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Carrasco CD, Bahle B, Simmons AM, Luck SJ. Using multivariate pattern analysis to increase effect sizes for event-related potential analyses. Psychophysiology 2024; 61:e14570. [PMID: 38516957 DOI: 10.1111/psyp.14570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/21/2024] [Accepted: 03/09/2024] [Indexed: 03/23/2024]
Abstract
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE data set and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two MVPA approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software). We assessed these approaches across seven widely studied ERP components (N170, N400, N2pc, P3b, lateral readiness potential, error related negativity, and mismatch negativity). Across all components, we found that multivariate approaches yielded effect sizes that were as large or larger than the effect sizes produced by univariate approaches. These results indicate that researchers could obtain larger effect sizes, and therefore greater statistical power, by using multivariate analysis of topographic voltage patterns instead of traditional univariate analyses in many ERP studies.
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Affiliation(s)
| | - Brett Bahle
- Center for Mind and Brain, University of California, Davis, California, USA
| | | | - Steven J Luck
- Center for Mind and Brain, University of California, Davis, California, USA
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Carrasco CD, Bahle B, Simmons AM, Luck SJ. Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.07.566051. [PMID: 37986854 PMCID: PMC10659264 DOI: 10.1101/2023.11.07.566051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Multivariate pattern analysis approaches can be applied to the topographic distribution of event-related potential (ERP) signals to 'decode' subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE dataset and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two multivariate pattern analysis approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software). We assessed these approaches across seven widely studied ERP components (N170, N400, N2pc, P3b, lateral readiness potential, error related negativity, and mismatch negativity). Across all components, we found that multivariate approaches yielded effect sizes that were as large or larger than the effect sizes produced by univariate approaches. These results indicate that researchers could obtain larger effect sizes, and therefore greater statistical power, by using multivariate analysis of topographic voltage patterns instead of traditional univariate analyses in many ERP studies.
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Affiliation(s)
| | - Brett Bahle
- Center for Mind & Brain, University of California, Davis
| | | | - Steven J Luck
- Center for Mind & Brain, University of California, Davis
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Amaral L, Besson G, Caparelli-Dáquer E, Bergström F, Almeida J. Temporal differences and commonalities between hand and tool neural processing. Sci Rep 2023; 13:22270. [PMID: 38097608 PMCID: PMC10721913 DOI: 10.1038/s41598-023-48180-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/23/2023] [Indexed: 12/17/2023] Open
Abstract
Object recognition is a complex cognitive process that relies on how the brain organizes object-related information. While spatial principles have been extensively studied, less studied temporal dynamics may also offer valuable insights into this process, particularly when neural processing overlaps for different categories, as it is the case of the categories of hands and tools. Here we focus on the differences and/or similarities between the time-courses of hand and tool processing under electroencephalography (EEG). Using multivariate pattern analysis, we compared, for different time points, classification accuracy for images of hands or tools when compared to images of animals. We show that for particular time intervals (~ 136-156 ms and ~ 252-328 ms), classification accuracy for hands and for tools differs. Furthermore, we show that classifiers trained to differentiate between tools and animals generalize their learning to classification of hand stimuli between ~ 260-320 ms and ~ 376-500 ms after stimulus onset. Classifiers trained to distinguish between hands and animals, on the other hand, were able to extend their learning to the classification of tools at ~ 150 ms. These findings suggest variations in semantic features and domain-specific differences between the two categories, with later-stage similarities potentially related to shared action processing for hands and tools.
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Affiliation(s)
- L Amaral
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC, USA.
| | - G Besson
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- CINEICC, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - E Caparelli-Dáquer
- Laboratory of Electrical Stimulation of the Nervous System (LabEEL), Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - F Bergström
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- CINEICC, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - J Almeida
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
- CINEICC, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
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Cavanagh P, Caplovitz GP, Lytchenko TK, Maechler MR, Tse PU, Sheinberg DL. The Architecture of Object-Based Attention. Psychon Bull Rev 2023; 30:1643-1667. [PMID: 37081283 DOI: 10.3758/s13423-023-02281-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
The allocation of attention to objects raises several intriguing questions: What are objects, how does attention access them, what anatomical regions are involved? Here, we review recent progress in the field to determine the mechanisms underlying object-based attention. First, findings from unconscious priming and cueing suggest that the preattentive targets of object-based attention can be fully developed object representations that have reached the level of identity. Next, the control of object-based attention appears to come from ventral visual areas specialized in object analysis that project downward to early visual areas. How feedback from object areas can accurately target the object's specific locations and features is unknown but recent work in autoencoding has made this plausible. Finally, we suggest that the three classic modes of attention may not be as independent as is commonly considered, and instead could all rely on object-based attention. Specifically, studies show that attention can be allocated to the separated members of a group-without affecting the space between them-matching the defining property of feature-based attention. At the same time, object-based attention directed to a single small item has the properties of space-based attention. We outline the architecture of object-based attention, the novel predictions it brings, and discuss how it works in parallel with other attention pathways.
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Affiliation(s)
- Patrick Cavanagh
- Department of Psychology, Glendon College, 2275 Bayview Avenue, North York, ON, M4N 3M6, Canada.
- CVR, York University, Toronto, ON, Canada.
| | | | | | | | | | - David L Sheinberg
- Department of Neuroscience, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
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Ayzenberg V, Simmons C, Behrmann M. Temporal asymmetries and interactions between dorsal and ventral visual pathways during object recognition. Cereb Cortex Commun 2023; 4:tgad003. [PMID: 36726794 PMCID: PMC9883614 DOI: 10.1093/texcom/tgad003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/15/2023] Open
Abstract
Despite their anatomical and functional distinctions, there is growing evidence that the dorsal and ventral visual pathways interact to support object recognition. However, the exact nature of these interactions remains poorly understood. Is the presence of identity-relevant object information in the dorsal pathway simply a byproduct of ventral input? Or, might the dorsal pathway be a source of input to the ventral pathway for object recognition? In the current study, we used high-density EEG-a technique with high temporal precision and spatial resolution sufficient to distinguish parietal and temporal lobes-to characterise the dynamics of dorsal and ventral pathways during object viewing. Using multivariate analyses, we found that category decoding in the dorsal pathway preceded that in the ventral pathway. Importantly, the dorsal pathway predicted the multivariate responses of the ventral pathway in a time-dependent manner, rather than the other way around. Together, these findings suggest that the dorsal pathway is a critical source of input to the ventral pathway for object recognition.
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Affiliation(s)
- Vladislav Ayzenberg
- Neuroscience Institute and Psychology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Claire Simmons
- School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Marlene Behrmann
- Neuroscience Institute and Psychology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA
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