Prof. Fang Fang’s group combined EEG and tACS to investigate the role of alpha oscillations in feature binding
In the external visual world, objects are composed of different visual features, such as color, motion and form. Those features are processed by segregated visual pathways and represented by specialized brain areas of cerebral cortex. However, our perception of the visual world is a whole, rather than isolated features. Therefore, how the visual system integrates isolated features together to create a unified representation of an object, which is also referred to as the binding problem, is a fundamental challenge for our visual system. The binding problem is one of the most puzzling and fascinating issues that brain and cognitive sciences have ever faced.
Figure 1. An illustration of the binding problem, from the website.
Neural oscillations are proposed to be a possible mechanism for feature binding - visual features belonging to the same object are coded through synchronous firing of neurons. In particular, gamma oscillations have been reported as a potential mechanism to solve this problem. However, gamma oscillations usually reflect local neural activity, whereas feature binding might involve interregional recurrent connectivity. Therefore, the role of neural oscillations in feature binding is still unclear.
Here, Fang Fang’s group combined electroencephalogram (EEG) and transcranial alternating current stimulation (tACS), and employed a bi-stable color-motion binding stimulus (Wu et al., 2004) to investigate the causal relationship between neural oscillations and feature binding. Subjects’ perception of the stimulus switched between the physical binding and the illusory (active) binding states.
Figure 2. Illustrations of EEG and tACS.
In the EEG experiment, researchers found that individual alpha power was negatively correlated with the time proportion of the active binding state. In addition, subjects’ perceptual switch rate was positively correlated with their individual alpha frequency (IAF).
Figure 3. EEG results. (A) Group averaged brain topographies of power differences in different bands from top and back views. From left to right are topographies in the theta (4-7 Hz), alpha (7-14 Hz), beta (15-30 Hz), and gamma (30-60 Hz) bands. (B) Group averaged brain topography of the alpha peak power difference. (C) Group averaged FFT power spectra for the physical binding state (light gray line) and the active binding state (dark gray line). (D) Group averaged powers in the theta, alpha, beta, and gamma bands for the two binding states. Error bars represent 1 SEM calculated across subjects.
In the tACS experiment, they found that with the entrainment of alpha oscillations by tACS, selectively changing alpha oscillations could shape subjects’ perceptual states of the color-motion binding. On one hand, applying tACS at IAF could effectively decrease the time proportion of the active binding state. On the other hand, delivering tACS at different temporal frequencies in the alpha band could change subjects’ perceptual switch rates - tACS at a higher frequency led to a faster perceptual switch through shortening perceptual epochs of the active binding.
Figure 4. tACS results. (A) Percentages of perceptual state time for the physical and active binding in the sham stimulation condition and the tACS condition. (B) Perceptual switch rates under tACS at IAF, IAF-2Hz, and IAF+2Hz. (C) Averaged durations of perceptual epochs for the physical and active binding at the three tACS frequencies. Error bars represent 1 SEM calculated across subjects.
Zhang Y.#*, Zhang Y.#, Cai P., Luo H., and Fang F.* (2019) The causal role of alpha oscillations in feature binding. Proceedings of the National Academy of Sciences of the United States of America. (# co-first authors) (in press)
Wu D. A., Kanai R., and Shimojo S. (2004) Vision: steady-state misbinding of colour and motion. Nature, 429, 262.