Legendary Marshmallow Test Yields Lessons for Everyday Challenges in Self-Control

Walter Mischel, the psychologist renowned for the groundbreaking study known as the “marshmallow test,” has finally decided to tell the story of that research for a general audience.

He dedicates the book, aptly titled The Marshmallow Test: Mastering Self-Control, to his now-grown daughters, saying they inspired him when they were young to study self-control in preschoolers.

“I saw dramatic changes in my own children,” says Mischel, the Robert Johnston Niven Professor of Humane Letters in Columbia’s Psychology Department. “I realized I was quite clueless about what was going on in their heads.”

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Mind-controlled prosthetic arms that work in daily life are now a reality

In January 2013 a Swedish arm amputee was the first person in the world to receive a prosthesis with a direct connection to bone, nerves and muscles. An article about this achievement and its long-term stability will now be published in the Science Translational Medicine journal.

“Going beyond the lab to allow the patient to face real-world challenges is the main contribution of this work,” says Max Ortiz Catalan, research scientist at Chalmers University of Technology and leading author of the publication.

“We have used osseointegration to create a long-term stable fusion between man and machine, where we have integrated them at different levels. The artificial arm is directly attached to the skeleton, thus providing mechanical stability. Then the human’s biological control system, that is nerves and muscles, is also interfaced to the machine’s control system via neuromuscular electrodes. This creates an intimate union between the body and the machine; between biology and mechatronics.”

The direct skeletal attachment is created by what is known as osseointegration, a technology in limb prostheses pioneered by associate professor Rickard Brånemark and his colleagues at Sahlgrenska University Hospital. Rickard Brånemark led the surgical implantation and collaborated closely with Max Ortiz Catalan and Professor Bo Håkansson at Chalmers University of Technology on this project.

The patient’s arm was amputated over ten years ago. Before the surgery, his prosthesis was controlled via electrodes placed over the skin. Robotic prostheses can be very advanced, but such a control system makes them unreliable and limits their functionality, and patients commonly reject them as a result.

Now, the patient has been given a control system that is directly connected to his own. He has a physically challenging job as a truck driver in northern Sweden, and since the surgery he has experienced that he can cope with all the situations he faces; everything from clamping his trailer load and operating machinery, to unpacking eggs and tying his children’s skates, regardless of the environmental conditions (read more about the benefits of the new technology below).

The patient is also one of the first in the world to take part in an effort to achieve long-term sensation via the prosthesis. Because the implant is a bidirectional interface, it can also be used to send signals in the opposite direction – from the prosthetic arm to the brain. This is the researchers’ next step, to clinically implement their findings on sensory feedback.

“Reliable communication between the prosthesis and the body has been the missing link for the clinical implementation of neural control and sensory feedback, and this is now in place,” says Max Ortiz Catalan. “So far we have shown that the patient has a long-term stable ability to perceive touch in different locations in the missing hand. Intuitive sensory feedback and control are crucial for interacting with the environment, for example to reliably hold an object despite disturbances or uncertainty. Today, no patient walks around with a prosthesis that provides such information, but we are working towards changing that in the very short term.”

The researchers plan to treat more patients with the novel technology later this year.

“We see this technology as an important step towards more natural control of artificial limbs,” says Max Ortiz Catalan. “It is the missing link for allowing sophisticated neural interfaces to control sophisticated prostheses. So far, this has only been possible in short experiments within controlled environments.”

Amputees discern familiar sensations across prosthetic hand

Even before he lost his right hand to an industrial accident 4 years ago, Igor Spetic had family open his medicine bottles. Cotton balls give him goose bumps.

Now, blindfolded during an experiment, he feels his arm hairs rise when a researcher brushes the back of his prosthetic hand with a cotton ball.

Spetic, of course, can’t feel the ball. But patterns of electric signals are sent by a computer into nerves in his arm and to his brain, which tells him different. “I knew immediately it was cotton,” he said.

That’s one of several types of sensation Spetic, of Madison, Ohio, can feel with the prosthetic system being developed by Case Western Reserve University and the Louis Stokes Cleveland Veterans Affairs Medical Center.

Spetic was excited just to “feel” again, and quickly received an unexpected benefit. The phantom pain he’d suffered, which he’s described as a vice crushing his closed fist, subsided almost completely. A second patient, who had less phantom pain after losing his right hand and much of his forearm in an accident, said his, too, is nearly gone.

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Neurons in human muscles emphasize stimulation from the outside world

Stretch sensors in our muscles participate in reflexes that serve the subconscious control of posture and movement. According to a new study published in the Journal of Neuroscience, these sensors respond weakly to muscle stretch caused by one’s voluntary action, and most strongly to stretch that is imposed by external forces. The ability to reflect causality in this manner can facilitate appropriate reflex control and accurate self-perception.  

“The results of the study show that stretch receptors in our muscles indicate more than which limb is moving or how fast; these sensors also adjust their signals according to who caused the movement,” says Michael Dimitriou, who conducted this study and is currently a post doc at the Department of Integrative Medical Biology, Umeå University, Sweden.

Normally, we can easily distinguish between movements we make ourselves and movements that are imposed on our body by external forces. The ability to discriminate between self-generated and externally generated sensory events is crucial for accurate perception and the control of posture and movement. This ability is also believed to form the foundation on which conscious self-awareness is built.

Such discrimination between self and other has previously been thought to arise as a result of complex computations performed in the brain, that use prior knowledge or memories of the consequences of own actions. But the study by Michael Dimitriou shows that information on the cause of a sensory effect can be provided in real-time by so-called ‘muscle spindles’, a class of stretch receptors found in most of our skeletal muscles.

