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Electrophysiological priming effects demonstrate independence and overlap of visual regularity representations in the extrastriate cortex
research-article
Author(s):
Alexis D. J. Makin
1
,
* ,
John Tyson-Carr
1 ,
Yiovanna Derpsch
1 ,
Giulia Rampone
1 ,
Marco Bertamini
1
,
2
Publication date (Electronic):
9 July 2021
Journal:
PLoS ONE
Publisher:
Public Library of Science
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
An Event Related Potential (ERP) component called the Sustained Posterior Negativity (SPN) is generated by regular visual patterns (e.g. vertical reflectional symmetry, horizontal reflectional symmetry or rotational symmetry). Behavioural studies suggest symmetry becomes increasingly salient when the exemplars update rapidly. In line with this, Experiment 1 (N = 48) found that SPN amplitude increased when three different reflectional symmetry patterns were presented sequentially. We call this effect ‘SPN priming’. We then exploited SPN priming to investigate independence of different symmetry representations. SPN priming did not survive changes in retinal location (Experiment 2, N = 48) or non-orthogonal changes in axis orientation (Experiment 3, N = 48). However, SPN priming transferred between vertical and horizontal axis orientations (Experiment 4, N = 48) and between reflectional and rotational symmetry (Experiment 5, N = 48). SPN priming is interesting in itself, and a useful new method for identifying functional boundaries of the symmetry response. We conclude that visual regularities at different retinal locations are coded independently. However, there is some overlap between different regularities presented at the same retinal location.
Related collections
Most cited references48
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- Abstract: found
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EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
Arnaud Delorme, Scott Makeig (2005)
We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
- Record: found
- Abstract: found
- Article: not found
Removing electroencephalographic artifacts by blind source separation.
T P Jung, S Makeig, C. Humphries … (2000)
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
- Record: found
- Abstract: found
- Article: not found
The mismatch negativity (MMN) in basic research of central auditory processing: a review.
R Näätänen, P Paavilainen, T Rinne … (2007)
In the present article, the basic research using the mismatch negativity (MMN) and analogous results obtained by using the magnetoencephalography (MEG) and other brain-imaging technologies is reviewed. This response is elicited by any discriminable change in auditory stimulation but recent studies extended the notion of the MMN even to higher-order cognitive processes such as those involving grammar and semantic meaning. Moreover, MMN data also show the presence of automatic intelligent processes such as stimulus anticipation at the level of auditory cortex. In addition, the MMN enables one to establish the brain processes underlying the initiation of attention switch to, conscious perception of, sound change in an unattended stimulus stream.
Author and article information
Contributors
Alexis D. J. Makin:
ORCID: https://orcid.org/0000-0002-4490-7400
Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole:
InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole:
SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole:
Writing – review & editing
John Tyson-Carr: Role: Data curationRole: Formal analysisRole: Writing – review & editing
Yiovanna Derpsch: Role: Data curationRole: Writing – review & editing
Giulia Rampone: Role: ConceptualizationRole: Writing – review & editing
Marco Bertamini:
ORCID: https://orcid.org/0000-0001-8617-6864
Role: ConceptualizationRole: ResourcesRole: Writing – review & editing
Tyler Davis: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS One
Journal ID (publisher-id): plos
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, CA USA
)
ISSN
(Electronic):
1932-6203
Publication date
(Electronic):
9
July
2021
Publication date Collection: 2021
Volume: 16
Issue: 7
Electronic Location Identifier: e0254361
Affiliations
Author notes
* E-mail:
Alexis.makin@
123456liverpool.ac.uk
Author information
Alexis D. J. Makin
https://orcid.org/0000-0002-4490-7400
Marco Bertamini
https://orcid.org/0000-0001-8617-6864
Article
Publisher ID: PONE-D-20-34598DOI: 10.1371/journal.pone.0254361
PMC ID: 8270198
PubMed ID: 34242360
SO-VID: 33641e1c-8cbf-40fd-b4c3-c2bc240d8159
Copyright © © 2021 Makin et al
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date
received
: 3
November
2020
Date
accepted
: 24
June
2021
Page count
Figures: 15,
Tables: 0,
Pages: 28
Funding
Funded by:
funder-id http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
Award ID: ES/S014691/1
Award Recipient
:
ORCID: https://orcid.org/0000-0002-4490-7400
Alexis D. J. Makin
Funded by:
funder-id http://dx.doi.org/10.13039/501100000269, Economic and Social Research Council;
Award ID: ES/K000187/1
Award Recipient
:
ORCID: https://orcid.org/0000-0001-8617-6864
Marco Bertamini
This project was part funded by an ESRC grant award to Marco Bertamini (ES/K000187/1)
and part funded by an ESRC grant awarded to Alexis Makin (ES/S014691/1).
Categories
Subject:
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Physics
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Classical Mechanics
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Event-Related Potentials
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Custom metadata
Data Availability All stimuli and image construction algorithms are available on Open Science Framework
(OSF), along with ERP analysis materials and Supplementary Materials 1 and 2 (
https://osf.io/2yjus/).
ScienceOpen disciplines: Uncategorized
Data availability:
ScienceOpen disciplines: Uncategorized