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The processing of continuous and complex auditory signals such as speech relies on the ability to use statistical cues (e.g. transitional probabilities). In this study, participants heard short auditory sequences composed either of Italian syllables or bird songs and completed a regularity-rating task. Behaviorally, participants were better at differentiating between levels of regularity in the syllable sequences than in the bird song sequences. Inter-individual differences in sensitivity to regularity for speech stimuli were correlated with variations in surface-based cortical thickness (CT). These correlations were found in several cortical areas including regions previously associated with statistical structure processing (e.g. bilateral superior temporal sulcus, left precentral sulcus and inferior frontal gyrus), as well other regions (e.g. left insula, bilateral superior frontal gyrus/sulcus and supramarginal gyrus). In all regions, this correlation was positive suggesting that thicker cortex is related to higher sensitivity to variations in the statistical structure of auditory sequences. Overall, these results suggest that inter-individual differences in CT within a distributed network of cortical regions involved in statistical structure processing, attention and memory is predictive of the ability to detect structural structure in auditory speech sequences.
Citation: Deschamps I, Hasson U, Tremblay P (2016) The Structural Correlates of Statistical Information Processing during Speech Perception. PLoS ONE 11(2): e0149375. https://doi.org/10.1371/journal.pone.0149375
Editor: Antoni Rodriguez-Fornells, University of Barcelona, SPAIN
Received: January 12, 2015; Accepted: February 1, 2016; Published: February 26, 2016
Copyright: © 2016 Deschamps et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are publicly available on the public repository at the University of Trento, Italy (UNITN): http://hdl.handle.net/11572/134214.
Funding: This study was supported by a research grant from the European Research Council under the 7th framework starting grant program (ERC-STG #263318) to U. Hasson and by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC #1958126) and Social Sciences and Humanities Research Council of Canada (SSHRC #430-2013-1084) to P. Tremblay. P. Tremblay holds a Career Award from the ''Fonds de Recherche du Québec – Santé'' (FRQS).
Competing interests: The authors have declared that no competing interests exist.
Auditory perception is a difficult task that involves the ability to process regularities (i.e. statistical structure) to make predictions about upcoming sounds. This ability relies, at least in part, on detecting the probabilities in which events co-occur [1, 2]. Transitional probabilities (TP) provide information regarding the regularity of auditory signals; this information can be used to predict upcoming sounds. The ability to detect TP is therefore a very important mechanism in the processing of auditory signals, both for language learning and language processing. In fact, the paradigmatic case is that of language acquisition during which children learn to use TP and other cues to segment speech into words, words into syllables, and syllables into phonemes. Indeed, from an early age, children are sensitive to TP in speech [3–6] and non-speech auditory signal [4]. Adults are also sensitive to TP in speech [7, 8] and non-speech auditory signals [4, 9–11], which suggests a general auditory competence, not specific to speech [4, 9–14]. Different underlying mechanisms have been proposed to account for these results. While some have proposed that TP and other statistics are computed/extracted online [4, 15] others have proposed that "chunking" mechanisms, dependent upon attention, memory and associative learning [16], underlie the apparent sensitivity to statistical structure. However, both approaches suggest that there is sensitivity to statistical regularities [17]. That is, the statistical structure of incoming auditory inputs must be detected.
Prior work using functional neuroimaging has identified the inferior frontal cortex (IFC), the superior temporal plane (STP), as well as subcortical regions (basal ganglia; BG) as sensitive to auditory statistical structure such as TP [18–27]. For example, regions within the frontal cortex, including the middle frontal gyrus (MFG) and the ventral premotor cortex (PMv), are sensitive to changes in perceived order in sequences of pure tones [18, 28], whereas the inferior frontal gyrus (IFG) is sensitive to TP in sequences of pure tones [22] and in syllable sequences in an artificial language [26]. Regions within the supratemporal plane are sensitive to TP in auditory tone sequences [22, 25], in bird songs [23] and in syllable sequences [21, 23, 29], as well as the sample entropy of pitch sequences [20]. Subcortical regions such as the BG are recruited during sequential structure learning using different modalities (e.g. visual, auditory and tactile) in which probabilistic relationships are manipulated [27, 30, 31].
While the relation between the processing of statistical structure and brain function has been studied extensively using fMRI, the relationship (if any) between brain anatomy and the processing of statistical structure remains poorly understood. Investigating this relationship might provide valuable insights into the underlying mechanisms involved in the processing of statistical structure, as gray matter is composed of neurons (including dendritic trees and spines), which are the building blocks in the transmission of information and brain functions. In fact, a recent review by Kanai and Rees [32] clearly demonstrates that brain morphometry has been repeatedly linked to inter-individual differences in numerous capacities (ranging from basic to higher cognitive functions). Specifically for language, two studies have demonstrated, using diffusion tensor imaging (DTI), that microstructural brain differences (i.e. integrity of white matter fiber tracts) predicted competencies often related to statistical learning; specifically, word-learning skills such as segmentation and rule learning [33, 34]. In these studies, increased fractional anisotropy or radial diffusivity was associated with better performance (i.e. less errors). Such results document a relationship between neurostructural differences and behavioral differences in performance on statistical learning abilities in healthy adults.
Other studies focusing on the more general relationship between brain structure and language or speech functions have also predominantly reported positive correlation between brain structure and performance. For example, with regards to auditory processing, it has been shown that musicians have greater CT in superior temporal surfaces than non-musicians [35]. In addition, relative pitch performance is predicted by higher CT in the bilateral intraparietal sulcus [36]. Other morphometric measures, such as white matter volume (WMV) and gray matter volume (GMV), also correlate with specific abilities. Successful learning of artificial grammars correlates with white matter integrity in left IFG (BA 44/45) [37]. In addition, successful learning of foreign speech sounds is correlated with higher WMV volume in the left Heschl's gyrus (HG) [38] and bilaterally in a region anterior to the parietal-occipital sulcus [39], while successful learning of pitch patterns in word contexts is correlated with GMV in the left HG [40]. The structural differences observed in HG are in agreement with functional (i.e. increased activity) and structural differences (i.e. increase gray matter volume) that have been observed in individuals with good auditory perception, such as musicians [35, 41, 42]. Taken together, these studies demonstrate that structural brain imaging is a powerful tool for investigating the neural organization of auditory and language performance.
Expanding on our prior work [23], here we examined whether cortical thickness (CT) correlates with sensitivity to statistical structure in auditory sequences. In our previous fMRI study, we reported that during passive listening, several brain regions, mainly within the superior temporal plane, were sensitive to the statistical structure of auditory sequences. In contrast, here we focused on the relationship between CT, a measure of the depth of the cortical mantle [43, 44] and an explicit measure of sensitivity to statistical structure. Since a few prior studies have documented converging results between structural and functional neuroimaging ([33, 45, 46], but see also [47, 48]), here we expected that inter-subject variability in the ability to differentiate different levels of statistical structure would positively correlate with CT in regions including IFG, MFG, PMv, STP, and BG. We also expected to find positive correlations in regions not typically identified in fMRI studies of statistical structure processing, as the ability to detect/perceive different levels of statistical structure may also draw on cognitive processes such as working memory and attention [2, 16, 49] not necessarily involved in more implicit tasks.
Materials and Methods
Twenty healthy right-handed [50] native speakers of Italian (9 females; 24±4.5 years (range: 18–40), education: 17.1±3.64 years), with normal self-reported hearing, and no history of language or neurological/neuropsychological disorders participated in this study. We report data from 18 participants as two participants were removed because the FreeSurfer analyses were lost in a hardware crash and could not be replicated with the same computers and software versions. The study was approved by the Human research ethics committee of the University of Trento in Italy. Written consent was obtained from all participants.
