Larson2013.Nfluences of Orthogonally-manipulated Valence and Arousal on Performance Monitoring Processes

7/21/2019 Larson2013.Nfluences of Orthogonally-manipulated Valence and Arousal on Performance Monitoring Processes

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What are the inuences of orthogonally-manipulated valence and arousalonperformance monitoring processes? The effects of affective state

Michael J. Larson a,b,, Alexander C. Gray a, Peter E. Clayson c, Rochelle Jones a, C. Brock Kirwan a,b

a Department of Psychology, Brigham Young University, Provo, UT, United Statesb Neuroscience Center, Brigham Young University, Provo, UT, United Statesc Department of Psychology, University of California-Los Angeles, Los Angeles, CA, United States

a b s t r a c ta r t i c l e i n f o

Article history:Received 20 September 2012

Received in revised form 14 December 2012

Accepted 2 January 2013

Available online 9 January 2013

Keywords:

Event-related potentials (ERPs)

Emotion

Mood

Error negativity

Attention

Psychopathology

Valence

Arousal

Studies of the inuence of affective state on the cognitive control process of performance monitoring are mixedand few studies have orthogonally manipulated affective valence and arousal. Performance monitoring can be

measured using behaviors (e.g., response times and error rates) and components of the event-related potentials

(ERPs), such as the error-related negativity (ERN), correct-related negativity (CRN), and post-error positivity

(Pe). We used a pre/post design and standard mood induction paradigm in 121 healthy participants randomly

assignedto orthogonalvalence(positive or negative) andarousal (high or low) conditions (i.e., happy, calm, anx-

ious, or sad mood states). Following mood induction, valence and arousal ratings differed between groups. Be-

havioral ndings showed decreased accuracy in participants with high arousal and negative valence (i.e.,

anxious condition), but no additional response time (RT), post-error slowing, or accuracy effects. Amplitude of

the CRN differentiated high and low valence, but was not related to arousal. Positive valence was associated

with decreased CRN amplitude even when baseline affect and demographic variables were controlled. Valence

and arousal did not signicantly differentiate the amplitude of the ERN, although the ERN minus CRN difference

wasrelatedto arousal but notvalence ratingsin multipleregression analyses. Affect-related differenceswere not

shown for the Pe. Findings provide a context to understand how dimensional aspects of emotional valence and

arousal inuence performance-monitoring processes and suggest a need for further research on the functional

role of the CRN and its relation to affective valence. 2013 Elsevier B.V. All rights reserved.

1. Introduction

Mood states can affect multiple cognitive processes, including mem-

ory (e.g., Chepenik et al., 2007), executive functioning (e.g., Phillips et al.,

2002), and attentional control (e.g., Jefferies et al., 2008). It is unclear,

however, whether cognitive control and, more specically, performance

monitoring abilities are inuenced by mood states. Performance moni-

toring refers to the cognitive control process of monitoring activities to

detect conict or inaccuracies that may require increased cognitive re-

sources (Carter et al., 1998). The neural bases of performance monitor-

ing, particularly with regard to response monitoring, can be measured

with millisecond accuracy using the error-related negativity (ERN),

correct-response negativity (CRN), and post-error positivity (Pe) com-

ponents of the scalp-recorded event-related potential (ERP).

The ERN is a negative potential that peaks approximately 50 ms after

an erroneousresponse.The ERNis larger(i.e.,more negative)in amplitude

on error trials relative to correct trials and consistently manifests across

multiple task situations and response modalities (Falkenstein et al., 1991;

Gehring et al., 1993; Nieuwenhuis et al., 2001). There are several theories

regarding the functional signicance of the ERN, with the most commonly

accepted theories indicating that the ERN represents the detection of

response-related conict, the detection of a mismatch between error and

correct trials, a reinforcement learning indicationof incorrectperformance

prediction, or an affective response to mistakes (see Hoffman and

Falkenstein, 2012; Olvet and Hajcak, 2008; van Veen and Carter, 2002).

The CRN occurs in the sametime frame as the ERN, but on correct tri-

als. The functional signicance of the CRN remains a matter of debate,

although some suggest that the CRN represents an accurate comparison

of correct and error representations,an index of level of response certain-

ty, an evaluation of whether a response strategy is adaptive, or an index

of the anticipated probability of a stimuluspresentation(Bartholow et al.,

2005; Endrass et al., 2012a; Ford, 1999; Pailing and Segalowitz, 2004;

Scheffers and Coles, 2000; Vidal et al., 2000). Source localization studies

indicate that the ERN and CRN have a similar frontal-medial scalp distri-

bution and originate broadly from areas near the anterior cingulate cor-

tex (ACC; Brazdil et al., 2005; Falkenstein et al., 2000; Roger et al.,

2010; Stemmer et al., 2004; van Veen and Carter, 2002).

The Pe has a posterior scalp distribution and occurs between 200 and

500 ms after an error (Falkenstein et al., 1991, 2000; Nieuwenhuis et al.,

International Journal of Psychophysiology 87 (2013) 327339

Correspondingauthorat: Departmentof Psychology and NeuroscienceCenter, Brigham

YoungUniversity,244 TLRB,Provo,UT 84602,UnitedStates.Tel.:+1 8014226125; fax: +1

801 422 0163.

E-mail address:[email protected](M.J. Larson).

0167-8760/$ see front matter 2013 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.ijpsycho.2013.01.005

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2001). The Pe typically follows the ERN and is thought to functionally re-ect conscious error processing (Endrass et al., 2007, 2012b; Hughesand

Yeung, 2011; Larson and Perlstein, 2009; Nieuwenhuiset al.,2001; Shalgi

et al., 2009) or continued processing of the affective signicance of an

error (Falkenstein et al., 2000; Overbeek et al., 2005). Source localization

studies also implicate the ACC, along withpotential additional areas such

as the insula, as the neural generators of the Pe (e.g., Herrmann et al.,

2004; Overbeek et al., 2005; Ullsperger et al., 2010).

Research investigating the in

uence of induced positive and nega-tive affect on reections of performance monitoring such as the ERN

and Pe is mixed (Clayson et al., 2012). Several studies suggest that the

ERN is not affected by changes in affective state (see Olvet and Hajcak,

2008). For example, ERN amplitude was not enhanced in individuals

with a spider phobia under conditions of symptom provocation, when

a live tarantula was held or placed in an aquarium next to the partici-

pant, but Pe amplitude was decreased potentially suggesting de-

creased awareness or orientation to errors (Moser et al., 2005).

Individuals in a sad mood condition did not show differences in ERN

or Pe amplitudes relative to a neutral group, unless they showed high

trait levels of neuroticism (Olvet and Hajcak, 2012). Our group recently

showed nonsignicant differences in ERN and Pe amplitudes between

groups of individuals receiving either encouraging or derogatory feed-

back on task performance (Clayson et al., 2012). Thesendings are con-

sistent with research that showed no differences in ERN amplitude

between conditions with and without trial-by-trial performance feed-

back (Olvet and Hajcak, 2009a) and between affective states such as

tension, anger, and depressed mood state (Tops et al., 2006).

