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Tytuł pozycji:

The Intensity of Early Attentional Processing, but Not Conflict Monitoring, Determines the Size of Subliminal Response Conflicts

Tytuł:
The Intensity of Early Attentional Processing, but Not Conflict Monitoring, Determines the Size of Subliminal Response Conflicts
Autorzy:
Wiebke Bensmann
Amirali Vahid
Christian Beste
Ann-Kathrin Stock
Temat:
attention
frontoparietal network
machine learning
subliminal priming
task set
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Źródło:
Frontiers in Human Neuroscience, Vol 13 (2019)
Wydawca:
Frontiers Media S.A., 2019.
Rok publikacji:
2019
Kolekcja:
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1662-5161
Relacje:
https://www.frontiersin.org/article/10.3389/fnhum.2019.00053/full; https://doaj.org/toc/1662-5161
DOI:
10.3389/fnhum.2019.00053
Dostęp URL:
https://doaj.org/article/8818c631e44b45b387aa4c660570e1ae  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.8818c631e44b45b387aa4c660570e1ae
Czasopismo naukowe
Response conflicts hamper goal-directed behavior and may be evoked by both consciously and subliminally (unconsciously) processed information. Yet, not much is known about the mechanisms and brain regions driving the size of subliminally induced conflicts. We hence combined a response conflict paradigm featuring subliminal primes and conscious flankers with in-depth neurophysiological (EEG) analyses, including source localization in a sample of N = 243 healthy subjects. Intra-individual differences in the size of subliminal conflicts were reflected both during early attentional stimulus processing (prime-associated N1 and target-associated P1 and N1 amplitudes) and conflict monitoring (N2 amplitudes). On the neuroanatomical level, this was reflected by activity modulations in the TPJ (BA39, BA40) and V2 (BA18), which are known to be involved in attentional stimulus processing and task set maintenance. In addition to a “standard” analysis of event-related potentials, we also conducted a purely data-driven machine learning approach using support vector machines (SVM) in order to identify neurophysiological features which do not only reflect the size of subliminal conflict, but actually allow to classify/predict it. This showed that only extremely early information processing (about 65 ms after the onset of the prime) was predictive of subliminal conflict size. Importantly, this predictive feature occurred before target information could even be processed and was reflected by activity in the left middle frontal gyrus (BA6) and insula (BA13). We conclude that differences in task set maintenance and potentially also in subliminal attentional processing of task-relevant features, but not conflict monitoring, determine the size of subliminally induced response conflicts.

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