Stimuli and Procedures
The stimuli were sequences of speech and non-speech sounds. The individual sounds that were concatenated into sequences were recorded with a sampling rate of 44 kHz, and edited to have an envelope of 225 ms, ±15 ms fade in and fade out, and equal root mean square (RMS) intensity. A complete description of the stimuli can be found in Tremblay et al., [23]. The speech sounds were 70 Italian frequent consonant-vowel (CV) syllables. These syllables were composed of combinations of five vowels (/a, e, i, o, u/) and twenty-four consonants. A native male Italian speaker pronounced these syllables in a sound-attenuated booth; they were recorded directly to disk. The non-speech stimuli were 70 unique bird sounds created from a high quality digital collection of bird sounds recorded at 44 KHz, and commercially available on iTunes (The Ultimate Sound Effects Collection: Birds; 2010 by HDsoundFX). Bird sounds were used because, like speech, they form a natural class of spectrally complex sounds.
For each category of stimuli (speech, non-speech), sequences of 8.8 seconds were created with 3 different levels of statistical structure (low, mid, high). For each category, a set of 63 unique sequences was generated. Each sequence contained four different sounds that were repeated 8 times within individual sequence, resulting in sequences of 32 sounds. Within individual sequences, sounds were separated by 50 ms of silence and presented at a rate of 3.6 Hz. To optimize discriminability, each speech sequence included 4 maximally different (acoustically) consonants and vowels, and each bird sound sequence contained 4 sounds from different bird species.
Formally, the statistical structure of the sequences was determined by transition probability (TP) matrices, which were manipulated experimentally. The sequences ranged from random (i.e. lack of statistical structure) to highly structured in three levels (low, mid, high). Each level was associated with a different degree of Markov Entropy (ME). ME is a measure of unpredictability: the more predictable a sequence is, the lower its ME value. In the low structure condition, each item was equally likely to appear at any point independently of the previously presented item (mean±SD ME = 1.84±.03; range: 1.8–1.9). It was thus impossible for participants to form correct expectations. In the mid structure (mean±SD ME = 1.51±.018; range 1.48–1.53) and high structure sequences (mean±SD ME = .79±.012; range: 0.78–0.81), the TP matrices were more constrained, which allowed participants to form expectations about upcoming sounds (audio excerpts can be found in S1 Text). The overall proportion of self-repetitions was set at 25% within all three types of sequences. This was done to control for repetition suppression effects [51–53]. In addition, in all conditions the marginal frequencies of the four stimuli were identically set to 25%; thus the only differentiating factor were transition probability constraints (i.e., Shannon's entropy was identical in all conditions, refer to Table 1). This experimental manipulation resulted in two within-subject factors: statistical structure (3 levels) and auditory category (2 levels).
Behavioral task and responses
Participants were first introduced to the syllables and bird sounds, to avoid any surprise effect during the main experiment and then they heard the 126 unique sequences during a passive listening task performed during a fMRI session (reported in [23]). Following the MRI session, participants were presented again with the same sequences while seated comfortably in front of a computer monitor, wearing a high quality headset through which the sound sequences were presented in random order using Presentation Software (Neurobehavioral Systems). On each trial, participants were asked to rate the degree of statistical structure (Regularity Sensitivity; RS) perceived in the sequences (scale of 1 [not structured] to 7 [highly structured]), and in 40% of all trials, they were also asked how many distinct sounds they perceived in the sequence, similarly rated using a 1–7 scale (See Fig 1). Participants were told that sequences containing repeating, predictable patterns (the following example PA-TA-KA-PA-TA-KA-PA-TA-KA was given) should be rated as highly structured (close to seven), while random, unpredictable sequences should be rated low (close to one). Here, we focus on the analysis of statistical structure ratings (RS) because it offers insights into participants' ability to detect changes in statistical structure in speech and non-speech sound sequences—i.e., to successfully differentiate between series solely on the basis of the transition constraints between sounds. Given that, to our knowledge, the RS scale has not been used in prior work, we assessed its internal consistency for each level of statistical structure (high, mid, low) for speech and non-speech sounds separately using a split-half reliability procedure [54]. That is, within each auditory category, for each level of statistical structure, we separated the odd trials from the even trials. We then calculated the split half coefficient for each level of statistical structure. The rating scale has good internal consistency for each level of statistical structure for speech and non-speech sounds (speech: high α = .95, mid α = .95, low α = .93; non-speech: high α = .73, mid α = .93, low = .89).
The trial number was displayed for 1000 ms and was followed by the an auditory sequence which lasted for 8800 ms. A question mark then appeared triggering participants to determine sequence regularity on a scale of 1 to 7.
For each auditory category (i.e. speech and non-speech) we used a repeated measures ordinal logistic regression (rmOLR) at the group level to determine whether participants' RS ratings could be predicted from the three levels of statistical structure (i.e. high, mid, low). First, for each participant, we calculated their average rating for each of the three levels of statistical structure (high, mid, low). Then, the average rating obtained for each statistical structure level was rounded to the nearest integer. Before running the rmOLR, we tested the assumption of collinearity using a linear regression with the ratings of statistical structure as the dependent variable and the actual level of statistical structure (high, mid, and low) as the independent variable. We found no violation of this assumption (Speech High: Tolerance = 0.789, VIF = 1.267; Speech Mid: Tolerance = 0.789, VIF = 1.267; Non-speech High: Tolerance = 0.789, VIF = 1.267; Non-speech Mid: Tolerance = 0.789, VIF = 1.267). Next we verified whether the assumption of proportional odds was met using a full likelihood ratio test comparing the residual of the fitted location model to a model with varying location parameters, (speech: χ2(4) = 9.033, p = 0.340; non-speech: χ2(4) = 1.710, p = 0.789).
A rmOLR was then performed to evaluate sensitivity to statistical structure separately for the speech and bird-song, with participants' RS ratings as the dependent variable and statistical structure levels (3) as the within-subject independent variable. For the speech sequences, the results from the rmOLR indicated a strong relation between participants' ratings and statistical structure, Wald χ2(2) = 19.688, p = 0.00005. This was shown statistically in that the odds ratio for being in a higher category of the dependent variable (i.e. higher rating on the 7 point scale) for the high versus low level of statistical structure was 34.34 (95% CI [7.2, 163.82]), which was statistically significant, Wald χ2(1) = 19.68, p = 0.000009. Similarly, the odds ratio of being in a higher category of the dependent variable for the mid versus low level of statistical structure and for the high versus mid level of statistical structure were also statistically significant (mid-low odds ratio = 1.839, 95% CI [1.02, 3.29], Wald χ2(1) = 4.17, p = 0.041; high-mid odds ratio = 18.68, 95% CI [4.59 76.07], Wald χ2(1) = 16.7, p = 0.00004). To summarize, for the speech sequences, participants' ratings tracked the level of regularity, and differentiated the high from mid-regularity levels as well as the mid from low-regularity levels.