Muscle spindles differ from other sensory receptors, such as stretch receptors in the skin, because their sensitivity can be controlled by the nervous system via specialized motor neurons. The purpose of this control has been unclear. The neural data presented by Michael Dimitriou indicates that these specialized motor neurons increase the sensitivity of stretch receptors when the body is exposed to an externally imposed stretch stimulus, such as when a falling ball is caught in the hand. Because amplified spindle responses mean stronger stretch reflexes, the resulting muscle activity instantly counteracts movement of the hand. When making a voluntary movement, however, the nervous system ‘automatically’ reduces the sensitivity of spindles in the stretching muscles, thereby making it possible for us to move without setting off strong stretch reflexes that would otherwise counteract movement. Uncontrollably strong stretch reflexes are commonly referred to as ‘spasticity’.

“These results provide an explanation of how reflexes can be functionally adjusted to help us achieve our everyday tasks, without requiring conscious control of reflex sensitivity or complex computations in the brain for predicting the sensory consequences of our actions,” says Michael Dimitriou.

He believes that these new findings are important both for understanding the neural mechanisms that underlie movement control and self-perception, but also for understanding pathological states where these mechanisms are disturbed.

“With these findings, we also get new insights into mechanisms whose malfunction may contribute to neuromuscular problems such as spasticity or alien hand syndrome (also known as ‘Dr. Strangelove syndrome’), and help identify potential treatment targets for these conditions,” says Michael Dimitriou.

How female fruit flies know when to say ‘yes’

A fundamental question in neurobiology is how animals, including humans, make decisions. A new study publishing in the open access journal PLOS Biology on October 7 reveals how fruit fly females make a very important decision: to either accept or reject male courtship. This decision appears to be generated by a very small number of excitatory neurons that use acetylcholine as their neurotransmitter located in three brain regions. This study provides the framework to understand how decisions are generated and suggests that a decision is reached because that option is literally the most exciting.

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Working memory hinders learning in schizophrenia

A new study pinpoints working memory as a source of learning difficulties in people with schizophrenia.

Working memory is known to be affected in the millions of people — about 1 percent of the population — who have schizophrenia, but it has been unclear whether that has a specific role in making learning more difficult, said Anne Collins, a postdoctoral researcher at Brown University and lead author of the study.

“We really tend to think of learning as a unitary, single process, but really it is not,” said Collins, who in 2012 along with co-author Michael Frank, associate professor of cognitive, linguistic, and psychological sciences, developed an experimental task and a computational model of cognition that can distinguish the contributions of working memory and reinforcement in the learning process. “We thought we could try to disentangle that here and see if the impairment was in both aspects, or only one of them.”

In the new study in the Journal of Neuroscience, cognitive scientists Collins and Frank collaborated with schizophrenia experts James Waltz and James Gold of the University of Maryland to measure the effects of working memory and reinforcement in learning by applying these methods. They found that only working memory was a source of impairment.

Learning about learning’s components

To find that out, they marshaled 49 volunteers with schizophrenia and an otherwise comparable set of 36 people without the condition to participate in the specially designed learning task. In each round, participants were shown a set of images and then were asked to push one of three buttons when they saw each image. With each button push they were told whether they had hit the correct button for that image. Over time, through trial and error, participants could learn which picture called for which button. With perfect memory, one wouldn’t need to see an image more than three times to learn the right button to push when it appeared.

The task explicitly involves employing the brain’s systems for working memory (keeping each image–button association in mind) and for reinforcement learning (wanting to repeat an action that led to the feedback of “correct” and to avoid one that produced “incorrect”). But in different rounds while the degree of reinforcement remained the same, the experimenters varied the number of images in the sets the volunteers saw, from two to six. What varied, therefore, was the degree to which working memory was taxed.

What the researchers found was that for both people with schizophrenia and for controls, the larger the image set size, the more trials it took to learn to press the correct button consistently for each image and the longer it took to react to each stimulus. People with schizophrenia generally performed worse on the task than healthy controls.

Those results show that as the task involved more images, it became harder to do – a matter of working memory, since the capacity to maintain information explicitly in memory is limited – but that alone did not prove that working memory was a source of learning problems for people with schizophrenia. They could also be doing worse because of a slower use of the reinforcement.

To determine that, the researchers used their computational models of how learning occurs in the brain to fit the experimental data. They asked what parameters in the models needed to vary to accurately predict the behavior they measured in people with and without schizophrenia.

That analysis revealed that varying parameters of working memory, such as capacity, but not parameters of reinforcement learning, accounted best for differences in behavior between the groups.

“With model-fitting techniques, I can look quantitatively, trial-by-trial and see that the model predicts subject’s choices,” she said. “The same model explains both the healthy group and the patient group, but with differences in parameters.”

That confirmed that working memory uniquely affected learning in people with schizophrenia, while reinforcement learning mechanisms did not, Collins said.

The study suggests that working memory could be a more important target than reinforcement learning among researchers and clinicians hoping to help improve learning for people with schizophrenia, Collins said.

Among mentally healthy people as well, the study illustrates that the different components of learning can be understood individually, even as they all interact in the brain to make learning happen.

“More broadly, it brings attention to the fact that we need to consider learning as a multiactor kind of behavior that can’t be just summarized by a single system,” Collins said. “It’s important to design tasks that can separate them out so we can extract different sources of variance and correctly match them to different neural systems.”

(Image: Shutterstock)