In contrast, a growing body of research suggests that affective states

may indeed alter neural indices of performance monitoring such as the

ERN and Pe. For example,Inzlicht and Al-Khindi (2012)cleverly used

the misattribution of arousal paradigm to show decreased-amplitude

ERN when participants could attribute their arousal to a placebo con-

coction rather than personal performance. Similarly, religious primes

can either decrease ERN amplitude in people who believe in God or in-

crease ERN amplitude in those who do not believe, suggesting that the

state emotion has person-specic effects on ERN amplitude (Inzlicht

and Tullett, 2010). Other studies that show such state-related changes

in ERN and Pe amplitudes have utilized affective pictures, faces, andmovie clips. For example, Larson et al. (2006)showed more negative

ERN amplitudes when errors were made during presentation of pleas-

ant pictures relative to unpleasant or neutral pictures.van Wouwe et

al. (2011)reported decreased-amplitude ERN following positive video

clips relative to following neutral video clips. In contrast, Wiswede et

al. (2009)showed increased ERN amplitude following unpleasant pic-

tures compared to neutral and pleasant pictures and Boksem et al.

(2011)reported increased ERN amplitudes during a Simon task using

emotionally-valent faces in the context of faces that showed an evalua-

tive (i.e., disgusted) expression relative to those that showed a pleasant

(i.e., happy) expression. There is considerable research that suggests

thataffective information can modulate bothearly and lateERP compo-

nents (e.g., Foti et al., 2009), but the relationship between induced

affective-state and ERN amplitude is not completely clear, although itappears thatincreased arousal or an absence of congruence with perfor-

mance and mood states contributes to increased-amplitude ERN.

The current literature on affective state and performance monitor-

ing, including the ERN, CRN, and Pe, is often confounded by a relative

absence of empirical quantication of changes in mood state. For exam-

ple, studies of picture viewing as a mood manipulation in studies of the

ERN and Pe (e.g.,Larson et al., 2006; Wiswede et al., 2009) imply that

changes in mood occurred during the performance monitoring task,

but these changes were not rigorously quantied (see Olvet and

Hajcak, 2012). Further, studies of state mood and ERN amplitudes

have tended to focus primarily on positive affect versus negative affect,

without attention to the possible interactions of mood-related valence

(i.e., negative versus positive) and mood-related arousal (i.e., low ver-

sus high). Indeed, we were unable to nd studies that examined

error-related performance monitoring along with orthogonal dimen-

sions of valence and arousal. Valence and arousal represent indepen-

dent dimensions of emotion, with valence ranging from pleasant to

unpleasant and affect describing the energy or level of activation associ-

ated with the affect, tending to range from low to high energy (Barrett

and Russell, 1998). Studies, such as the current investigation, orthogo-

nally investigating valence and arousal effects will inform the eld by

providing a way to systematically examine the unique contributions

of these dimensions as well as their potential interactive effects. Thesestudies have implication for performance monitoring in both healthy

individuals and those with affective dysregulation such as depression

or anxiety, particularlysince the impact of affect on conict-related per-

formance monitoring processes has not been fully established.

To address these shortcomings in the extant literature, the cur-

rent examination quantied changes in mood-related arousal and

valence to assess the relationship between changes in mood state

and performance-monitoring indices (i.e., ERN, CRN, Pe) using a pre-

viously veried mood-induction procedure that involves music and

mood-related rumination across a range of valence and arousal com-

binations (Eich et al., 2007; Jefferies et al., 2008). Specic mood induc-

tion conditions included sad (negative valence, low arousal), calm

(positive valence, low arousal), anxious (negative valence,higharousal),

and happy (positive valence, high arousal). We utilized a pre-mood in-

duction to post-mood induction design so that each participant could

serve as his or her own control. That is, each participant completed a

neutral baseline for comparison in order to make the strongest possible

inferences without the added error that would come from including an-

other between-groups neutral condition.

The mixedndings in the state-related performance monitoring lit-

erature make determining hypotheses regarding the distinctive contri-

butions of emotional valence and arousal difcult; however, there are

some intimations for expectations in the current literature. Individuals

with chronically-high levels of negative valence and arousal (e.g., those

with clinical elevations in anxiety or obsessivecompulsive disorder)

tend to show increased ERN amplitudes relative to individuals with

more neutral valence and arousal levels (e.g., Aarts and Pourtois, 2010;

Endrass et al., 2008; Gehring et al., 2000; Olvet andHajcak,2008). Thelit-

erature on affective traits and performance monitoring is more variablefor individuals with chronic levels of negative valence and low arousal

(e.g., those with chronic sadness or depression), but seems to suggest

slightly increased ERN amplitudes (or error minus correct differences)

relative to psychiatrically-healthy controls (Georgiadi et al., 2011;

Olvet and Hajcak, 2008; Weinberg et al., 2010). The research on positive

valence (regardless of arousal) is still in its infancy and does not provide

clear suggestions, with some studies showing an increased ERN ampli-

tudes associated with increased levels of happiness and calmness

(West and Travers, 2008), whereas others show decreased (i.e., more

positive) ERN amplitudes in individuals with higher satisfaction with

life and no relationship with other positive personality traits including

positive affect and optimism (Larson et al., 2010c).

Based on this literature and the state mood induction literature to

date, we hypothesized that ERN amplitude would increase during anegative-valence, high-arousal mood state, namely, anxiety. We also

hypothesized that ERN amplitude would increase during a negative-

valence, low-arousal moodstate, namely, sadness. We did not have spe-

cic hypothesesabout theCRNor Pe based on a relativelackof informa-

tion in the current literature.

2. Materials and methods

2.1. Participants

All participants provided written informed consent as approved by

the local Institutional Review Board. Participants were recruited from

undergraduate psychology courses. Exclusion criteria, assessed via par-

ticipant self-report, included current or previous diagnosis of a

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psychiatric disorder, current psychoactive medication use, current

substance abuse or dependence, neurological disorders, head injury,

left-handedness, uncorrected visual impairment. These exclusion

criteria were chosen to decrease the potential inuence of individual

differences in the data. In line with previous work on the internal con-

sistency of the ERN, an additional exclusion criterion was fewer than

six error trials retained for single subject averaging after artifact rejec-

tion and correction (Larson et al., 2010a; Olvet and Hajcak, 2009b).

Final study enrollment included 121 individuals randomly assigned tofour different groups: anxious (n=29; 16 female; mean age=20.0

[1.9] years; mean education=13.6 [1.4] years), calm (n=35; 19 fe-

male; mean age= 19.8 [1.7] years; mean education= 13.3 [1.1]

years), happy (n=29; 13 female; mean age=20.3 [2.4] years; mean

education=13.4 [1.4] years), or sad (n=28; 17 female; mean age=

21.3 [5.9] years; mean education=13.5 [1.4] years). No differences

were shown between groups for male-to-female ratio, 2(3)=1.50,p=.68, age,F(3,117)=1.15,p =.33, or years of education,F(3,117)=

0.19,p =.90.

2.2. Baseline positive and negative affect

All participants completed the Positive and Negative Affect Sched-

ule (PANAS;Watson et al., 1988) prior to the mood induction task to

measure baseline levels of positive and negative affect. There were no

signicant between-group differences for level of negative affect,

F(3,117)=.85, p =.47, or level of positive affect, F(3,117)=2.16,

p =.10. Mean (standard deviation) positive affect ratings were 33.5

(5.4) for individuals in the anxious conditions, 35.3 (5.5) for happy,

32.4 (6.7) for sad, and 31.8 (5.8) for calm. For negative affect mean

ratings were 20.4 (4.3) for anxious, 19.2 (5.0) for happy, 18.8 (5.7)

for sad, and 18.7 (4.0) for calm.

