scholarly journals Decoding Digits and Dice with Magnetoencephalography: Evidence for a Shared Representation of Magnitude

2018 ◽  
Vol 30 (7) ◽  
pp. 999-1010 ◽  
Author(s):  
Lina Teichmann ◽  
Tijl Grootswagers ◽  
Thomas Carlson ◽  
Anina N. Rich
Keyword(s):  
Time Course ◽  
Numerical Cognition ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Multivariate Pattern Analysis ◽  
Ongoing Debate ◽  
Activation Patterns ◽  
Magnitude Processing ◽  
Multivariate Pattern

Numerical format describes the way magnitude is conveyed, for example, as a digit (“3”) or Roman numeral (“III”). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography offers an opportunity to record brain activity with high temporal resolution. Multivariate pattern analysis applied to magnetoencephalographic data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can cross-decode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests a similar representation of these numerical symbols. In addition, results from a time generalization analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

scholarly journals Decoding digits and dice with Magnetoencephalography: Evidence for a shared representation of magnitude

2018 ◽  
Author(s):  
A. Lina Teichmann ◽  
Tijl Grootswagers ◽  
Thomas Carlson ◽  
Anina N. Rich
Keyword(s):  
Time Course ◽  
Numerical Cognition ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Multivariate Pattern Analysis ◽  
Ongoing Debate ◽  
Activation Patterns ◽  
Magnitude Processing ◽  
Multivariate Pattern

AbstractNumerical format describes the way magnitude is conveyed, for example as a digit (‘3’) or Roman Numeral (‘III’). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography (MEG) offers an opportunity to record brain activity with high temporal resolution. Multivariate Pattern Analysis (MVPA) applied to MEG data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can crossdecode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests similar representation of these numerical symbols. Additionally, results from a time-generalisation analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

Revisiting the Role of the Fusiform Face Area in Expertise

2016 ◽  
Vol 28 (9) ◽  
pp. 1345-1357 ◽  
Author(s):  
Merim Bilalić
Keyword(s):  
Pattern Analysis ◽  
Spatial Relations ◽  
Fusiform Face Area ◽  
Multivariate Pattern Analysis ◽  
Multiple Objects ◽  
Activation Patterns ◽  
Face Area ◽  
Single Category ◽  
Multivariate Pattern

The fusiform face area (FFA) is considered to be a highly specialized brain module because of its central importance for face perception. However, many researchers claim that the FFA is a general visual expertise module that distinguishes between individual examples within a single category. Here, I circumvent the shortcomings of some previous studies on the FFA controversy by using chess stimuli, which do not visually resemble faces, together with more sensitive methods of analysis such as multivariate pattern analysis. I also extend the previous research by presenting chess positions, complex scenes with multiple objects, and their interrelations to chess experts and novices as well as isolated chess objects. The first experiment demonstrates that chess expertise modulated the FFA activation when chess positions were presented. In contrast, single chess objects did not produce different activation patterns among experts and novices even when the multivariate pattern analysis was used. The second experiment focused on the single chess objects and featured an explicit task of identifying the chess objects but failed to demonstrate expertise effects in the FFA. The experiments provide support for the general expertise view of the FFA function but also extend the scope of our understanding about the function of the FFA. The FFA does not merely distinguish between different exemplars within the same category of stimuli. More likely, it parses complex multiobject stimuli that contain numerous functional and spatial relations.

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 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 Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

2017 ◽  
Vol 29 (4) ◽  
pp. 677-697 ◽  
Author(s):  
Tijl Grootswagers ◽  
Susan G. Wardle ◽  
Thomas A. Carlson
Keyword(s):  
Time Series ◽  
Cognitive Neuroscience ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Activation Patterns ◽  
Cognitive States ◽  
Classifier Selection ◽  
Computer Interfaces ◽  
Neuroimaging Data ◽  
Multivariate Pattern

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

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