Performance monitoring beyond choice tasks: The time course of force execution monitoring investigated by event-related potentials and multivariate pattern analysis

Refreshing is the component cognitive process of directing reflective attention to one of several active mental representations. Previous studies using fMRI suggested that refresh tasks involve a component process of initiating refreshing as well as the top–down modulation of representational regions central to refreshing. However, those studies were limited by fMRI's low temporal resolution. In this study, we used EEG to examine the time course of refreshing on the scale of milliseconds rather than seconds. ERP analyses showed that a typical refresh task does have a distinct electrophysiological response as compared to a control condition and includes at least two main temporal components: an earlier (∼400 msec) positive peak reminiscent of a P3 response and a later (∼800–1400 msec) sustained positivity over several sites reminiscent of the late directing attention positivity. Overall, the evoked potentials for refreshing representations from three different visual categories (faces, scenes, words) were similar, but multivariate pattern analysis showed that some category information was nonetheless present in the EEG signal. When related to previous fMRI studies, these results are consistent with a two-phase model, with the first phase dominated by frontal control signals involved in initiating refreshing and the second by the top–down modulation of posterior perceptual cortical areas that constitutes refreshing a representation. This study also lays the foundation for future studies of the neural correlates of reflective attention at a finer temporal resolution than is possible using fMRI.

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The Decision Decoding ToolBOX (DDTBOX) – A Multivariate Pattern Analysis Toolbox for Event-Related Potentials

Neuroinformatics ◽

10.1007/s12021-018-9375-z ◽

2018 ◽

Vol 17 (1) ◽

pp. 27-42 ◽

Cited By ~ 10

Author(s):

Stefan Bode ◽

Daniel Feuerriegel ◽

Daniel Bennett ◽

Phillip M. Alday

Keyword(s):

Pattern Analysis ◽

Event Related Potentials ◽

Multivariate Pattern Analysis ◽

Related Potentials ◽

Multivariate Pattern

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Characterizing the neural signature of face processing in Williams syndrome via multivariate pattern analysis and event related potentials

Neuropsychologia ◽

10.1016/j.neuropsychologia.2020.107440 ◽

2020 ◽

Vol 142 ◽

pp. 107440

Author(s):

Emily K. Farran ◽

Inês Mares ◽

Michael Papasavva ◽

Fraser W. Smith ◽

Louise Ewing ◽

...

Keyword(s):

Williams Syndrome ◽

Face Processing ◽

Pattern Analysis ◽

Event Related Potentials ◽

Multivariate Pattern Analysis ◽

Related Potentials ◽

Neural Signature ◽

Multivariate Pattern

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The Decision Decoding ToolBOX (DDTBOX) – A multivariate pattern analysis toolbox for event-related potentials

10.1101/153189 ◽

2017 ◽

Cited By ~ 2

Author(s):

Stefan Bode ◽

Daniel Feuerriegel ◽

Daniel Bennett ◽

Phillip M. Alday

Keyword(s):

Open Source ◽

Pattern Analysis ◽

Event Related Potentials ◽

Event Related Potential ◽

Statistical Testing ◽

Support Vector ◽

Single Subject ◽

Multivariate Pattern Analysis ◽

Related Potentials ◽

Multivariate Pattern

AbstractIn recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig, 2004; Lopez-Calderon and Luck, 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

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Time-resolved multivariate pattern analysis of infant EEG data

10.1101/2021.06.16.448720 ◽

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.

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

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01257 ◽

2018 ◽

Vol 30 (7) ◽

pp. 999-1010 ◽

Cited By ~ 13

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.

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

10.1101/249342 ◽

2018 ◽

Cited By ~ 1

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.

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