scholarly journals Neural correlates of conscious visual perception lie outside the visual system: evidence from the double-drift illusion

2019 ◽  
Author(s):  
Sirui Liu ◽  
Qing Yu ◽  
Peter U. Tse ◽  
Patrick Cavanagh
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Activity Patterns ◽  
Neural Correlates ◽  
Multivariate Pattern Analysis ◽  
Visual Areas ◽  
Visual Cortices ◽  
Multivariate Pattern ◽  
The Brain

SummaryWhen perception differs from the physical stimulus, as it does for visual illusions and binocular rivalry, the opportunity arises to localize where perception emerges in the visual processing hierarchy. Representations prior to that stage differ from the eventual conscious percept even though they provide input to it. Here we investigate where and how a remarkable misperception of position emerges in the brain. This “double-drift” illusion causes a dramatic mismatch between retinal and perceived location, producing a perceived path that can differ from its physical path by 45° or more [1]. The deviations in the perceived trajectory can accumulate over at least a second [1] whereas other motion-induced position shifts accumulate over only 80 to 100 ms before saturating [2]. Using fMRI and multivariate pattern analysis, we find that the illusory path does not share activity patterns with a matched physical path in any early visual areas. In contrast, a whole-brain searchlight analysis reveals a shared representation in more anterior regions of the brain. These higher-order areas would have the longer time constants required to accumulate the small moment-to-moment position offsets that presumably originate in early visual cortices, and then transform these sensory inputs into a final conscious percept. The dissociation between perception and the activity in early sensory cortex suggests that perceived position does not emerge in what is traditionally regarded as the visual system but emerges instead at a much higher level.

Role of the occipito-temporal theta rhythm in hand visual identification

2020 ◽  
Vol 123 (1) ◽  
pp. 167-177 ◽  
Author(s):  
Quentin Moreau ◽  
Eleonora Parrotta ◽  
Vanessa Era ◽  
Maria Luisa Martelli ◽  
Matteo Candidi
Keyword(s):  
Visual Processing ◽  
Functional Role ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Theta Band ◽  
Time Frequency ◽  
Visual Identification ◽  
Multivariate Pattern ◽  
Healthy Participants

Neuroimaging and EEG studies have shown that passive observation of the full body and of specific body parts is associated with 1) activity of an occipito-temporal region named the extrastriate body area (EBA), 2) amplitude modulations of a specific posterior event-related potential (ERP) component (N1/N190), and 3) a theta-band (4–7 Hz) synchronization recorded from occipito-temporal electrodes compatible with the location of EBA. To characterize the functional role of the occipito-temporal theta-band increase during the processing of body-part stimuli, we recorded EEG from healthy participants while they were engaged in an identification task (match-to-sample) of images of hands and nonbody control images (leaves). In addition to confirming that occipito-temporal electrodes show a larger N1 for hand images compared with control stimuli, cluster-based analysis revealed an occipito-temporal cluster showing an increased theta power when hands are presented (compared with leaves) and show that this theta increase is higher for identified hands compared with nonidentified ones while not being significantly different between not identified nonhand stimuli. Finally, single trial multivariate pattern analysis revealed that time-frequency modulation in the theta band is a better marker for classifying the identification of hand images than the ERP modulation. The present results support the notion that theta activity over the occipito-temporal cortex is an informative marker of hand visual processing and may reflect the activity of a network coding for stimulus identity. NEW & NOTEWORTHY Hands provide crucial information regarding the identity of others, which is a key information for social processes. We recorded EEG activity of healthy participants during the visual identification of hand images. The combination of univariate and multivariate pattern analysis in time- and time-frequency domain highlights the functional role of theta (4–7 Hz) activity over visual areas during hand identification and emphasizes the robustness of this neuromarker in occipito-temporal visual processing dynamics.

scholarly journals Representational confusion: the plausible consequence of demeaning your data

2017 ◽  
Author(s):  
Fernando M. Ramírez
Keyword(s):  
Neural Coding ◽  
Pattern Analysis ◽  
Brain Area ◽  
Multivariate Pattern Analysis ◽  
Class Membership ◽  
Multivariate Pattern ◽  
Potential Interactions ◽  
The Impact ◽  
The Brain ◽  
Extract Information

