January 18, 2013
NIH Podcast Episode #0178
Balintfy: Welcome to episode 178 of the new NIH Research Radio. The new NIH Research Radio is your source for weekly news and information about the ongoing medical research at the National Institutes of Health – NIH . . . Turning Discovery Into Health®. I'm your host Joe Balintfy, and coming up in this episode our news summary at the end of the program includes items on:
- funding for Alzheimer’s disease research
- biomarkers for Parkinson’s disease, and
- a preliminary study on autism
But first, our feature story...
Infant looking, learning
Balintfy: NIH funded researchers at the University of Iowa, have shown how infants learn by taking inventory of things they see. I’m talking with Dr. John Spencer, a psychology professor and co-author of a paper published in the journal Cognitive Science.
Spencer: Right. Well we all know that infancy is a time of great change so parents experience this directly so, you know, six weeks olds are radically different than six months olds. Given the pace of change, there’s a lot of interest right now in early intervention. The idea is simple, if we could work with parents to create just the right experiences for infants, we might have a profound impact on the most at- risk children.
Now to make this idea work, there’s a couple of challenges we have to overcome. The first is identifying infants at risk and there are a number of ways to do that. For example, we do a fair amount of work looking at preterm infants. They’re known to be at risk for cognitive deficits later in life, special needs in the classroom, etc. But the second challenge is how do we intervene? So at the end of the day, what exactly are we going to ask parents to do?
Now to intervene at that level of detail, we need to know how infants learn and develop so it’s hard to intervene if you don’t know the basics and that’s really where our paper comes in. So our paper focuses in on a key form of learning early in infancy, learning about the visual world, presents a mathematical model called the dynamic neural field model that explain how infants learn to look, how they visually explore their world, and how they learn to look, how changes over learning change the nature of visual exploration. What we show in the model is that if we can understand sort of the details of infant’s visual exploration, we can identify the situations that might be most beneficial for learning. And the hope is that as this work unfolds, we can actually use the model to optimize interventions for the most at-risk children.
Balintfy: Can you explain more about how infants learn by looking. I think in a lot of ways we would think this is common sense, of course my baby looks around and learns things. How can we distinguish between the scientific discovery and what we already know as parents?
Spencer: Yeah. So exactly, right. There’s this – you know, you have the intuition that you know if your eyes are closed, you don’t learn a lot of about the visual world right. But what the paper really talks about is this sort of step-by-step dance between looking and learning.
So let’s imagine you have an infant seated at a table or sitting in the parent’s lap and there’s maybe a number of attractive toys sitting in front of them. What we talk about is the first look somewhere could be for any variety of reasons. Maybe there’s a really attractive toy or maybe the parent picks up one of the objects and moves it which captures the infant’s attention. So they make an initial look at that object and then what happens is that starts a process of a memory formation. First, the infant enters what we call a working memory state, sort of holds the features of that object actively in mind. As the infant enters that working memory state, that leaves a little bit of a learning trace, which is going to linger even when the infant looks away at another object.
What we are really interested in is how the second-by-second details of looking somewhere, learning a little bit, and then releasing fixation to look somewhere else, how all those memory traces accumulate to create learning over months and years of development.
Balintfy: And how were you able to study that, did you have real infants that you were watching?
Spencer: Sure. In this particular paper, it was all computer simulations of data that had been collected with real infants in other studies. So we used the model to try to replicate the infant’s patterns of looking through time and, you know, in a sense that validates the computer model and then the goal is that computer model can become a tool for research or for other types of research in the future.
Balintfy: So it’s data from real infants, but a mathematical or computer model. I think most people when they hear about computer models they know what the meteorologist is using to predict our weather. But how can you explain the implications of this research and these models?
Spencer: Yeah. Well I think the implications really are trying to create a sophisticated tool. You know, that weather model that you mentioned is a nice example right. Weather is extremely complicated and we all take that for granted and maybe give the weatherman some slack when they get it wrong. But when we’re dealing with infants who are at risk, the stakes are pretty high and I think it’s important to take, you know, the same type of cutting edge technology that we use in the weather when we think about at risk infants. So in a sense what we’re doing is trying to take computer models that are as sophisticated as those weather models and say are they up to the task of predicting what an infant will do second by second as they look around the world. Then what we really are excited about is we could take those computer models and run millions and millions of simulations, which is like simulating the day-by-day details of an infant’s life and ask can we create development in these models.
Balintfy: Is there a concrete or specific next step? Dr. Spencer, how do you feel this research is going to hit the road, so to speak?
Spencer: Yeah. Well right now, where we are in the process is you know, the paper we’re talking about was our initial validation of this approach. Let me give you one concrete example to tell you how we anchored that to some of the future work we want to do. So in our paper, we show that we basically took a model of a very young infant and we put one model in a virtual world that had a responsive parent or a responsive context. So in that model’s world, every time it looked at an object, it got a little bit of an extra support to continue to dwell on that object. Okay. So a little bit of extra but maybe like the parent picking up that object and jiggling it around trying to keep the infant’s attention focused there for just a little bit longer.
The other infant model had an unresponsive environment and so when that model looks somewhere, there was a little bit of an extra input at other locations, okay, so a little bit of a distraction in that model’s world. What we did is we showed that these two models over learning diverged such that the model that was in that responsive context or let’s say with the responsive parent not only learned more but it learned better. So after many, many episodes of learning, that model not only could distinguish known from novel objects but it had developed the ability to really have fine grain discrimination like really subtle detection of differences from one object to the next.
So the idea is that validates that we can now put those models in a real context let’s say with these two artificial parents. Now let’s take it one step further and can we do something like instruct the parents to be responsive in particular ways. We should then be able to predict where their infants will be a couple of months down the road and we could actually test that prediction with real infants.
Balintfy: Is it too early to speculate whether this will have implications for things like autism or attention deficit disorders, where we don’t know what the causes might be we can run the models to see if that has any impact?
Spencer: Yeah. We have a number of projects right now looking at sort of that early assessment question. So we’re looking at premature infants in collaboration with folks at the University of Iowa Children’s Hospital. We’re very interested in siblings of children diagnosed with an autism spectrum disorder because they’re at risk for ASD. And it’s been a little trickier to look at the risk for ADHD but actually our work on preterm infants may give insights there because premature infants are at risk for ADHD down the road.
Balintfy: Thanks to Dr. John Spencer, an NIH funded researcher and psychology professor at the University of Iowa. For more about his research and the paper he co-authored in the journal Cognitive Science, visit the website now.uiowa.edu. For details on autism spectrum disorders and ADHD, visit www.nimh.nih.gov, and for information on childhood development, visit www.nichd.nih.gov.
And coming up, those news stories about Alzheimer’s disease research, a biomarker for Parkinson’s disease, and autism spectrum disorders. That’s next on NIH Research Radio.
(BREAK FOR PUBLIC SERVICE ANNOUNCEMENT)
Balintfy: Now for some recent news headlines from NIH, here’s Craig Fritz.
Fritz: A new NIH initiative aims to accelerate the search for biomarkers in Parkinson's disease. Biomarkers are changes in the body that can be used to predict, diagnose or monitor a disease. The effort will improve collaboration among researchers and help patients get involved in clinical studies. A lack of biomarkers for Parkinson's has been a major challenge for developing better treatments. The Parkinson’s Disease Biomarkers Program supports efforts to invent new technologies and analysis tools for biomarker discovery, to identify and validate biomarkers in patients, and to share biomarker data and resources across the Parkinson's community.
With new research funding from NIH, the nation’s premier Alzheimer’s disease study network will undertake four major studies aimed at finding new treatments for the disease. The award supports the latest projects of the Alzheimer’s Disease Cooperative Study, a national consortium of academic medical centers and clinics set up by NIH in 1991 to collaborate on the development of Alzheimer’s treatments and diagnostic tools. In this round of studies, researchers will test drug and exercise interventions in people in the early stages of the disease, examine a medication to reduce agitation in people with Alzheimer’s dementia, and test a cutting-edge approach to speed testing of drugs in clinical trials.
A new preliminary study by NIH has confirmed that some children, who were accurately diagnosed with autism in early childhood, lose the symptoms and the diagnosis as they grow older. The research team made the finding by carefully documenting a prior diagnosis of autism in a small group of school-age children and young adults with no current symptoms of the disorder. The report is the first of a series that will probe more deeply into the nature of the change in these children’s status. Having been diagnosed at one time with an autism spectrum disorder, these young people now appear to be on par with typically developing peers. The study team is reviewing records on the types of interventions the children received, and to what extent they may have played a role in the transition.
For this NIH news update, I’m Craig Fritz.
Balintfy: You can get more information on these news items at www.nih.gov/news.
Balintfy: And that’s it for this episode of the new NIH Research Radio. Please join us again next Friday, January 18 when our next edition will be available. Coming up in that episode…
One of the aspects and challenges in the management of chronic pain is the difficulty that doctors have to make with respect to the need of initiating an opioid medication and the potential negative effects that these medications have.
If you have any questions or comments about this program, or have a story suggestion for a future episode, please let me know. Send an email to NIHRadio@mail.nih.gov. Also, please consider following NIH Radio via Twitter @NIHRadio, or on Facebook. Until next week, I'm your host, Joe Balintfy. Thanks for listening.
Announcer: NIH Research Radio is a presentation of the NIH Radio News Service, part of the News Media Branch, Office of Communications and Public Liaison in the Office of the Director at the National Institutes of Health in Bethesda, Maryland, an agency of the US Department of Health and Human Services.
About This Podcast
Spokesperson:Dr. John P. Spencer
Topic:infant, infants, learn, learning, looking, memory, model, computer model, mathematical model, children, at-risk, parent, parents, Alzheimer’s disease, Parkinson’s disease, autism, autism spectrum disorder
Additional Info: Infants learn to look and look to learn