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M A Giese

Publications and source records attributed to M A Giese.

4 recordsLinked to original sources
PubMed2002

Measurement of generalization fields for the recognition of biological motion.

The human visual system processes complex biological motion stimuli with high sensitivity and selectivity. The characterization of spatio-temporal generalization in the perception of biological motion is still a largely unresolved problem. We present an experiment that investigates how the visual system responds to motion stimuli that interpolate spatio-temporally between natural biological motion patterns. Inspired by analogous studies in stationary object recognition, we generated stimuli that interpolate between natural perceptual categories by morphing. Spatio-temporal morphs between natural movement patterns were obtained with a technique that allows to calculate linear combinations of spatio-temporal patterns. The weights of such linear combinations define a linear metric space over the set of generated movement patterns, so that the spatio-temporal similarity of the motion patterns can be quantified. In our experiments, we found smooth and continuous variation of the categorization probabilities with the weights of the prototypes in the morphs. For bipedal locomotion patterns we could accurately predict the perceived properties of the morphs by linear combinations of the perceived properties of the prototypes. Such predictions were not possible for morphs between locomotion and very dissimilar movements. We conclude that the visual system shows generalization within classes of motion patterns with similar basic structure, such as bipedal locomotion.

Adult↗
PubMed1996

Identification of the nonlinear state-space dynamics of the action-perception cycle for visually induced postural sway.

Human subjects standing in a sinusoidally moving visual environment display postural sway with characteristic dynamical properties. We analyzed the spatiotemporal properties of this sway in an experiment in which the frequency of the visual motion was varied. We found a constant gain near 1, which implies that the sway motion matches the spatial parameters of the visual motion for a large range of frequencies. A linear dynamical model with constant parameters was compared quantitatively with the data. Its failure to describe correctly the spatiotemporal properties of the system led us to consider adaptive and nonlinear models. To differentiate between possible alternative structures we directly fitted nonlinear differential equations to the sway and visual motion trajectories on a trial-by-trial basis. We found that the eigenfrequency of the fitted model adapts strongly to the visual motion frequency. The damping coefficient decreases with increasing frequency. This indicates that the system destabilizes its postural state in the inertial frame. This leads to a faster internal dynamics which is capable of synchronizing posture with fast-moving visual environments. Using an algorithm which allows the identification of essentially nonlinear terms of the dynamics we found small nonlinear contributions. These nonlinearities are not consistent with a limit-cycle dynamics, accounting for the robustness of the amplitude of postural sway against frequency variations. We interpret out results in terms of active generation of postural sway specified by sensory information. We derive also a number of conclusions for a behavior-oriented analysis of the postural system.

Humans↗
PubMed1994

Frequency dependence of the action-perception cycle for postural control in a moving visual environment: relative phase dynamics.

When standing human subjects are exposed to a moving visual environment, the induced postural sway displays varying degrees of coherence with the visual information. In our experiment we varied the frequency of an oscillatory visual display and analysed the temporal relationship between visual motion and sway. We found that subjects maintain sizeable sway amplitudes even as temporal coherence with the display is lost. Postural sway tended to phase lead (for frequencies below 0.2 Hz) or phase lag (above 0.3 Hz). However, we also observed at a fixed frequency, highly variable phase relationships in which a preferred range of phase lags is prevalent, but phase jumps occur that return the system into the preferred range after phase has begun drifting out of the preferred regime. By comparing the results quantitatively with a dynamical model (the sine-circle map), we show that this effect can be understood as a form of relative coordination and arises through an instability of the dynamics of the action-perception cycle. Because such instabilities cannot arise in passively driven systems, we conclude that postural sway in this situation is actively generated as rhythmic movement which is coupled dynamically to the visual motion.

Humans↗