We have a strong and ongoing research effort involving psychophysical research and computational modeling of object perception, including how objects, contours, and surfaces are perceived from information that is fragmentary in space and time, and how we perceive and represent shape. In recent work, we have developed a neural-style implementation of early contour connections underlying connections of contour fragments across gaps. In ongoing efforts, we are developing a more comprehensive framework for understanding object formation -- extending the basic geometric relations governing interpolation (generation of illusory and occluded contours and surfaces) from 2D static cases to 3D perception and spatiotemporal object perception, in which objects are constructed across gaps in both space and time. We are applying classification imaging techniques to the problem of spatiotemporal interpolation, exploring the types of motions that support dynamic object formation, and studying object formation across gaps in the context of multiple object tracking.
Fantoni, C., Hilger, J., Gerbino, W. & Kellman, P. J. (in press). Surface interpolation and 3D relatability. Journal of Vision.
Fantoni, C., Gerbino, W. & Kellman, P.J. (in press). Approximation, torsion, and amodally completed surfaces. Vision Research.
Keane, B. P., Lu, H., & Kellman, P. J. (2007). Classification images reveal spatiotemporal interpolation in illusory figures. Vision Research, 47, 3460-3475.
Kellman, P.J., Garrigan, P.B., Shipley, T.F. & Keane, B.P. (2007). Postscript: Identity and constraints in models of object formation. Psychological Review, 114(2): 502-508.
Kellman, P.J., Garrigan, P.B., Shipley, T.F. & Keane, B.P. (2007). Interpolation processes in object perception: A reply to Anderson. Psychological Review, 114(2): 488-502
Kellman, P.J. & Garrigan, P.B. (2007). Segmentation, grouping, and shape: Some Hochbergian questions. In M. A. Peterson, B. Gillam & H. A. Sedgwick, (Eds.) Julian Hochberg on the perception of pictures, films, and the world, NY: Oxford University Press.
Palmer, E. M., Kellman, P. J., & Shipley, T. F. (2006). A theory of dynamic occluded and illusory object perception. Journal of Experimental Psychology: General, 135, 513–541. (Selected by the American Psychological Association as best paper published in JEP: General in 2006 by a young investigator (Evan Palmer).)
Kellman, P.J. & Arterberry, M.A. (2006). Infant visual perception. In R. Siegler and D. Kuhn (Eds.), Handbook of Child Psychology, Sixth Edition, Volume 2: Cognition, Perception, and Language. New York: Wiley.
Kellman, P.J., Garrigan, P., & Shipley, T. F. (2005). Object interpolation in three dimensions. Psychological Review, Vol. 112, No. 3, 586-609.
Kellman, P.J., Garrigan, P., Yin, C., Shipley, T. & Machado, L. (2005). 3D interpolation in object perception: Evidence from an objective performance paradigm. Journal of Experimental Psychology: Human Perception & Performance, 31, 558-583.
Guttman, S.E. & Kellman, P.J. (2004). Contour interpolation revealed by a dot localization paradigm. Vision Research, 44(15), 1799-1815.
Kellman, P.J. (2003). Perceptual processes that create objects from fragments. Proceedings of the 2003 IEEE International Joint Conference on Neural Networks.
Kellman, P.J. (2003). Segmentation and grouping in object perception: A 4-dimensional approach. In M. Behrmann and R. Kimchi (Eds.). Perceptual Organization in Vision: Behavioral and Neural Perspectives: The 31st Carnegie Symposium on Cognition. Hillsdale, NJ: Erlbaum.
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.
Kellman, P.J. & Garrigan, P.B. (in press). Perceptual learning and human expertise. Physics of Life Reviews.
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.
Massey, C.M., Kellman, P.J., Roth, Z. & Burke, T. (in press). 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.
Garrigan, P.B. & Kellman, P.J. (2008). Perceptual learning depends on perceptual constancy. Proceedings of the National Academy of Sciences (USA), Vol. 105, 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.
This work focuses on high-level perceptual learning, especially research and modeling of human abilities to discover and encode abstract relations (as in perception of a shape, a melody, or in language). We are also working to relate examples of so-called high level and low-level perceptual learning, an effort that is tending to show that, following Eleanor Gibson's classic view, selection and discovery of invariance characterize perceptual learning across levels, and even improvements that appear "low-level" and sensory involve substantial contributions of perceptual organization.
Kellman, P.J. & Garrigan, P.B. (in press). Perceptual learning and human expertise. Physics of Life Reviews.
Garrigan, P.B. & Kellman, P.J. (2008). Perceptual learning depends on perceptual constancy. Proceedings of the National Academy of Sciences (USA), Vol. 105, No. 6, 2248-2253.
Kellman, P.J. (2002). Perceptual learning. In R. Gallistel (Ed.), Stevens' handbook of experimental psychology, Third edition, Vol. 3 (Learning, motivation and emotion), John Wiley & Sons, 259-299.