AbstractThe use of multivariate pattern analysis (MVPA) methods has enjoyed this past decade a rapid increase in popularity among neuroscientists. More recently, similarity-based multivariate methods aiming not only to extract information regarding the class membership of stimuli from their associated brain patterns, say, decode a face from a potato, but to understand the form of the underlying representational structure associated with stimulus dimensions of interest, say, 2D grating or 3D face orientation, have flourished under the name of Representational Similarity Analysis (RSA). However, data-preprocessing steps implemented prior to RSA can significantly change the covariance (and correlation) structure of the data, hence possibly leading to representational confusion—i.e., a researcher inferring that brain area A encodes information according to representational scheme X, and not Y, when the opposite is true. Here, I demonstrate with simulations that time-series demeaning (including z-scoring) can plausibly lead to representational confusion. Further, I expose potential interactions between the effects of data demeaning and how the brain happens to encode information. Finally, I emphasize the importance in the context of similarity analyses of at least occasionally explicitly considering the direction of pattern vectors in multivariate space, rather than focusing exclusively on the relative location of their endpoints. Overall, I expect this article will promote awareness of the impact of data demeaning on inferences regarding representational structure and neural coding.

scholarly journals Spatiotemporally Resolved Multivariate Pattern Analysis for M/EEG

2021 ◽  
Author(s):  
Cameron J Higgins ◽  
Diego Vidaurre ◽  
Nils Kolling ◽  
Yunzhe Liu ◽  
Tim Behrens ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Activity Patterns ◽  
Multivariate Pattern Analysis ◽  
Model Framework ◽  
Model Inversion ◽  
Classification And Regression ◽  
Multivariate Pattern ◽  
Generative Encoding

An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. Whilst EEG and MEG offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion allows predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in future.

scholarly journals Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data

2020 ◽  
Author(s):  
Andrew E. Silva ◽  
Benjamin Thompson ◽  
Zili Liu
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Flicker Noise ◽  
Univariate Analysis ◽  
Motion Processing ◽  
List Type ◽  
Multivariate Pattern Analysis ◽  
Motion Energy ◽  
Multivariate Pattern

AbstractThis study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy which is processed by low-level motion detectors. Models of motion processing propose a mechanism called motion opponency that reduces the processing of flicker noise. Motion opponency involves the pooling of local motion signals to calculate an overall motion direction. A neural correlate of motion opponency has been observed in human area MT+/V5 using fMRI, whereby stimuli with perfectly balanced motion energy constructed from dots moving in counter-phase elicit a weaker BOLD response than non-balanced (in-phase) motion stimuli. Building on this previous work, we used multivariate pattern analysis to examine whether the patterns of brain activation elicited by motion opponent stimuli resemble the activation elicited by flicker noise across the human visual cortex. Robust multivariate signatures of opponency were observed in V5 and in V3A. Our results support the notion that V5 is centrally involved in motion opponency and in the reduction of flicker noise during visual processing. Furthermore, these results demonstrate the utility of powerful multivariate analysis methods in revealing the role of additional visual areas, such as V3A, in opponency and in motion processing more generally.HighlightsOpponency is demonstrated in multivariate and univariate analysis of V5 data.Multivariate fMRI also implicates V3A in motion opponency.Multivariate analyses are useful for examining opponency throughout visual cortex.

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

scholarly journals Guilty by association: How group-based (collective) guilt arises in the brain

2019 ◽  
Author(s):  
Zhiai Li ◽  
Hongbo Yu ◽  
Yongdi Zhou ◽  
Tobias Kalenscher ◽  
Xiaolin Zhou
Keyword(s):  
Functional Mri ◽  
Pattern Analysis ◽  
Group Membership ◽  
Shared Responsibility ◽  
Multivariate Pattern Analysis ◽  
Collective Guilt ◽  
Social Emotions ◽  
Group Members ◽  
Multivariate Pattern ◽  
The Brain

AbstractPeople do not only feel guilty for transgressions of social norms/expectations that they are causally responsible for, but they also feel guilty for transgressions committed by those they identify as in-group (i.e., collective or group-based guilt). However, the neurocognitive basis of group-based guilt and its relation to personal guilt are unknown. To address these questions, we combined functional MRI with an interaction-based minimal group paradigm in which participants either directly caused harm to victims (i.e., personal guilt), or observed in-group members cause harm to the victims (i.e., group-based guilt). In three experiments (N = 90), we demonstrated that perceived shared responsibility with in-group members in the transgression predicted behavioral and neural manifestations of group-based guilt. Multivariate pattern analysis of the functional MRI data showed that group-based guilt recruited a similar brain representation in anterior middle cingulate cortex as personal guilt. These results have broaden our understanding of how group membership is integrated into social emotions.

