The Neural Representation of Concepts in Bilinguals: An Evaluation of Factors Influencing Cross-language Overlap Using fMRI-based Multivariate Pattern Analysis

161 p.The neurocognitive mechanisms that support the generalization of semantic representations across different languages remain to be determined. Current psycholinguistic models propose that semantic representations are likely to overlap across languages, although there is evidence also to the contrary. Neuroimaging studies observed that brain activity patterns associated with the meaning of words may be similar across languages. However, the factors that mediate cross-language generalization of semantic representations are not known. In a series of functional MRI research studies, we investigate how factors including state of visual awareness, depth of word processing and lexico-semantic characteristics of words influence cross-language generalization of semantic representations. Using multivariate pattern analysis, we found that fully conscious and deep processing of high concrete and high frequency words leads to above-chance cross-language generalization in putative areas of the semantic network. These results have ramifications for existing psycholinguistic models and theories of meaning representation.bcbl:basque center on cognition, brain & languag

Decoding the Meaning of Unconsciously Processed Words Using fMRI-based MVPA

Available online 21 February 2019Does 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 fMRI-based 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 across-language 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.D.S. acknowledges support from the Spanish Ministry of Economy and Competitiveness (MINECO), through the ’Severo Ochoa’ Programme for Centres/Units of Excellence in R&D (SEV-2015-490; grant number BES-2016-078130). The authors also thank Cesar Caballero Gaudes for his support with the imaging protocol and BCBL's lab staff for their help in fMRI acquisition

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface

Published: 7 September 2018People 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 NIRSThis research received no external funding

A rare case of three years disease free survival in a locally advanced parathyroid carcinoma successfully excised by complete surgical resection

Parathyroid carcinoma (PC) is one of the rarest malignancies making approximately 0.005% of all cancers. It may arise sporadically or less commonly, in conjunction with genetic endocrine syndromes. Due to the rarity of the disease, no general consensus or definitive guidelines exist for its pre-operative diagnosis, management, or follow up. Surgical tumor removal is the gold standard treatment to prevent its recurrence. Parathyroid carcinoma has a high recurrence rate ranging from 40 to 60% in recent literature. We report a case of a seventy-year-old elderly female with locally advanced parathyroid carcinoma successfully surgically excised completely with a 3 year disease free survival period without adjuvant chemotherapy or radiotherapy

Neurocognitive Mechanisms Supporting the Generalization of Concepts Across Languages

The neurocognitive mechanisms that support the generalization of semantic representations across different languages remain to be determined. Current psycholinguistic models propose that semantic representations are likely to overlap across languages, although there is evidence also to the contrary. Neuroimaging studies observed that brain activity patterns associated with the meaning of words may be similar across languages. However, the factors that mediate cross-language generalization of semantic representations are not known. We here identify a key factor: the depth of processing. Human participants were asked to process visual words as they underwent functional MRI. We found that, during shallow processing, multivariate pattern classifiers could decode the word semantic category within each language in putative substrates of the semantic network, but there was no evidence of cross-language generalization in the shallow processing context. By contrast, when the depth of processing was higher, significant cross-language generalization was observed in several regions, including inferior parietal, ventromedial, lateral temporal, and inferior frontal cortex. These results support the distributed-only view of semantic processing and favour models based on multiple semantic hubs. The results also have ramifications for psycholinguistic models of word processing such as the BIA+, which by default assumes non-selective access to both native and second languages

Neurocognitive mechanisms supporting the generalization of concepts across languages

Available online 31 December 2020.The neurocognitive mechanisms that support the generalization of semantic representations across different languages remain to be determined. Current psycholinguistic models propose that semantic representations are likely to overlap across languages, although there is evidence also to the contrary. Neuroimaging studies observed that brain activity patterns associated with the meaning of words may be similar across languages. However, the factors that mediate cross-language generalization of semantic representations are not known. We here identify a key factor: the depth of processing. Human participants were asked to process visual words as they underwent functional MRI. We found that, during shallow processing, multivariate pattern classifiers could decode the word semantic category within each language in putative substrates of the semantic network, but there was no evidence of cross-language generalization in the shallow processing context. By contrast, when the depth of processing was higher, significant cross-language generalization was observed in several regions, including inferior parietal, ventromedial, lateral temporal, and inferior frontal cortex. These results are in keeping with distributed-only views of semantic processing and favour models based on multiple semantic hubs. The results also have ramifications for existing psycholinguistic models of word processing such as the BIA+, which by default assumes non-selective access to both native and second languagesD.S. acknowledges support from the Basque Government through the BERC 2018–2021 program, from the Spanish Ministry of Economy and Competitiveness, through the ’Severo Ochoa’ Programme for Centres/Units of Excellence in R&D (SEV-2015-490) and also from project grants PSI2016-76443-P from MINECO and PI-2017-25 from the Basque Government

Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning

Published:20 May 2020How 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.D.S. acknowledges support from the Basque Government through the BERC 2018-2021 programme, from the Spanish Ministry of Economy and Competitiveness, through the ‘Severo Ochoa’ Programme for Centres/Units of Excellence in R&D (SEV-2015-490) and also from project grants PSI2016-76443-P from MINECO and PI-2017-25 from the Basque Government. R.S. acknowledges support by the Basque Government (IT1244-19 and ELKARTEK programmes), and the Spanish Ministry of Economy and Competitiveness MINECO (project TIN2016-78365-R)

Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review

Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research

Multi-level refinement feature pyramid network for scale imbalance object detection

Object detection becomes a challenge due to diversity of object scales. In general, modern object detectors use feature pyramid to learn multi-scale representation for better results. However, current versions of feature pyramid are insufficient to handle scale imbalance, as it is inefficient to integrate semantic information across different scales. Here, we reformulate feature pyramid construction as a feature reconfiguration process. We propose a detection network, Multi-level Refinement Feature pyramid Network, to combine high-level features (i.e., semantic information), middle-level feature and low-level feature (i.e., boundary information), in a highly-nonlinear yet efficient manner. A novel contextual features module is proposed, which consists of global attention and local reconfigurations. It efficiently gathers task-oriented contextual features across different scales and spatial locations (i.e., lightweight local reconfiguration and global attention). To evaluate significance of proposed model, we designed and trained end-to-end single stage detector called MRFDet by assimilating it into Single Shot Detector (SSD), and it achieved better detection performance compared to most recent single-stage objects detectors. MRFDet achieves an AP of 45.2 with MS-COCO and an improvement in $mAP$ of 4.5% with VOC