A division of labor between power and phase coherence in encoding attention to stimulus streams

Alessandro Tavano, David Poeppel

Research output: Contribution to journalArticle

Abstract

Both time-based (when) and feature-based (what) aspects of attention facilitate behavior, so it is natural to hypothesize additive effects. We tested this conjecture by recording response behavior and electroencephalographic (EEG) data to auditory pitch changes, embedded at different time lags in a continuous sound stream. Participants reacted more rapidly to larger rather than smaller feature change magnitudes (deviancy), as well as to changes appearing after longer rather than shorter waiting times (hazard rate of response times). However, the feature and time dimensions of attention separately contributed to response speed, with no significant interaction. Notably, phase coherence at low frequencies (delta and theta bands, 1–7 Hz) predominantly reflected attention capture by feature changes, while oscillatory power at higher frequency bands, alpha (8–12 Hz) and beta (13–25 Hz) reflected the orienting of attention in time. Power and phase coherence predicted different portions of response speed variance, suggesting a division of labor in encoding sensory attention in complex auditory scenes.

Original languageEnglish (US)
Pages (from-to)146-156
Number of pages11
JournalNeuroImage
Volume193
DOIs
StatePublished - Jun 1 2019

Fingerprint

Reaction Time
Power (Psychology)

Keywords

  • Attention
  • Deviancy
  • Oscillation
  • Phase coherence
  • Reaction time

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Cite this

A division of labor between power and phase coherence in encoding attention to stimulus streams. / Tavano, Alessandro; Poeppel, David.

In: NeuroImage, Vol. 193, 01.06.2019, p. 146-156.

Research output: Contribution to journalArticle

@article{4b16a51d1f0d4902984f3f7ec61a2349,
title = "A division of labor between power and phase coherence in encoding attention to stimulus streams",
abstract = "Both time-based (when) and feature-based (what) aspects of attention facilitate behavior, so it is natural to hypothesize additive effects. We tested this conjecture by recording response behavior and electroencephalographic (EEG) data to auditory pitch changes, embedded at different time lags in a continuous sound stream. Participants reacted more rapidly to larger rather than smaller feature change magnitudes (deviancy), as well as to changes appearing after longer rather than shorter waiting times (hazard rate of response times). However, the feature and time dimensions of attention separately contributed to response speed, with no significant interaction. Notably, phase coherence at low frequencies (delta and theta bands, 1–7 Hz) predominantly reflected attention capture by feature changes, while oscillatory power at higher frequency bands, alpha (8–12 Hz) and beta (13–25 Hz) reflected the orienting of attention in time. Power and phase coherence predicted different portions of response speed variance, suggesting a division of labor in encoding sensory attention in complex auditory scenes.",
keywords = "Attention, Deviancy, Oscillation, Phase coherence, Reaction time",
author = "Alessandro Tavano and David Poeppel",
year = "2019",
month = "6",
day = "1",
doi = "10.1016/j.neuroimage.2019.03.018",
language = "English (US)",
volume = "193",
pages = "146--156",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - A division of labor between power and phase coherence in encoding attention to stimulus streams

AU - Tavano, Alessandro

AU - Poeppel, David

PY - 2019/6/1

Y1 - 2019/6/1

N2 - Both time-based (when) and feature-based (what) aspects of attention facilitate behavior, so it is natural to hypothesize additive effects. We tested this conjecture by recording response behavior and electroencephalographic (EEG) data to auditory pitch changes, embedded at different time lags in a continuous sound stream. Participants reacted more rapidly to larger rather than smaller feature change magnitudes (deviancy), as well as to changes appearing after longer rather than shorter waiting times (hazard rate of response times). However, the feature and time dimensions of attention separately contributed to response speed, with no significant interaction. Notably, phase coherence at low frequencies (delta and theta bands, 1–7 Hz) predominantly reflected attention capture by feature changes, while oscillatory power at higher frequency bands, alpha (8–12 Hz) and beta (13–25 Hz) reflected the orienting of attention in time. Power and phase coherence predicted different portions of response speed variance, suggesting a division of labor in encoding sensory attention in complex auditory scenes.

AB - Both time-based (when) and feature-based (what) aspects of attention facilitate behavior, so it is natural to hypothesize additive effects. We tested this conjecture by recording response behavior and electroencephalographic (EEG) data to auditory pitch changes, embedded at different time lags in a continuous sound stream. Participants reacted more rapidly to larger rather than smaller feature change magnitudes (deviancy), as well as to changes appearing after longer rather than shorter waiting times (hazard rate of response times). However, the feature and time dimensions of attention separately contributed to response speed, with no significant interaction. Notably, phase coherence at low frequencies (delta and theta bands, 1–7 Hz) predominantly reflected attention capture by feature changes, while oscillatory power at higher frequency bands, alpha (8–12 Hz) and beta (13–25 Hz) reflected the orienting of attention in time. Power and phase coherence predicted different portions of response speed variance, suggesting a division of labor in encoding sensory attention in complex auditory scenes.

KW - Attention

KW - Deviancy

KW - Oscillation

KW - Phase coherence

KW - Reaction time

UR - http://www.scopus.com/inward/record.url?scp=85063037012&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063037012&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2019.03.018

DO - 10.1016/j.neuroimage.2019.03.018

M3 - Article

VL - 193

SP - 146

EP - 156

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -