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How Task Set and Task Switching Modulate Perceptual Processes: Is Recognition of Facial Emotion an Exception? Cover

How Task Set and Task Switching Modulate Perceptual Processes: Is Recognition of Facial Emotion an Exception?

Open Access
|Aug 2021

Figures & Tables

joc-4-1-179-g1.png
Figure 1

Illustration of stimuli (A), comprising the face of one individual with two different neutral expressions (with letters “G” and “E” superimposed), an angry expression (with “A” superimposed) and a fearful expression (with “S” superimposed). The sequence of displays on one trial is shown in panel B.

joc-4-1-179-g2.png
Figure 2

RTs and error rates by CSI, task, switch/repeat and emotional expression (“Emo”: emotional expressions; “Neu”: neutral expressions).

joc-4-1-179-g3.png
Figure 3

Grand-average ERPs for the face task as a function of emotional expression and switch/repeat. The electrodes were selected to illustrate both the Emotional Expression Effect (EEE, in frontal electrodes), or encompass the N170 peak in temporo-occipital electrodes.

joc-4-1-179-g4.png
Figure 4

The emotional-neutral ERP difference (the EEE) for the face task as a function of switch/repeat: the upper panel shows the difference waves averaged for a subset of left frontal electrodes where the EEE is maximal (see Method); the lower panel shows the spline-interpolated scalp distribution of the EEE.

joc-4-1-179-g5.png
Figure 5

Grand-average ERPs as a function of emotional expression and task shown for the same set of electrodes as in Figure 3.

joc-4-1-179-g6.png
Figure 6

The emotional-neutral ERP difference (the EEE) for the two tasks. As in Figure 4, difference waves averaged for the left-frontal cluster of electrodes is displayed above the scalp distribution of the EEE.

Table 1

Descriptive and inferential statistics for the analysis of the switch-induced delay on the face task trials, with segments extracted from the difference wave based on the time-windows used for the amplitude analyses.

WINDOW WIDTH (START, END) IN MSSHIFTS (LEFT, RIGHT) IN STEPS OF 2 MSSHIFT WITH HIGHEST CORRELATION IN MST
(19)
P95% CONFIDENCE INTERVALSBAYES FACTOR
100 (150–250)3020.050.96[39.4, –37.4]0.23
100 (250–350)30–2–0.150.88[12.3, –14.3]0.23
200 (150–350)6020.20.84[10.7, –8.7]0.24
Table 2

Descriptive and inferential statistics for the analysis of the switch-induced delay on the face task trials, with segments centred around the peak of the EEE.

WINDOW WIDTH (START, END) IN MSSHIFTS (LEFT, RIGHT) IN STEPS OF 2 MSSHIFT WITH HIGHEST CORRELATION (MS)T (19)PCONFIDENCE INTERVALSBAYES FACTOR
150 (224–374)5040.330.75[13.9, –9.9]0.24
200 (200–400)6040.290.77[15.5, –11.5]0.24
250 (276–426)7080.70.5[15.3, –7.3]0.29
300 (150–450)80100.820.4[16.9, –6.9]0.32
joc-4-1-179-g7.png
Figure 7

The scalp distribution of the vowel-consonant ERP difference as a function of task.

DOI: https://doi.org/10.5334/joc.179 | Journal eISSN: 2514-4820
Language: English
Submitted on: May 17, 2020
|
Accepted on: Jun 30, 2021
|
Published on: Aug 5, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Heike Elchlepp, Stephen Monsell, Aureliu Lavric, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.