For the non-speech sequences, the results from the rmOLR indicated that the level of actual statistical structure influenced the ratings, Wald χ2(2) = 13.93, p = 0.001. However, we found that participants appeared to discriminate the high regularity from the other two conditions, but did not discriminate the mid-regularity and low-regularity conditions. Statistically, this was seen in that the odds ratio of being in a higher category of the dependent variable was only statistically significant for the high versus low level of statistical structure (odds ratio = 3.73, 95% CI [1.78, 7.79], Wald χ2(1) = 12.22, p = 0.0005) and high versus mid level of statistical structure (odds ratio = 3.07, 95% CI [1.43, 6.56], Wald χ2(1) = 8.31, p = 0.004). For the mid versus low level of statistical structure the odds ratio of being in a higher category of the dependent variable was not significant (odds ratio = 1.216, 95% CI [0.58 to 2.57], Wald χ2(1) = 0.261, p = 0.61). Thus, unlike for the speech sequences, for the non-speech sequences participant did not appear to detect the difference in statistical structure between the mid versus low level. Because of this weaker sensitivity to statistical structure in bird-song sequences, we excluded the bird songs from the morphometry analyses. The remaining analyses therefore focus on the relationship between CT and the sensitivity to statistical structure in speech sequences.
After establishing that, at the group level, participants could perceive differences in statistical structure among the 3 levels for the speech sequences, we derived one index per participant quantifying the degree to which changes in statistical structure were associated with changes in ratings. For each participant we calculated, the degree to which ratings predicted the actual level of statistical structure of a sequence. An ordinal logistic regression (OLR) with ratings as the dependent variable and statistical structure levels (3) as the independent variable was computed for each participant. This procedure returned a Wald Chi-Square statistics per participant (the higher the Chi-Square, the more participant's ratings predicted the level of statistical structure of the speech sequences). After verifying that the distribution of the Wald Chi-Square statistics across all subjects was normal, these statistics were used as predictors in the brain-behavior analysis that forms the core of this study (See Fig 2).
A. Scatterplot illustrating the distribution of the Wald chi-square statistics for speech.
Image acquisition
Two T1-weighted 3D MPRAGE structural images were acquired [1x1x1 mm3, GRAPPA iPAT = 2, 5:36min each]. One was optimized for optimal contrast [MPRAGE_CNR] between gray and white matter tissue [TE/TR/TI/flip angle = 4.18ms/2700ms/1020ms/7°] and the other was optimized for signal to noise ratio [MPRAGE_SNR] in gray and white matter tissue [TE/TR/TI/flip angle = 3.37 ms / 2500 ms /1200 ms / 12°]. The two were co-registered and averaged to obtain a single high quality structural image per participant.
MRI Image analysis
Pre-processing of the T1-images.
CT measures and subcortical volumetric brain measures were obtained using Freesurfer v5.3.0 [55–57]. First, a surface representation of each participant's anatomy was created by inflating each hemisphere of the averaged anatomical volume to a surface representation. The resulting surface representation was aligned to a template of average curvature. These surface representations were obtained by submitting each participant's structural image to a series of steps that included: (1) motion correction (across the two volumes) and affine transformation to Talairach space, (2) intensity normalization, (3) removal of non-brain voxels using skull stripping algorithms, (4) segmentation of gray and white matter as well as cerebrospinal fluid and (5) surface tessellation. At each step, intermediate results were inspected and manual interventions were performed when required to correct topological assignment errors. These included mainly manual annotations to establish white-matter/gray-matter boundaries (control-point intervention) and manual removal of residual dura mater or skull. The surface representations were then parcellated using an automated parcellation scheme [57, 58]. This scheme relies on a probabilistic algorithm that incorporates the anatomical convention of Duvernoy [59]. The anatomical accuracy of this method is high and approaches the accuracy of manual parcellations [57, 60, 61].
Investigation of the relationship between brain morphometry and behavior.
Our main objective was to examine the relationship between CT and sensitivity to statistical structure. This was accomplished in two ways: (1) by conducting a whole-brain vertex-wise analysis correlating CT with sensitivity to statistical structure, (2) by examining the relationship between subcortical volumes in relation to sensitivity to statistical structure (these were not included in the cortical-surface analysis in #1) and (3) by investigating whether mean CT measures within functionally-defined regions of interest (ROIs) correlated with sensitivity to statistical structure. We report the analyses in that order.
Whole-brain analyses.
Whole-brain CT measures were obtained from FreeSurfer routines calculating the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface [43]. The CT maps were created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. We exported the individual maps to SUMA (AFNI's 3D cortical surface mapping module; [62]) where we conducted group-level analyses on the surface. Prior to the group-level analyses, we smoothed the individual CT data using a 10-mm full-width-at-half-maximum Heat kernel.
For the whole-brain CT measures, we tested, using a vertex-wise linear regression model (AFNI covariate analysis option in the 3dttest++ program), whether CT correlated with the statistical sensitivity index that we derived for each participant. Age was included in the analysis as a covariate to control for the well-established relation between age and CT [63, 64]. We also conducted an additional vertex-wise linear regression model in which sex was included as an additional covariate. This analysis is detailed in S2 Text. The resulting group maps were corrected for multiple comparisons using the Monte Carlo simulation procedure implemented in FreeSurfer. Only areas in which CT significantly correlated with the statistical sensitivity index were included in the final corrected maps (individual vertex threshold of p < 0.05, corrected for multiple comparisons to achieve a whole-brain family-wise error (FWE) rate of p < 0.05 (clusters ≥ 437 vertices)).
Subcortical volumes.
To examine subcortical structures, we used the automated procedure for volumetric segmentation implemented in FreeSurfer. This procedure relies on the segmentation of subcortical structure, which is based on voxel intensity, spatial comparisons with a probabilistic training atlas as well as comparisons to neighboring voxel labels [61]. For each bilateral subcortical structure (thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus acumbens and cerebellum), a volume measurement was obtained from the native anatomical data. FreeSurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [65]. For each subcortical volume, we then examined whether sensitivity to statistical structure predicted volume using a multiple regression model in which the sensitivity index, age and intracranial volume were included as predictors. We also conducted additional multiple regressions in which sex was also included as a predictor. This analysis is detailed in S2 Text. The resulting p-values were corrected for multiple comparisons using a False Discovery Rate (FDR) procedure (q* = 0.05, i = 16, 2 hemispheres x 8 ROIs).
Functional ROIs.
To further explore the relationship between CT and sensitivity to statistical structure, we created a set of seven masks based on the functional data reported [23]. Masks were based on the regions that showed a significant effect of statistical structure (Fig 3A; [23]). For each participant, the average thickness of each of the 7 clusters was extracted. For each ROI, we then examined whether sensitivity to statistical structure predicted CT using a multiple regression model in which the sensitivity index, and age were included as predictors. We also conducted additional multiple regressions in which the sensitivity index, age and sex were included as predictors. This analysis is detailed in S2 Text. The resulting p-values were corrected for multiple comparisons using a False Discovery Rate (FDR) procedure (q* = 0.05, i = 7).
Results are shown on the group-average inflated white matter inflated surface. All analyses are controlled for multiple comparisons.
Whole-brain results
The whole-brain analysis targeted cortical regions in which CT correlated with participant's sensitivity to statistical structure. We identified several regions showing such a correlation, and importantly, all correlations were positive: thicker cortex was associated with a higher sensitivity to statistical regularities in auditory syllable sequences. We found such correlations in the bilateral SMG, the bilateral angular gyrus/superior temporal sulcus (AG/STS), the bilateral superior frontal gyrus and sulcus (SFG/S), the superior portion of the left precentral sulcus (PrCS), the left postcentral gyrus and central sulcus (PoCG/CS), the ventral portion of the anterior insula (vAI), the left precentral sulcus and MFG (PrCS/MFG), the IFG pars triangularis and anterior insula (IFGpt/AI), the right temporal pole and anterior portion of the superior temporal gyrus (STGa), and the right paracentral gyrus and sulcus (Refer to Figs 3 and 4 and Table 2, for a complete list).
