Perceptual Learning: Applications to Education

My lab at UCLA has been in the news twice recently, which is very exciting for us! You may have seen this article in the NYTimes last week or this interview on CBS’ The Early Show this morning. Both stories are about perceptual learning and its applications to education. I thought in this post I would expand on those ideas to give you a deeper sense of what perceptual learning is, why we study it, and what its limitations are.
What does it mean to learn?

Traditionally, when we talk about learning, we are referring to declarative and procedural knowledge. Declarative knowledge is knowledge that can be stated, such as definitions, historical dates, ideas, rules, principles, theories, and so forth. Procedural knowledge is the ability to carry out sequences of actions to accomplish something, such as solving math problems, riding a bike, or cooking. To say you have learned something often means that you are either able to repeat that information back when asked or you are able successfully carry out the procedure that was taught. In general, education focuses on teaching students these two kinds of knowledge and not much else, but recently, promising innovations for improving instruction in these areas have arisen out of scientific research on expertise.

What makes experts so smart?

Expertise is an interesting and relevant field of research to look at because experts have learned something special about their field beyond the declarative and procedural side that makes them faster and more efficient at processing information and solving problems. Specifically, experts are able to deal with information more efficiently and effectively than novices because they are able to “see” patterns and underlying structure in information. These patterns are focused around a deep understanding of what information in a scene or a problem is relevant to the task at hand, and what is not. With lots of practice, experts learn to pay attention only to the bits of information that they need while ignoring the rest, and they also begin to see informational patterns that don’t pop out to novices.

For example, beginning physics students often struggle when solving problems that have to do with circular motion. The relevant features of circular motion have to do with the speed an object is moving, its mass, and its distance from the center of rotation, but most word problems include much more information that those three quantities. Novices have a hard time knowing what information is important and what is not, so it takes them longer to solve the problem and they may not be very accurate. But an expert, on the other hand, can look past surface features like what kind of object it is and whether it’s on a string, a racetrack, or a rollercoaster loop and boil the problem down to its essential features. They also learn to identify some features that are not given directly in the problem but can be inferred from the situation, such as whether the object is in equilibrium or whether there is conservation of energy. Most novices are just as capable as experts at applying definitions and principles and carrying out the algebraic manipulations to reach a solution, yet they are still slower and make more mistakes. The important point here is not that experts know more than novices, but that they see problems differently. The kind of learning that leads to this seeing ability is something we call perceptual learning.

What is perceptual learning?

Perceptual learning is defined as experience- or practice-induced changes in the pickup of information. In her 1969 book, Principles of Perceptual Learning and Development, Eleanor Gibson outlines three basic trends that characterize perceptual learning and development: increasing specificity of discrimination, optimization of attention, and increasing economy of information pickup. In plain English, that means that perceptual learning leads to an increased ability to decide what features are relevant and what are not, an optimal level of attention that is guided by the learner’s knowledge of what to look for, and increased speed and efficiency in identifying those key features. These three trends can be summarized as discovery and fluency effects, where discovery is related to what features of information we attend to and fluency is related to the speed and manner of search (i.e. serial versus parallel) for those features. Whereas novices focus on simple features, are less selective in what they attend to, and process those features serially (one at a time), experts, on the other hand, are highly selective and able to perceive “chunks” or relations of features as single units, thus leading to parallel processing (simultaneous evaluation of multiple features), a reduced attentional load, and faster response times.

Although perceptual learning seems to be the missing link between novices and experts, it’s a type of learning that has not been capitalized on in schools. This perceptual ability has generally been chalked up to years and years of experience, and novices have had no choice but to invest that time if they truly wished to gain expertise in a field. However, new technologies have begun to experiment with ways of speeding up the process of perceptual learning, and my lab at UCLA is very involved in this development.

Perceptual Learning Modules at UCLA

Basically, the concept of our perceptual learning modules (PLMs) is to give participants the opportunity to interact with problems in a meaningful way without asking them to solve problems like in a traditional classroom. The PLM I am working with right now is called the Algebraic Transformations PLM, and its purpose is to help students understand the structure of equations and how terms can move without affecting the equality of both sides. In this particular module, each trial screen shows one complex equation and the student chooses from 4 options the correct legal transformation of that equation. They are not solving to find the value of any variables, but they do have to know and understand the rules of algebraic manipulations. As they complete the module, they advance through levels of mastery that are based on improvements in their accuracy and response times.

What is so remarkable about this module is that practice with the PLM actually cut students’ response times by two-thirds from pre- to post-test when solving algebra problems, even though they were mid-year algebra students who had been practicing solving problems for months. The other modules have shown similar improvements in accuracy related to multiple representations of linear relationships (translating between word problems, graphs, and equations) and to solving problems with fractions. Previous research from our lab has shown similar perceptual learning effects with other subject material such as reading aviations controls and identifying injuries in x-rays.

Limitations and Implications

There are limits, however, to what perceptual learning can do, as well as to our knowledge of how much we can accomplish with technology like this. A hallmark of perceptual learning is that it is task-specific. So training on “seeing” solutions to algebra problems won’t help you with your chemistry homework (sorry). Also, perceptual learning is a process that most people can’t verbalize; even though we have evidence of significant improvement in processing information, our participants are not necessarily learning anything declarative or procedural. Future research will be directed towards testing the limits of what students can learn from tasks like our PLMs, but for the moment we don’t know how much conscious knowledge students are gleaning from the experience.

For these reasons, and many others, this technology will never replace real life teachers, tutors, and mentors. There is no doubt that we need to learn declarative and procedural knowledge – we are only suggesting that perhaps a view of education that ignores the problem of “seeing” is too narrow. Implementing perceptual learning tasks won’t make your kids into geniuses, but it might help them overcome the frustration of tackling a new and difficult subject where it’s hard to know where to start.

Further Reading

If you want to learn more about perceptual learning, expertise, and applications to educational practice, I would suggest checking out these readings:
• Gibson, E J. (1969). Principles of perceptual learning and development. East Norwalk, CT, US: Appleton-Century-Crofts.
• Kellman, P.J. & Garrigan, P.B. (2009). Perceptual learning and human expertise. Physics of Life Reviews, Vol. 6, No. 2, 53-84.
• Kellman, P.J., Massey, C.M & Son, J. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science (Special Issue on Perceptual Learning), 2(2): 285-305.

And of course you can always ask questions in the comments!