EEG frequency tagging reveals higher order intermodulation components as neural markers of learned holistic shape representations
Vergeer, Mark ; Kogo, Naoki ; Nikolaev, Andrey R LU
; Alp, Nihan
; Loozen, Veerle
; Schraepen, Brenda
and Wagemans, Johan
(2018)
In Vision Research
152.
p.91-100
- Abstract
Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly... (More)
Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.
(Less)
- author
- Vergeer, Mark
; Kogo, Naoki
; Nikolaev, Andrey R
LU
; Alp, Nihan
; Loozen, Veerle
; Schraepen, Brenda
and Wagemans, Johan
- publishing date
- 2018-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adult, Biomarkers, Electroencephalography, Evoked Potentials, Visual/physiology, Female, Fixation, Ocular/physiology, Form Perception/physiology, Humans, Learning/physiology, Male, Pattern Recognition, Visual/physiology, Young Adult
- in
- Vision Research
- volume
- 152
- pages
- 10 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85042664428
- pmid:29474892
- ISSN
- 1878-5646
- DOI
- 10.1016/j.visres.2018.01.007
- language
- English
- LU publication?
- no
- additional info
- Copyright © 2018 Elsevier Ltd. All rights reserved.
- id
- 98e1904e-9529-4eeb-b5c6-e14046a47523
- date added to LUP
- 2019-10-21 19:26:17
- date last changed
- 2024-07-10 04:35:47
@article{98e1904e-9529-4eeb-b5c6-e14046a47523,
abstract = {{<p>Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.</p>}},
author = {{Vergeer, Mark and Kogo, Naoki and Nikolaev, Andrey R and Alp, Nihan and Loozen, Veerle and Schraepen, Brenda and Wagemans, Johan}},
issn = {{1878-5646}},
keywords = {{Adult; Biomarkers; Electroencephalography; Evoked Potentials, Visual/physiology; Female; Fixation, Ocular/physiology; Form Perception/physiology; Humans; Learning/physiology; Male; Pattern Recognition, Visual/physiology; Young Adult}},
language = {{eng}},
pages = {{91--100}},
publisher = {{Elsevier}},
series = {{Vision Research}},
title = {{EEG frequency tagging reveals higher order intermodulation components as neural markers of learned holistic shape representations}},
url = {{http://dx.doi.org/10.1016/j.visres.2018.01.007}},
doi = {{10.1016/j.visres.2018.01.007}},
volume = {{152}},
year = {{2018}},
}