SMG = supramarginal gyrus; STS = superior temporal sulcus.
Analyses of subcortical volume and functional ROIs
No relationship was found between GMV and sensitivity to statistical structure in any of the subcortical structures even at an uncorrected threshold. In addition, no relationship was found between CT and sensitivity to statistical structure in any of the functional ROIs even at an uncorrected threshold.
This is the first study to investigate the potential relationship between cortical thickness in healthy adults and the ability to detect statistical structure in non-deterministic sequences of speech sounds. Our main finding is that the thickness of several cortical areas correlates positively with sensitivity to statistical structure in auditory syllable sequences.
Given that the cellular and molecular mechanisms that determine CT are complex, and that structural MRI cannot identify the source of CT variations (e.g. increased neuronal or synaptic density, increased density in glial cells), the link between brain anatomy and performance can be interpreted in several ways. Neuronal changes that could affect CT include neurogenesis, synaptogenesis, increased dendritic formation, neuronal morphology, and neuronal arrangement, as well as the natural processes of synaptic pruning [66, 67]. In the present study, the relation between CT and sensitivity to statistical structure was positive indicating that thicker cortex was associated with better performance (i.e. better ability to perceived differences between statistical structures). More specifically, the whole-brain analyses revealed that increased sensitivity to statistical structure was associated with thicker cortex in regions that have been previously associated with the processing of statistical structure using fMRI (i.e. bilateral AG/pSTS, left PrCG, left MFG), as well as regions typically associated with cognitive functions such as memory and attentional processes (bilateral SMG, bilateral SFG\S, left vAI). These findings are discussed in the following paragraphs.
Behavior/CT correlations in areas implicated in statistical processing
Among the regions in which CT co-varied with participant's sensitivity to statistical structure for speech, the left ventral IFGpt, the left MTG, the left PrCS, and the bilateral STS are regions that have previously been linked to the processing of statistical information in auditory and language stimuli [22, 28, 29, 68]. In particular, prior work suggests that the involvement of the left ventral IFG might be related to the processing of auditory sequential information. For example, Nastase [22] reported that the left ventral IFG, a region similar to the one reported in the current study, differentiated between levels of disorders in auditory sequences of pure tones. Clusters of activation within this region have also been reported during musical sequence processing [69, 70] and rule learning in an artificial language [33, 71]. Taken together, these findings are consistent with the notion that the left ventral IFG might provide a neural basis for processing statistical information based on statistical cues (i.e. TP) present in auditory signals. One possibility is that in the current study, the left ventral IFG relied on TP for the construction of predictions regarding upcoming information in speech sequences.
In addition, we also found a positive correlation between CT and sensitivity to statistical structure in the left MFG, a region similar to the one reported in Tobia et al., [18] during the active monitoring of order change in tone sequences. Interestingly, this region was not recruited during the passive detection of order changes in tone sequences [18] or during the passive detection of statistical structure in speech and non-speech sounds [23]. This suggests that attentional demands modulate the network of brain regions involved in the detection of statistical structure. The correlation between CT and sensitivity to statistical structure within the left MFG supports the involvement of this region in the explicit evaluation of statistical structure, but not during passive processing. We also observed a positive correlation in the bilateral STS, a region that has been associated with the ongoing computation of transitional probabilities [21, 29]. McNealy et al., [21, 29] have linked the superior temporal plane (including STS) to the processing of statistics. In sum, in the current work, which focused on the structural correlates associated with the explicit assessment of statistical structure, our results identified a subset of regions that are typically associated with the processing of statistical information using fMRI.
Behavior/CT correlations in areas implicated in attentional and memory processes
Interestingly, our results reveal correlations between CT and brain areas traditionally implicated in working memory (e.g., bilateral ventral SMG, SFS) [72, 73] and attentional processes (vAI) [74–76]. For instance, prior work has documented the involvement of the SMG during the manipulation of sequences of phonemes/syllables in working memory [77, 78] and during the learning of an artificial language [21, 29]. In experiments investigating the automatic processing of auditory syllable sequences with different levels of statistical structure, and in experiments investigating non-verbal auditory sequences, SMG activation is not typically documented [18, 22, 23], suggesting that the anterior SMG is involved in maintaining phonological information in memory [79]. This hypothesis is supported by the present finding of a relationship between CT and the processing of sequences of syllables, as the processing of syllable sequences requires the updating of phonological information in working memory as the sequence unfolds over time. Similarly, we also found a significant correlation in the bilateral SFS. This might suggest a close relationship between working memory and the ability to detect changes in statistical structure, given that the bilateral SFS/G is engaged in working memory [80, 81].
In addition, within the left vAI, thicker CT was associated with greater sensitivity to statistical structure. While left vAI is not usually associated with the processing of statistical information, it is a region that is active across a variety of tasks encompassing a broad range of processing domains [82, 83], including speech [84], interoceptive awareness [85], and emotions [86], and it has been associated with the ventral attention network in studies examining responses to oddball stimuli [87]. It has been suggested that the anterior insula plays a role in domain-general attention control [76, 88]. Thus, one possibility is that our finding for this region reflects inter-individual attentional differences whereby individuals with thicker CT would be better at focusing their attention and, as a result, would be better at detecting differences in statistical structure among the three levels of statistical structure. This view is congruent with the notion that the ability to detect changes in statistical structure relies, at least partly, on other cognitive functions such as attention and memory [2, 16, 49]. Future studies are needed to clarify the relationship between attention, memory and statistical information processing.
Absence of Behavior/CT correlations in areas sensitive to statistics during passive listening
We note that we did not find correlations between CT and sensitivity to statistical structure within the functional ROIs based on our previous work, which identified regions in which brain activation tracked statistical structure during passive listening. Some of these regions (the right medial transverse temporal gyrus and the bilateral medial transverse temporal sulci) only tracked statistics only tracked statistical structure in non-speech sounds, while our dependent measure here was derived from sensitivity to speech sounds (see section Behavioral tasks and responses for the rationale). However, in our previous work, other regions of the primary auditory cortex, the bilateral lateral transverse temporal gyrus and sulcus and the medial transverse temporal gyrus, tracked statistical structure regardless of auditory category. Yet, in the present study, we found no correlation between cortical thickness and sensitivity to statistical structure within these regions. This lack of correspondence between functional and morphometric data may reflect the fact that the crux of computations of TP takes place on higher cortical areas of the processing hierarchy. For instance, one possibility is that A1 receives afferent inputs after regularity has been coded by higher-level regions (e.g. SMG, STS). The predictions generated in these higher-level regions would then be evaluated against inputs in A1. Hence, while A1 would not necessarily code for statistical structure, the predictions generated by these higher-level regions would still have an impact on the activity in A1 (top-down effect). This is congruent with a recent study by Tobia et al., [28] in which the authors found afferent connectivity from the superior temporal sulcus to the left A1 during the perception of sequences of tones.
