Earl Miller is quoted in a Time article about the dangers of multitasking:
You Asked: Are My Devices Messing With My Brain? Time (May 13, 2015)
http://time.com/3855911/phone-addiction-digital-distraction/
““Every time you switch your focus from one thing to another, there’s something called a switch-cost,” says Dr. Earl Miller, a professor of neuroscience at Massachusetts Institute of Technology. “Your brain stumbles a bit, and it requires time to get back to where it was before it was distracted.” ““You’re not able to think as deeply on something when you’re being distracted every few minutes,” Miller adds. “And thinking deeply is where real insights come from.”
Miller Lab alumnus, Andreas Nieder, continues his epic investigations into the neural basis of number sense. Here, Viswanathan and Nieder show that training to make numerosity judgments sharpens neural selectivity in frontal cortex but not in parietal cortex. It seems that the number representations in parietal cortex are innate whereas in the frontal cortex, they are learned.
Miller Lab alumnus David Freedman and colleagues present a model that shows how categorical neural activity can develop through learning. As a result of top-down influences from decision neurons, categorical representations develop in neurons that show choice-correlated activity fluctuations. They test the model via recordings from parietal cortex.
Choice-correlated activity fluctuations underlie learning of neuronal category representation
Tatiana A. Engel, Warasinee Chaisangmongkon, David J. Freedman & Xiao-Jing Wang
Miller Lab alumnus Melissa Warden has been awarded a Sloan Research Fellowship.
2015 Sloan Research Fellows
We could not be prouder of her if she were a Little Lebowski Urban Achiever.
Frequency-specific hippocampal-prefrontal interactions during associative learning
Brincat, S.L. and Miller, E.K. (2015) Nature Neuroscience, advanced online publication
Abstract:
Much of our knowledge of the world depends on learning associations (for example, face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive and mnemonic abilities are akin to those of humans. We found functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, whereas HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC and increased with learning. Rapid object associative learning may occur in PFC, whereas HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands.
MIT News Office: Neurons hum at different frequencies to tell the brain which memories it should store.
New discovery from the Miller Lab
Anne Trafton | MIT News Office
February 23, 2015
Our brains generate a constant hum of activity: As neurons fire, they produce brain waves that oscillate at different frequencies. Long thought to be merely a byproduct of neuron activity, recent studies suggest that these waves may play a critical role in communication between different parts of the brain.
A new study from MIT neuroscientists adds to that evidence. The researchers found that two brain regions that are key to learning — the hippocampus and the prefrontal cortex — use two different brain-wave frequencies to communicate as the brain learns to associate unrelated objects. Whenever the brain correctly links the objects, the waves oscillate at a higher frequency, called “beta,” and when the guess is incorrect, the waves oscillate at a lower “theta” frequency. Read more
MIT News Office: Neurons hum at different frequencies to tell the brain which memories it should store.
New discovery from the Miller Lab
Anne Trafton | MIT News Office
February 23, 2015
Our brains generate a constant hum of activity: As neurons fire, they produce brain waves that oscillate at different frequencies. Long thought to be merely a byproduct of neuron activity, recent studies suggest that these waves may play a critical role in communication between different parts of the brain.
A new study from MIT neuroscientists adds to that evidence. The researchers found that two brain regions that are key to learning — the hippocampus and the prefrontal cortex — use two different brain-wave frequencies to communicate as the brain learns to associate unrelated objects. Whenever the brain correctly links the objects, the waves oscillate at a higher frequency, called “beta,” and when the guess is incorrect, the waves oscillate at a lower “theta” frequency. Read more
Miller, E.K. and Buschman, T.J. (2015) Working memory capacity: Limits on the bandwidth of cognition. Daedalus, Vol. 144, No. 1, Pages 112-122. View PDF
Why can your brain store a lifetime of experiences but process only a few thoughts at once? In this article we discuss “cognitive capacity” (the number of items that can be held “in mind” simultaneously) and suggest that the limit is inherent to processing based on oscillatory brain rhythms, or “brain waves,” which may regulate neural communication. Neurons that “hum” together temporarily “wire” together, allowing the brain to form and re-form networks on the fly, which may explain a hallmark of intelligence and cognition: mental flexibility. But this comes at a cost; only a small number of thoughts can fit into each wave. This explains why you should never talk on a mobile phone when driving.
