TWO MOTION SENSOR POPULATIONS, AS REVEALED BY TEST PATTERN TEMPORAL FREQUENCY Neuroethology Group Padualaan 8 NL-3583 CH Utrecht Netherlands

Slides:



Advertisements
Similar presentations
Hearing relative phases for two harmonic components D. Timothy Ives 1, H. Martin Reimann 2, Ralph van Dinther 1 and Roy D. Patterson 1 1. Introduction.
Advertisements

PSYC 1000 Lecture 21. Selective Attention: Stroop.
December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.
Motion and Ambiguity Russ DuBois. Ambiguity = the possibility to interpret a stimulus in two or more ways Q: Can motion play a part in our interpretation.
The Role of Speed Lines in Subtle Motion Judgments Jason Allen & Nestor Matthews Department of Psychology, Denison University, Granville OH USA Purpose:
Computer Vision – Fundamentals of Human Vision Hanyang University Jong-Il Park.
Neural mechanisms for timing visual events are spatially selective in real-world coordinates. David Burr, Arianna Tozzi, & Concetta Morrone.
Introduction ATTENTION SPANS MULTIPLE STIMULUS DIMENSIONS IN MACAQUE VISUAL CORTEX Jitendra Sharma*, James Schummers, Hiroki Sugihara, Paymon Hosseini.
December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.
Neuronal Adaptation to Visual Motion in Area MT of the Macaque -Kohn & Movshon 지각 심리 전공 박정애.
1 Computational Vision CSCI 363, Fall 2012 Lecture 21 Motion II.
DECREASED FLICKER SENSITIVITY WITH A SCANNED LASER DISPLAY. J.P. Kelly 1, H.L. Pryor, E.S. Viirre, T. Furness III. 1 Children's Hospital & Medical Center;
CONCLUSIONS * T WO ( SUB ) POPULATIONS OF MOTION SENSORS WITH WITH DIFFERENT CHARACTERISTICS MUST BE RESPONSIBLE FOR THIS DIFFERENCE IN MAE- DIRECTION.
1 Perception and VR MONT 104S, Fall 2008 Lecture 6 Seeing Motion.
Computational Vision CSCI 363, Fall 2012 Lecture 22 Motion III
MIB Transition for Real and After-image Seiichiro Naito, Ryo Shohara, & Makoto Katsumura Human and Information Science, Tokai University, JAPAN P20-61.
CORNELL UNIVERSITY CS 764 Seminar in Computer Vision Attention in visual tasks.
Motion Perception Arash Afraz. What is motion? Is it infinite number of stops in infinite number of moments? Motion as an old philosophical paradox!
Date of download: 6/29/2016 The Association for Research in Vision and Ophthalmology Copyright © All rights reserved. From: Judging the shape of.
Jenna Kelly1,2 & Nestor Matthews2
Journal of Vision. 2008;8(13):9. doi: / Figure Legend:
Journal of Vision. 2012;12(6):33. doi: / Figure Legend:
Volume 94, Issue 1, Pages (January 2008)
Journal of Vision. 2011;11(6):14. doi: / Figure Legend:
From: Motion processing with two eyes in three dimensions
Contribution of spatial and temporal integration in heading perception
Interacting Roles of Attention and Visual Salience in V4
A Motion Direction Map in Macaque V2
One-Dimensional Dynamics of Attention and Decision Making in LIP
Responses to Spatial Contrast in the Mouse Suprachiasmatic Nuclei
Perceptual Echoes at 10 Hz in the Human Brain
Volume 21, Issue 19, Pages (October 2011)
Robert J. Snowden, Alan B. Milne  Current Biology 
Volume 66, Issue 6, Pages (June 2010)
Braden A. Purcell, Roozbeh Kiani  Neuron 
Yu-Cheng Pei, Steven S. Hsiao, James C. Craig, Sliman J. Bensmaia 
Volume 74, Issue 5, Pages (June 2012)
Motion-Based Analysis of Spatial Patterns by the Human Visual System
Attentional Modulations Related to Spatial Gating but Not to Allocation of Limited Resources in Primate V1  Yuzhi Chen, Eyal Seidemann  Neuron  Volume.
Responses of Collicular Fixation Neurons to Gaze Shift Perturbations in Head- Unrestrained Monkey Reveal Gaze Feedback Control  Woo Young Choi, Daniel.
Volume 36, Issue 5, Pages (December 2002)
Volume 75, Issue 1, Pages (July 2012)
Anna Lisa Stöckl, David Charles O’Carroll, Eric James Warrant 
Liu D. Liu, Christopher C. Pack  Neuron 
Consequences of the Oculomotor Cycle for the Dynamics of Perception
Prediction of Orientation Selectivity from Receptive Field Architecture in Simple Cells of Cat Visual Cortex  Ilan Lampl, Jeffrey S. Anderson, Deda C.
Uma R. Karmarkar, Dean V. Buonomano  Neuron 
Volume 25, Issue 3, Pages (February 2015)
Volume 27, Issue 21, Pages e3 (November 2017)
Segregation of Object and Background Motion in Visual Area MT
Patrick Kaifosh, Attila Losonczy  Neuron 
Consequences of the Oculomotor Cycle for the Dynamics of Perception
Local and Global Contrast Adaptation in Retinal Ganglion Cells
Shin'ya Nishida, Alan Johnston  Current Biology 
Timescales of Inference in Visual Adaptation
Stephen V. David, Benjamin Y. Hayden, James A. Mazer, Jack L. Gallant 
The Normalization Model of Attention
Receptive Fields of Disparity-Tuned Simple Cells in Macaque V1
Short-Term Memory for Figure-Ground Organization in the Visual Cortex
Dynamic Shape Synthesis in Posterior Inferotemporal Cortex
Albert V. van den Berg, Jaap A. Beintema  Neuron 
Higher-Order Figure Discrimination in Fly and Human Vision
Volume 74, Issue 1, Pages (April 2012)
End-Stopping and the Aperture Problem
Volume 21, Issue 23, Pages (December 2011)
Judging Peripheral Change: Attentional and Stimulus-Driven Effects
Successive contrast Simultaneous contrast
Valerio Mante, Vincent Bonin, Matteo Carandini  Neuron 
Neurophysiology of the BOLD fMRI Signal in Awake Monkeys
Patrick Kaifosh, Attila Losonczy  Neuron 
Presentation transcript:

TWO MOTION SENSOR POPULATIONS, AS REVEALED BY TEST PATTERN TEMPORAL FREQUENCY Neuroethology Group Padualaan 8 NL-3583 CH Utrecht Netherlands M.J. van der Smagt, F.A.J. Verstraten & W.A. van de Grind

Introduction 4Motion aftereffects (MAEs) tested with stationary test patterns, such as Static Visual Noise (SVN) occur for adaptation speeds up to about 25 deg/s. 4It was shown recently 1 that MAEs tested with Dynamic Visual Noise (DVN) occur for much higher adaptation velocities (up to 80 deg/s). 4‘Static MAEs’ are dominant for lower adaptation speeds, whereas ‘dynamic MAEs’ are dominant in the high speed range. (figure 1) 4Transparent motion containing one fast and one slow velocity results in an MAE opposite to the fast vector when tested with DVN, and opposite the slow vector when SVN is used as test.

4Transparent motion containing one fast and one slow velocity results in a transparent MAE 2 when a new test pattern is used, which contains both SVN and DVN characteristics (see figure 2). Again the DVN-component of this combined test pattern seems to move rapidly opposite to the fast adaptation vector, and the SVN-component slowly opposite to the slow adaptation vector. 4SVN and DVN patterns differ from each other primarily in temporal characteristics. SVN is refreshed at 0 Hz, DVN at 45 Hz. (note these are temporal cut-off frequencies since these kind of patterns are temporally as well as spatially broad-band) 4At least two temporal channels (‘sustained’ and ‘transient’) have been shown to exist in human motion vision. 3 4Here we examine the effect of test-pattern-refresh-frequency on the MAE, in relation to adaptation speed.

Figure 1 MAE durations as function of type of test pattern (data from ref. 1). Arrows show the speed combinations that are used in the present experiment. Figure 2 The space-time plots give an example of adaptation and test stimuli that lead to the transparent MAE (ref 2.) space ADAPT space TEST component 1 component 2superimposed image adaptation speed (deg/s) fast mixed slow static test dynamic test

Methods 4The adaptation stimulus consisted of two superimposed Random-Pixel-Arrays, that moved transparently in orthogonal directions (figure 3) behind a circular window. Three speed combinations were used (see figure 1): 3Slow : 1.3 deg/s and 4 deg/s 3Fast : 12 deg/s and 36 deg/s 3Mixed : 4 deg/s and 12 deg/s 4The test stimulus consisted of DVN of which the refresh frequency was varied across trials from 0 Hz (SVN) to 90 Hz. 4Three observers adapted 45 s to the transparent motion while fixating on a dot in the center. The test stimulus was shown for 3 s, after which the screen turned grey and an arrow appeared, which was to be aligned with the MAE direction.

