Brain-Training To Improve Memory Boosts Fluid Intelligence

Brain-training efforts designed to improve working memory can also boost scores in general problem-solving ability and improve fluid intelligence, according to new University of Michigan research.

New findings show that multiple efforts designed to improve memory skills similarly improve fluid intelligence. (Credit: iStockphoto/Kiyoshi Takahase Segundo)

"Considering the fundamental importance of fluid intelligence in everyday life and its predictive power for a large variety of intellectual tasks and professional success, we believe that our findings may be highly relevant to applications in education," U-M psychology researchers Susanne Jaeggi and Martin Buschkuehl concluded.

Many psychologists believe general intelligence can be separated into "fluid" and "crystalline" components. Fluid intelligence—considered one of the most important factors in learning—applies to all problems while crystallized intelligence consists of skills useful for specific tasks.

"Working memory and fluid intelligence both seem to rely on similar neural networks,'' Jaeggi said. "Our study does not permit us to know how long the training-gain persists. Longitudinal studies will be required to address that issue."

Previously, many psychologists believed the only way to increase fluid intelligence was through direct practice of the tests themselves, rather than by training. But the new findings show that multiple efforts designed to improve memory skills similarly improve fluid intelligence.

After initially giving subjects a standard test for fluid intelligence, the researchers gave subjects a series of training exercises designed to improve their working memory.

The training was given to four groups, who repeated the exercises for eight, 12, 17, or 19 days. After the training, the researchers re-tested the subjects' fluid intelligence.

Although the performance of untrained controls improved slightly, the trained subjects showed a significant performance improvement, which increased with time spent training.

"The more training, the more improvement in fluid intelligence," Jaeggi said.

The researchers suggest that the training exercises strengthened multiple "executive processes" in the brain that function in problem-solving, noting that fluid intelligence is usually seen as "robust against influences of education and socialization, and it is commonly seen as having a strong hereditary component."

The research is detailed in a recent  article in the Proceedings of the National Academy of Sciences (PNAS).

source:sciencedaily.

On the Hunt for Universal Intelligence

 How do you use a scientific method to measure the intelligence of a human being, an animal, a machine or an extra-terrestrial? So far this has not been possible, but a team of Spanish and Australian researchers have taken a first step towards this by presenting the foundations to be used as a basis for this method in the journalArtificial Intelligence, and have also put forward a new intelligence test.

On the hunt for the universal intelligence test. (Credit: SINC)

We have developed an 'anytime' intelligence test, in other words a test that can be interrupted at any time, but that gives a more accurate idea of the intelligence of the test subject if there is a longer time available in which to carry it out," José Hernández-Orallo, a researcher at the Polytechnic University of Valencia (UPV), said.

This is just one of the many determining factors of the universal intelligence test. "The others are that it can be applied to any subject -- whether biological or not -- at any point in its development (child or adult, for example), for any system now or in the future, and with any level of intelligence or speed," points out Hernández-Orallo.

The researcher, along with his colleague David L. Dowe of the Monash University, Clayton (Australia), have suggested the use of mathematical and computational concepts in order to encompass all these conditions. The study forms part of the "Anytime Universal Intelligence" project, in which other scientists from the UPV and the Complutense University of Madrid are taking part.

The authors have used interactive exercises in settings with a difficulty level estimated by calculating the so-called 'Kolmogorov complexity' (they measure the number of computational resources needed to describe an object or a piece of information). This makes them different from traditional psychometric tests and artificial intelligence tests (Turing test).

Use in artificial intelligence

The most direct application of this study is in the field of artificial intelligence. Until now there has not been any way of checking whether current systems are more intelligent than the ones in use 20 years ago, "but the existence of tests with these characteristics may make it possible to systematically evaluate the progress of this discipline," says Hernández-Orallo.

And what is even "more important" is that there were no theories or tools to evaluate and compare future intelligent systems that could demonstrate intelligence greater than human intelligence.

The implications of a universal intelligence test also impact on many other disciplines. This could have a significant impact on most cognitive sciences, since any discipline depends largely on the specific techniques and systems used in it and the mathematical basis that underpins it.

"The universal and unified evaluation of intelligence, be it human, non-human animal, artificial or extraterrestrial, has not been approached from a scientific viewpoint before, and this is a first step," the researcher concludes.

source:sciencedaily.

Researchers Probe Computer 'Commonsense Knowledge'

Challenge a simple pocket calculator at arithmetic and you may be left in the dust. But even the most sophisticated computer cannot match the reasoning of a youngster who looks outside, sees a fresh snowfall, and knows how to bundle up for the frosty outdoors.

Few can challenge a simple pocket calculator at arithmetic. But even the most sophisticated computer cannot match the reasoning of a youngster. (Credit: iStockphoto)

For artificial intelligence scientists, enabling computers to have such human-level intelligence requires a commonsense knowledge base that can evolve and learn new things. But it's an elusive goal.

"It's been the Holy Grail of artificial intelligence research since its early days to answer questions that a young child can answer about the world," says Robert Sloan, professor and head of computer science at the University of Illinois at Chicago. "We're still a long way from that."

Sloan and colleague Gyorgy Turan, professor of mathematics, statistics and computer science at UIC, hope to build theoretical foundations that will bring artificial intelligence closer to everyday human reasoning. They were recently awarded a three-year, $500,000 National Science Foundation grant to develop algorithms for use in building commonsense knowledge bases that can evolve.

"You can view this evolving process as a kind of learning about the world by a computer," said Turan. "Our task is to understand the problem, find useful mathematical models, understand the basic mathematical properties and, hopefully, provide some efficient computational methods and algorithms in those models."

Part of the work will involve looking at the construction of current Web-based commonsense knowledge base systems, such as Cycorp's "Cyc" and MIT's "Open Mind Common Sense," that allow any user to enter bits of knowledge considered relevant, useful or interesting.

Turan and Sloan will consider questions such as how to deal with contradictory information that is entered and how to organize knowledge in formats that are useful for deriving further knowledge.

"The issue is how to process new information that comes in over time," said Sloan. "One crisply defined algorithmic problem is how do you incorporate the new information both efficiently and in a reasonable way? Of course, defining the meaning of 'reasonable' is a challenging problem in itself."

The UIC researchers will work with graduate students and postdoctoral staff to concentrate on the interaction between different subtasks of evolving commonsense knowledge bases and on developing efficient computational methods.

Sloan and Turan hope their work will find applications in the artificial intelligence field, possibly through improved robots and other automated devices.

"Currently we're studying abstract mathematical versions of these problems, but we hope the conclusions will lead to useful, practical tools," said Turan.

source:sciencedaily.