2.3. Procedure overview

Upon arrival for the study session, participants were randomly

assigned to one of four mood-induction conditions: anxious, calm,

happy, or sad using a random number generator (participants were

not told their condition until the mood induction procedure began).We did not include a neutral condition because each participant com-

pleted a neutral baseline and could, thus, serve as his or her own con-

trol. Subsequently, an initial modied Eriksen Flanker task (described

below) was administered while electroencephalogram (EEG) and be-

havioral data were recorded. After the anker task, participants com-

pleted the rst rating of the affect grid (pre-mood induction affect

grid) then began the mood induction procedure, which lasted 10 min

and is described in detail below. Five minutes into the mood induction

procedure, participants completed an additional affect grid (mid-mood

induction affect grid). Immediately following the mood induction proce-

dure the affect grid was again completed (post-mood induction affect

grid). Participants promptlybegan a second administration of theanker

task after the mood induction procedure. Mood grids weresubsequently

completed approximately 5 min into the second task, 10 min into thesecond task, and at completion of the task. Thus, participants completed

the affect grids six times (pre-mood induction, mid-mood induction,

post-mood induction, 5-minute anker, 10-minute anker, and

post-anker). Following the second anker task participants were

debriefed to ensure they were in a safe mood state prior to leaving the

experiment and compensated with course credit.

2.4. Modiedanker task

Participants completed a modied version of the Eriksen Flanker

task before mood induction and after mood induction (Eriksen and

Eriksen, 1974). An identical task was used both before and after

mood induction procedures. Each trial consisted of either congruent

(bbbbb

, >>>>>) or incongruent (bb

>bb

, >>b

>>) arrow stimuli

presented in white on a black background of a 17 in. computer mon-

itor approximately 20 in. from the participant's head. Participants

were instructed to respond as quickly and accurately as possible

with a right-hand key press. An index-ngerbutton press was used

if the target stimulus (i.e., middle arrow) pointed to the left and a

middle-nger button press was used if the target stimulus pointed

to the right. Flanker stimuli were presented 100 ms prior to the

onset of the target stimulus, which remained on the screen for

500 ms. The ITI varied randomly between 800 ms, 1000 ms, and1200 ms, with a mean of 1000 ms. Three blocks of 200 trials (600

total trials) were presented, with 300 congruent trials (50%) and

300 incongruent trials (50%). Participants completed 24 practice trials

prior to beginning the rstanker task.

2.5. Affect ratings grid

Valence and arousal were rated using a 9 9 affect grid (e.g.,Russell

et al., 1989). Specically, the grid consisted of a 9-point valence rating

across the x-axis and a 9-point arousal rating across the y-axis. The

9-point axes were rated from extremely low (4) to extremely high

(+4). Valence was dened as the unpleasantness or pleasantness of

the mood. Arousal was dened as the level of energy or activation the

participant was experiencing (e.g., from extremely high energy to ex-

tremely low energy; seeJefferies et al., 2008). Unless otherwise speci-ed below we took the average of all of the valence and arousal

ratings except the baseline rating for use in correlational analyses.

2.6. Mood induction procedure

Following completion of the rst modied Eriksen Flanker task,

participants completed a mood-induction procedure that were select-

ed to closely replicate a previous study on attentional control and in-

duced mood (Jefferies et al., 2008). Participants were seated in a

comfortable chair and instructed to develop a particular mood by lis-

tening to mood-appropriate musical selections and ruminating about

mood-appropriate past events for 10-minutes. Musical selections

were the same as those in the Jefferies et al. paper; an appendix

with the precise song selections is included in that paper. Musicwas played over speakers attached to a laptop computer.

2.7. Electrophysiological data recording and reduction

Electroencephalogram was recorded from 128 scalp sites using a

geodesic sensor net and Electrical Geodesics, Inc. (EGI; Eugene, OR)am-

plier system (20 K nominal gain, bandpass=.10100 Hz). During re-

cording, EEG was referenced to the vertex electrode and digitized

continuously at 250 Hz with a 24-bit analog-to-digital converter. Im-

pedances were maintained below 50 k. Data were digitally low-pass

ltered at 30 Hz.

Individual-subject response-locked averages were calculated sepa-

rately for the pre-mood induction and post-mood induction anker

tasks using a window from

400 ms prior to participant response to800 ms following participant response. Errors of omission were exclud-

ed from ERP analyses. Waveforms were baseline corrected using a

200 ms window from 400 ms to 200 ms prior to participant re-

sponse. This baseline was chosen such that pre-response error-related

changes in the ERP would not be obscured. Eye blinks were removed

from the segmented waveforms using independent components analy-

sis (ICA) in the ERP PCA Toolkit (Dien, 2010). The ICA components that

correlated at least .9 with the scalp topography of two blink templates

were removed from the data (Dien et al., 2010). Trials were considered

unusable if more than 15%of channels were markedbad. Channels were

marked bad if the fast average amplitude exceeded 100V or if the dif-

ferential average amplitude exceeded 50V. Data were average

re-referenced and used the polar average reference effect (PARE)

correction.

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Correct-trial (CRN) and error-trial (ERN) amplitudes were averaged

across four fronto-central electrode sites (numbers 6 [FCz], 7, 106, and

Ref [Cz]; see Larson et al., 2012, for sensor layout) and extracted as

the mean amplitude from 0 to 100 ms following participant response.

Error-trial and correct-trial Pe amplitudes were extracted as the mean

amplitude from 200 ms to 400 ms post-response across ve centro-

parietal electrode sites (31, 54, 62 [Pz], 79, and 80).

2.8. Statistical analysis

To ensure the mood-induction procedure was effective, we rst

conducted 4-Group (anxious, calm, happy, sad)6-Time analyses of

variance (ANOVAs) on valence and arousal ratings collected using

the affect grid. For all ANOVAs, partial-eta2 (p2) is reported for

ANOVA effect sizes and the HuynhFeldt epsilon adjustment was ap-

plied to correct for possible violations of sphericity for factors with

more than two levels. We followed the procedures outlined in

Jefferies et al. (2008) for subsequent behavioral and ERP data analyses

by conducting separate group-based ANOVAs and orthogonal valence

by arousal ANOVAs. More specically, mean response times (RTs) and

accuracy were initially analyzed using separate 4-Group2-Session

(pre-mood induction, post-mood induction) 2-Congruency (congru-

ent, incongruent) mixed-model ANOVAs, whereas separate 4-Group

2-Session 2-Accuracy (correct, incorrect) ANOVAs were conducted

on ERNand Pe amplitudes. To further understand theorthogonal contri-

butions of valence and arousal, we next conducted separate orthogonal

2-Valence (negative, positive)2-Arousal (low, high)2-Session (pre-,

post-induction)2-Congruency (congruent, incongruent) ANOVAs on

behavioral data and 2-Valence2-Arousal2-Session2-Accuracy

analyses on ERP data.

We next sought to determinethe relationship of arousal and valence

as continuous variables with ERP indices of performance monitoring. To

do this, we conducted three separate hierarchical regression analyses

with post-mood induction ERN amplitude, CRN amplitude, and the

error-minus-correct ERN amplitude as dependent variables. We chose

not to include Pe amplitudes as dependent variables in the regression

analyses due to the absence of relationship with mood induction in

the ANOVA-basedndings(see below). Independent variables in there-gression analyses in addition to valence and arousal values were chosen

to ensure that ndings were not better accounted for by demographic

variables such as sex, age, or education,or individualtrait-related differ-

ences in positive or negative affect, previously shown to be related to the

ERPs of interest (e.g., Compton et al., 2008; Larson et al., 2011; Luu et al.,

2000; Moser et al., 2012). Thus, for the independent variables, the rst

step of the hierarchical regression included sex, age, education, PANAS

positive affect, and PANAS negative affect scores. The second step

added theaverage of measurements two through six(i.e., mid-mood in-

duction to theend of the task) of the valence and arousal ratings as well

as the valence by arousal interaction.

3. Results

3.1. Mood induction manipulation check

Werst examined whether the mood induction procedure induced

the intended moodstates. See Table 1 for mean arousaland valence rat-

ings foreach group acrossthe sixratings.Consistent with previousstud-

ies using the same mood induction process (e.g., Jefferies et al., 2008),

participants at baseline were in a generally positive valence (mean

valence=2.2 [1.5]) with neutral arousal (mean arousal=.9 [1.8])

prior to beginning the mood induction procedure.

A 4-Group6-Time repeated measures ANOVA on arousal scores

showed a non-signicant trend toward a main effect of time,

F(3.2,371.7)=2.46,p =.06, p2=.02, and, more importantly, a signi-

cant Group Time interaction, (9.5,371.7)= 7.29,pb .001, p2=.16, in-

dicating signicant changes in mood as a function of group over time.

There were no signicant group differences for pre-mood induction

arousal ratings,F(3,117)=.11, p =.96,p2=.003. Orthogonal contrasts

showed that, consistent with expectations, individuals in the anxious

and happy groups (high arousal) showedhigher arousal ratings than in-

dividuals in the calm and sad groups(low arousal) across all post-mood

induction ratings,psb .03, with the exception of rating number six,p =

.09 (seeTable 1). Individuals in the happy and anxious groups did not

differ in arousal ratings at any time point, ps>.14; individuals in the

sad and calm groups showed similar arousal ratings, with only a slightdifference at rating number three, p =.05, but at no other time point

ps>.07.

Forvalence ratings both themain effectof time, F(3.7,429.1)=17.89,pb .001, p

2=.13, and the Group Time interaction, F(11.0,429.1)=

11.47, pb .001, p2=.23, were signicant. There were baseline differ-

ences in valence between groups, F(3,117)=2.98, p =.03, p2=.07,

that were the result of the sad group having more positive valence rat-

ings than the anxious and happy groups,ps= .05 (seeTable 1); howev-

er, following the mood induction procedures all valence ratings were

signicantin the expected directions. Specically, individuals in the anx-

ious and sad groups (negative valence) showed signicantly lower va-

lence scores than those in the happy and calm groups (positive

valence) at all post mood induction time points,psb .001, with no differ-

ences between the anxious and sad groups, ps>.17. There were no

post-mood induction differences in valence for individuals in the

happy and calm groups across all measurements, ps>.52. Thus, consis-

tent with previous research, results indicated that the mood induction

procedure signicantly altered valence and arousal ratings in a manner

consistent with happy (positive valence, high arousal), calm (positive

valence, low arousal), sad (negative valence, low arousal) and anxious

(negative valence, high arousal) emotions (seeJefferies et al., 2008).

3.2. Response times and accuracy

Mean RT and accuracy data as a function of group are presented in

Table 2. As noted above, data were rst analyzed with a Group

Session Congruency ANOVA. The ANOVA on mean RTsshowed a sig-

nicant main effect of congruency with longer RTs to incongruent

than congruent trials, F(1,117)=1,133.11, pb .001, p2=.91. Themain effect of session was also signicant with longer RTs during

the rst session compared to the second session, F(1,117)=206.06,

pb .001, p2=.64. Most relevant to the current study, there were no

signicant main effects or interaction as a function of group member-

ship,Fsb1.27,ps>.29, indicating that there were no RT-related group

differences as a function of mood induction condition.

Mean post-error and post-correct trial RTs are shown in Table 2.

There was consistent post-error slowing across conditions as evidenced

byasignicant main effectof accuracy, F(1,117)=128.48,pb .001,p2=

.52, with slower RTs following errors than following correct responses.

There was also a signicant main effect of session,F(1,117)=209.27,

pb .001,p2=.64, post-response RTs were consistently faster in the sec-

ond session than the rst session, but this did not differ by accuracy

Table 1

Mean (standard deviation) arousal and valence ratings by group.

Group Baseline (1) Rating 2 Rating 3 Rating 4 Rating 5 Rating 6

Arousal ratings

Anxious 0.9 (1.9) 2.7 (1.3) 2.4 (1.5) 2.2 (1.6) 1.6 (2.0) 1.2 (2.2)

Ha ppy 1.0 (2 .0) 2 .1 ( 1.9) 2 .3 ( 1.8) 1.5 ( 1.9) 1.0 ( 2.1) 0 .6 (2 .2 )

Sad 0.7 (1.8) 0.6 (1.6) 1.0 (1.5) 0.2 (1.7) 0.0 (1.9) 0.0 (2.0)

Calm 0.8 (1.8) 0.2 (1.7) 0.1 (1.8) 0.6 (1.6) 0.5 (21.9) 0.5 (2.0)

Valence ratings

Anxious 1.8 (1.7) 0.5 (1.7) 0.3 (1.9) 0.2 (1.7) 0.6 (1.8) 0.2 (1.7)

H app y 1.8 ( 1.6) 3 .2 ( 1.3) 3 .3 ( 1.5) 2.2 ( 1.2) 1.6 ( 1.5) 1.4 (1 .7 )

Sad 2.8 ( 1.2) 0 .4 ( 1.7) 0 .0 ( 1.7) 0.5 ( 1.6) 0.3 ( 1.6) 0.8 (1 .6 )

C alm 2.3 ( 1.3) 3 .0 ( 1.3) 3 .1 ( 1.6) 2.3 ( 1.3) 1.7 ( 1.5) 1.7 (1 .5 )



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condition. There were no signicant main effects or interactions related

to group membership,Fsb0.29,ps>.83.

The ANOVA on accuracy revealed a signicant main effect of congru-

ency (worse accuracy on incongruent than congruent trials),F(1,117)=

196.64,pb .001,p2=.63. Themain effects of session,F(1,117)=2.09,p=

.15, p2=.02, and group,F(1,117)=0.92, p =.44, p

2=.02, were not sig-

nicant. There were, however, trend-level GroupSession, F(3,117)=

2.58, p=.06, p2=.056, and GroupSessionCongruency interactions,

F(3,117)=2.54, p =.06, p2=.06. Subsequent SessionCongruency

ANOVAs as a function of group showed a main effect of session for

only the anxiety group, F(1,28)=25.95, pb .001, p2=.48, with worseaccuracy following the mood induction procedure than before the pro-

cedure. There was a signicant Session Congruency interaction for the

anxiety and calm groups, F(1,28)=27.44, pb .001, p2=.49, and

F(1,28)=6.41,p =.02,p2=.16, respectively. Comparison of each con-

gruency individually by session showed that individuals in the anxiety

group had worse performance on incongruent trials from session one

to session twot(28)=5.93, pb .001, but there were no differences be-

tween sessions for congruent trials t(28)=0.16, p =.87. There were

no signicant differences between sessions by congruency for individ-

uals in the calm group, tsb1.50,ps>.15. Taken together, although the

omnibus effect was only signicant at trend level, participants in the

anxiety group showed worse performance following the mood induc-

tion procedures, particularly on the more difcult incongruent trials.

