“If the only tool you have is a hammer, you tend to see every problem as a nail.” - Abraham Maslow
In Jabar & Anderson (2015), we highlight that probable orientations are robustly linked to better perceptual precision. We also advanced a hypothesis: Perhaps the result of probability learning is to change the neural tuning of visual processing neurons, leading to these precision changes. We do know from animal neurophysiology that orientation information is processed early in the primary visual cortex (V1). But how would we see if probability affects such processing?
Flip through any introductory text to cognition and you will invariably find a section comparing one form of neurophysiological investigation to another. Among the prime contenders are fMRI and EEG, and the trade-off between them is of temporal and spatial ‘resolution’. On one hand, fMRI directly measures changes in blood oxygenation levels in a 3d space, and these changes can be localized well. The drawback, owing to its reliance on an indirect measure of brain activity, and that blood flow adjustments take time, is a loss in temporal precision. By conrast, EEG measures voltage changes which stem from neural activity. In that, it captures changes in brain activity quickly. The downside? The electrical activity travels through the brain, to the scalp, and it is difficult to ascertain where the signal recorded originates. Coarse localization is simple enough: Visual processing is lateralized, and presenting stimuli in the left or right visual field causes an increase in activity measureable at the contralateral occipital electrode (as compared to the ipsilateral one). Localization at a finer scale is more difficult.
Difficult, but not impossible. Neurons receive inputs from their dendrites and depolarise along their axon. Their specific anatomical orientation determines the polarity of the voltage measured across a location, and acts as a ‘dipole’. With thousands of neurons with similiar orientation, these dipoles summed yield measurable signals at the scalp. The V1 cortex has an anatomical peculiarity that makes its dipoles, and thus the resulting voltage changes, unique. Orientation selective neurons in V1 are found along the banks of the calcarine cortex. Neurons sensitive to upper visual field stimuli are located on the lower bank, with the reverse mapping for lower-field stimuli. As explored by Di Russo and colleagues (2002), this leads to the situation where early visual components in the EEG recordings are flipped in polarity when comparing upper and lower field stimuli. This location-dependent ‘C1’ component is unique to the V1 cortex. The image below is from the Di Russo paper, localizing the dipoles elicited by stimuli in different locations.
Can we use this component to see the effects of probability on the V1 cortex? We have launched an investigation into the topic, and the preliminary data we have obtained is encouraging. We showed participants Gabor stimuli on one of four quadrants, with some orientations being more likely than others. With a small sample, we already seem to be seeing a component that looks like a C1.
Blue = High probability orientation, Red = Low.
Solid line = lower field stimuli. Dotted = Upper.
That first component (hence the name ‘C1’), might be an indicator of V1 activity for 3 main reasons:
1) It occurs early (time zero is stimuli onset).
2) It is maximal in occipital (Oz) and occipito-parietal (POz) electrodes. At least as compared to central (Cz) elecrodes.
3) It flips in polarity depending on stimulus location.
And of course, we might as well look at the whole set of scalp recordings across time!
Top left plot is when the stimuli was shown on the top left, etc.
Red reflects positive going voltage changes, blue is negative.
The plot in the middle represents the average Oz amplitude at the timepoint indicated.
Green marker represents time when Gabor was shown (0ms to 50ms).
Will probability affect the C1 signal? What would that mean? Stay tuned for updates!
Di Russo, F., Martínez, A., Sereno, M. I., Pitzalis, S., & Hillyard, S. A. (2002). Cortical sources of the early components of the visual evoked potential. Human brain mapping, 15(2), 95-111.