We often fail to see something that at other times is readily detectable. Because the visual stimulus itself is unchanged, this variability in conscious awareness is likely related to changes in the brain. Here we show that the phase of EEG ␣ rhythm measured over posterior brain regions can reliably predict both subsequent visual detection and stimulus-elicited cortical activation levels in a metacontrast masking paradigm. When a visual target presentation coincides with the trough of an ␣ wave, cortical activation is suppressed as early as 100 ms after stimulus onset, and observers are less likely to detect the target. Thus, during one ␣ cycle lasting 100 ms, the human brain goes through a rapid oscillation in excitability, which directly influences the probability that an environmental stimulus will reach conscious awareness. Moreover, ERPs to the appearance of a fixation cross before the target predict its detection, further suggesting that cortical excitability level may mediate target detection. A novel theory of cortical inhibition is proposed in which increased ␣ power represents a "pulsed inhibition" of cortical activity that affects visual awareness.

Alpha oscillations are ubiquitous in the brain, but their role in cortical processing remains a matter of debate. Recently, evidence has begun to accumulate in support of a role for alpha oscillations in attention selection and control. Here we first review evidence that 8–12 Hz oscillations in the brain have a general inhibitory role in cognitive processing, with an emphasis on their role in visual processing. Then, we summarize the evidence in support of our recent proposal that alpha represents a pulsed-inhibition of ongoing neural activity. The phase of the ongoing electroencephalography can influence evoked activity and subsequent processing, and we propose that alpha exerts its inhibitory role through alternating microstates of inhibition and excitation. Finally, we discuss evidence that this pulsed-inhibition can be entrained to rhythmic stimuli in the environment, such that preferential processing occurs for stimuli at predictable moments. The entrainment of preferential phase may provide a mechanism for temporal attention in the brain. This pulsed inhibitory account of alpha has important implications for many common cognitive phenomena, such as the attentional blink, and seems to indicate that our visual experience may at least some times be coming through in waves.

The biased competition theory of selective attention has been an influential neural theory of attention, motivating numerous animal and human studies of visual attention and visual representation. There is now neural evidence in favor of all three of its most basic principles: that representation in the visual system is competitive; that both top-down and bottom-up biasing mechanisms influence the ongoing competition; and that competition is integrated across brain systems. We review the evidence in favor of these three principles, and in particular, findings related to six more specific neural predictions derived from these original principles.

Human subjects are extremely efficient at categorizing natural scenes, despite the fact that different classes of natural scenes often share similar image statistics. Thus far, however, it is unknown where and how complex natural scene categories are encoded and discriminated in the brain. We used functional magnetic resonance imaging (fMRI) and distributed pattern analysis to ask what regions of the brain can differentiate natural scene categories (such as forests vs mountains vs beaches). Using completely different exemplars of six natural scene categories for training and testing ensured that the classification algorithm was learning patterns associated with the category in general and not specific exemplars. We found that area V1, the parahippocampal place area (PPA), retrosplenial cortex (RSC), and lateral occipital complex (LOC) all contain information that distinguishes among natural scene categories. More importantly, correlations with human behavioral experiments suggest that the information present in the PPA, RSC, and LOC is likely to contribute to natural scene categorization by humans. Specifically, error patterns of predictions based on fMRI signals in these areas were significantly correlated with the behavioral errors of the subjects. Furthermore, both behavioral categorization performance and predictions from PPA exhibited a significant decrease in accuracy when scenes were presented up-down inverted. Together these results suggest that a network of regions, including the PPA, RSC, and LOC, contribute to the human ability to categorize natural scenes.

Functional magnetic resonance imaging (fMRI) of subjects attempting to detect a visual change occurring during a screen flicker was used to distinguish the neural correlates of change detection from those of change blindness. Change detection resulted in enhanced activity in the parietal and right dorsolateral prefrontal cortex as well as category-selective regions of the extrastriate visual cortex (for example, fusiform gyrus for changing faces). Although change blindness resulted in some extrastriate activity, the dorsal activations were clearly absent. These results demonstrate the importance of parietal and dorsolateral frontal activations for conscious detection of changes in properties coded in the ventral visual pathway, and thus suggest a key involvement of dorsal-ventral interactions in visual awareness.