Tag Archives: neuroscience

Pupil study reveals learning styles, brain activity (Nature Neuroscience)

Test of learning styles experiment

To test people’s learning styles, participants were presented with a choice between two images (objects or words) and rewarded according to their choices. In this exercise, to maximize reward, participants had to learn by trial and error that office-related images provide a higher reward than food-related images (semantic, or related to meaning), and that grayscale images provide a higher reward than color images (visual features). Image credit: Nature Neuroscience.

By Catherine Zandonella, Office of the Dean for Research

People are often said to have “learning styles” – for example, some people pay attention to visual details while others grab onto abstract concepts and meanings. A new study from Princeton University researchers found that changes in pupil size can reveal whether people are learning using their dominant learning style, or whether they are learning in modes outside of that style.

The researchers found that pupil dilation was smaller when people learned using their usual style and larger when people diverged from their normal style. The study was published in the journal Nature Neuroscience.

The study compared brain activity in individuals with two different learning styles – those who learn best by absorbing concrete visual details and those who are better at learning abstract concepts or meanings.

“We showed that changes in pupil dilation are associated with the degree to which learners use the style for which they have a predisposition,” said Eldar Eran, a graduate student in the Princeton Neuroscience Institute, who led the study.

The researchers used changes in pupil size as an indicator of variations in “neural gain,” which can be thought of as an amplifier of neural communication: when gain is increased, excited neurons become even more active and inhibited neurons become even less active. Smaller pupil dilation indicates more neural gain and larger pupil dilation indicates less neural gain.

The team showed that neural gain was correlated with different modes of communication between parts of the brain. In studies of human volunteers undergoing brain scans, when neural gain was high, communication tended to be tightly concentrated in certain regions of the brain that govern specific tasks, whereas low neural gain is associated with communication across wider regions of the brain.

“We showed that the brain has different modes of communication,” said Yael Niv, assistant professor of psychology and the Princeton Neuroscience Institute, “one mode where everything talks to everything else, and another mode where communication is more segregated into areas that don’t talk to each other.” The study also involved Jonathan Cohen, Princeton’s Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience.

These modes are linked to the level of neural gain and to learning style, Niv said. Neural gain can be thought of as a contrast amplifier that increases intensity of both activation and inhibition of communication among brain areas. “If one area is trying to activate another, or trying to inhibit another – both effects are stronger, everything is more potent,” she said. “This is correlated with communication being segregated into clusters of activation in the brain, so each network is talking to itself loudly, but connections across networks are inhibited. In situations of lower gain, however, the areas can talk to each other across networks, so information flows more globally.”

“These two modes [of communication in the brain] seem to be associated with different constraints on learning,” she said. “According to our study, in the mode where everything talks to everything, learning is very flexible. In contrast, in the mode where communication is stronger and more focused, but also more segregated between brain areas, subjects were more true to their personal learning style. Neither of these modes are better than the other – in both cases participants were equally successful in the task, but in different ways.”

“We interpreted these results to mean that although we tend to have a dominant learning style, we are not a slave to that style, and when operating in the proper mode, we can overcome dominant styles to learn in other ways,” she said.

This research was funded by NIH grants R03 DA029073 and R01 MH098861, a Howard Hughes Medical Institute International Student Research fellowship, and a Sloan Research Fellowship. The authors also wish to thank the generous support of the Regina and John Scully Center for the Neuroscience of Mind and Behavior within the Princeton Neuroscience Institute.

Read the article

Eldar, Eran, Jonathan D Cohen & Yael Niv. 2013. The effects of neural gain on attention and learning.  Nature Neuroscience. Published online June 16, 2013 doi:10.1038/nn.3428

Researchers discover workings of brain’s ‘GPS system’ (Nature)

By Catherine Zandonella, Office of the Dean for Research

Just as a global positioning system (GPS) helps find your location, the brain has an internal system for helping determine the body’s location as it moves through its surroundings.

A new study from researchers at Princeton University provides evidence for how the brain performs this feat. The study, published in the journal Nature, indicates that certain position-tracking neurons — called grid cells — ramp their activity up and down by working together in a collective way to determine location, rather than each cell acting on its own as was proposed by a competing theory.