Another possible explanation is that regions involved in the processing of statistical information as identified using fMRI do not necessarily correlate with structural differences within these regions. This is consistent with several studies in the literature investigating brain structure-function relationships. For instance, Hoeft et al., [48], used fMRI to compare brain activation associated with visual rhyme judgments in dyslexic adolescents, age-matched and reading-matched controls. In the dyslexic adolescents, hypoactivations were found within the left inferior parietal lobule (IPL) and the bilateral fusiform/lingual gyri as compared to controls. Interestingly, the hypoactivation in left IPL was the only one that was accompanied by significant differences in grey matter volume between dyslexic adolescents and age-matched controls (i.e. smaller grey matter volume in dyslexic adolescents). All other regions of hypoactivations (i.e. bilateral fusiform) had no relation to brain structure. Similarly, Nyberg et al., [89] reported that while many cortical regions exhibited less activation as a function of age during a semantic categorization task, this pattern observed could only be explained in terms of grey matter volume differences in one of these regions (i.e. right mid-frontal cortex). For the other regions, no relationship between brain structure and function was found. Relatedly, Kadosh et al., [90] found variations in brain activation as a function of age during a facial expression detection task in the left fusiform gyrus and the right inferior temporal gyrus. These variations in brain activation correlated with white matter volume (WMV) differences in the left fusiform gyrus and the right inferior temporal gyrus as well as grey matter volume (GMV) volume differences in the left fusiform gyrus. A positive correlation between brain activation and age was also found during a gaze detection task within the left supramarginal gyrus. This correlation was not related to differences in grey or white matter volume in that region. In addition, Tremblay et al., [47] demonstrated that most age differences in task-related BOLD signal magnitude during speech tasks are not related to neurostructural changes. Taken together, these results suggest that functional activation is not systematically related to brain structure, or to specific morphometric measures (e.g. CT, GMV, WMV).
Alternatively, it is also possible that the neural mechanisms associated with passive listening to statistical regularities (as assessed in our previous fMRI study) are distinct, at least in part, from the mechanisms underlying active assessment of statistical regularity (as assessed in the present study). Therefore, while in both studies the same auditory sequences were used, the nature of the tasks differed (active vs. passive). One possibility is that the brain regions identified here are ones that utilize attention and memory to process speech sounds; these regions might not show differential responses to different levels of regularity (as more attentive individuals may do so independent of regularity), but their structure could co-vary with one's ability to discriminate levels of regularity. Another possibility is that the regions identified in the functional study are involved in performing low-level 'chunking' operations that are linked to the transition probability structure during online listening, but do not strongly contribute to the explicit evaluation of overall input disorder.
Overall, our results support the idea that the ability to detect differences in statistical structure in auditory sequences relies on several different perceptual, cognitive and executive mechanisms including attention and memory, in addition to mechanisms more directly related to statistical structure processing. This idea is supported by the observation that most of the regions in which CT correlated with sensitivity to statistical structure have been described by Chen et al. [91] as being part of three different modular CT correlations networks (among the six modules that were proposed) (see [91, 92], for a recent review). These studies demonstrate that CT organization follows a modular network structure matching specific functions such as auditory and language processing. In the current study, we observed significant correlations within regions involved in higher-level cognitive functions (executive/strategic; Module 1), sensorimotor/spatial functions (Module 2) and auditory processing/language functions (Module 5) [91]. While sensitivity to statistical structure appears to rely, to a large extent, on domain-general neural resources, the results of the current study cannot distinguish between models that seek to differentiate the numerous mechanisms underlying sensitivity to statistical structure. As such, our results are congruent both with models that posit that sensitivity to structure is the end result of computing transition probabilities [15] as well as with models that propose that sensitivity to statistical structure is a by-product of "chunking" mechanisms that rely on memory, attention and associative learning [16].
We detected inter-individual differences in sensitivity to statistical structure in sub-lexical speech sequences using a behavioral procedure, and at the whole-brain level using vertex morphometry. In addition to showing, for the first time, that sensitivity to statistical structure can be investigated using surface-based brain morphometry, we also show that the neural system identified are dominantly cortical and include both specialized and domain-general areas. This suggests that domain-general regions not specific to auditory or statistical processing support the ability to detect statistical structure, a mechanism that may play an important part in our day-to-day life, allowing us to form expectations about future events and using this information to interact more efficiently with our complex environments. More studies are needed to replicate our findings using larger sample size, and to gain further insights into the complex relationship between brain structure, brain functions, and the processing of statistical information.
Supporting Information
S1 Fig. Cortical thickness results corrected for sex.
A. Red: sensitivity to statistics controlled for age and sex. Yellow: sex effects controlled for age and sensitivity to statistics. B. Overlap between sex and statistical effects.
This study was supported by a research grant from the European Research Council under the 7th framework starting grant program (ERC-STG #263318) to U. Hasson and by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC #1958126) and Social Sciences and Humanities Research Council of Canada (SSHRC #430-2013-1084) to P. Tremblay. P. Tremblay holds a Career Award from the ''Fonds de Recherche du Québec—Santé" (FRQS). We thank Margaret Moreno for her help collecting data, the MRI staff of the Functional Neuroimaging Lab at the Center for Mind & Brain Sciences (CIMeC) at the University of Trento, and all the participants. The data presented in this article can be accessed from the Research Registry of the University of Trento at http://hdl.handle.net/11572/134214
Author Contributions
Conceived and designed the experiments: PT UH. Performed the experiments: PT. Analyzed the data: PT ID. Wrote the paper: ID PT UH.
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text:
Buddha Air
Clients: Buddha Air Pvt Ltd
Industry: Airlines
Website: https://www.buddhaair.com
- Project Background:After the exhaustive search for the right partner, Buddha Air choose Social Aves for search engine optimization of a website. Based on the analysis of their website we recommended they prioritize rethinking their booking experience in order to improve conversion rates, repeat visits and mobile engagement rates.
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- Solution:Starting with in-depth keyword analysis the SEO team of Social Aves put together organic SEO strategy for Buddha Air Pvt Ltd that included a full re-optimization of their existing website content, URls and Meta Tags, H1 tags, and alt images attributes, and more for each and every page. After the completion of on-page optimization, our team is focused in increasing backlink profile of Buddha Air's and internet marketing promotion with various strategies including article marketing, client blogging, business directories.
- Results: Within one year , Social Aves was able to increase Buddha Air's google organic search traffic by 79.39% with a 56.52% in new visitors which generated a number of new sales.

subset: commoncrawl


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One week at a vacation home in Sedona, AZ
- Item Number
- 14-001
- Estimated Value
- Sold
- $1,100 USD to Live Event Bidder
- Number of Bids
- 2 - Bid History
Item Description
Enjoy a one week stay in Sedona, AZ, close to some of the best hiking and mountain biking trails in the country. Stay in a southwest style, single level home, with spectacular views from every window. The house sleeps 6-8 comfortably. Three bedrooms, including a studio bedroom with two trundle beds, and two bathrooms. Dates are flexible, but will need to be worked out with the owners (to accommodate Park School and Carver Center breaks). Owners ask that you pay for cleaning service ($225) after your stay.
Special Instructions
Sorry, no airfare included.
Donated By:
Jane and Brian Sims
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As the summer ends and the back-to-school sales start haunting your shopping centers, ease into the academic season with some free educational podcasts. The iTunes U app gives you access to courses.
It is essentially 15th-century Spanish, with words mixed in from Portuguese, French, Italian, Arabic, Greek, Turkish and Hebrew — and it is rich with music, poetry, proverbs, folktales and more. Learn.
That means if you want to learn Arabic. Duolingo is free to use, and Rosetta Stone offers a free trial to give you a taste for the way it teaches. Give them both a go and see if one grabs you more.
