Human Perception Lab

Research

Perception and Cognition: Applications to Learning Technology

In this work we use principles of perception and cognition and results of our research and others to develop and optimize computer-based learning technology. This work focuses on two innovation areas: perceptual learning methods and adaptive learning technology.

Perceptual learning methods lead learners to extract invariant structure from variable instances and transfer their knowledge to the classification of novel instances and structures. This work addresses dimensions of learning that are crucial to expertise in any domain but are poorly addressed by both conventional instructional methods and by existing learning technology.

Our patented adaptive learning methods use the individual learner's speed and accuracy to determine the recurrence of items or categories in learning. Our algorithms produce sequencing of short, interactive trials that implements a number of important laws of learning and tend to optimize the efficiency of learning of whole sets of items or categories.

This work is ideal for students hoping to obtain strong training in cognitive science research but also wishing to apply their training to real-world learning contexts. Opportunities for support under a US Department of Education grant, and opportunities for involvement in a growing learning technology company are available.

Examples of Learning Modules

  • From the Start to End PLM
  • From the Histopathology PLM
  • From the Wrist X-Ray PLM

Researchers

  • Philip J. Kellman Philip J. Kellman
  • Everett Mettler Everett Mettler
  • Carolyn Bufford Carolyn Bufford
  • Tim Burke Tim Burke

Collaborators

  • face Christine Massey (Penn)
  • face Sally Krasne (UCLA)
  • face Erik Dutson (UCLA)
  • face Cheryl Hein (UCLA)

Selected Publications

Kellman, P.J., & Massey, C.M. (2013) erceptual learning, cognition, and expertise. In B.H. Ross (Ed.), The Psychology of Learning and Motivation (Vol. 58, 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, pp. 4-6.
Mettler, E., Massey, C., & Kellman, P. J. (2011). Improving adaptive learning technology through the use of response times. In L. Carlson, C. Holscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2532-2537). Boston, MA: Cognitive Science Society.
Thai, K. P., 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.
Massey, C. M., Kellman, P. J., Roth, Z. & Burke, T. (2010). Perceptual learning and adaptive learning technology: Developing new approaches to mathematics learning in the classroom. In Stein, N.L. (Ed.), Developmental and learning sciences go to school: Implications for education.
Kellman, P. J., Massey, C. M., & Son, J. (2009). 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.
Kellman, P. J. & Garrigan, P. B. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53-84.
Kellman, P.J., Massey, C.M., Roth, Z., Burke, T., Zucker, J., Saw, A., Aguero, K.E. & Wise, J.A. (2008). Perceptual learning and the technology of expertise: Studies in fraction learning and algebra. Pragmatics & Cognition, Special Issue on Cognition and Technology.
Garrigan, P.B. & Kellman, P.J. (2008). Perceptual learning depends on perceptual constancy. Proceedings of the National Academy of Sciences (USA), Vol. 85, No. 6, 2248-2253.
Palmer, E.M., Clausner, T. C. & Kellman, P.J. (2008). Enhancing air traffic displays via perceptual cues. ACM Transactions on Applied Perception (TAP), Vol. 5(1), 1-22.