Cognitive control processes involving prefrontal cortex allow humans to overrule and inhibit habitual responses to optimize performance in new and challenging situations, and traditional views hold that cognitive control is tightly linked with consciousness. We used functional magnetic resonance imaging to investigate to what extent unconscious "no-go" stimuli are capable of reaching cortical areas involved in inhibitory control, particularly the inferior frontal cortex (IFC) and the pre-supplementary motor area (pre-SMA). Participants performed a go/no-go task that included conscious (weakly masked) no-go trials, unconscious (strongly masked) no-go trials, as well as go trials. Replicating typical neuroimaging findings, response inhibition on conscious no-go stimuli was associated with a (mostly right-lateralized) frontoparietal "inhibition network." Here, we demonstrate, however, that an unconscious no-go stimulus also can activate prefrontal control networks, most prominently the IFC and the pre-SMA. Moreover, if it does so, it brings about a substantial slowdown in the speed of responding, as if participants attempted to inhibit their response but just failed to withhold it completely. Interestingly, overall activation in this "unconscious inhibition network" correlated positively with the amount of slowdown triggered by unconscious no-go stimuli. In addition, neural differences between conscious and unconscious control are revealed. These results expand our understanding of the limits and depths of unconscious information processing in the human brain and demonstrate that prefrontal cognitive control functions are not exclusively influenced by conscious information.

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.

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