High School Musical 3 Take My Hand Lyrics Believe it or not, this month marks the nine-year anniversary of High School Musical! Take this quiz to see how many of the songs you remember. Oh My Disney. Search. Search the website Close. Facebook Instagram YouTube. Explore. Quiz; News;. How Well Do You Know the Lyrics from High School Musical? Oh My Disney. Family
The Courses are available in English, French, and Arabic. beginner level, the SMART course will prepare you for a successful career as an international travel professional in a dynamic and exciting.
I didn't learn this maneuver at a self-defense. I signed up for the free Greeting Phrases in Arabic course and the $34.99, 13-lesson course called "DSLR photography for beginners." Though I'm quite.
Arizona Teacher Certification Requirements Apr 24, 2017. An Arizona bill is headed toward passage that would do just that by eliminating some of Arizona's teacher certification requirements. Jun 26, 2017. In Arizona, charter school "teachers" are exempt from state certification requirements. A Brookings Institute study found that in the 2012-13. Certified Staff. TEACHER CERTIFICATION. Arizona Department of Education 1535
This online course is lead. might be enough of a reward. Although Arabic is a rather daunting language to learn, this course aims to make it easy to learn the basics and progress fast. Arabic.
With DW's free online language courses you will discover the German culture and language. Beginner courses and courses for advanced learners help everyone at all skill levels learn and improve their.
The MLTC offers courses in Arabic, French, German, Italian, Latin, Portuguese and Spanish from beginners' to. for credits can apply online through the add-drop procedure. Undergraduate students may.
St Thomas Engineering College Kazhakuttom The NSS volunteers are with them. The NSS unit of the College of Engineering collected essential items from ST College at Pappanamcode, Heera College, St Thomas College, Mohandas College, PRS college, The project was completed within the specified time and CECL received the 1986 Gleaner Honour Award 'for dramatic engineering breakthrough in record time in
Duolingo, for the uninitiated, is a cross-platform app where users can learn languages for free, though they can also cough up $. the Pittsburgh-based company finally rolled out support for Arabic.
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That's because Marquette's Spanish and Italian curriculums use Skype, a free Internet phone service. little – if any – of the conversation time so crucial to learning. "On average in any class of.
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Lingua.ly is a cloud-based language learning technology that provides learners with a free dictionary and learning platform to look up words in English, French, Spanish, Arabic or Hebrew. suitable.
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She teaches courses in Arabic, French and. The evening lecture is free for museum members and students with ID; $15 for nonmembers. Reservations are required for all programs and may be made online.
In the coming months, Scripts will add simplified Hanzi, Thai, Kanji, Arabic, Hebrew. popular language spoken at home after Spanish. Yet, for many beginners who want to get started with learning.


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We're all for showing some spring skin but this dress will give you up much more than a peep. Find friends making use of your Facebook, Gmail or Yahoo contacts. Browse other individuals boards and see ideas for anything from cooking, and decorating, to technology and travel. And what exactly are we dressing our pooches because this holiday. Your content can be excellent, but without one or more image, Pinterest won't accept it. Connect using your followers outside pinterest login with the addition of a link in your website and enabling Facebook and Twitter. Log in for a account after you've completed the registration process, and then go through the "Schedule Pins" tab for the left side of your respective screen. If there is a blog or website, Pinterest offers Pin It and board-sharing widgets you may add in your website. Create pins coming from a laptop or desktop computer while using the plus icon shown near your reputation and photo with the top on the Pinterest screen. Today, I'm going to share with you with you how to set up a Pinterest icon on Safari.
Choose from the large listing of established categories that interest you. The guiding visual metaphor of Pinterest is really a pinboard, and a lot activity about the social interest-sharing network involves certain things: pins and boards. You can't help make your profile completely private, but hiding it from yahoo and google prevents your profile from appearing from the results when someone searches for your business or username. Put aside the belief that you'd be wearing a gown made of sugar-coated marshmallows: look into the neckline for this thing. Warning: Pinterest is extremely addictive even in case you're not seeking something specific you'll be able to get lost all night and hours searching people's boards. Create your wedding board so it is possible to share it along with your florist, party planner, and brides maids. From jewelry to scarves & gloves, these handmade gifts are perfect for your stylish gals on the list. And if that suits you set these options so you're pins show up on your own facebook feed plus your twitter stream. Since when will be the hidden baskets and eggs not enough. If you desire to unfollow an user, select the user's name in the bottom in the pin; should you want to unfollow a board, select the name with the board with the bottom with the pin.
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Michio Kaku And Other Scientits Physicist, Futurist, Bestselling Author, Popularizer of Science. Exploration with Michio Kaku, is an hour long radio program on science, technology, politics, and the environment.It is broadcast each week on WBAI
(Image stolen from Glenn Peoples) Cowardly Richard Dawkins refuses to debate William Lane Craig on this latest UK tour. (H/T Mary) Excerpt: Richard Dawkins has made his name as the scourge of organised religion who branded the Roman Catholic Church "evil" and once called the Pope "a leering old villain in a frock". But he…
Average Sperm Morphology Percentage Fibonacci Code In C Jun 25, 2016 · Write a C++ program , that prints all the Fibonacci numbers , which are smaller than or equal to a number k(k≥2) ,which
Welborn Dr., 6008-Ari Burstein and Laura Dawkins to John. 11706-Kenneth M. and Richard M. Barnes to Eileen Chen and Hu Lin, $376,000. Hobbs Dr., 705-Hector A. Mastrapa to Stephen A. Dean and Pamela.
Craig. Richard M. Lytle to Mary Lou Schmidt, Carolina Shores. Robert Conley and wife Patricia F. to Charles W. Harding and wife Bonnie V., Saltaire Village. Richard M. Neall and wife Margaret A. to.
Reddit gives you the best of the internet in one place. Get a constantly updating feed of breaking news, fun stories, pics, memes, and videos just for you. Passionate about something niche? Reddit has thousands of vibrant communities with people that share your interests. Alternatively, find out what's trending across all of Reddit on r/popular.
Julien to Diane V. Stark, 639 Quail Ave. Wellington Condo – Michael Nadermann to William Rinz, 1752 Pattington Circle, $175,500. Westview Village – Todd Craig to Larry R. Messick, 6317 Shelby Lane,
William H, II and Shartory D; Amount: $193,500; Oak Creek 03 (Moore) Blks 8-10, Lot 25, Block 9 Seller(s): Daily, Rebecca S Paddock, Richard L; Buyer(s): Looney- Williams, Francy D Williams, David P;.
May 14, 2011 · Richard Dawkins won't debate William Lane Craig (updated) The Telegraph has noticed the Richard Dawkins has declined to debate with William Lane Craig this Autumn: "I have no intention of assisting Craig in his relentless drive for self-promotion," he said.
William Lane Craig On Richard Dawkins – Part 1 – 2010 – Popular Christian Videos. Here are a couple of more videos of Dr. Craig.
Cunningham to Richard L. Ridgway. Lewis Jr. to William B, Richardson Jr., one-quarter interest in tract, 200-acres, Peach Run, Slate district, exempt. * Harold E. Ours to Craig Wilson, tract, 2,970.
Julien to Diane V. Stark, 639 Quail Ave. Wellington Condo – Michael Nadermann to William Rinz, 1752 Pattington Circle, $175,500. Westview Village – Todd Craig to Larry R. Messick, 6317 Shelby Lane,
Celano Celano, Mario, 76, of Palm Bay, FL passed away on June 18th, 2009 at William Childs Hospice House. American Burial & Cremation Center At Jennings Funeral Home.* Lane Lane, John Richard "Dick.
