Selected Abstracts

Optic Flow and Beyond. (2004). Vaina LM, Beardsley SA, Rushton S (Eds.), Synthese Library, Kluwer Academic Press.

Optic flow provides all the information necessary to guide a walking human or a mobile robot to its target. Over the past 50 years, a body of research on optic flow spanning the disciplines of neurophysiology, psychophysics, experimental psychology, brain imaging and computational modelling has accumulated. Today, when we survey the field, we find independent lines of research have now converged and many arguments have been resolved; simultaneously the underpinning assumptions of flow theory are being questioned and alternative accounts of the visual guidance of locomotion proposed. At this critical juncture, this volume offers a timely review of what has been learnt and pointers to where the field is going.

Chapters

Section 1: Optic flow - Neurophysiology & Psychophysics 1

1. Multiple Cortical Representations of Optic Flow Processing
Milena Raffi and Ralph M. Siegel 3

2. Optic Flow and Vestibular Self-Movement Cues: Multi-Sensory Interactions in Cortical Area MST
Charles J. Duffy and William K. Page 23

3. A Visual Mechanism for Extraction of Heading Information in Complex Flow Fields
Michael W. von Grünau and Marta Iordanova 45

4. Eye Movements and an Object-Based Model of Heading Perception
Ranxiao F. Wang and James E. Cutting 61

5. Short-Latency Eye Movements: Evidence for Rapid, Parallel Processing of Optic Flow
Fred A. Miles, C. Busettini, G. S. Masson, and D. S. Yang 79

6. Functional Neuroanatomy of Heading Perception in Humans
Lucia M. Vaina and Sergei Soloviev 109

7. The Event Structure of Motion Perception
Martin H. Fischer and Heiko Hecht 139

8. Modeling Observer and Object Motion Perception
Constance S. Royden 159

9. Linking Perception and Neurophysiology for Motion Pattern Processing: The Computational Power of Inhibitory Connections in Cortex
Scott A. Beardsley and Lucia M. Vaina 183

10. Circular Receptive Field Structures for Flow Analysis and Heading Detection
Jaap A.Beintema, Albert V.van den Berg, and Markus Lappe 223

11. Parametric Measurements of Optic Flow by Humans
José F. Barraza and Norberto M. Grzywacz 249

12. Fast Processing of Image Motion Patterns Arising from 3-D Translational Motion
Venkarataraman Sundareswaran, Scott A. Beardsley, and Lucia M. Vaina 273

13. On the Computation of Image Motion and Heading in a 3-D Cluttered Scene
Michael S. Langer and Richard Mann 289

14. From Optic Flow to Laws of Control
William H. Warren & Brett R. Fajen 307

15. Egocentric Direction and Locomotion
Simon K. Rushton 339

16. The Utility of not Changing Direction and the Visual Guidance of Locomotion
Simon K. Rushton and Julie M. Harris 363

17. Gaze Behaviors During Adaptive Human Locomotion: Insights into how Vision is used to Regulate Locomotion
Aftab E. Patla 383

18. How do We Control High Speed Steering?
John P. Wann and Richard M. Wilkie 401

19. Model-Based Control of Perception/Action
Jack M. Loomis and Andrew C. Beall 421

20. Neural Model for Biological Movement Recognition: A Neurophysiologically Plausible Theory
Martin A. Giese 443

21. Controlling Bipedal Movement Using Optic Flow
M. Anthony Lewis 471

Liu Z, Vaina LM. (1995). "Stimulus specific learning: a consequence of stimulus specific experiments?" Perception, 24.

Stimulus specificity is typical in perceptual learning studies -- subjects' improved performance after repeated trials under one stimulus condition, for a specific stimulus attribute A, does not transfer to a significantly different attribute B. From the viewpoint of "learning statistical properties of stimulus ensemble", stimulus specificity could mean that subjects have learned specific aspects of A, but since B is not a sample from the A ensemble, learning does not transfer from A to B. Thus stimulus specificity could be a special case but not necessarily a general rule. An alternative would be that learning is due to the specific experimental procedure, e.g., A alone is extensively tested before B is. We employed an experimental paradigm with an interleaved stimulus sequence A-A-B A-A-B A-A-B ... . Both A and B consisted of two noisy random dot motion stimuli presented sequentially. The mean direction of the dots was either 135 degrees or 165 degrees in A, and 315 degrees or 345 degrees in B. In a two-temporal-alternative forced-choice task, five subjects discriminated if the two directions in each condition were the same or different. Every subject's accuracy score for the 260 B trials was higher than that for the first 260 of the 520 A trials (90.92% vs 88.69%, F(1,4)=13.25, p<0.022). This implies that the learning rate for B was higher than that for A, indicating a positive transfer of A -> B, which is not predicted by a stimulus specific learning. Implications of this case study to perceptual learning in general will also be addressed.