Consistent withJefferies et al. (2008)we next analyzed RTand accu-racy data using orthogonal Valence (negative, positive)Arousal (low,

high)Session (pre-, post-induction)Congruency (congruent, incon-

gruent) ANOVAs. For RTs (including post-error and post-correct RTs),

there were no signicant main effects or interactions involving arousal

or valence,Fsb3.37,ps>.07. For accuracy, however, there were signi-

cant ValenceArousalSession,F(1,117)=3.86,p =.05,p2=.03, and

Arousal Session Congruency interactions, F(1,117)=4.83, p=.03,

p2=.04. Subsequent Valence ArousalCongruency ANOVAs sepa-

rately for pre-mood induction and post-mood induction sessions

showed no valence or arousal main effects or interactions pre-mood in-

duction, Fsb .94,ps> .33 or post-mood induction Fsb1.44,ps> .23. Sim-

ilarly, the separate ArousalCongruency ANOVAs conducted for each

separate session were nonsignicant,Fsb1.51,ps>.22. In sum, the or-

thogonal valence and arousal conditions did not have specic effects

on RT or accuracy data that differentiated the pre-mood induction re-

sults from the post-induction results.

3.3. Event-related potentials

Mean ERN and Pe component amplitude data as a function of group

are presented in Table 2. For the anxious group, ERPs contained an aver-

age standard deviation (minimum to maximum) of 502 43 (410 to

567) for correct trials and 21 22 (6 to 111) for error trials during the

rst session and 45291 (146 to 556) for correct trials and 2818

(10 to 95) for error trials during the second session. For the calmgroup, 49857 (298 to 571) correct trials and 2730 (6 to 187)

error trials were retained for averaging during the rst session and

48571 (223 to 575) correct trials and 2314 (6 to 56) error trials

were retained for averaging during the second session. For the happy

group, 48571 (308 to 566) correct trials and 3026 (6 to 130)

error trials were retained for averaging during the rst session and

46175 (265 to 543) correct trials and 2616 (6 to 68) error trials

were retained for averaging during the second session. For the sad

group, 50051 (388 to 569) correct trials and 2421 (7 to 88) error

trials were retained for averaging during the rst session and 489

58 (388 to 580) correct trials and 2417 (6 to 68) error trials were

retained for averaging during the second session. Notably, groups

showed similar numbers of trials retained for averaging across condi-

tions and sessions (Fsb1.6, ps>.20). Using the method proposed bySchimmel (1967), groups also showed similar noise estimates across

conditions and sessions (Fsb2.0, ps>.13). Thus, number of trials and

levels of background noise did not likely bias ERP ndings.

3.4. Error-related negativity

Grand average ERP waveforms for the ERN and CRN pre-mood in-

duction are presented inFig. 1; post-mood induction ERN and CRN

waveforms are presented inFig. 2and difference waves overlaid for

pre-mood induction and post-mood induction session are presented

in Fig. 3. A Group Session Accuracy ANOVA on ERN amplitude

showed a signicant main effect of accuracy with more negative

error-trial amplitude than correct-trial amplitude, F(1,117)=252.65,

pb

.001, p2

=.68. The main effect of session was also signicant,

Table 2

Behavioral and event-related potential data as a function of Group and Session.

Anxious Happy Sad Calm

(n=29) (n=29) (n= 28) (n= 35)

Mean SD Mean SD Mean SD Mean SD

Pre-Mood

Induction

Congruent-trial RTs (ms) 410 46 403 48 399 37 405 40

Incongruent-trial RTs (ms) 476 52 466 45 462 37 462 49

Post-correct RTs (ms) 443 45 433 41 431 34 433 41

Post-error RTs (ms) 490 80 485 64 483 59 486 77Congruent-trial Accuracy 98% 2% 97% 4% 98% 2% 98% 2%

Incongruent-trial Accuracy 94% 5% 92% 7% 93% 6% 93% 4%

CRN amplitude (V) 1.2 1.9 1.2 1.4 1.3 1.5 2.0 1.7

ERN amplitude (V) 1.9 1.3 1.4 1.3 1.6 1.5 1.7 1.1

Correct-trial Pe amplitude (V) 1.1 .9 -.9 1.2 1.0 1.2 -.8 1.0

Error-trial Pe amplitude (V) 4.5 3.4 3.2 2.7 4.5 3.2 4.9 3.3

Post-Mood

Induction

Congruent-trial RTs (ms) 378 32 373 33 372 31 372 34

Incongruent-trial RTs (ms) 435 36 434 38 433 34 424 40

Post-correct RTs (ms) 410 32 407 34 406 31 400 35

Post-error RTs (ms) 432 46 426 48 423 57 420 48

Congruent-trial Accuracy 98% 2% 98% 1% 99% 1% 98% 2%

Incongruent-trial Accuracy 90% 5% 91% 6% 92% 6% 92% 4%

CRN amplitude (V) 1.8 1.8 2.1 1.7 1.7 1.5 3.0 1.9

ERN amplitude (V) 1.6 1.2 1.4 1.4 1.2 1.3 2.0 1.3

Correct-trial Pe amplitude (V) .9 .9 .8 1.1 1.1 1.1 .8 1.3

Error-trial Pe amplitude (V) 4.3 2.6 4.1 2.7 5.4 2.9 5.8 3.8

Note. RT=response time; CRN=correct-related negativity; ERN=error-related negativity; Pe=post-error positivity.



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F(1,117)=52.19, pb .001, p2=.31; overall ERP amplitude decreased

(i.e., became more positive) from therst session to the second session.

The main effect of group was not signicant,F(3,117)=1.69, p =.17,

p2=.04. Notably, the Group SessionAccuracy interaction was

signicant, F(3,117)=5.24, p=.002, p2=.12; the remaining interac-

tions in the omnibus ANOVA involving group were not signicant

(Fsb .31, ps>.82). When decomposed as a function of session, there

was no signicant GroupAccuracy interaction prior to mood induction,

Fig. 1.Grand average waveforms as a function of mood-induction group for session 1 representing the correct-related negativity (CRN) and error-related negativity (ERN) averaged

across fronto-medial electrode locations.

Fig. 2.Grand average waveforms as a function of mood-induction group for session 2 representing the correct-related negativity (CRN) and error-related negativity (ERN) averaged

across fronto-medial electrode locations.

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F(3,117)=1.07,p=.36,p2=.03, whereas the GroupAccuracy interac-

tion was signicant on the post-mood induction data, F(3,117)=4.62,

p=.004,p2=.11, indicating signicant change in ERPs as a function of

group following the mood induction procedure.

To further decompose these ndings, we conducted Session

Accuracy ANOVAs separately for each group. The main effect of ses-

sion was signicant for all groups, Fs>7.92, psb .008, with decreased-

amplitude (i.e., more positive) ERPs on the post-mood induction ses-sion relative to the pre-mood induction session. The Session Accuracy

interaction was only signicant for the individuals in the happy,

F(1,28)=7.02, p =.01, p2=.20, and calm groups, F(1,28)=35.37,

pb .001, p2=.51 (i.e., the two positive valence groups). These differ-

ences were primarily driven by the CRN, as CRN amplitude was de-

creased (i.e., more positive) on the post-mood induction session for

individuals in both the happy, t(28)=5.00, pb .001, and calm groups,

t(34)=7.10, pb .001; whereas, ERN amplitude did not signicantly dif-

fer between pre- and post-mood induction sessions for individuals in

the calm and happy groups, tsb1.8,ps>.07. Thus, CRN amplitude de-

creased signicantly following the mood induction procedure in the

positive valence conditions. Amplitude of the ERN was not affected by

group membership following the mood induction procedure.

