TWO MOTION SENSOR POPULATIONS, AS REVEALED BY TEST PATTERN TEMPORAL

FREQUENCY

H elm h o ltz In stitu u t

Scho o l fo r A u to no m o u s Sy stem s R esearch

Universiteit Utrecht

Neuroethology GroupPadualaan 8

NL-3583 CH UtrechtNetherlands

M.J.vanderSmagt@bio.uu.nl

M.J. van der Smagt, F.A.J. Verstraten & W.A. van de Grind

Introduction Motion aftereffects (MAEs) tested with stationary test

patterns, such as Static Visual Noise (SVN) occur for adaptation speeds up to about 25 deg/s.

It was shown recently1 that MAEs tested with Dynamic Visual Noise (DVN) occur for much higher adaptation velocities (up to 80 deg/s).

‘Static MAEs’ are dominant for lower adaptation speeds, whereas ‘dynamic MAEs’ are dominant in the high speed range. (figure 1)

Transparent 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.

Transparent motion containing one fast and one slow velocity results in a transparent MAE2 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.

SVN 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)

At least two temporal channels (‘sustained’ and ‘transient’) have been shown to exist in human motion vision.3

Here we examine the effect of test-pattern-refresh-frequency on the MAE, in relation to adaptation speed.

Figure 1MAE 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 2The space-time plots give an example of adaptation and test stimuli that lead to the transparent MAE (ref 2.)

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Methods The 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):

Slow : 1.3 deg/s and 4 deg/sFast : 12 deg/s and 36 deg/sMixed : 4 deg/s and 12 deg/s

The test stimulus consisted of DVN of which the refresh frequency was varied across trials from 0 Hz (SVN) to 90 Hz.

Three 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.

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Figure 3 Example of the adaptation stimulus (left) and space-time plots of the some of the test-pattern-refresh-frequencies (right)

Results Irrespective 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.

In 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.

Around 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. (r2 mixed > 0.98; r2 slow < 0.75; r2 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.

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Conclusions Adaptation to higher speeds is revealed in the MAE when

DVN patterns with refresh frequencies above 20 Hz are used.

Adaptation to lower speeds is revealed in the MAE when SVN patterns or DVN patterns with refresh frequencies below 20 Hz are used.

The 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 channels3 in motion vision.

However, temporal frequency alone cannot explain all the differences between the slow and fast channel (see below).

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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 storyTemporal Frequency cannot be the whole story DVN test patterns are temporally broad-band

and high refresh frequencies are thus temporal cut-off frequencies.

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).

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).

Although there are no low TFs in this type of test stimulus, a recurring pattern is clearly apparent to the observers.

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.

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References1. Verstraten FAJ, van der Smagt MJ & van de Grind WA

(1998) Aftereffect of high speed motion. Perception 27, 47-59.

2. 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, 149-162.

- Anderson SJ & Burr DC (1985) Spatial and temporal selectivity of the human motion detection system. Vision Research 25, 1147-1154.- Hess RF & Snowden RJ (1992) Temporal properties of human visual filters: number, shapes and spatial covariation. Vision Research 32, 47-59.

AcknowledgementsMS is supported by the Netherlands organization for scientific research (NWO-ALW). FV is a fellow of the Royal Academy of arts and sciences (KNAW).