scholarly journals Individual face and house-related eye movement patterns distinctively activate FFA and PPA in the absence of faces and houses

2019 ◽  
Author(s):  
Lihui Wang ◽  
Florian Baumgartner ◽  
Falko R. Kaule ◽  
Michael Hanke ◽  
Stefan Pollmann
Keyword(s):  
Pattern Analysis ◽  
Perception And Action ◽  
Fusiform Face Area ◽  
Multivariate Pattern Analysis ◽  
Face Area ◽  
Gaze Patterns ◽  
Visual Areas ◽  
Uniform Background ◽  
Multivariate Pattern ◽  
Parahippocampal Place Area

We investigated if the fusiform face area (FFA) and the parahippocampal place area (PPA) contain a representation of fixation sequences that are typically used when looking at faces or houses. For this purpose, we instructed observers to follow a dot presented on a uniform background. The dot's movements represented gaze paths acquired separately while observers were looking at face or house pictures. Even when gaze dispersion differences were controlled, face- and house-associated gaze patterns could be discriminated by multivariate pattern analysis in the FFA and PPA. The discrimination of face- and house-associated gaze patterns in FFA and PPA was more sensitive for the current observer’s own gazes than for another observer’s gaze. Moreover, the discrimination of the observer’s own gaze patterns was specific to FFA and PPA, but was not observed in early visual areas (V1 – V4) or superior parietal lobule and frontal eye fields. These findings indicate a link between perception and action - the complex gaze patterns that are used to explore faces and houses - in the FFA and PPA.

scholarly journals Mapping brain activation and information during category-specific visual working memory

2012 ◽  
Vol 107 (2) ◽  
pp. 628-639 ◽  
Author(s):  
David E. J. Linden ◽  
Nikolaas N. Oosterhof ◽  
Christoph Klein ◽  
Paul E. Downing
Keyword(s):  
Working Memory ◽  
Cortical Activation ◽  
Multivariate Pattern Analysis ◽  
Frontoparietal Network ◽  
Visual Areas ◽  
Occipitotemporal Cortex ◽  
Initial Processing ◽  
Multivariate Pattern ◽  
The Brain ◽  
Term Maintenance

How is working memory for different visual categories supported in the brain? Do the same principles of cortical specialization that govern the initial processing and encoding of visual stimuli also apply to their short-term maintenance? We investigated these questions with a delayed discrimination paradigm for faces, bodies, flowers, and scenes and applied both univariate and multivariate analyses to functional magnetic resonance imaging (fMRI) data. Activity during encoding followed the well-known specialization in posterior areas. During the delay interval, activity shifted to frontal and parietal regions but was not specialized for category. Conversely, activity in visual areas returned to baseline during that interval but showed some evidence of category specialization on multivariate pattern analysis (MVPA). We conclude that principles of cortical activation differ between encoding and maintenance of visual material. Whereas perceptual processes rely on specialized regions in occipitotemporal cortex, maintenance involves the activation of a frontoparietal network that seems to require little specialization at the category level. We also confirm previous findings that MVPA can extract information from fMRI signals in the absence of suprathreshold activation and that such signals from visual areas can reflect the material stored in memory.

scholarly journals Individual face- and house-related eye movement patterns distinctively activate FFA and PPA

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Lihui Wang ◽  
Florian Baumgartner ◽  
Falko R. Kaule ◽  
Michael Hanke ◽  
Stefan Pollmann
Keyword(s):  
Pattern Analysis ◽  
Perception And Action ◽  
Fusiform Face Area ◽  
Multivariate Pattern Analysis ◽  
Face Area ◽  
Gaze Patterns ◽  
Visual Areas ◽  
Uniform Background ◽  
Multivariate Pattern ◽  
Parahippocampal Place Area

AbstractWe investigated if the fusiform face area (FFA) and the parahippocampal place area (PPA) contain a representation of fixation sequences that are typically used when looking at faces or houses. Here, we instructed observers to follow a dot presented on a uniform background. The dot’s movements represented gaze paths acquired separately from observers looking at face or house pictures. Even when gaze dispersion differences were controlled, face- and house-associated gaze patterns could be discriminated by fMRI multivariate pattern analysis in FFA and PPA, more so for the current observer’s own gazes than for another observer’s gaze. The discrimination of the observer’s own gaze patterns was not observed in early visual areas (V1 – V4) or superior parietal lobule and frontal eye fields. These findings indicate a link between perception and action—the complex gaze patterns that are used to explore faces and houses—in the FFA and PPA.

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