Perceptual Learning

Leading Learners to Quickly Recognize Key Features and Patterns and Apply That Understanding to New Problems

Perceptual Learning

Leading Learners to Quickly Recognize Key Features and Patterns and Apply That Understanding to New Problems

A Missing Key to Learning

Learning research shows that perceptual learning is a missing key to learning—one that is not well understood and not systematically addressed in typical instruction.

What is Perceptual Learning?

A characteristic of experts in any domain is that they selectively pick up relevant information, discover important patterns, and see key structure in new cases or situations. They are able to extract structure quickly and fluently with little effort (cognitive load), freeing up attention for higher-level problem solving.

Expertise and perceptual learning are tightly interwoven. Perceptual learning refers to changes in the way we pick up information as a result of experience or practice and is achieved through exposure to varied examples. Your abilities to correctly identify new cats, new songs by your favorite band, and your best friend’s voice on the phone depends on prior perceptual learning. So do the abilities of expert chemists, algebra teachers, and air traffic controllers.

  • Examples 1: Radiologists need to be proficient in reading x-rays to pick out abnormalities.
  • Example 2: Pilots need to be able to decipher critical information from an array of instruments.
  • Example 3: Orchestra conductors can look at a score, hear every instrument in their head, and simultaneously monitor what the musicians are playing. Photo by Jimmy Baikovicius | CC BY | cropped and resized from original.

Our brain is good at this kind of learning, if given the right kinds of input. And these inputs are mostly absent and hard to give in traditional educational settings. It had been thought that this kind of learning could not be systematically taught and could only be slowly acquired through experience. Until now.

PALMs: Perceptual and Adaptive Learning Modules

At Insight Learning Technology, we are introducing the first learning software of its kind—products that apply perceptual (and adaptive) learning to some of the toughest subjects in many domains. Perceptual and Adaptive Learning Modules (PALMs) offer, for the first time, systematic ways of teaching pattern recognition.

Based on years of research in cognitive science, we have developed learning technology that accelerates perceptual learning. PALMs targeting crucial domains in mathematics and science have been shown to have powerful, long-lasting effects on students' learning, and can be generalized for solving many kinds of problems. Our patented perceptual learning technology can apply to almost any learning domain, and its potential has only begun to be realized.

  • MultiRep Insight
  • Slice & Clone
  • Core Math Insight
  • Start to End
  • Electrocardiography Insight
  • Anatomy Insight
  • Histopathology Insight
  • Area Measurement

Our PALMs, as well as stand-alone Adaptive Learning Modules for factual or procedural learning, utilize our patented adaptive learning technology to tailor interactive learning events to each individual learner. These methods use learner performance, including both accuracy and speed, to make factual, procedural, or perceptual learning more efficient—often cutting learning time in half.

Publications

  • Rimoin, L., Altieri, L., Craft, N., Krasne, S., & Kellman, P. J. (2015). Training pattern recognition of skin lesion morphology, configuration, and distribution. Journal of the American Academy of Dermatology, 72(3), 489-495.
  • Bufford, C. A., Mettler, E., Geller, E. H., & Kellman, P. J. (2014). The psychophysics of algebra expertise: Mathematics perceptual learning interventions produce durable encoding changes. In P. Bellow, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 272-277). Austin, TX: Cognitive Science Society.
  • Mettler, E., & Kellman, P. J. (2014). Adaptive response-time-based category sequencing in perceptual learning. Vision Research, 99, 111-123.
  • Krasne, S., Hillman, J. D., Kellman, P. J. & Drake, T. A. (2013). Applying perceptual and adaptive learning techniques for teaching introductory histopathology. Journal of Pathology Informatics, 4, 34-41.
  • Kellman, P. J. (2013). Adaptive and perceptual learning technologies in medical education and training. [Supplement issue.] Military Medicine, 178, 98-106.
  • Kellman, P. J., & Massey, C. M. (2013) Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 58, pp. 117-165). Amsterdam: Elsevier Inc.
  • Wise, J., & Kellman, P. J. (2011). Changing the face of learning: Perceptual learning, the path to expert pattern recognition.California Association of Independent Schools (CAIS) Faculty Newsletter, Fall 2011, 4-6.
  • Thai, K., Mettler, E., & Kellman, P. J. (2011). Basic information processing effects from perceptual learning in complex, real-world domains. In L. Carlson, C. Holscher, & T Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 555-560). Boston, MA: Cognitive Science Society.
  • Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students' pattern recognition, structure extraction, and fluency. [Special issue on perceptual learning]. Topics in Cognitive Science, 2(2), 285-305.