2311-Richard A. Kennedy Jr. to Paul Edward Lanzillotta, $350,000. Pawlet Dr., 1954-Rolessia R. Holman to William J. Nicoll. Benemerito to Robert F. and Helen V. Rhoades, $279,900. Blade Green Lane,
William Russell Hamm, 54, of Stokesdale and Janis Ruth Smith, 44, of Archdale. Adam Lane Wright, 39, and Cynthia Dawn Phillips. Bishop, tract, $560. Richard Craig Canady and Penny Hanes Canady to.
May 14, 2011 · A war of words has broken out between the best selling author of The God Delusion, and his critics, who see his refusal to take on the American academic, William Lane Craig, as a "glaring.
Bidwell Lane. Cox Rd., 640-Richard L. and Sherry L. Reynolds to Craig Champman, $295,000. Holland Cliffs Rd., 3101-HSBC Bank USA National Association to Jeanne M. Prochownik, $350,000. Plum Point.
Dr. William Lane Craig is one of Christianity's leading defenders and many individuals over the years have attempted to arrange a debate between Dr. Craig and Richard Dawkins. Richard Dawkins has offered various ruses on why he will not debate William Lane Craig, which Dr. Craig supporters have shown were inconsistent and merely a dodge to.
The William Lane Craig vs (Not-) Richard Dawkins Saga Archive Many of you know already that I spent a large part of 2011 launching a series of youtube commentaries against Richard Dawkins, for refusing to debate William Lane Craig (and specifically,
Jan 14, 2013 · คำตอบที่ดีที่สุด: William Lane Craig is an amazing debater, mostly because he knows his arguments better than anyone else, he speaks well and throws PHD level bullshit at you. you can see it in his debate against Hitchens, and Hitchens ignored it. that's exactly what would happen in a Craig/Dawkins debate, except Dawkins would probably call him an idiot.
Landmark Performance Corp. to Meyer, Craig J. and Corrigan-Meyer. 21750 Mandy Lane, $675,000. Nelson Builders Inc. to Moss, Henry P. and Melanie L., 619 Brentwood Drive, $374,000. Doose, Richard D.
Richard C. Vanroekel. Sparks, 2927 N. Sunny Lane, July 30, Chapter 13. Patricia Marlene and Park William Waldroup Jr., P.O. Box 8094, July 27, Chapter 13. FORDYCE Jimmy Dale Thompson, 2542 Ark. 273.
William Craig Lane. 60 likes. Community. Facebook is showing information to help you better understand the purpose of a Page.
Nov 02, 2011 · Doppleganger. Sorry to disappoint you but I'm afraid I don't feel qualified to speak for Richard Dawkins, or anyone else for that matter. Noticeably, you felt unable or unwilling to comment upon the substance of the article, upon the points I raised in my blog, or upon William Lane Craig's advocacy of genocide and child murder based on Christian principles.
Wayne Smith $100, George and Ronda Huskey $50, Richard F. Brown $250, Casey J. and Marsha E. Fox $50, Ray and Dee Ramirez $100, Bill Ware $500, David E. Craig $500. John Skaggs $100, William Boyce.
Dawkins. Lane, 4781-Richard W. Marker to Nicole J. Broussard, $221,000. Four Seasons Dr., 17135-Paul V. Brown to Aubrey D. Whitten, $300,000. Mammoth Cave Loop, 4024-MMHH Investment Corp. to Terry.
6830: John V. 2232-34-36-38: William M. Doby Sr. Estate to Robert C. Bourgeois III, $146,300. Alba Road E. 20501: Russell Trahan and Lynette Callegan Trahan to Kenneth Hill, $275,000. Athis St.
Dr. William Lane Craig Responds to Dawkins Book; On Richard Dawkins and Daniel Dennett (Audio only) Richard Dawkins Cowers Away from William Lane Craig; Who Designed The Designer? a response to Dawkins' The God Delusion; Why Is Richard Dawkins So Popular? Will Richard Dawkins debate William Lane Craig? William Lane Craig's Debate Resume
William Lane Craig On Richard Dawkins – Part 2 – 2010 – Popular Christian Videos
May 14, 2011 · Richard Dawkins won't debate William Lane Craig (updated) The Telegraph has noticed the Richard Dawkins has declined to debate with William Lane Craig this Autumn: "I have no intention of assisting Craig in his relentless drive for self-promotion," he said.
Fibonacci Code In C Jun 25, 2016 · Write a C++ program , that prints all the Fibonacci numbers , which are smaller than or equal to a number k(k≥2) ,which was entered by the
All the Ways the Endgame Trailers Faked Us Out. Game of Thrones: Season 8, Episode 4 Review. HBO, Starbucks Respond to Game of Thrones' Accidental Coffee Cup
Les Properties Vingt Sept Inc. to Heady, William R., Unit 6, SE Peck Plaza, $260,000. Malacaria, Richard V to Marazia, Henry. U.S. Bank Trust to Young, Craig A., Lot 16, Block 12, Plantation.
V. Bradley and Gretchen E., $1,260,000, 2700 Riverbend Rd. Gosner, William A. Jr. and Krista M. to Tran, Kevin B. and Yost, Suzanne M., $415,000, 3836 Buck Hill Circle. NES Family Trust, Smith, Nancy.
William. Inc., A/V service, 9544 Cannon Drive, Orlando, 32817. Kidz R 4 Uz, child care services, 7132 Coral Cove Drive, Orlando, 32818. Sean Shearouse, painting, 7223 Pinion Drive, Orlando, 32818.
History: Eichhorn, Jennifer Janell; and Libman, Jared Lane. Kyle C.; Dawkins, Sharon V.; Dixon, James Wendell; Evans, Richard Allen; James Jr., Jerry Neal; Keene, Sheri Lynn; Pasket, Terence M.;.
Counterpoints: Christopher Hitchens vs. William Lane Craig on the Timing of Christ's Coming Christopher Hitchens : "I've taken the best advice I can on how long Homo sapiens has been on the planet. Carl Sagan, Richard Dawkins, many others, and many discrepant views from theirs, reckon it's not more than 250,000 years, a quarter of a million years.


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Search Canada or Alaska
Explore Canada & Alaska map
Call for reservations and questions
Monday - Friday 7.30am - 5.30pm
Saturday 9am - 5pm
Pacific time
Email firstname.lastname@example.com
Or contact your travel professional
Subscribe for expert travel tips and inspiration.
Get inspired with our FREE Canada & Alaska brochure.
Please note that prices are listed in USD.
Traveled with us? Tell us what you thought of your trip!
Bear Viewing
From grizzly bears to polar bears and British Columbia rainforest to arctic tundra, various bears have made Canada their home for millions of years. It's unsurprising, then, that bear viewing tours in Canada are better than most other countries around the world, and that our bear tours offer the unique aspect of getting up close and personal with these unique creatures on their turf. While on a Canada grizzly bear viewing tour, we ensure that you encounter these majestic creatures in a safe and secure way, with minimal impact on the environment and the help of professional guides. From staying at family-owned floating lodges to trekking with guides that have been leading walks for years, you'll feel entirely at ease with a wild bear just feet from where you stand – an experience you and your loved ones will cherish for a lifetime.
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Page 638 of 1051
Staff Accomplishments
Carbaugh elected as Health Physics Society Fellow
Congratulations to EED's Gene Carbaugh on his recent election to Fellow status in the Health Physics Society. He'll be inducted at the HPS Annual Meeting in late June in Salt Lake City. Fellows are senior members of the Society who have been nominated and elected by peers for making significant administrative, educational, and/or scientific contributions to the profession of health physics. Gene joined PNNL in 1980 and currently manages the Hanford Internal Dosimetry Program. He's a Certified Health Physicist who has established himself as one of the leading applied internal dosimetrists in the nation and is known for his many contributions to the health physics profession.