ADAPT 0 Hz space 30 Hz 90 Hz TEST Figure 3 Example of the adaptation stimulus (left) and space-time plots of the some of the test-pattern-refresh-frequencies (right)

Results 4Irrespective of the test-pattern-refresh-frequency, the observers indicated a more or less constant MAE direction for both the Slow and the Fast condition (figure 4). Although they reported weak MAEs and the task to be difficult for high refresh frequencies in the Slow, and low frequencies in the Fast condition. 4In the Mixed condition the MAE is more opposite the fast (12 deg/s) adaptation component for test frequencies > 20 Hz, while more opposite the slow (4 deg/s) adaptation vector for frequencies < 20 Hz. 4Around 20 Hz observers were inconsistent in their direction judgements, sometimes judging the MAE to be more opposite the slow adaptation vector, sometimes more opposite the fast. There is no smooth transition of MAE direction as function of test frequency (figure 5).

Figure 4 MAE directions as a function of test-pattern-refresh-frequency for a typical observer. Green diamonds are for the Slow condition, blue circles for the Fast and red squares for the Mixed condition. Curves are sigmoidal functions fitted through the data. (r 2 mixed > 0.98; r 2 slow < 0.75; r 2 fast <0.1) Figure 5 Same as figure 4 for three observers (individual data points) and only the Mixed condition. Note that there is no gradual change in the MAE direction with increasing test-pattern-refresh-frequency. test pattern refresh frequency (Hz) static ° 180°0° ADAPTTEST MS PH FV static test pattern refresh frequency (Hz) Mixed condition

Conclusions 4Adaptation to higher speeds is revealed in the MAE when DVN patterns with refresh frequencies above 20 Hz are used. 4Adaptation to lower speeds is revealed in the MAE when SVN patterns or DVN patterns with refresh frequencies below 20 Hz are used. 4The almost stepwise transition in the Mixed condition indicates the existence of two rather independent motion sensor populations which can be identified by their velocity (slow and fast) and temporal frequency (low and high) preference (see figure 6). This finding is compatible with the distinction between sustained and transient channels 3 in motion vision. 4However, temporal frequency alone cannot explain all the differences between the slow and fast channel (see below).

speed Figure 6 Schematic representation of the main findings. Temporal frequency sensitivity does not appear to increase with speed sensitivity. Rather there appear to be two separate populations: One tuned to higher speeds and higher TFs, the other to lower speeds and lower TFs.

Temporal Frequency cannot be the whole story 3 DVN test patterns are temporally broad-band and high refresh frequencies are thus temporal cut-off frequencies. 3 A Random-Pixel-Array of which each bright pixel becomes dark and each dark pixel bright every few frames is temporally more narrow-band (although still broadband spatially; see top right). 3 When such a contrast reversing pattern, with a high reversal frequency, is used as test pattern, adaptation to slow motion (as well as high speed adaptation) yields strong MAEs (see bottom right). 3 Although there are no low TFs in this type of test stimulus, a recurring pattern is clearly apparent to the observers. 3 Closer scrutiny of the MAE showed that for higher speeds the MAE appeared perceptually different from that at low adaptation speeds, again indicating that we are dealing with two separate motion channels PH adaptation speed (deg/s) static dynamic contrast reversing refresh & reversal freq. 30 HZ dynamic reversing space

References 1.Verstraten FAJ, van der Smagt MJ & van de Grind WA (1998) Aftereffect of high speed motion. Perception 27, van der Smagt MJ, Vertraten FAJ & van de Grind (1998) The aftereffect of transparent motion: integration or segregation is determined by the type of test pattern. Perception 27, 50b (suppl) 3.e.g.-Kulikowski JJ & Tolhurst DJ (1973) Psychophysical evidence for sustained and transient detectors in human vision. Journal of Physiology 232, Anderson SJ & Burr DC (1985) Spatial and temporal selectivity of the human motion detection system. Vision Research 25, Hess RF & Snowden RJ (1992) Temporal properties of human visual filters: number, shapes and spatial covariation. Vision Research 32, Acknowledgements MS is supported by the Netherlands organization for scientific research (NWO-ALW). FV is a fellow of the Royal Academy of arts and sciences (KNAW).