What are the influences of orthogonally-manipulated valence and arousal on performance monitoring processes? The effects of affective state
Highlights
► Examined orthogonal contributions of valence and arousal to performance monitoring. ► Negative valence with high arousal (anxiety) showed decreased accuracy. ► CRN amplitude was related to positive valence, but not arousal. ► ERN minus CRN amplitude difference related to arousal, but not valence. ► Overall ERN and Pe amplitudes did not significantly differ by valence or arousal.
Introduction
Mood states can affect multiple cognitive processes, including memory (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 specifically, performance monitoring abilities are influenced by mood states. Performance monitoring refers to the cognitive control process of monitoring activities to detect conflict or inaccuracies that may require increased cognitive resources (Carter et al., 1998). The neural bases of performance monitoring, 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) components of the scalp-recorded event-related potential (ERP).
The ERN is a negative potential that peaks approximately 50 ms after an erroneous response. The ERN is 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 significance of the ERN, with the most commonly accepted theories indicating that the ERN represents the detection of response-related conflict, the detection of a mismatch between error and correct trials, a reinforcement learning indication of incorrect performance 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 same time frame as the ERN, but on correct trials. The functional significance 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 certainty, an evaluation of whether a response strategy is adaptive, or an index of the anticipated probability of a stimulus presentation (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 distribution and originate broadly from areas near the anterior cingulate cortex (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, Falkenstein et al., 2000, Nieuwenhuis et al., 2001). The Pe typically follows the ERN and is thought to functionally reflect conscious error processing (Endrass et al., 2007, Endrass et al., 2012b, Hughes and Yeung, 2011, Larson and Perlstein, 2009, Nieuwenhuis et al., 2001, Shalgi et al., 2009) or continued processing of the affective significance of an error (Falkenstein et al., 2000, Overbeek et al., 2005). Source localization studies also implicate the ACC, along with potential 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 influence of induced positive and negative affect on reflections 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 participant, but Pe amplitude was decreased — potentially suggesting decreased 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 nonsignificant differences in ERN and Pe amplitudes between groups of individuals receiving either encouraging or derogatory feedback on task performance (Clayson et al., 2012). These findings are consistent with research that showed no differences in ERN amplitude between conditions with and without trial-by-trial performance feedback (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 concoction rather than personal performance. Similarly, religious primes can either decrease ERN amplitude in people who believe in God or increase ERN amplitude in those who do not believe, suggesting that the state emotion has person-specific 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, and movie clips. For example, Larson et al. (2006) showed more negative ERN amplitudes when errors were made during presentation of pleasant 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 pictures 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 evaluative (i.e., disgusted) expression relative to those that showed a pleasant (i.e., happy) expression. There is considerable research that suggests that affective information can modulate both early and late ERP components (e.g., Foti et al., 2009), but the relationship between induced affective-state and ERN amplitude is not completely clear, although it appears that increased arousal or an absence of congruence with performance and mood states contributes to increased-amplitude ERN.
The current literature on affective state and performance monitoring, including the ERN, CRN, and Pe, is often confounded by a relative absence of empirical quantification of changes in mood state. For example, 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 quantified (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 versus high). Indeed, we were unable to find studies that examined error-related performance monitoring along with orthogonal dimensions of valence and arousal. Valence and arousal represent independent dimensions of emotion, with valence ranging from pleasant to unpleasant and affect describing the energy or level of activation associated with the affect, tending to range from low to high energy (Barrett and Russell, 1998). Studies, such as the current investigation, orthogonally investigating valence and arousal effects will inform the field by providing a way to systematically examine the unique contributions of these dimensions as well as their potential interactive effects. These studies have implication for performance monitoring in both healthy individuals and those with affective dysregulation such as depression or anxiety, particularly since the impact of affect on conflict-related performance monitoring processes has not been fully established.
To address these shortcomings in the extant literature, the current examination quantified 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 previously verified mood-induction procedure that involves music and mood-related rumination across a range of valence and arousal combinations (Eich et al., 2007, Jefferies et al., 2008). Specific mood induction conditions included sad (negative valence, low arousal), calm (positive valence, low arousal), anxious (negative valence, high arousal), and happy (positive valence, high arousal). We utilized a pre-mood induction 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 another between-groups neutral condition.
The mixed findings in the state-related performance monitoring literature make determining hypotheses regarding the distinctive contributions of emotional valence and arousal difficult; 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 obsessive–compulsive 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 and Hajcak, 2008). The literature on affective traits and performance monitoring is more variable for 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 amplitudes 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 a negative-valence, high-arousal mood state, namely, anxiety. We also hypothesized that ERN amplitude would increase during a negative-valence, low-arousal mood state, namely, sadness. We did not have specific hypotheses about the CRN or Pe based on a relative lack of information in the current literature.
Section snippets
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 participant self-report, included current or previous diagnosis of a 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
Mood induction manipulation check
We first examined whether the mood induction procedure induced the intended mood states. See Table 1 for mean arousal and valence ratings for each group across the six ratings. Consistent with previous studies 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-Group × 6-Time repeated measures
Discussion
We aimed to examine the influences of affective valence and arousal on electrophysiological and behavioral indices of response-related performance monitoring (i.e., ERN, CRN, Pe) using a standard mood induction paradigm. To address some of the inconsistencies in the literature we ensured that the mood induction procedure altered participant self-reported mood and specifically examined both the interactive (i.e., group-based) and orthogonal roles of valence and arousal. Participant ratings of
Acknowledgment
We gratefully acknowledge the assistance of Justin Hoskin, Kevin Voisin, and Jordan Davis in data collection. The Brigham Young University Office of Research and Creative Activities provided funding for this study. The authors report no conflicts of interest.
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