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 Decoding the Neural Representation of Self and Person Knowledge with Multivariate Pattern Analysis and Data Driven Approaches

2018 ◽  
Author(s):  
Dylan D. Wagner ◽  
Robert Chavez ◽  
Timothy W. Broom
Keyword(s):  
Cognitive Neuroscience ◽  
Conceptual Knowledge ◽  
Person Perception ◽  
Pattern Analysis ◽  
Social Groups ◽  
The Self ◽  
Neural Representation ◽  
Data Driven ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

Multivariate pattern analysis and data driven approaches to understanding how the human brain encodes sensory information and higher level conceptual knowledge have become increasingly dominant in visual and cognitive neuroscience, however it is only in recent years that these methods have been applied to the domain of social information processing. This review examines recent research in the field of social cognitive neuroscience focusing on how multivariate pattern analysis (e.g., pattern classification, representational similarity analysis) and data-driven methods (e.g, reverse correlation, intersubject correlation) have been used to decode and characterize high-level information about the self, other persons and social groups. We begin with a review of what is known about how self-referential processing and person perception are represented in the medial prefrontal cortex based on conventional activation-based neuroimaging approaches. This is followed by a non-technical overview of current multivariate pattern-based and data-driven neuroimaging methods designed to characterize and/or decode neural representations. The remainder of the review focuses on examining how these methods have been applied to the topic of self, person perception and the perception of social groups. Throughout, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, the use of naturalistic stimuli) and discuss several theoretical challenges that arise from the application of these new methods to the question of how the brain represents knowledge about the self and others.

Stable Task Representations under Attentional Load Revealed with Multivariate Pattern Analysis of Human Brain Activity

2015 ◽  
Vol 27 (9) ◽  
pp. 1789-1800 ◽  
Author(s):  
Jason L. Chan ◽  
Aaron Kucyi ◽  
Joseph F. X. DeSouza
Keyword(s):  
Neural Activity ◽  
Pattern Analysis ◽  
Univariate Analysis ◽  
Visual Presentation ◽  
Error Rates ◽  
Oculomotor Control ◽  
Multivariate Pattern Analysis ◽  
Attentional Load ◽  
Multivariate Pattern ◽  
Task Representations

Performing multiple tasks concurrently places a load on limited attentional resources and results in disrupted task performance. Although human neuroimaging studies have investigated the neural correlates of attentional load, how attentional load affects task processing is poorly understood. Here, task-related neural activity was investigated using fMRI with conventional univariate analysis and multivariate pattern analysis (MVPA) while participants performed blocks of prosaccades and antisaccades, either with or without a rapid serial visual presentation (RSVP) task. Performing prosaccades and antisaccades with RSVP increased error rates and RTs, decreased mean activation in frontoparietal brain areas associated with oculomotor control, and eliminated differences in activation between prosaccades and antisaccades. However, task identity could be decoded from spatial patterns of activation both in the absence and presence of an attentional load. Furthermore, in the FEFs and intraparietal sulcus, these spatial representations were found to be similar using cross-trial-type MVPA, which suggests stability under attentional load. These results demonstrate that attentional load may disrupt the strength of task-related neural activity, rather than the identity of task representations.

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.

Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

2016 ◽  
Vol 39 ◽  
Author(s):  
Carolyn Parkinson ◽  
Thalia Wheatley
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Neural Population ◽  
Multivariate Pattern Analysis ◽  
Population Responses ◽  
Population Codes ◽  
Multivariate Pattern ◽  
Insight Into

AbstractMultivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

scholarly journals An empirically-driven guide on using Bayes Factors for M/EEG decoding

2021 ◽  
Author(s):  
Lina Teichmann ◽  
Denise Moerel ◽  
Chris I Baker ◽  
Tijl Grootswagers
Keyword(s):  
Time Series ◽  
Cognitive Science ◽  
Pattern Analysis ◽  
Analysis Data ◽  
Bayes Factors ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

Bayes Factors can be used to provide quantifiable evidence for contrasting hypotheses and have thus become increasingly popular in cognitive science. However, Bayes Factors are rarely used to statistically assess the results of neuroimaging experiments. Here, we provide an empirically-driven guide on implementing Bayes Factors for time-series neural decoding results. Using real and simulated Magnetoencephalography (MEG) data, we examine how parameters such as the shape of the prior and data size affect Bayes Factors. Additionally, we discuss benefits Bayes Factors bring to analysing multivariate pattern analysis data and show how using Bayes Factors can be used instead or in addition to traditional frequentist approaches.

scholarly journals Fast ventral stream neural activity enables rapid visual categorization

2015 ◽  
Author(s):  
Maxime Cauchoix ◽  
Sébastien M Crouzet ◽  
Denis Fize ◽  
Thomas Serre
Keyword(s):  
Neural Activity ◽  
Stimulus Onset ◽  
Ventral Stream ◽  
Natural Scenes ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Visual Categorization ◽  
Shape Processing ◽  
Perceptual Abilities ◽  
Multivariate Pattern

Primates can recognize objects embedded in complex natural scenes in a glimpse. Rapid categorization paradigms have been extensively used to study our core perceptual abilities when the visual system is forced to operate under strong time constraints. However, the neural underpinning of rapid categorization remains to be understood, and the incredible speed of sight has yet to be reconciled with modern ventral stream cortical theories of shape processing. Here we recorded multichannel subdural electrocorticogram (ECoG) signals from intermediate areas (V4/PIT) of the ventral stream of the visual cortex while monkeys were actively engaged in detecting the presence or absence of animal targets in natural scenes. Using multivariate pattern analysis (MVPA) techniques, we quantified at millisecond precision task-relevant signals conveyed by ECoG data. Reliable neural decoding was possible shortly after stimulus onset from single trials with a degree of generalization to experimental manipulations closely mimicking monkeys’ accuracy and reaction time. Together, the present study suggests that rapid ventral stream neural activity induces a selective task-relevant signal subsequently used to drive visual categorization.

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 Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Temporal Information ◽  
Single Subject ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
Over Time

AbstractBackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.ConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

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