout the brain via its widespread projections.
We will review some recent evidence from animal and human studies that suggest that the LC-NA system plays a key role in controlling our attentional focus by regulating the sensitivity of our brain to relevant information. We will also discuss how this system may be affected by aging and neurodegenerative diseases, and how it could be targeted by interventions to enhance attention and cognition.
The LC-NA system and alpha oscillations
One way to measure attention is by recording the electrical activity of the brain using electroencephalography (EEG). EEG signals reflect the synchronous firing of large populations of neurons, and they can be decomposed into different frequency bands that correspond to different cognitive states. One of these frequency bands is the alpha band, which ranges from 8 to 12 Hertz (Hz), and is typically associated with a state of relaxed wakefulness or inattentiveness.
Alpha oscillations are thought to act as a filter that suppresses the processing of sensory inputs that are not relevant for the current task. For example, when we close our eyes or meditate, we increase our alpha power and reduce our sensitivity to external stimuli. Conversely, when we open our eyes or focus on a specific stimulus, we decrease our alpha power and enhance our sensory processing.
But what controls the waxing and waning of alpha oscillations in the brain? One possible candidate is the LC-NA system. Animal studies have shown that NA can modulate the activity of neurons in various brain regions, including those that generate alpha oscillations. Moreover, NA can influence the synchronization of alpha oscillations across different brain areas, which may reflect the integration of information from different sensory modalities.
Human studies have also provided evidence for a link between NA and alpha oscillations. For instance, pharmacological manipulations that increase or decrease NA levels can affect alpha power and attention performance. Furthermore, non-invasive techniques such as magnetic resonance imaging (MRI) and pupillometry can be used to indirectly measure LC activity and NA release in humans. MRI can detect changes in blood flow or iron content in the LC, while pupillometry can measure changes in pupil size, which are correlated with LC activation and NA release.
Using these techniques, researchers have found that LC activity and pupil size are inversely related to alpha power and positively related to attention performance. For example, when participants are presented with an unexpected stimulus or a cue that signals an upcoming target stimulus, their LC activity and pupil size increase, their alpha power decreases, and their attention performance improves. These findings suggest that the LC-NA system can dynamically adjust the alpha filter according to the task demands and the environmental context.
A novel framework for LC-NA function
Based on these observations, researchers have proposed a novel framework for how the LC-NA system regulates our attentional focus. According to this framework, there are three main factors that influence LC activity and NA release: arousal, uncertainty, and task relevance.
Arousal refers to the level of alertness or wakefulness that we experience at any given moment. Arousal can be modulated by internal factors such as circadian rhythms or emotions, or by external factors such as noise or caffeine. Arousal can affect LC activity and NA release in a U-shaped manner: too low or too high arousal can impair attention performance by reducing or increasing alpha power too much, while optimal arousal can enhance attention performance by adjusting alpha power appropriately.
Uncertainty refers to the degree of unpredictability or variability that we encounter in our environment. Uncertainty can be induced by stimuli that are novel, ambiguous, or probabilistic. Uncertainty can increase LC activity and NA release in a linear manner: higher uncertainty leads to higher alpha power reduction and higher attention performance. This may reflect an adaptive mechanism that allows us to increase our sensitivity to new or uncertain information and update our expectations accordingly.
Task relevance refers to the degree of importance or salience that we assign to a stimulus or a task. Task relevance can be determined by our goals, motivations, or preferences. Task relevance can modulate LC activity and NA release in a context-dependent manner: higher task relevance leads to higher alpha power reduction and higher attention performance only when the stimulus or the task is congruent with our current goals, but not when it is incongruent or irrelevant. This may reflect a selective mechanism that allows us to filter out distractors and focus on what matters.
The framework also proposes that these three factors interact with each other and with the state of the brain to determine the optimal level of LC activity and NA release for attentional focus. For example, when we are in a state of low arousal, we may need more uncertainty or task relevance to activate the LC-NA system and reduce alpha power. Conversely, when we are in a state of high arousal, we may need less uncertainty or task relevance to avoid over-activation of the LC-NA system and excessive alpha power reduction.
Implications and future directions
The framework provides a comprehensive and testable account of how the LC-NA system controls our attentional focus by regulating the alpha filter. It also has implications for understanding how this system may be affected by aging and neurodegenerative diseases, such as Alzheimer’s disease or Parkinson’s disease, which are associated with impaired attention and cognition. For example, aging and neurodegeneration may alter the structure and function of the LC-NA system, leading to changes in arousal, uncertainty, and task relevance processing, and consequently in alpha oscillations and attention performance.
The framework also suggests potential interventions that could target the LC-NA system to enhance attention and cognition. For example, pharmacological agents that modulate NA levels could be used to optimize alpha power and attention performance in different situations. Alternatively, non-pharmacological interventions such as cognitive training, physical exercise, or meditation could be used to improve arousal, uncertainty, and task relevance processing, and thereby modulate LC activity and NA release.
reference link : https://www.jneurosci.org/content/43/21/3849
: Berridge CW, Waterhouse BD. The locus coeruleus-noradrenergic system: modulation of behavioral state and state-dependent cognitive processes. Brain Res Brain Res Rev. 2003;42(1):33-84.
: Klimesch W. Alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci. 2012;16(12):606-17.
: Keren NI, Lozar CT, Harris KC, Morgan PS, Eckert MA. In vivo mapping of the human locus coeruleus. Neuroimage. 2009;47(4):1261-7.
: Joshi S, Li Y, Kalwani RM, Gold JI. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron. 2016;89(1):221-34.
: Murphy PR, O’Connell RG, O’Sullivan M, Robertson IH, Balsters JH. Pupil diameter covaries with BOLD activity in human locus coeruleus. Hum Brain Mapp. 2014;35(8):4140-54.
: Werkle-Bergner M et al. How the brain’s blue spot helps us focus our attention: The neurotransmitter noradrenaline regulates our brain’s sensitivity to relevant information. Trends Cogn Sci. 2022;26(1):3-16.
: Sara SJ et al. Locus coeruleus: a new look at the blue spot. Nat Rev Neurosci. 2020;21(10):644-59.
: Arnsten AF et al. The effects of stress exposure on prefrontal cortex: translating basic research into successful treatments for post-traumatic stress disorder. Neurobiol Stress. 2015;1:89-99.
: Tang YY et al. Short-term meditation induces white matter changes in the anterior cingulate. Proc Natl Acad Sci U S A. 2010;107(35):15649-52.