Radio New Zealand: Interview with Professor Earl Miller about Multi-tasking and technology
Originally aired on Afternoons, Tuesday 20 January 2015
Getting back into work routines, after a holiday break, is something many of us will already have come to grips with in recent weeks. And these routines seem to get busier all the time, as modern technology allows us to perform more and more tasks ourselves, quickly, on our tablets and smart phones. But at what cost? MIT neuroscientist Professor Earl Miller is an expert on divided attention. He argues our addiction to technology is actually making us less efficient.
(Back row, left to right) Vanu Bose, ’87, SM ’94, PhD ’99, son of Amar Bose; Earl Miller, the Picower Professor of Neuroscience; Jeff Grossman, an associate professor of materials science and engineering; Janet Conrad, a professor of physics; Alan Oppenheim, the Ford Professor of Engineering; and President L. Rafael Reif; and (front row, left to right) Joel Voldman, a professor of electrical engineering and computer science; Gabriel Bousquet, a PhD student in mechanical engineering; and Nicola Ferralis, a research scientist for materials science and engineering.
Andre Bastos and colleagues review an update the communication-through-coherence (CTC) hypothesis. They propose that bi-directional cortical communication involves separate feedforward and feedback mechanisms that are separate both anatomically and spectrally.
Task Dependence of Visual and Category Representations in Prefrontal and Inferior Temporal Cortices
Jillian L. McKee, Maximilian Riesenhuber, Earl K. Miller, and David J. Freedman
Visual categorization is an essential perceptual and cognitive process for assigning behavioral significance to incoming stimuli. Categorization depends on sensory processing of stimulus features as well as flexible cognitive processing for classifying stimuli according to the current behavioral context. Neurophysiological studies suggest that the prefrontal cortex (PFC) and the inferior temporal cortex (ITC) are involved in visual shape categorization. However, their precise roles in the perceptual and cognitive aspects of the categorization process are unclear, as the two areas have not been directly compared during changing task contexts. To address this, we examined the impact of task relevance on categorization-related activity in PFC and ITC by recording from both areas as monkeys alternated between a shape categorization and passive viewing tasks. As monkeys viewed the same stimuli in both tasks, the impact of task relevance on encoding in each area could be compared. While both areas showed task-dependent modulations of neuronal activity, the patterns of results differed markedly. PFC, but not ITC, neurons showed a modest increase in firing rates when stimuli were task relevant. PFC also showed significantly stronger category selectivity during the task compared with passive viewing, while task-dependent modulations of category selectivity in ITC were weak and occurred with a long latency. Finally, both areas showed an enhancement of stimulus selectivity during the task compared with passive viewing. Together, this suggests that the ITC and PFC show differing degrees of task-dependent flexibility and are preferentially involved in the perceptual and cognitive aspects of the categorization process, respectively.
Bose grants reward risk
Five innovative, high-risk projects launch with support from Prof. Amar G. Bose Research Grants
The Motivated Brain
Helle Bundgaard, Jefferson Roy
Buy it at Amazon
This book is the missing link, connecting motivation and modern brain science. Upon purchasing The Motivated Brain, you get free access to the motivation assessment Motivation Factor™ Indicator. Completing the assessment makes it easier to read the book and gives you a better understanding of your own motivation.
Motivation can crumble starting with the Energy Drainers that distract from the current goal, clutter the mind, and waste resources. Next, Needs not being met further hinders motivation by invoking the stress response that hijacks the brain. When this occurs, it is almost impossible to live with a greater Purpose in mind and to use our Talents effectively. Together, this makes us less resistant to setbacks while further reducing our motivation. Not to mention the short and long-term effects on physical health and well-being.
This book reviews the relevant brain areas and circuits thought to be involved in the Hierarchy of Motivation. While not an exhaustive study, our goal is for you to come away with a sense that the four levels in the Hierarchy of Motivation are connected and build on each other, not only in the Motivation Factor Framework, but also in the brain.
As with many frameworks that require learning and change, the proof is in the trying. We are confident that by tapping into established neural circuits and behaviors; the positive changes of increased personal awareness and personal growth can be attained by anyone who tries. We hope to convince you that not only can the brain be trained; it can be motivated!
They got my experiment wrong, but spelled my name right:
Biology of Consciousness: Bridging the Mind-Body Gap?
The Huffington Post 10/30/14
Goal-direction and top-down control
Timothy J. Buschman and Earl K. Miller
We review the neural mechanisms that support top-down control of behavior. We suggest that goal-directed behavior utilizes two systems that work in concert. A basal ganglia-centered system quickly learns simple, fixed goal-directed behaviors while a prefrontal cortex-centered system gradually learns more complex (abstract or long-term) goal-directed behaviors. Interactions between these two systems allows top-down control mechanisms to learn how to direct behavior towards a goal but also how to guide behavior when faced with a novel situation.
