Current Projects:
Dynamic representation of visual space
Our percept of the space around us is both stable and dynamic. We see how objects or people move through space but when we move by ourselves the world appears at rest even though its image in the eye is actually moving. How is a stable and consistent percept of a the world achieved in a dynamic visual context? We study this question by analyzing visual illusions that break the stable and consistent percept of the world. A particular question is the perception of space during saccadic eye movements, the quick changes of gaze that align the axis of sight with objects of interest. Every saccade changes the image of the world on the retina. We perform more than a hundred thousand saccades every day but never experience the world to move. However, during some tens of milliseconds before and during an eye movement this spatial stability is broken. Visual stimuli that are briefly flashed during this time are seen at grossly distorted positions. These effects show the dynamic process of space representation at work. We study the role of visual references, attention, and trans-saccadic memory in this process. We also investigate the plasticity of saccadic eye movements and the effects of saccadic adaptation on visual space perception.
Financial Support:
Human Frontier Science Program RG149/1999B (1999-2003)
DFG LA952/2 (2005-2011)
BMBF Visuo-spatial cognition (2007-2009)
EU FP7 Eyeshots (2008-2011)
Partners:
Concetta Morrone, Università Vita-Salute San Raffaele, Milano
Mickey Goldberg, Columbia University, New York
Frank Bremmer, Phillipp-Universität Marburg
Mark Greenlee, Universität Regensburg
Christoph Ploner, Charite Berlin
Hans-Otto Karnath, Universität Tübingen
Thérèse Collins, Universität Hamburg
Karine Doré-Mazars, CNRS - Université Paris 5
Eyeshots Consortium
Selected Publications:
Hamker, F. H., Zirnsak, M., Calow, D. & Lappe, M. (2008). The peri-saccadic perception of objects and space. PLoS Comp. Biol. 4(2):e31,1-15
Collins, T., Dore-Mazars, K. & Lappe, M. (2007). Motor space structures perceptual space: evidence from human saccadic adaptation. Brain Research 1172:32-39.
H. Awater and M. Lappe (2006). Mislocalization of perceived saccade target position induced by peri-saccadic visual stimulation. Journal of Neuroscience, 26(1):12-20.
M. Kaiser & M.
Lappe (2004). Perisaccadic Mislocalization Orthogonal to Saccade Direction.
Neuron, 41 (2): 293-300.
H. Awater, D. Burr,
M. Lappe, M. C. Morrone & M. E. Goldberg (2005). The effect of saccadic
adaptation on the localization of visual targets. Journal of Neurophysiology,
93: 3605-3614.
M. Lappe, H. Awater
& B. Krekelberg (2000). Postsaccadic visual references generate presaccadic
compression of space. Nature, 403:892-985.
Perception of
Biological Motion
Recognition of the
actions and movements of people is among the most important tasks of vision.
But it is also one of the most difficult, because the movement of the body has
many degrees of freedom and is non-rigid. Yet, the brain has developed
exquisite capabilities to recognize this 'biological motion'. It is possible to
infer actions, gender or even identity of a person from the movement of only a
few points on the body. How can such a rich description be obtained from so
little information? We study how biological motion perception is achieved in
the visual system and develop a computer model using similar strategies. A
particular question is whether biological motion perception is derived from
motion or from form signals. We have developed a variant of Gunnar Johannsson's
'point-light display' which prohibits the direct use of motion signals. This
stimulus demonstrates that dynamic form cues alone can support the perception
of biological motion. We have developed a neurocomputational model that shows
how biological motion can be inferred from form cues in a sequence of body
postures. Our model assumes that the visual system matches incoming visual
information about body posture against body shape templates, presumably
contained in areas of the cortical form pathway. The distribution of activity
over these template neurons indicates body posture and orientation. The
perception of body movement is achieved in a second stage in which the temporal
sequence of body postures is analyzed.
Demos:
'Classical Walker'
Stimulus (.mov about 750 kb)
'SFL-Walker'
Stimulus (.mov about 350 kb)
Financial Support:
BioFuture Preis des
Bundesministerium für Bildung und Forschung (1999-2005)
Partners:
Lucia Vaina, Boston University
& Harvard Medical School
Raimund Kleiser, Heinrich Heine
Universität Düsseldorf
Rüdiger Seitz, Heinrich Heine
Universität Düsseldorf
Luciano Fadiga, Università Ferrara
Christo Pantev, IBB, Universität
Münster
Selected Publications:
J. A. Beintema &
M. Lappe (2002). Perception of biological motion without local image motion.
Proceedings of the National Academy of Sciences, 99: 5661-5663.
J. Lange, K. Georg
and M. Lappe (2006). Visual perception of biological motion by form: A
template-matching analysis. Journal of Vision, 6(8):836-849.
J. Lange & M.
Lappe (2006). A model of biological motion perception from configural form
cues. Journal of Neuroscience, 26(11):2894-2906.
L. Michels, M. Lappe
& L. M. Vaina (2005). Visual areas involved in the perception of human
movement from dynamic form analysis. NeuroReport, 16(10):1037-1041.
de Lussanet, M. H.