To understand the contributions of valence and arousal, the orthog-onal Valence Arousal Session Accuracy ANOVA showed nonsigni-

cant main effects of valence and arousal, Fsb2.97,ps> .09; however, the

ValenceArousalAccuracy, F(1,117)=3.96, p=.05, p2=.03, and

ValenceSessionAccuracy,F(1,117)=12.42,pb .001, p2=.10, inter-

actions were signicant. Subsequent decomposition separated by pre-

and post-mood induction was consistent with thendings in the previ-

ous ANOVA and showed no signicant main effects or interactions with

arousal or valence pre-mood induction, Fsb2.03,ps> .15. Post-mood in-

duction the AccuracyValence, F(1,117)=6.06, p =.02, p2=.05, and

AccuracyValenceArousal, F(1,117)=5.50,p =.02,p2=.05, interac-

tions were signicant. Amplitude of the CRN after mood induction was

signicantly different as a function of high and low valence, t(119)=

2.63,p =.01, but not arousal, t(119)=1.50,p =.14; whereasamplitude

of theERN after mood induction did not signicantly differ as a function

of arousal, t(119)=.64,p=.53, or valence, t(119)=1.44,p=.15. These

results support the group-relatedndings and suggest that positive va-

lence inuences amplitude of the CRN, but overall arousal does not af-

fect ERN or CRN amplitude.

3.5. Post-error positivity

Grand average pre-mood induction Pe waveforms are presentedinFig. 4; post-mood induction Pe waveforms are presented inFig. 5

and the difference waves as a function of pre-mood induction and

post-mood induction session are presented in Fig. 6. A Group

Session Accuracy ANOVA on Pe amplitude showed a main effect of

accuracy with more positive Pe amplitude for error trials than for cor-

rect trials, F(1,117)=464.09,p b .001, p2=.80. Overall Pe amplitude

was also more positive during the second session than during the

rst session as indicated by a main effect of session, F(1,117)=7.45,p =.007, p

2=.06. There were no signicant main effects or interac-

tions as a function of group, Fsb2.20,ps>.09, indicating that mood

induction group did not signicantly inuence Pe amplitudes. The or-

thogonal Valence Arousal Session Accuracy on Pe amplitudes

yielded only additional Accuracy Arousal interaction, F(1,117)=

5.01, p =.03, p2

=.04, with decreased-amplitude error-trial Pe inthe high arousal group relative to the low arousal group. There were

no signicant main effects or interactions with session and valence

or arousal, suggesting that the mood induction procedure did not sig-

nicantly inuence the amplitude of the Pe.

3.6. Hierarchical regression analyses

Hierarchical regression analyses included all participants regard-

less of group and are summarized inTable 3for the CRN,Table 4for

the ERN, andTable 5for the ERN amplitude. Independent variables

in the rst step included baseline levels of positive and negative af-

fect, sex, age, and years of education. The second step added the aver-

age arousal and valence ratings for all time points except baseline and

the valence by arousal interaction. Variance ination factors (VIFs)

Fig. 3.Grand average difference waveforms (ERN minus CRN) for pre-mood induction and post-mood induction sessions.



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between predictors for both steps were well within acceptable limits

(Cohen et al., 2003; seeTables 3 to 5).

For the model with CRN amplitude as the dependent variable, the

overall model was not signicant at the rst step, F(5,115)=1.36,

p =.25, and accounted for 5.6% of the variance in CRN amplitude.

When the second step added the mean valence, arousal, and the va-

lence by arousal interaction, the overall model became signicant,

F(8,112)=2.03,p =.05, and accounted for 12.6% of the variance in

Fig. 4. Grand average waveforms as a function of mood-induction group for session 1 representing the post-error positivity (Pe) for correct and error trials averaged across

centro-parietal electrode locations.

Fig. 5. Grand average waveforms as a function of mood-induction group for session 2 representing the post-error positivity (Pe) for correct and error trials averaged across

centro-parietal electrode locations.

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CRN amplitude. The R2 change from the rst to second step was sig-

nicant, F(3,112)=3.02, p =.03. The valence rating variable was a

signicant predictor of CRN amplitude, =0.27, p =.009, along

with years of education, =0.22, p =.04. There were no additional

signicant predictors. Findings indicate that valence is a modest pre-

dictor of CRN amplitude even when baseline positive and negative af-

fect, demographic variables, and arousal are controlled.

The regression model for ERN amplitude was also not signicant inthe rst step, F(5,115)=.25, p =.94, and accounted for only 1.1% of

the variance in ERN amplitude (seeTable 4). The model at the second

step was also not signicant, F(8,112)=.98, p =.46, and accounted

for 6.5% of the variance in ERN amplitude. The R2 change from the

rst step to the second step was not signicant, F(3,112)=2.18, p =

.09. The valence and arousal rating predictors were the only

near-signicant predictors in the model at, = .20, p =.055, and,= .20, p=.067, respectively. These ndings suggest a small rela-

tionship between valence and arousal ratings and ERN amplitude

when baseline affective and demographic variables are controlled, al-

though the overall model was not signicant.

Neither the rst step nor the second step of the regression

predicting ERN amplitude were signicant, F(5,115)=1.17, p=

.33;F(8,112)=1.62,p =.13, respectively. The rst model accounted

for 4.8% of the variance; the second model accounted for 10.4% of

the variance inERN amplitude. The R2 change was not signicantly

different between the two blocks,F(3,112)=2.31,p =.08. The arous-

al ratings variable was a signicant predictor of ERN amplitude,

=

0.24,p =.03, along with years of education, =0.21, p =.05.There were no additional signicant predictors indicating a potential

relationship betweenERN amplitude and arousal along with educa-

tion, although the model did not account for signicant additional

variance beyond demographic factors and positive/negative affect.

4. Discussion

We aimed to examine the inuences of affective valence and arousal

on electrophysiological and behavioral indices of response-related per-

formance monitoring (i.e., ERN, CRN, Pe) using a standard mood induc-

tion paradigm. To address some of the inconsistencies in the literature

Fig. 6.Grand average difference waveforms (error-trial Pe minus correct-trial Pe) for pre-mood induction and post-mood induction sessions.

Table 3

Hierarchical regression models with correct-related negativity (CRN) amplitude as the dependent variable.

Step R 2 R2 change R 2 changep-value B(std. error) Beta p-value VIF

First Step Sex .056 .25 .10 (.35) .03 .78 1.12

Age (years) .09 (.06) .17 .14 1.51

Education (years) .31 (.15) .23 .04 1.44

PANAS Positive Affect .02 (.03) .05 .57 1.06

PANAS Negative Affect .04 (.04) .11 .24 1.06

Second Step Sex .126 .07 .03 .08 (.34) .02 .82 1.12

Age (years) .10 (.06) .18 .10 1.52

Education (years) .30 (.14) .22 .04 1.45



Valence .30 (.12) .27 .009 1.31

Arousal .05 (.11) .05 .67 1.44

Valence by Arousal .09 (.05) .19 .10 1.73

Note. Statistics are for the nal regression models including all predictors. PANAS=positive and negative affect scale; VIF=variance ination factor.



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we ensured that the mood induction procedure altered participant

self-reported mood and specically examinedboththe interactive (i.e.,

group-based) and orthogonal roles of valence and arousal. Participant

ratings of valence and arousal indicated that the mood induction para-

digm was effective in inducing the expected happy, calm, anxious, and

sad moods. Specically, participants in the calm and happy groups

showed increased self-reported positive valence relative to those in

the sad and anxious groups; participants in the anxious and happy

mood induction groups showed increased arousal ratings relative to

those in the calm and sad groups. There was a signicant change in

mood relative to the baseline task across all mood induction conditions.

Thus, addressing a common concern about a lack of verication regard-

ing mood changes in performance monitoring studies, the mood induc-

tion procedure was effective in inducing the expected mood states

consistent with previous research using the same procedures and

these changes seemedto generally remain present through theduration

of the task (seeJefferies et al., 2008).

