Search

The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but, in such theories, divisions into categories of concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, arousal). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. Electroencephalography (EEG) is on par with the temporal resolution of cognitive behavior and source reconstruction. Using high-density set-ups, it is able to yield a spatial resolution in the scale of millimeters, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in the scalp domain has been detected. Traditional methods will potentially miss out on the fine-grained effects of semantic features as they are possibly obscured by the mixing of source activity due to volume conduction. For this reason, we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low arousal words, and in this way shed light on how word categories are represented in the brain.

Access to meaning from speech input is a high-speed process. However, models of word comprehension stipulate that words must be analyzed at phonological, grammatical and semantic levels. Here we used event-related potentials (ERPs) to compare on-line semantic categorization (natural/manufactured) and grammatical gender categorization (masculine/feminine) of spoken monosyllabic French noun pairs. Twenty four native French speakers were instructed to spot pairs of nouns in which both stimuli pertained to a category specified prior to each block. They could make a decision either after processing the first noun (i.e., ignore the second, Release condition), or after processing both (Hold condition). Virtually identical N4 components affecting Hold and Release ERPs in relation to category expectations were elicited by the first noun in both the tasks. Their topography was intermediary between that of the N400 and that of the Left Anterior Negativity. Despite shorter Release reaction times (RTs) for gender decisions than semantic ones, Release and Hold ERPs diverged 84 ms earlier in the semantic context than in the gender context, indicating faster onset for the processing of meaning. Conversely, the offset of Hold/Release differences was observed 42 ms earlier in the gender task than in the semantic task, suggesting an earlier completion for gender categorization. In sum, electrophysiological differences induced by semantic operations commenced earlier but recovered later than those relating to grammatical gender analysis. In convergence with previous Lateralized Readiness Potential and N200 studies, our results suggest that conscious semantic access can precede access to other types of lexical information during word comprehension in highly controlled conditions. [Copyright &y& Elsevier]

How does the human brain combine a finite number of words to form an infinite variety of sentences? According to the Memory, Unification and Control (MUC) model, sentence processing requires long-range feedback from the left inferior frontal cortex (LIFC) to left posterior temporal cortex (LPTC). Single word processing however may only require feedforward propagation of semantic information from sensory regions to LPTC. Here we tested the claim that long-range feedback is required for sentence processing by reducing visual awareness of words using a masking technique. Masking disrupts feedback processing while leaving feedforward processing relatively intact. Previous studies have shown that masked single words still elicit an N400 ERP effect, a neural signature of semantic incongruency. However, whether multiple words can be combined to form a sentence under reduced levels of awareness is controversial. To investigate this issue, we performed two experiments in which we measured electroencephalography (EEG) while 40 subjects performed a masked priming task. Words were presented either successively or simultaneously, thereby forming a short sentence that could be congruent or incongruent with a target picture. This sentence condition was compared with a typical single word condition. In the masked condition we only found an N400 effect for single words, whereas in the unmasked condition we observed an N400 effect for both unmasked sentences and single words. Our findings suggest that long-range feedback processing is required for sentence processing, but not for single word processing. • We disrupted long-distance feedback processing by means of visual masking. • With EEG, we compared masked/unmasked processing of sentences and single words. • We observed an N400 effect for both unmasked sentences and single words. • We only observed an N400 effect for masked single words, not for sentences. • Long-distance feedback is crucial for sentence processing. [ABSTRACT FROM AUTHOR]

The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but, in such theories, divisions into categories of concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, arousal). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. Electroencephalography (EEG) is on par with the temporal resolution of cognitive behavior and source reconstruction. Using high-density set-ups, it is able to yield a spatial resolution in the scale of millimeters, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in the scalp domain has been detected. Traditional methods will potentially miss out on the fine-grained effects of semantic features as they are possibly obscured by the mixing of source activity due to volume conduction. For this reason, we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low arousal words, and in this way shed light on how word categories are represented in the brain.