Understanding the distinction between conscious and unconscious cognition remains a priority in psychology and neuroscience. A comprehensive neurocognitive account of conscious awareness will not be possible without a sound framework to isolate and understand unconscious information processing. Here we provide a brain-based framework that allows the identification of unconscious processes even with null effects on behaviour.

Does the human brain elicit patterns of activity associated with the meaning of words in the absence of conscious awareness? Do such non-conscious semantic representations generalize across languages? This study aimed to address these questions using fMRIbased multivariate pattern analysis (MVPA) in a masked word paradigm. Animal and non-animal words were visually presented in two different languages (i.e. Spanish and Basque). Words were presented very briefly and were masked. On each trial, participants identified the semantic category and provided a visibility rating of the word. A support vector machine (SVM) was used to decode word category from multivoxel patterns of BOLD responses in seven canonical semantic regions of a left-lateralized network that were prespecified based on a previous meta-analysis. We show that the semantic category of non-conscious words (i.e. associated with null visual experience and chance-level discrimination performance) can be significantly decoded from BOLD response patterns. For Spanish, such discriminative patterns of BOLD responses were consistently found in inferior parietal lobe, dorsomedial prefrontal cortex, inferior frontal gyrus and posterior cingulate gyrus. While for Basque, these were found in ventromedial temporal lobe and posterior cingulate gyrus. All of the areas identified have previously been associated with semantic processing in studies involving animals-tools and animals-artifacts contrasts. In conscious trials, such patterns were found to be distributed over all seven regions of the semantic network in both Spanish and Basque. However, we found no evidence of acrosslanguage generalization. These results demonstrate that even in the absence of conscious awareness and lack of behavioural sensitivity to the words, putative semantic brain areas carry information related to the meanings of the words. The generalization of semantic representations across languages, however, may require deeper conscious semantic access.

How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain–Computer Interfaces (BCIs) based on NIRS.