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 Outcome contingency selectively affects the neural coding of outcomes but not of tasks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David Wisniewski ◽  
Birte Forstmann ◽  
Marcel Brass
Keyword(s):  
Decision Making ◽  
Parietal Cortex ◽  
Neural Coding ◽  
Free Choice ◽  
Pattern Analysis ◽  
Maintenance Phase ◽  
Large Network ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

AbstractValue-based decision-making is ubiquitous in every-day life, and critically depends on the contingency between choices and their outcomes. Only if outcomes are contingent on our choices can we make meaningful value-based decisions. Here, we investigate the effect of outcome contingency on the neural coding of rewards and tasks. Participants performed a reversal-learning paradigm in which reward outcomes were contingent on trial-by-trial choices, and performed a ‘free choice’ paradigm in which rewards were random and not contingent on choices. We hypothesized that contingent outcomes enhance the neural coding of rewards and tasks, which was tested using multivariate pattern analysis of fMRI data. Reward outcomes were encoded in a large network including the striatum, dmPFC and parietal cortex, and these representations were indeed amplified for contingent rewards. Tasks were encoded in the dmPFC at the time of decision-making, and in parietal cortex in a subsequent maintenance phase. We found no evidence for contingency-dependent modulations of task signals, demonstrating highly similar coding across contingency conditions. Our findings suggest selective effects of contingency on reward coding only, and further highlight the role of dmPFC and parietal cortex in value-based decision-making, as these were the only regions strongly involved in both reward and task coding.

scholarly journals Time-resolved multivariate pattern analysis of infant EEG data

2021 ◽  
Author(s):  
Kira Ashton ◽  
Benjamin Zinszer ◽  
Radoslaw Cichy ◽  
Charles Nelson ◽  
Richard Aslin ◽  
...  
Keyword(s):  
Time Course ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Time Resolved ◽  
Promising Tool ◽  
Eeg Data ◽  
Neuroimaging Data ◽  
Linear Svm ◽  
Multivariate Pattern ◽  
The Impact

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA methods have recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. A pipeline for time-resolved MVPA based on linear SVM classification is described and implemented with accompanying code in both Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above chance classification accuracy. Extensions of the core pipeline are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

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 Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

2019 ◽  
Author(s):  
Andrew A. Chen ◽  
Joanne C. Beer ◽  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Russell T. Shinohara ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neuroimaging Data ◽  
Mean And Variance ◽  
The Mean ◽  
Improved Performance ◽  
Sensitivity Specificity ◽  
Multivariate Pattern ◽  
The Brain ◽  
Multivariate Image

AbstractTo acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing scanner-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of multivariate pattern analysis (MVPA) has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing scanner effects in mean and variance may not be sufficient for MVPA. This stems from the fact that such methods fail to address how correlations between measurements can vary across scanners. Data from the Alzheimer’s Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across scanners and that popular harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization.

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.

scholarly journals Concurrent contextual and time-distant mnemonic information co-exist as feedback in human visual cortex.

2021 ◽  
Author(s):  
Javier Ortiz-Tudela ◽  
Johanna Bergmann ◽  
Matthew Bennett ◽  
Isabelle Ehrlich ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Experience ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Early Visual Cortex ◽  
Efficient Processing ◽  
Sensory Evidence ◽  
Past Experiences ◽  
Multivariate Pattern ◽  
The Brain

Efficient processing of visual environment necessitates the integration of incoming sensory evidence with concurrent contextual inputs and mnemonic content from our past experiences. To delineate how this integration takes place in the brain, we studied modulations of feedback neural patterns in non-stimulated areas of the early visual cortex in humans (i.e., V1 and V2). Using functional magnetic resonance imaging and multivariate pattern analysis, we show that both, concurrent contextual and time-distant mnemonic information, coexist in V1/V2 as feedback signals. The extent to which mnemonic information is reinstated in V1/V2 depends on whether the information is retrieved episodically or semantically. These results demonstrate that our stream of visual experience contains not just information from the visual surrounding, but also memory-based predictions internally generated in the brain.

scholarly journals Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Jin Gu ◽  
Baolin Liu ◽  
Weiran Yan ◽  
Qiaomu Miao ◽  
Jianguo Wei
Keyword(s):  
Pattern Analysis ◽  
Spatial Information ◽  
Occipital Cortex ◽  
Scene Recognition ◽  
Multivariate Pattern Analysis ◽  
Lateral Occipital Complex ◽  
Average Signal Intensity ◽  
Multivariate Pattern ◽  
The Impact ◽  
Lateral Occipital Cortex

Significant objects in a scene can make a great contribution to scene recognition. Besides the three scene-selective regions: parahippocampal place area (PPA), retrosplenial complex (RSC), and occipital place area (OPA), some neuroimaging studies have shown that the lateral occipital complex (LOC) is also engaged in scene recognition processing. In this study, the multivariate pattern analysis was adopted to explore the object-scene association in scene recognition when different amounts of significant objects were masked. The scene classification only succeeded in the intact scene in the ROIs. In addition, the average signal intensity in LOC [including the lateral occipital cortex (LO) and the posterior fusiform area (pF)] decreased when there were masked objects, but such a decrease was not observed in scene-selective regions. These results suggested that LOC was sensitive to the loss of significant objects and mainly involved in scene recognition by the object-scene semantic association. The performance of the scene-selective areas may be mainly due to the fact that they responded to the change of the scene's entire attribute, such as the spatial information, when they were employed in the scene recognition processing. These findings further enrich our knowledge of the significant objects' influence on the activation pattern during the process of scene recognition.

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