E., Fadiga, L., Michels, L., Seitz, R. J., Kleiser, R. & Lappe, M. (2008).
Interaction of Visual Hemifield and Body View in Biological Motion Perception.
European Journal of Neuroscience 27:514-522.
Michels,
L., Kleiser, R., de Lussanet, M. H., Seitz, R. J., Lappe, M. (2009). Brain
activity for peripheral biological motion in the posterior superior temporal
gyrus and the fusiform gyrus: Dependence on visual hemifield and view.
NeuroImage 45:151-159
Visual navigation
and spatial orientation
How do we perceive and
control our movements within the environment? When we walk around, ride a bike,
or drive a car the image of the world that we see is moving. The pattern of
image motion experienced during self-motion is called optic flow. Optic flow is
used be the visual system to control our movement through space. We try to
understand how optic flow is analyzed by the visual system and how it is used
to estimate self-motion. We combine a computer model of how populations of
neurons in the brain process and analyze complex flow patterns with
experimental studies that look at the perception of optic flow by human
subjects. We have looked at how our brain combines the many visual, motor, and
vestibular cues to self-motion that it has available. A specific questions is
the relationship between optic flow an eye movements, i.e., how optic flow
induces eye movements, how eye movements affect the structure of the optic flow
that arrives in the eye, and how neurons may exploit the statistical structure
of the flow to optimally extract self-motion. We also study how travel distance
can be estimated from the visual input during self-motion. Next to our interest
in the underlying mechanisms of optic flow analysis in the visual system we
also try to put these mechanisms to work in technical settings of computer
vision and virtual reality.
Financial Support:
DFG
Sonderforschungsbereich 509 'Neurovision' an der
Ruhr-Universität Bochum (1996-2004)
DFG LA952/3
(2005-2008)
EU FET-Projekt
ECoVision (2002-2005)
EU FET-Projekt
Drivsco (2005-2009)
Partners:
Drivsco Consortium
ECoVision Konsortium
Laurence Harris, York University,
Toronto
Frank Steinicke, Informatik, WWU
Münster
Selected Publications:
Calow, D.
and Lappe, M. (2008). Efficient encoding of natural optic flow. Network:
Computation in Neural Systems. 19(3):183-212.
Calow, D.
& Lappe, M. (2007). Local statistics of retinal optic flow for self-motion
through natural sceneries. Network: Computation in Neural Systems
18(4):343-374.
M. Lappe, M. Jenkin,
L. R. Harris (2007). Travel distance estimation from visual motion by leaky
Path integration. Article DOI: 10.1007/s00221-006-0835-6. Exp. Brain Res.
180:35-48.
H. Frenz & M.
Lappe (2005). Absolute travel distance from optic flow. Vision Research,
45(13): 1679-1692.
M. Lappe, F. Bremmer
& A. V. van den Berg (1999). Perception of self-motion from visual flow.
Trends in Cognitive Sciences, 3:329-336.
Lappe, M.
(2000). Computational mechanisms for optic flow analysis in primate cortex. In
M. Lappe, editor, Neuronal Processing of Optic Flow, Int.Rev.Neurobiol., 44:235-268.
Visual attention,
object/category recognition and its cognitive control
Vision provides a rich
collection of information about our environment. If we have to orient ourselves
in a novel visual environment or have to search for a person we have to attend
to certain aspects in the scene. There are a number of theories and models of
attention each on different levels of abstraction. In this research project, we
explore i) the mechanism of attention and ii) how attention emerges. Perception
is considered as an active process: planing stages in the frontal areas modify
perception in early stages to construct the needed information from the
environmental input. We persue a model driven approach to explore different
levels of attention in object and category recognition. The scope is on
building functional models of cortical and subcortical areas in the primate
brain based on physiological and anatomical findings. The function of the
prefrontal cortex and basal ganglia will be an integral part of these models.
The validity of the models should also be demonstrated by testing their
performance on real world category/object recognition tasks.
Financial Support:
DFG HA2630/2-1
(2000-2002)
DFG HA2630/3
(2006-2008)
Selected Publications:
Vitay,
J., Hamker, F. H. (2008). Sustained activities and retrival in a computational
model of perirhinal cortex. Journal of Cognitive Neuroscience, 20:11, pp.
1993-2005.
Hamker, F.H.,
Wiltschut, J. (2007). Hebbian learning in a model with dynamic rate-coded
neurons: an alternative to the generative model approach for learning receptive
fields from natural scenes. Network, Computation in Neural Systems, 18:
249-266.
Hamker, F. H.,
Zirnsak, M. (2006). V4 receptive field dynamics as predicted by a systems-level
model of visual attention using feedback from the frontal eye field. Neural
Networks, 19:1371-1382.
Hamker, F. H.
(2006). Modeling feature-based attention as an active top-down inference
process. BioSystems, 86:91-99.
F. H. Hamker (2005).
The Reentry Hypothesis: The Putative Interaction of the Frontal Eye Field,
Ventrolateral Prefrontal Cortex, and Areas V4, IT for Attention and Eye
Movement. Cerebral Cortex, 15:431-447.
F. H. Hamker (2004).
A dynamic model of how feature cues guide spatial attention. Vision Research,
44:501-521.
