Ventral and Dorsal Pathways Relate Differently to Visual Awareness of Body Postures under Continuous Flash Suppression

Zhan, Minye; Goebel, Rainer; Gelder, Béatrice de

doi:10.1523/eneuro.0285-17.2017

Cited by 30 publications

(24 citation statements)

References 49 publications

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“…In accordance to the two-stream theory of visual perception (Milner & Goodale, 2008), the majority of the following studies also pointed to the association between conscious perception and activities in the ventral ROIs. According to these studies, a visible target induces stronger signal in the ventral ROIs than an invisible target (Hesselmann, Hebart, & Malach, 2011;Hesselmann & Malach, 2011;Ludwig, Kathmann, Sterzer, & Hesselmann, 2015;Zhan, Goebel, & de Gelder, 2018). The results regarding the neural correlates of nonconscious perception with CFS, however, are mixed, and did not provide supporting evidence for this theory.…”

Section: Processing Of Objects In Dorsal Vs Ventral Visual Streammentioning

confidence: 88%

Continuous flash suppression: Known and unknowns

Pournaghdali

Schwartz

2020

Psychon Bull Rev

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Studies utilizing continuous flash suppression (CFS) provide valuable information regarding conscious and nonconscious perception. There are, however, crucial unanswered questions regarding the mechanisms of suppression and the level of visual processing in the absence of consciousness with CFS. Research suggests that the answers to these questions depend on the experimental configuration and how we assess consciousness in these studies. The aim of this review is to evaluate the impact of different experimental configurations and the assessment of consciousness on the results of the previous CFS studies. We review studies that evaluated the influence of different experimental configuration on the depth of suppression with CFS and discuss how different assessments of consciousness may impact the results of CFS studies. Finally, we review behavioral and brain recording studies of CFS. In conclusion, previous studies provide evidence for survival of low-level visual information and complete impairment of high-level visual information under the influence of CFS. That is, studies suggest that nonconscious perception of lower-level visual information happens with CFS but there is no evidence for nonconscious highlevel recognition with CFS.

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Section: Processing Of Objects In Dorsal Vs Ventral Visual Streammentioning

confidence: 88%

Continuous flash suppression: Known and unknowns

Pournaghdali

Schwartz

2020

Psychon Bull Rev

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“…Moreover, brain structure segmentation is an early step in functional MRI (fMRI) study pipelines, as neuroscientists need to isolate specific brain structures before analysing the spatiotemporal patterns of activity within them. Manual segmentation, although considered to be the gold standard in terms of accuracy, is time consuming [1]. Therefore, neuroscience studies began to exploit computer vision to process data from increasingly performing MRI scanners and ease the interpretation of brain data, intrinsically characterised by a strong inter-subject variability.…”

Section: Introductionmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

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Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.

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“…For the visual distractor task, participants indicated whether the fixation cross turned lighter or darker during the trial. A separate localizer session was also performed where participants passively viewed stimuli of faces, bodies, houses, tools and words in blocks; see (Zhan M et al 2018) for details.…”

Section: Methodsmentioning

confidence: 99%

Decoding of emotion expression in the face, body and voice reveals sensory modality specific representations

Vaessen

2019

Preprint

Self Cite

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1 0 1 1 1 2 2 ABSTRACT 1 3A central issue in affective science is whether the brain represents the emotional expressions of 1 4 faces, bodies and voices as abstract categories in which auditory and visual information converge 1 5 3 1 are recognized effortlessly and responded to spontaneously when rapid adaptive actions are 3 2 required. The specifics of the subjective experience in the natural environment determine which 3 3 affective signal dominates and triggers the adaptive behavior. Rarely are the face, the whole 3 4body and the voice equally salient. That is, the actual conditions under which we react to an 3 5 angry face may be different from those of hearing an angry voice or viewing whole body 3 6 movements. For example, we see faces from close by and therefore personal familiarity may play 3 7 a role in how we react to the angry face. This is less so for the voice or the whole body, both of 3 8 which already prompt reactions when seen or heard from a distance while information about 3 9 personal identity is not yet available or needed for action preparation. Thus, the angry body 4 0 expression viewed from a distance and the angry face expression seen from closeby may each 4 1 trigger a different reaction as behavior needs to be adapted to the concrete context. Therefore, a 4 2 representation of affective meaning that is sensitive to the spatiotemporal parameters may seem 4 3 desirable rather than an abstract system of higher order concepts as traditionally envisaged by 4 4 emotion theorists (Ekman P and D Cordaro 2011); but see (Lindquist KA et al. 2012). 5Research on the brain correlates of emotion has favored the traditional notion of abstract 4 6 neural representations of basic emotions and this has also been the dominant rationale for 4 7 multisensory research. Studies comparing how not just the face but also the voice and the whole 4 8 body convey emotions have followed this overall basic emotion perspective and asked where in 4 9 7 1 basic emotions with discrete brain correlates continues to generate controversy (Kragel PA and 7 2 KS LaBar 2016; Saarimaki H et al. 2016). Second, detailed meta-analyses of crossmodal and 7 3 multisensory studies, whether they are reviewing the findings about each separate modality or 7 4 the results of crossmodal studies (Dricu M and S Fruhholz 2016; Schirmer A and R Adolphs 7 5 2017), provide a mixed picture. Furthermore, these meta-analyses also show that a number of 7 6 methodological obstacles stand in the way of valid comparisons across studies. That is, taking 7 7 into account the role of task (incidental perception, passive perception, and explicit evaluation of 7 8 emotional expression) and the use of appropriate control stimuli reduces the number of studies 7 9 that can validly be compared. Third, findings from studies that pay attention to individual 8 0 differences and to clinical aspects reveal individual differences in sensory salience and 8 1 dominance in clinical populations, for example in autism and schizophrenia. For example, (Karle 8 2 KN et al. 20...

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Ventral and Dorsal Pathways Relate Differently to Visual Awareness of Body Postures under Continuous Flash Suppression

Cited by 30 publications

References 49 publications

Continuous flash suppression: Known and unknowns

Continuous flash suppression: Known and unknowns

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Decoding of emotion expression in the face, body and voice reveals sensory modality specific representations

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