Behavioralperformancefor RTsshowed strong practice effects across

sessions. That is, participants responded considerably faster, including

speed of post-error and post-correct RTs, on the post-mood induction

anker task relative to the pre-mood induction task regardless of stimu-luscongruency. Thus,it is possiblethat theincreased familiarity with the

task masked the inuence of changes in affective state for RTs at the

post-mood induction session. Purely between-group research that

minimizes practice effects may be needed to accurately determine if

there were effects of mood induction on study RTs. For accuracy,

however, there was not a signicant difference between sessions

when collapsed across groups and congruencies suggesting that the

increased familiarity did not inuence accuracy or, more broadly,

attention to the task.

Accuracyndings showed worse performance from the pre-mood

induction session to the post-mood induction session only in those in

the anxiety group, particularly for incongruent trials. These ndings

did not hold for the other groups or for orthogonal analyses of valence

and arousal. That is, when groups were collapsed to compare high

versus low arousal and positive versus negative valence, the differ-

ences from pre-mood induction to post-mood induction were not sig-

nicant. These results suggest that the combination of high arousal

and negative affect in the anxious condition may inuence perfor-

mance beyond what would be present using only negative affect or

high arousal. Previous studies using similar anker tasks show in-

creased error rates for individuals in low valence and high arousal

groups or when there is increased emotional arousal consistent

with the current results (e.g., Larson et al., 2006; van Steenbergen et

al., 2010). However, other studies using similar paradigms showed

no relation between mood state and accuracy (e.g., Kuhbandner and

Zehetleitner, 2011; McConnell and Shore, 2011). Increased research

is necessary to clarify the relationship between induced mood and

cognitive control accuracy on tasks such as the anker.

Event-related potential ndings showed the clearest ndings with

regard to the CRN. Amplitude of the CRN was smaller (i.e., more pos-

itive amplitude) with more positive valence. Group analyses showed

smaller CRN amplitudes in individuals in the happy and calm condi-tions on the post-mood induction session, but did not differfor the in-

dividuals in the anxious and sad conditions. Similarly, orthogonal

valence and arousal analyses indicated decreased-amplitude CRN

was associated with more positive valence. There were no differences

in pre-mood induction ERPs. Regression analyses supported these re-

lationships and indicated that demographic factors, or levels of posi-

tive and negative affect cannot account for the ndings. Thus, it

appears that valence following experimental mood induction is a

modest predictor of CRN amplitude, particularly as valence trends in

the positive direction. Notably, CRN amplitude was not associated

with arousal ratings in any analysis. In contrast, both ANOVA and

Table 4

Hierarchical regression models with error-related negativity (ERN) amplitude as the dependent variable.


First Step Sex .011 .94 .17 (.26) .06 .51 1.12

Age (years) .03 (.05) .06 .57 1.51

Education (years) .05 (.11) .05 .66 1.44



Second Step Sex .065 .06 .09 .16 (.25) .06 .52 1.12

Age (years)

.03 (.04)

.07 .54 1.52Education (years) .06 (.11) .06 .61 1.45



Valence .17 (.09) .20 .06 1.31

Arousal .15 (.08) .20 .07 1.44



Table 5

Hierarchical regression models with ERN minus CRN (ERN) amplitude as the dependent variable.


First Step Sex .048 .33 .43 (.49) .08 .38 1.12

Age (years) .14 (.09) .18 .11 1.51

Education (years) .41 (.21) .21 .06 1.44



Second Step Sex .104 .06 .08 .41 (.49) .08 .41 1.12

Age (years) .15 (.09) .20 .08 1.52

Education (years) .41 (.21) .21 .05 1.45



Valence .03 (.17) .02 .85 1.31

Arousal .36 (.16) .24 .03 1.44





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regression analyses did not suggest a unique relationship between

valence or arousal and amplitude of the ERN. The ERN minus CRN dif-

ference was related to arousal and education in the regression analy-

ses that controlled for demographic and affective variables. There

appear to be differences in the relationships between valence, arous-

al, CRN amplitude, andERN amplitude whereas ERN amplitude

showed only nonsignicant relationships with both valence and

arousal in regression analyses and no group-related differences in

the ANOVAs.These ndings suggest a potential dissociation of CRN and ERN

amplitudes on indices of valence and arousal following experimental

mood induction. Research to date directly examining the CRN is

sparse and few clear theories of the functional signicance of the

CRN, particularly with regard to affect-related effects, exist. Several

studies show CRN amplitude is more negative when an individual is

not certain about the accuracy of his or her response relative to

when the individual is sure of response accuracy (Pailing and

Segalowitz, 2004; Pietschmann et al., 2011; Scheffers and Coles,

2000).Olvet et al. (2010) showed CRNamplitudes were related to de-

pression severity in a group of individuals with major depressive dis-

order, with increased CRN amplitudes associated with increased

depression severity. To explain these ndings, the authors suggested

that individuals with depression could be more uncertain about their

responses, may expect to make more errors than controls, or may

generally have a heightened performance monitoring response to

correct trials. Current ndings are in the same direction (i.e., more pos-

itive valence associated with decreased CRN amplitude). Thus, it is pos-

sible that there is decreased uncertainty or a different expectation about

performance relative to those whorated mood valence more negatively.

It is also possible that the CRN is sensitive to the current emotion of the

participant. Consistent with this possibility, one study showed increased

CRN amplitude following presentation of aversive stimuli relative to

neutral stimuli (Simon-Thomas and Knight, 2005), although CRNampli-

tude differences is not always present as a function of valence following

presentation of affective pictures (e.g., Larson et al., 2006). Other studies

reportenhanced ERNand CRN amplitudes for individuals with high anx-

iety and obsessivecompulsive symptoms that correlate with anxiety

symptom severity (e.g.,Endrass et al., 2008, 2010; Hajcak and Simons,2002). Neuroimaging studies further supported increased levels of

correct-trial related activity with high levels of anxiety (e.g.,Ursu et al.,

2003) suggesting that current emotionalstatusmay inuenceneural ac-

tivity associated with both correct and error trials, with the possibility

that both the correct and error-related processes may reect some

form of comparison process inuenced by affective state. The functional

signicance of the CRN and its role in the processing and utilization of

emotional information is not fully claried by the current results; how-

ever, these ndings strongly suggest that the CRN is most closely linked

to the valence of an individual's affective state, but not arousal.

There were no signicant differences between arousal and valence

conditions with ERN amplitude, although regression analyses suggest

a modest relationship between the ERN minus CRN difference and

arousal levels. These ndings were surprising and contrary to our hy-potheses of increased arousal and negative affect being associated

with increased ERN amplitude. Amplitude of the ERN is consistently in-

creased when there is increased motivation to be vigilant to the task at

hand, such as when there is reward at stake (e.g., Ganushchak and

Schiller, 2008; Hajcak et al., 2005; Sturmer et al., 2011),when partici-

pants are informed that their performance is being evaluated

(Bonnefond et al., 2011), or when accuracy of performance is empha-

sized (e.g.,Gehring et al., 1993). Increased ERN amplitudes have also

been related to improved performance on neuropsychological tests of

attention and executive function (Larson and Clayson, 2011), internal

adjustments of attention (Larson et al., 2012), increased trait levels of

persistence (Tops and Boksem, 2010), and higher levels of empathy

a trait requiring vigilance both to the environment and the actions of

others (Larson et al., 2010b; Santesso and Segalowitz, 2009). Further,

ERNamplitude is increased in the context of evaluative facial expres-

sions or derogatory performance-based comments rather than when

participants are viewing pleasant expressions or receiving positive

performance feedback (Boksem et al., 2011; Unger et al., 2012) and

the picture-based studies of state-related ERN amplitude changes

(e.g.,Larson et al., 2006; Wiswede et al., 2009) could be interpreted

by level of attention engagement to the task-related picture stimuli

(seeClayson et al., 2012). It is possible that the long anker tasks

led to disproportionately decreased engagement in the high positivevalence conditions, but did not affect arousal and, subsequently, am-

plitude of the ERN.

