Review
The motion aftereffect reloaded

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The motion aftereffect is a robust illusion of visual motion resulting from exposure to a moving pattern. There is a widely accepted explanation of it in terms of changes in the response of cortical direction-selective neurons. Research has distinguished several variants of the effect. Converging recent evidence from different experimental techniques (psychophysics, single-unit recording, brain imaging, transcranial magnetic stimulation, visual evoked potentials and magnetoencephalography) reveals that adaptation is not confined to one or even two cortical areas, but occurs at multiple levels of processing involved in visual motion analysis. A tentative motion-processing framework is described, based on motion aftereffect research. Recent ideas on the function of adaptation see it as a form of gain control that maximises the efficiency of information transmission at multiple levels of the visual pathway.

Introduction

After prolonged adaptation to a visual scene moving in a certain direction, observation of a stationary scene evokes an experience of motion in the opposite direction. This ancient perceptual effect, called the motion aftereffect (MAE) 1, 2, is easy to generate and very robust. Research on the MAE has had a crucial role in the development of theories relating motion perception to neural activity in the brain. Sutherland [3] was the first to suggest a simple neural explanation of the MAE, inspired by Hubel and Wiesel’s [4] discovery of direction-selective cortical cells in the cat:

‘…the direction in which something is seen to move might depend on the ratios of firing in cells sensitive to movement in different directions, and after prolonged movement in one direction a stationary image would produce less firing in the cells which had just been stimulated than normally, hence movement in the opposite direction would be seen to occur’ (p.227 in Ref. [3]).

In 1963, Barlow and Hill [5] reported adaptation-induced changes in responsiveness in single cells in the rabbit retina, and Sutherland’s [3] ratio account of the effect gained wide acceptance. Later discoveries of adaptation effects in cat and primate cortex encouraged the general view that the origin of the MAE was probably adaptation in motion-selective cells in primary visual cortex. The essential principle of population coding in the MAE is still universally accepted, but discoveries made possible with the introduction of new experimental techniques indicate that important changes to theoretical explanations of the MAE are required. These discoveries include work in human psychophysics 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, primate physiology 25, 26, 27, 28, human neuroimaging 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, human electrophysiology (Visual Evoked Potentials [VEPs]), magnetoencephalography (MEG) 39, 40, 41, 42, 43, 44 and transcranial stimulation 45, 46. Results indicate that the MAE is an amalgam of neural adaptation at several visual cortical sites. This short review offers a fresh appraisal of the MAE and its neural basis, based on this recent research.

Section snippets

Psychophysical evidence: how many aftereffects?

The classical MAE seen in natural viewing conditions involves a static test pattern; after one observes movement for a while, such as a waterfall or the view from a moving vehicle, subsequently viewed stationary objects seem to move. We shall refer to this effect as the static MAE or SMAE. In the late twentieth century, laboratory researchers began using dynamic test patterns such as dynamic visual noise or counter-phase flicker to study the aftereffects of motion adaptation. A dynamic visual

Single-unit recordings

Important recent studies by Kohn and Movshon 25, 26 measured adaptation-induced changes in the response of direction-selective cells in macaque MT (previously reported in Refs 27, 28). One of their aims was to determine whether adaptation effects occur at the level of MT, or are inherited in responses fed forward from V1 cells. In the latter case, the spatial extent of adaptation in MT should be limited by the smaller size of receptive fields in V1. Kohn and Movshon [25] did indeed find

Human brain imaging

Results from recent functional magnetic resonance imaging (fMRI) studies of human motion processing support a functional distinction between at least two populations of motion sensors, responsive respectively to first- and second-order motion, but these populations do not seem to occupy anatomically segregated locations. Ashida et al. [29], for instance, employed a fMRI adaptation paradigm: when repeated presentation of similar stimuli reduced the blood oxygen level dependent (BOLD) response,

Human transcranial stimulation studies

Stewart and colleagues [43] were the first to succeed in reducing the duration of SMAE (but not of the colour aftereffect) with magnetic stimulation over MT, indicating a role for MT in the SMAE. Théoret et al. [46] applied repetitive transcranial magnetic stimulation (rTMS) over MT during a storage period in between MAE adaptation and testing. Stimulation shortened the duration of the subsequent MAE, compared to a control condition without rTMS. There was little effect of stimulation to V1 on

VEPs and MEG

Which components of the VEP reflect activity related specifically to the MAE? Human electrophysiological studies have shown that the amplitude of a negativity peak at ∼200 ms (N2) is affected by motion adaptation [40], but it is not clear whether this effect is direction selective. More recently, Kobayashi et al. [41] found a significant bilateral increase of a positive component at ∼160 ms (P160) in the occipitotemporal region after motion adaptation. They also observed a laterally biased effect

Conclusions

Figure 4 is a simple functional diagram that attempts to summarize the main stages of visual motion processing from the perspective of the motion aftereffect research reviewed here. Motion sensors in the earliest cortical areas (V1, V2 and V3) feed into a computation underlying the perception of ‘static’ and also into a local motion integration stage. First-order motion sensors tuned to slow velocities contribute to ‘static’ computations, whereas first-order sensors tuned to higher velocities

Acknowledgements

This work was supported by the Wellcome Trust (WT082816MA) and the CARIPARO foundation (2005).

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