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Neural Correlates of Subliminal Language Processing.

Axelrod V, Bar M, Rees G, Yovel G - Cereb. Cortex (2014)

Bottom Line: The results of several functional magnetic resonance imaging studies have suggested that unconscious lexical and semantic processing is confined to the posterior temporal lobe, without involvement of the frontal lobe-the regions that are indispensable for conscious language processing.We found that subjectively and objectively invisible meaningful sentences and unpronounceable nonwords could be discriminated not only in the left posterior superior temporal sulcus (STS), but critically, also in the left middle frontal gyrus.We conclude that frontal lobes play a role in unconscious language processing and that activation of the frontal lobes per se might not be sufficient for achieving conscious awareness.

View Article: PubMed Central - PubMed

Affiliation: The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel UCL Institute of Cognitive Neuroscience.

No MeSH data available.


Related in: MedlinePlus

Probabilistic group-level masks of language network (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html) projected on a SPM template T1 image. The names of the regions are: 1—left angular gyrus, 2—left supramarginal gyrus, 3—left posterior STS, 4—left middle anterior temporal, 5—left anterior temporal, 6—left orbital inferior frontal gyrus, 7—left inferior frontal gyrus, 8—left middle frontal gyrus, 9—left superior frontal gyrus, 10—right posterior STS, 11—right middle anterior temporal. Regions’ IDs correspond to the IDs in Table 3.
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BHU022F2: Probabilistic group-level masks of language network (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html) projected on a SPM template T1 image. The names of the regions are: 1—left angular gyrus, 2—left supramarginal gyrus, 3—left posterior STS, 4—left middle anterior temporal, 5—left anterior temporal, 6—left orbital inferior frontal gyrus, 7—left inferior frontal gyrus, 8—left middle frontal gyrus, 9—left superior frontal gyrus, 10—right posterior STS, 11—right middle anterior temporal. Regions’ IDs correspond to the IDs in Table 3.

Mentions: For the language functional localizer (visible text) we estimated a GLM model (HRF boxcar function) with 2 regressors: meaningful sentences and nonwords. We used the contrast “meaningful sentences > nonwords” to identify a network of language processing regions for each participant (Fedorenko et al. 2010, 2011, 2012). To constraint individual GLM-defined functional activations we used probabilistic group-level functional masks (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html). Thus, for each mask region/participant based on individual “meaningful sentences > nonwords” GLM contrast we selected a contiguous cluster of most selective voxels (number of voxels is specified below). The regions defined by the masks are shown in Figure 2. There were 11 regions in total: 5 regions in the left parieto-temporal lobe (angular gyrus, supramarginal gyrus, posterior STS, middle anterior temporal gyrus, and anterior temporal gyrus), 2 regions in the right hemisphere of the temporal lobe (posterior STS, middle anterior temporal gyrus) and 4 regions in the left hemisphere of the frontal lobe (orbital inferior frontal gyrus, inferior frontal gyrus, middle frontal gyrus, superior frontal gyrus). Critically, as multivariate prediction is influenced by region of interest (ROI) size (e.g., Eger et al. 2008; Walther et al. 2009; Said et al. 2010) we ensured the ROIs of different regions were of an equal size of 100 voxels (1200 mm3). In additional analyses we also explored a range of different ROI sizes of 50 and 150 voxels. The ROI size could not be increased further since the size of probabilistic group-level functional masks (Fedorenko et al. 2010) of some of the regions (e.g., left superior frontal gyrus) was <200 voxels. Defining ROIs of equal size was undertaken using custom MATLAB code, where for each region/participant the code selected the contiguous cluster of voxels with the highest z-score values relating to the “meaningful sentences > nonwords” contrast in the independent localizer with visible stimuli (similar procedure had been previously applied for face-selective voxels here [Axelrod and Yovel 2012]). The list of the ROIs (100 voxels size) with their coordinates and average z-score values can be found in Table 3.Figure 2.


Neural Correlates of Subliminal Language Processing.

