In masking, a stimulus is rendered invisible through the presentation of a second stimulus shortly after the first. Over the years, authors have typically explained masking by postulating some early disruption process. In these feedforward-type explanations, the mask somehow “catches up” with the target stimulus, disrupting its processing either through lateral or interchannel inhibition. However, studies from recent years indicate that visual perception—and most notably visual awareness itself—may depend strongly on cortico-cortical feedback connections from higher to lower visual areas. This has led some researchers to propose that masking derives its effectiveness from selectively interrupting these reentrant processes. In this experiment, we used electroencephalogram measurements to determine what happens in the human visual cortex during detection of a texture-defined square under nonmasked (seen) and masked (unseen) conditions. Electro-encephalogram derivatives that are typically associated with reentrant processing turn out to be absent in the masked condition. Moreover, extrastriate visual areas are still activated early on by both seen and unseen stimuli, as shown by scalp surface Laplacian current source-density maps. This conclusively shows that feedforward processing is preserved, even when subject performance is at chance as determined by objective measures. From these results, we conclude that masking derives its effectiveness, at least partly, from disrupting reentrant processing, thereby interfering with the neural mechanisms of figure-ground segmentation and visual awareness itself.

To further our understanding of the function of conscious experience we need to know which cognitive processes require awareness and which do not. Here, we show that an unconscious stimulus can trigger inhibitory control processes, commonly ascribed to conscious control mechanisms. We combined the metacontrast masking paradigm and the Go/No-Go paradigm to study whether unconscious No-Go signals can actively trigger high-level inhibitory control processes, strongly associated with the prefrontal cortex (PFC). Behaviorally, unconscious No-Go signals sometimes triggered response inhibition to the level of complete response termination and yielded a slow down in the speed of responses that were not inhibited. Electroencephalographic recordings showed that unconscious No-Go signals elicit two neural events: (1) an early occipital event and (2) a frontocentral event somewhat later in time. The first neural event represents the visual encoding of the unconscious No-Go stimulus, and is also present in a control experiment where the masked stimulus has no behavioral relevance. The second event is unique to the Go/No-Go experiment, and shows the subsequent implementation of inhibitory control in the PFC. The size of the frontal activity pattern correlated highly with the impact of unconscious No-Go signals on subsequent behavior. We conclude that unconscious stimuli can influence whether a task will be performed or interrupted, and thus exert a form of cognitive control. These findings challenge traditional views concerning the proposed relationship between awareness and cognitive control and stretch the alleged limits and depth of unconscious information processing.

In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.

In texture segregation, an example of scene segmentation, we can discern two different processes: texture boundary detection and subsequent surface segregation [Lamme, V. A. F., Rodriguez-Rodriguez, V., & Spekreijse, H. Separate processing dynamics for texture elements, boundaries and surfaces in primary visual cortex of the macaque monkey. Cerebral Cortex, 9, 406–413, 1999]. Neural correlates of texture boundary detection have been found in monkey V1 [Sillito, A. M., Grieve, K. L., Jones, H. E., Cudeiro, J., & Davis, J. Visual cortical mechanisms detecting focal orientation discontinuities. Nature, 378, 492–496, 1995; Grosof, D. H., Shapley, R. M., & Hawken, M. J. Macaque-V1 neurons can signal illusory contours. Nature, 365, 550–552, 1993], but whether surface segregation occurs in monkey V1 [Rossi, A. F., Desimone, R., & Ungerleider, L. G. Contextual modulation in primary visual cortex of macaques. Journal of Neuroscience, 21, 1698–1709, 2001; Lamme, V. A. F. The neurophysiology of figure ground segregation in primary visual-cortex. Journal of Neuroscience, 15, 1605–1615, 1995], and whether boundary detection or surface segregation signals can also be measured in human V1, is more controversial [Kastner, S., De Weerd, P., & Ungerleider, L. G. Texture segregation in the human visual cortex: A functional MRI study. Journal of Neurophysiology, 83, 2453–2457, 2000]. Here we present electroencephalography (EEG) and functional magnetic resonance imaging data that have been recorded with a paradigm that makes it possible to differentiate between boundary detection and scene segmentation in humans. In this way, we were able to show with EEG that neural correlates of texture boundary detection are first present in the early visual cortex around 92 msec and then spread toward the parietal and temporal lobes. Correlates of surface segregation first appear in temporal areas (around 112 msec) and from there appear to spread to parietal, and back to occipital areas. After 208 msec, correlates of surface segregation and boundary detection also appear in more frontal areas. Blood oxygenation level-dependent magnetic resonance imaging results show correlates of boundary detection and surface segregation in all early visual areas including V1. We conclude that texture boundaries are detected in a feedforward fashion and are represented at increasing latencies in higher visual areas. Surface segregation, on the other hand, is represented in “reverse hierarchical” fashion and seems to arise from feedback signals toward early visual areas such as V1.

The visual system has the remarkable ability to integrate fragmentary visual input into a perceptually organized collection of surfaces and objects, a process we refer to as perceptual integration. Despite a long tradition of perception research, it is not known whether access to consciousness is required to complete perceptual integration. To investigate this question, we manipulated access to consciousness using the attentional blink. We show that, behaviorally, the attentional blink impairs conscious decisions about the presence of integrated surface structure from fragmented input. However, despite conscious access being impaired, the ability to decode the presence of integrated percepts remains intact, as shown through multivariate classification analyses of electroencephalogram (EEG) data. In contrast, when disrupting perception through masking, decisions about integrated percepts and decoding of integrated percepts are impaired in tandem, while leaving feedforward representations intact. Together, these data show that access consciousness and perceptual integration can be dissociated.