Miller Lab Alumnus Tim Buschman is one of the winners of the NIH Director’s New Innovator Award.
According to the NIH website: The award “is designed specifically to support unusually creative new investigators with highly innovative research ideas at an early stage of their career when they may lack the preliminary data required for an R01 grant.”
We couldn’t be prouder of him if we were a Little Lebowski Urban Achiever.
“Linked” is the operative term here. Earl Miller is quoted in a New York Magazine article about a study that finds less gray matter in people who multitask more. Earl points out that the study does not necessarily mean that multitasking decreases brain matter. It could be that people with less gray matter are more impulsive and thus more prone to multitasking.
At this risk of kvelling, in 2011 we published a paper (Buschman et al., 2011) showing independent visual working memory capacities in the right vs left visual hemifields. We were told “no way” and “that’s impossible”. Since then, a bunch of papers have supported this. Here’s another one.
Wang et al used FMRI and found that brain networks primarily interact with ipsilateral, not contralateral networks. Thus, the brain emphasizes processing within each hemisphere (visual hemifield) and minimizes across-hemisphere processing.
Also see:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
IFLScience: Brain Waves Synchronize for Faster Learning
Summary:
As our thoughts dart from this to that, our brains absorb and analyze new information at a rapid pace. According to a new study, these quickly changing brain states may be encoded by the synchronization of brain waves across different brain regions. Waves originating from two areas involved in learning couple to form new communication circuits when monkeys learn to categorize different patterns of dots.
Read more here
A (very brief) mention of the new paper by Antzoulatos and Miller (2014) on National Public Radio.
The paper:
Antzoulatos, E.G. and Miller, E.K. (in press) “Increases in functional connectivity between the prefrontal cortex and striatum during category learning.” Neuron. View PDF
Huffington Post article about the evils of multitasking.
You’re Not Busy, You Just Think You Are: 7 Ways To Find More Time The Huffington Post UK | By Georgia James Posted: 13/06/2014 15:00 BST
(with quotes from Earl Miller)
New Miller Lab paper in press and online at Neuron:
Antzoulatos EG and Miller EK (in press) Increases in Functional Connectivity between Prefrontal Cortex and Striatum during Category Learning. Neuron, in press.
DOI: http://dx.doi.org/10.1016/j.neuron.2014.05.005
Animals were trained to learn new category groupings by trial and error. Once they started to “get” the categories, there was an increase in beta-band synchrony between the prefrontal cortex and striatum, two brain areas critical for learning. By the time the categories were well-learned, the beta synchrony between the areas became category-specific, that is, unique sets of sites in the prefrontal cortex and striatum showed increased beta synchrony for the two different categories. This suggests that synchronization of brain rhythms can quickly establish new functional brain circuits and thus support cognitive flexibility, a hallmark of intelligence.
MIT Press release:
Synchronized brain waves enable rapid learning
MIT study finds neurons that hum together encode new information.
A well-known correlate of working memory is sustained neural activity that bridges short gaps in time. It is well-established in the primate brain, but what about birds? They have working memory. (In fact, there is a lot of classic work that detailed the behavioral characteristics of working memory in pigeons).
Miller Lab alumnus Andreas Nieder and crew trained crows to perform a working memory task and found sustained activity in the nidopallium caudolaterale (NCL). This is presumably a neural correlate of the crow’s visual working memory.
Now if crows could only pass that causality test.
See lectures from the Cognitive Rhythms Collaborative conference on Rhythmic Dynamics and Cognition, which took place on June 4, 2013 at MIT.
Talks:
Elizabeth Buffalo: Neural Signals for Memory and Space in the Primate Medial Temporal Lobe
Earl K. Miller: Cognition is Rhythmic
Robert Knight: Oscillations and Human PFC
Peter Ulhaas: Neural Oscillations in Schizophrenia: Perspectives from MEG
Charles Schroeder: Neural Substrates of Temporal Prediction in Active Sensing
Peter Brown: Beta Oscillations in the Human Basal Ganglia
Christa van Dort: Optogenetic Activation of Cholinergic Neurons in the PPT Induces REM Sleep
Rosalyn Doran: Dynamic Causal Modeling and Neurophysiology
Liam Paninski: Statistical Neuroscience
Astrid Prinz: How do rhythmically active circuits “analyze” their own activity?