Axelrod V, Bar M, Rees G, Yovel G - Cereb. Cortex (2014)

Probabilistic group-level masks of language network (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html) projected on a SPM template T1 image. The names of the regions are: 1—left angular gyrus, 2—left supramarginal gyrus, 3—left posterior STS, 4—left middle anterior temporal, 5—left anterior temporal, 6—left orbital inferior frontal gyrus, 7—left inferior frontal gyrus, 8—left middle frontal gyrus, 9—left superior frontal gyrus, 10—right posterior STS, 11—right middle anterior temporal. Regions’ IDs correspond to the IDs in Table 3.
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BHU022F2: Probabilistic group-level masks of language network (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html) projected on a SPM template T1 image. The names of the regions are: 1—left angular gyrus, 2—left supramarginal gyrus, 3—left posterior STS, 4—left middle anterior temporal, 5—left anterior temporal, 6—left orbital inferior frontal gyrus, 7—left inferior frontal gyrus, 8—left middle frontal gyrus, 9—left superior frontal gyrus, 10—right posterior STS, 11—right middle anterior temporal. Regions’ IDs correspond to the IDs in Table 3.
Mentions: For the language functional localizer (visible text) we estimated a GLM model (HRF boxcar function) with 2 regressors: meaningful sentences and nonwords. We used the contrast “meaningful sentences > nonwords” to identify a network of language processing regions for each participant (Fedorenko et al. 2010, 2011, 2012). To constraint individual GLM-defined functional activations we used probabilistic group-level functional masks (Fedorenko et al. 2010; http://web.mit.edu/evelina9/www/funcloc/funcloc_parcels.html). Thus, for each mask region/participant based on individual “meaningful sentences > nonwords” GLM contrast we selected a contiguous cluster of most selective voxels (number of voxels is specified below). The regions defined by the masks are shown in Figure 2. There were 11 regions in total: 5 regions in the left parieto-temporal lobe (angular gyrus, supramarginal gyrus, posterior STS, middle anterior temporal gyrus, and anterior temporal gyrus), 2 regions in the right hemisphere of the temporal lobe (posterior STS, middle anterior temporal gyrus) and 4 regions in the left hemisphere of the frontal lobe (orbital inferior frontal gyrus, inferior frontal gyrus, middle frontal gyrus, superior frontal gyrus). Critically, as multivariate prediction is influenced by region of interest (ROI) size (e.g., Eger et al. 2008; Walther et al. 2009; Said et al. 2010) we ensured the ROIs of different regions were of an equal size of 100 voxels (1200 mm3). In additional analyses we also explored a range of different ROI sizes of 50 and 150 voxels. The ROI size could not be increased further since the size of probabilistic group-level functional masks (Fedorenko et al. 2010) of some of the regions (e.g., left superior frontal gyrus) was <200 voxels. Defining ROIs of equal size was undertaken using custom MATLAB code, where for each region/participant the code selected the contiguous cluster of voxels with the highest z-score values relating to the “meaningful sentences > nonwords” contrast in the independent localizer with visible stimuli (similar procedure had been previously applied for face-selective voxels here [Axelrod and Yovel 2012]). The list of the ROIs (100 voxels size) with their coordinates and average z-score values can be found in Table 3.Figure 2.

Bottom Line: The results of several functional magnetic resonance imaging studies have suggested that unconscious lexical and semantic processing is confined to the posterior temporal lobe, without involvement of the frontal lobe-the regions that are indispensable for conscious language processing.We found that subjectively and objectively invisible meaningful sentences and unpronounceable nonwords could be discriminated not only in the left posterior superior temporal sulcus (STS), but critically, also in the left middle frontal gyrus.We conclude that frontal lobes play a role in unconscious language processing and that activation of the frontal lobes per se might not be sufficient for achieving conscious awareness.

View Article: PubMed Central - PubMed

Affiliation: The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel UCL Institute of Cognitive Neuroscience.

No MeSH data available.


Related in: MedlinePlus