Grid cells are neurons that become electrically active, or “fire,” as animals travel in an environment. First discovered in the mid-2000s, each cell fires when the body moves to specific locations, for example in a room. Amazingly, these locations are arranged in a hexagonal pattern like spaces on a Chinese checker board.  (See figure.)

Tank_Brain_GPS

As the mouse moves around in a square arena (left), a single grid cell in the mouse’s brain becomes active, or spikes, when the animal arrives at particular locations in the arena (right). These locations are arranged in a hexagonal pattern. The red dots indicate the mouse’s location in the arena when the grid cell fired. (Image credit: Cristina Domnisoru, Princeton University)

“Together, the grid cells form a representation of space,” said David Tank, Princeton’s Henry L. Hillman Professor in Molecular Biology and leader of the study. “Our research focused on the mechanisms at work in the neural system that forms these hexagonal patterns,” he said. The first author on the paper was graduate student Cristina Domnisoru, who conducted the experiments together with postdoctoral researcher Amina Kinkhabwala.

Domnisoru measured the electrical signals inside individual grid cells in mouse brains while the animals traversed a computer-generated virtual environment, developed previously in the Tank lab. The animals moved on a mouse-sized treadmill while watching a video screen in a set-up that is similar to video-game virtual reality systems used by humans.

She found that the cell’s electrical activity, measured as the difference in voltage between the inside and outside of the cell, started low and then ramped up, growing larger as the mouse reached each point on the hexagonal grid and then falling off as the mouse moved away from that point.

This ramping pattern corresponded with a proposed mechanism of neural computation called an attractor network. The brain is made up of vast numbers of neurons connected together into networks, and the attractor network is a theoretical model of how patterns of connected neurons can give rise to brain activity by collectively working together. The attractor network theory was first proposed 30 years ago by John Hopfield, Princeton’s Howard A. Prior Professor in the Life Sciences, Emeritus.

The team found that their measurements of grid cell activity corresponded with the attractor network model but not a competing theory, the oscillatory interference model. This competing theory proposed that grid cells use rhythmic activity patterns, or oscillations, which can be thought of as many fast clocks ticking in synchrony, to calculate where animals are located. Although the Princeton  researchers detected rhythmic activity inside most neurons, the activity patterns did not appear to participate in position calculations.

Read the abstract.

Domnisoru, Cristina, Amina A. Kinkhabwala & David W. Tank. 2013. Membrane potential dynamics of grid cells. Nature. doi:10.1038/nature11973. Published online Feb. 10, 2013.

This work was supported by the National Institute of Neurological Disorders and Stroke under award numbers 5RC1NS068148-02 and 1R37NS081242-01, the National Institute of Mental Health under award number 5R01MH083686-04, a National Institutes of Health Postdoctoral Fellowship grant F32NS070514-01A1 (A.A.K.), and a National Science Foundation Graduate Research Fellowship (C.D.).

 

 

How our brains keep track of where we are in the world (Journal of Neuroscience)

Libraries, supermarkets, classrooms…the world is full of places that look very similar, and yet our brains always seem to keep track of where we are. In a new study published in the Journal of Neuroscience, researchers at Princeton University and Ohio State University have uncovered one way in which the brain does this.

Similar-looking places can be distinguished from each other because of differences in what we experience when navigating to them. As we head toward a destination, our brains catalogue details such as other nearby buildings, the look of the doorway, even the people nearby.

The researchers discovered that the parahippocampal cortex, a part of the visual system that analyzes the current scene in front of us, also incorporates the details leading up to the scene, or its “temporal context.” As a result, even when two scenes look identical, we create different memory traces for them when their temporal contexts are different. Ultimately, this can help our brains to keep track of where we are in the world.

Learn more about Nicholas Turk-Browne‘s research at Princeton University.

Journal Citation: Turk-Browne NB, Simon MG, Sederberg PB. Scene representations in parahippocampal cortex depend on temporal context.  J Neurosci. 2012 May 23;32(21):7202-7.