The role of alpha oscillations in spatial attention: limited evidence for a suppression account

Abstract

Covert spatial attention allows us to prioritize visual processing at relevant locations. A fast growing literature suggests that alpha-band (8-12 Hz) oscillations play a key role in this core cognitive process. It is clear that alpha-band activity tracks both the locus and timing of covert spatial orienting. There is limited evidence, however, for the widely embraced view that alpha oscillations suppress irrelevant visual information during spatial selection. Extant evidence is equally compatible with an account in which alpha activity enables spatial selection through signal enhancement rather than distractor suppression. Thus, more work is needed to characterize the computational role of alpha activity in spatial attention.

Figure 1.. Reconstructing alpha channel tuning functions with an inverted encoding model.

Inverted encoding models (IEMs) are a powerful tool for reconstructing population-level representations from aggregate measures of neural activity (e.g. EEG or fMRI). We and others have used the IEM approach to reconstruct spatially selective channel tuning functions (CTFs) from the scalp distribution of alpha-band power [11,12]. This approach assumes that alpha-band power at each electrode reflects the combined activity of a number of spatially tuned channels (or neuronal populations), each tuned for a different spatial position. Each curve shown in (a) shows the predicted response of each of eight spatially selective channels (C1-C8) across eight possible attended locations (right). The IEM analysis proceeds in two stages. In the training phase (b), we estimate the relative contribution of each channel to the response measured at each electrode (called channel weights). For a given attended location, the predicted response of each channel can be derived from the functions in (a). The example shown here is for an attended location at 45°. Because the predicted channel responses vary as a function of the attended location, by varying the attended location it is possible to estimate how strongly each channel contributes to activity measured at each electrode (i.e., the channel weights). In the test phase (c), using an independent set of data, we use the channel weights obtained in the training phase to estimate the profile of channel responses given the observed pattern of activity across the scalp. The example shown here is for an attended location at 135°. The resulting CTF reflects the spatial selectivity of population-level alpha-band activity. Adapted from [11].

Figure 2.. Alpha-band oscillations enable spatially and temporally resolved tracking of covert spatial attention.

(a) Alpha CTFs precisely track where attention is deployed following an attentional cue. Observers performed a spatial-cueing task (left). A central cue (a cross with one uniquely colored arm) directed observers to attend one of eight place holders. After delay, a target digit was presented among distractor letters and then masked with a pound sign. The plot on the right shows the reconstructed alpha CTFs across time for each of eight locations. A channel offset of 0° corresponds to the channel tuned for the cued location. The yellow band in each subplot shows the peak channel response, which tracked the cued location start around 300 ms after cue onset. (b) The time course of alpha-band CTFs track the latency of target selection during visual search. Observers searched for a target (a vertical or horizontal bar) among distractors and reported the orientation of the target (left). The plots on the right show the selectivity of target-related CTFs as for easy and hard search (upper) and as a function of response times regardless of search condition (lower). Spatially selective activity that tracked the target position emerged earlier during easy search than during hard search, and earlier on trials with fast RTs than on trials with slow RTs. Adapted from [11].