Amplitude of the Pe (error or correct) didnot differentiate mood in-

duction groups andwas not disproportionately inuencedby valence or

arousal. A growing body of research suggests that the Pe is primarily re-

lated to the conscious awareness and recognition that an error has been

committed processing (Endrass et al., 2007; Hewig et al., 2011; Hughes

and Yeung, 2011; Larson and Perlstein, 2009; Nieuwenhuis et al., 2001;

Shalgi et al., 2009), but few studies show a connection between Pe am-

plitude and emotionally-salient stimuli or mood states (Clayson et al.,

2012; Larson et al., 2010c). There are a few studies of emotional traits

that do show Pe-related differences. For example, decreased Pe ampli-

tude has been shown in individuals with high levels of negative affect

and depression relative to controls (e.g., Hajcak et al., 2004; Olvet et

al., 2010; Schrijvers et al., 2008), but this nding is not consistent

(e.g.,Chiu and Deldin, 2007). Other studies show heightened Pe ampli-

tudes in individuals with obsessivecompulsive disorder (OCD) or per-

fectionistic traits (e.g.,Santesso et al., 2006; Schrijvers et al., 2010), but

these are also not consistent (Endrass et al., 2008, 2010).As a whole,re-

sults of the current study, the absence ofndings in other studies of

state-related emotion and Pe, and the variability in trait-related emo-

tion ndings and the Pe lead us to the conclusion that there is not a re-

lationship between state-related mood changes and Pe amplitude.

Thendings of the current study are interesting in the context of

recent results that indicate RT adjustments following conict stimuli

(i.e., conict adaptation) are larger in negative valence relative to

positive valence conditions, but not related to arousal, and attentional

capture to distractors, discussed primarily in terms of control of

on-going attentional resources, is associated with high arousal statesindependent of valence (Kuhbandner and Zehetleitner, 2011; van

Steenbergen et al., 2010). One interpretation of these ndings is from

a cognitive dissonance perspective, wherein conict adaptation reects

an adaptive avoidance of incompatibility that is larger in negative va-

lence conditions (van Steenbergen et al., 2010). Our results would

argue against such an interpretation, as valence was related with the

CRN where the correct and actual responses are congruent, rather than

the ERN where the correct and actual responsesare incongruent. Anoth-

er possible interpretation comes from the build-and-broaden theory

wherein positive affect is thought to broaden selective processing of in-

formation and could subsequently weaken attention control or control

adaptation (Fredrickson, 1998; Kuhbandner and Zehetleitner, 2011;

Roweet al., 2007). Thus, it is possible that CRNamplitude was decreased

in positive affect conditions due to lower control, but why then did theERN not concomitantly reduce with positive valence when impulsive

or inattention errors would be more common than in the correct trial

condition? Further, the widening of attention with positive mood has

been recently challenged (Bruyneel et al., in press). Notably, post-error

slowing, another form of conict-related adjustment, did not differ as

a function of mood condition in this study. So, the conict adaptation re-

sults that are modulatedby valence conditiondo notgeneralize to all as-

pects of conict-related adjustments in performance.

Arousal was related to the ERN minus CRN difference, consistent

with previous affect-related research and the possibility of some de-

gree of attention narrowing in individuals in high arousal conditions

(Kuhbandner and Zehetleitner, 2011; Olvet and Hajcak, 2008). The

potentially informative part about this nding is that response-

related information that is common to both the ERN and the CRN is



12/13

removed by the difference score; however, as these components are

now being thought of as reecting unique processes the exact impli-

cations of this nding are unclear. Future research integrating the

ndings from the current study and studies of conict-related control

is needed for a rm understanding of the inuences of valence and

arousal on cognitive control abilities. Regardless, our ndings in

conjunction with these conict adaptation results indicate that

state-related affect can indeed dynamically alter neural and behavior-

al manifestations of cognitive control.Our results should be interpreted within the context of several

strengths andweaknesses.An aspect that is botha strength anda weak-

ness of the current study is the pre-mood induction to post-mood in-

duction design wherein participants' performance after the mood

induction procedure could be compared to their baseline performance.

This is a strength because each participant served as his or her owncon-

trol with a neutral baselinelevel of performance. However, the RT fa-

cilitation due to task practice and familiarity as well as the potential

disengagement and fatigue associated with the task is a weakness and

may be a large contributor to the current ndings. Several previous

studies using between-group designs (e.g., designs where there was

no baseline task, only comparison with a neutralgroup that did not

complete a mood induction protocol) show RT-related changes as a

function of mood condition on attention and cognitive control tasks

(e.g.,Jefferies et al., 2008; Martin and Kerns, 2011; Rowe et al., 2007).

Notably, however, research on mood-related changes in RTs on

anker-type tasks is mixed. Some researchers report no RT differences

between pleasant and neutral mood induction groups (Bruyneel et al.,

in press; Martin and Kerns, 2011), whereas others report increasedanker interferencein participantsin a positive mood conditionrelative

to negative or neutral (Rowe et al., 2007). Thus, the inuence of

state-related changes in mood on conict-related tasks such as the

anker remains unclear; however, it is likely that there was some de-

gree of decreased attention and vigilance lowering with increased

time on task (c.f.,Bonnefond et al., 2011).

A secondarea that is both a strengthand a weaknessis inthe area of

the mood induction paradigm. A strength is that participants' ratings

showed a signicant differentiation of the mood induction conditions,

particularly along the lines of valence and arousal. The weakness of themood induction procedures is the possibility of a demand effect because

participantswere told their conditionin order to have them imagine and

rehearsea mood-congruent eventduring the induction procedure. Thus,

the results could primarily represent a susceptibility to this demand ef-

fect rather than true changes associated with state affect differences. Fi-

nally,a third andprimary strength is thedimensional examinationof the

results using orthogonal valence and arousal ratings, something that has

not yet been done in the performance monitoring ERP literature.

In conclusion, study results indicate decreased CRN amplitude re-

lated to higher (i.e., more pleasant) valence and some suggestion of

increased ERN minus CRN differences associated with higher arousal.

Current ndings provide a context in which to interpret the inu-

ences of state-related mood changes on indices of performance mon-

itoring (i.e., ERN, CRN, Pe) and call for further research on thefunctional role of the CRN in performance monitoring. Affect-related

differences were not shown for the Pe. The present examination illus-

trates the utility of considering dimensional aspects of mood in inves-

tigating performance-monitoring processes. As such, we recommend

that dimensional aspects of arousal and valence be considered in fu-

turestudies of performance monitoring relating to ERN, CRN, and Pe

amplitudes in both groups with differing levels of valence and arousal

(e.g., individuals with depressive or anxiety disorders) as well as in

state-related studies of the ERN and CRN.

Acknowledgment

We gratefully acknowledge the assistance of Justin Hoskin, Kevin

Voisin, and Jordan Davis in data collection. The Brigham Young

University Ofce of Research and Creative Activities provided funding

for this study. The authors report no conicts of interest.

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Larson2013.Nfluences of Orthogonally-manipulated Valence and Arousal on Performance Monitoring Processes