Page 638 of 1051


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Northern parts of Oman receive rain
MUSCAT, April 23 – Parts of Sultanate experienced heavy to moderate rains on Tuesday. The Governorates of South Al Batinah, Al Dakhiliyah, North Al Sharqiyah experienced heavy to moderate rains which led to overflowing of Wadis. The Governorate of Muscat also experienced moderate rains. Rains reported in Al Amerat, Qurayyat and Al Seeb. "On Wednesday there is a similar weather condition expected for areas around Al Hajar Mountains," Oman Met Office's weather forecaster noted. Sea will be moderate to rough along the Musandam coast and Oman Sea. The wave height could go up to three metres. Meanwhile, temperatures in Al Amerat reached 42 degrees Celsius crossing Fahud which recorded a high of 40 on Tuesday.


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tag:blogger.com,1999:blog-6999078.post108689103563921398..comments2019-12-12T02:56:11.021-05:00Comments on Southwest Florida Online - Sunday Morning News: Don Brownehttp://firstname.lastname@example.com:blogger.com,1999:blog-6999078.post-1087173035044783442004-06-13T20:30:00.000-04:002004-06-13T20:30:00.000-04:00It is very sad that a man with the reputation of M...It is very sad that a man with the reputation of Mr. Simmons find himself breaking the very rules that he approved. I wonder if he would have been caught if he wasn't the County Commissioner?

If Mr. Simmons Company was doing something wrong why wasn't he just advised and given the opportunity to comply. Was this just a campaign year excuse to take him off the County Commission?Hendry County Infohttps://email@example.com


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Featuring NADINE does Moving Chic
Sunday, May 6, 2018
8:00 pm / Doors open at 7:30pm
Tickets: $12/$10 in advance
Olden Yolk is a New York-based group whose penchant for dystopian folk, abstract poeticism, and motorik rhythms have enveloped them in a sound uniquely of-the-moment yet simultaneously time-tested. The project is led by songwriters, vocalists, and multi-instrumentalists Shane Butler and Caity Shaffer, whose interlaced vocals are found guiding each composition on their enlivening self-titled debut. The project was initially conceived in 2012 by Butler as an outlet for one-off songs and visual art while touring and releasing albums with the band Quilt (Mexican Summer). Following the release of a split-record with Weyes Blood in 2014, Olden Yolk became a collaborative entity.
Their debut full-length, slated for release in February 2018 (Trouble in Mind), ruminates on questions surrounding love, self-doubt, and locating autonomy amidst burgeoning unrest. Wrought with hazy melancholy and halcyon joy, Butler and Shaffer's lilting vocals play off one another through a devotional dialogue, taking form in haunting choral melodies and candid rock n' roll. These songs are ecstatic odes to the life of the city; to the subway platforms, kiosks, and monuments which enliven and encompass our collectivity, elevating into an urban-psychedelia.
On the album, Butler and Shaffer are joined by drummer Dan Drohan (Tei Shi, Uni Ika Ai) and guitarist Jesse DeFrancesco who round out the studio sessions and live-band. Drohan's deep passion for jazz, hip-hop, and experimental percussion come to fore while Defrancesco's minimal yet powerful guitar ambiences are heard swelling in the peripheries of each song. The album was recorded at Gary's Electric in NYC by Jarvis Taveniere (Woods) with co-production, electronics, and mixing by Jon Nellen (Ginla, Terrible Records). Other guests, such as multi-instrumentalist John Andrews (Woods, Quilt, The Yawns) and violinist Jake Falby (Mutual Benefit, Julie Byrne), add to the mercurial nature of the record, creating a landscape tinged with beatific songwriting and transgressive underpinnings.
Instagram: @oldenyolk
Twitter: @oldenyolk
NADINE does Moving Chic opens.
Nadine Does Moving Chic is vocalist Nadia Hulett & Friends exploring what feels the most right now, inspired by:
the pains
the lightness
the strong-willed desire for better, clearer communication (skills/tools please welcome)
Moving attention to the body's senses, reactions, longings.
NADINE's songs have one foot standing firm in pop, but ebb and flow with exploration and experiment. Polyphonic melodies swing and gambol, instrumental layers take generous flourishes and unexpected turns with an ear to the wondrous and occasionally weird, crafting jazz-tinged lounge-pop all held together by Hulett's characteristic vocals, strong with a sincerity and gentleness that holds the listener. Let go of old ideas. Listen for tricks of the light. What does it feel like?


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Artist: John Legend
John Stephens (born December 28, 1978 in Springfield, Ohio), better known as John Legend, is a Grammy award winning R&B singer-songwriter, multi-instrumentalist, poet and actor signed to Kanye West's G.O.O.D. Music label. Prior to the release of his debut album Get Lifted in 2004, Legend's career gained momentum through a series of successful collaborations with multiple established artists such as Jay-Z, Alicia Keys and Lauryn Hill. He released his second album, Once Again in 2006, and his latest release, 2008's Evolver showcased a more experimental and upbeat side of Legend. He is the recipient of six Grammy Awards, and in 2007 he received the special Starlight award from the Songwriters Hall of Fame. You can get Cheap John Legend Tickets for his next performances.
Legend also played the piano on Lauryn Hill's 1998 song "Everything is Everything" and sang background vocals on Jay-Z's "Encore" and Alicia Keys' "You Don't Know My Name". Legend frequently cites gospel, R&B, and hip hop music as his main musical influences.
Legend released his critically acclaimed and Billboard Top Ten second album, Once Again, in October 2006. Produced by Raphael Saadiq, Kanye West and will.i.am, it is again a mixture of gospel and classic soul showcasing his extraordinary voice, particularly on "Show Me".
Grammy Award Wins:
*2005 Best New Artist
*2005 Best Male R&B Vocal Performance: "Ordinary People"
*2005 Best R&B Album- Get Lifted
*2007 Best Male R&B Vocal Performance: Heaven
*2007 Best R&B Performance by a Duo or Group: Family Affair
Neo-soul singer and pianist John Legend combined the raw fervor of contemporaries Cody ChesnuTT and the burning precision of D'Angelo. Born John Stephens, Legend was a child prodigy who grew up in Ohio, where he began singing gospel and playing piano at the tender age of five. Legend left Ohio at 16 to attend college in Philadelphia, and it was there that he first found a larger audience. Not yet out of his teens, Legend was tapped to play piano on Lauryn Hill's "Everything Is Everything" in 1998. After completing college, he moved to New York, where he began to build a loyal following playing in nightclubs and releasing CDs that he would sell at shows. He also became an in-demand session musician, playing and occasionally writing for a wide array of artis
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John Legend, All Of Me - Single Review - Contact Music (Reviews)
John Legend - Love in the Future - Exclaim! (Reviews)
Love in the Future? Speaking of which, any forthcoming John Legend documentary should be titled The Curious Case of John Stephens. For a musical prodigy that first burst out of the gate with a lively debut album (2004's Get Lifted), Legend has settled into a languid musical stride, one akin to a Las Vegas lounge singer. As one of the initial members of Kanye West's G.O.O.D. Music imprint, the vivacious soul spark initially exuded by the Ohio-born artist has withered into a commercially boring drone. While he never was a "hip-hop star," as some culturally lazy writers have...Read More
Love in the Future by John Legend - ArtistDirect
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Top Albums
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