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1 Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2014 Reward Positivity, but Not Feedback Negativity, Is Sensitive to Reward History (Trial Sequence Reward Context) Srikant R. Kothur Follow this and additional works at the FSU Digital Library. For more information, please contact

2 FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES REWARD POSITIVITY, BUT NOT FEEDBACK NEGATIVITY, IS SENSITIVE TO REWARD HISTORY (TRIAL SEQUENCE REWARD CONTEXT) By SRIKANT R. KOTHUR A Thesis submitted to the Department of Psychology in partial fulfillment of the requirements for the degree of Master of Science Degree Awarded: Fall Semester, 2014

3 Srikant Kothur defended this thesis on July 31, The members of the supervisory committee were: Norman B. Schmidt Professor Directing Thesis Thomas Joiner Committee Member Walter Boot Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that thesis has been approved in accordance with university requirements. ii

4 TABLE OF CONTENTS List of Tables... iv List of Figures...v Abstract... vi 1. INTRODUCTION Overview Feedback Negativity Reward Positivity Phase Dynamics Reward Processing: Implications for Understanding Behavior Current Study Aims METHOD Participants Procedure Gambling Task Physiological Data Acquisition Data Preprocessing Data Reduction Data Analysis Plan RESULTS DISCUSSION...23 APPENDICES...28 A. FIGURES...28 B. TABLES...34 C. IRB APPROVAL AND RE-APPROVAL LETTER...37 D. SAMPLE INFORMED CONSENT FORM...41 REFERENCES...43 BIOGRAPHICAL SKETCH...53 iii

5 LIST OF TABLES 1 Gain-Loss Differences of Time-Domain and Time-Frequency Components Delta-RP and Theta-FN Polynomial Contrasts in a One-Way ANOVA with Four Reward Sequence Contexts (Previous-Current: Gain-Gain, Loss-Gain, Gain-Loss, Loss-Loss) Contrasts of Sequence Context Categories by TF Component Assessing the Effect of Previous Reward on Delta-RP and Theta-FN: 2 (Current Reward: Gain or Loss) x 2 (Previous Reward: Gain or Loss) Repeated Measures ANOVA Post-Feedback Reaction Time: Sequence Context Pairwise Comparisons...36 iv

6 LIST OF FIGURES 1 Time-Frequency representation of gain vs. loss Gehring gambling task trial types Trial sequence context Delta-RP reward context line plot Theta-FN reward context line plot Delta-RP main effects for Previous Reward and Current Reward Theta-FN interaction between Previous Reward and Current Reward v

7 ABSTRACT Recent research using TF analysis has suggested that two processes underlie performance feedback event-related potentials (ERPs): a reward sensitive process in the delta range (deltareward positivity; 0-3 Hz) and a loss sensitive process in the theta range (theta-feedback negativity; 3-7 Hz). In addition to being sensitive to gain outcomes, delta-rp appears to be sensitive to more complex reward context information, such as alternative outcomes and reward magnitude. The current study evaluated delta-rp with respect to reward history, a previously unassessed example of reward context information. Reward history considers trial sequence context and the specific reward outcomes on previous trials, relative to the current trial. The current study assessed the extent to which delta-rp is not only sensitive to primary feedback (i.e. current outcome), but also reward history, Further, the current study evaluated whether theta-fn is best characterized as a loss sensitive index of primary feedback characteristics. A college sample (N=43) completed a common gambling task, while EEG data was recorded. TF-PCA was utilized to parse overlapping delta and theta activity during the the traditional time-domain FN- P300 period. Delta-RP was sensitive to both current trial reward feedback and more distal reward feedback. The current study determined that delta-rp linearly scaled with the amount of overall reward accumulated over the course of two trials. Theta-FN was primarily sensitive to the current trial outcome with greater activity in response to loss feedback. Further, analysis of behavioral correlates suggested that consecutive loss feedback resulted in a decrease in subsequent reaction time. Overall, results suggest that delta-rp may be a dynamic index of more complex reward information. vi

8 CHAPTER ONE INTRODUCTION 1.1 Overview Reward outcomes processing are critical to learning and maintaining goal-directed behavior. Recently, gain and loss aspects of reward have been closely investigated within gambling feedback tasks (Bernat, Nelson, Steele, Gehring, & Patrick, 2011; Gehring & Willoughby, 2002; Hajcak, Moser, Holroyd, & Simons, 2006; Holroyd, Hajcak, & Larsen, 2006; Yeung & Sanfey, 2004). These tasks generally ask individuals to make choices, from which they receive feedback indicating whether they have won or lost money. Event-releated potential (ERP) electroencephalogram (EEG) measures have been utilized in studies of this behavior and have traditionally been analyzed in the time-domain (TD), which is amplitude changes across time. However, this technique poses a problem with respect to disentangling multiple neurocognitive processes that tend to be summed in the ERP. Time-frequency (TF) signal processing is an emerging method for characterizing these neurocognitive processes (Bernat, Williams, & Gehring, 2005; Bernat, Nelson, Steele, Gehring, & Patrick, 2011; Bernat, Malone, Williams, Patrick, & Iacono, 2007). By taking into account the spectral characteristics of the ERP waveform, TF analysis has suggested that two processes underlie feedback ERPs: a reward sensitive process in the delta range (0-3 Hz) and a loss sensitive process in the theta range (3-7 Hz). Traditional time-domain approaches have focused on the feedback negativity (FN), which is a negative polarity deflection in the ERP occurring around ms after feedback onset, just before the P300 component (Gehring & Willoughby, 2002; Hajcak, Moser, Holroyd, & Simons, 2007; Holroyd & Coles, 2002; Kreussel et al., 2012; Miltner, Braun, & Coles, 1997; Nieuwenhuis, Yeung, Holroyd, Schurger, & Cohen, 2004; Wu & Zhou, 2009; Yeung & Sanfey, 2004). The FN has been shown to be increased in response to loss outcomes, but not to gains, and to be mainly sensitive to primary loss/gain feedback parameters, but not to secondary parameters such as the loss magnitude or the outcome relative to unchosen alternative outcomes (Hajcak et al., 2006b; Sato et al., 2005; Yeung & Sanfey, 2004). Recent work has begun to detail a reward positivity (RP) component, occurring at the same time as the FN, that is sensitive to gain instead of loss outcomes (Baker & Holroyd, 2011b; Holroyd, Pakzad-Vaezi, & Krigolson, 1

9 2008). Recent time-frequency analysis approaches have provided a delineation of the FN (theta- FN; occuring in the theta frequency band, 3-7 Hz) from the RP (delta-rp; occuring in the delta band, 0-3 Hz) (Bernat et al., 2007; Bernat et al., 2011). Findings from this work support the idea that theta-fn and delta-rp are independent, co-occurring processes. For example, gain-loss differences for theta and delta were found to be uncorrelated during the FN-P300 time period (Bernat et al., 2011). This suggests that theta-fn and delta-rp are not yoked expressions of the same underlying neural process, but instead index distinguishable processes. In contrast to the FN research suggesting primary sensitivity to the most salient current-trial feedback characteristics (Nieuwenhuis et al., 2004) delta-rp appears to index an array of secondary feedback characteristics (i.e. beyond the primary gain aspect), including reward magnitude and alternative outcomes (Bernat, Nelson, & Baskin-Sommers, in review). The current study aims to assess whether delta-rp is sensitive to reward processing context when extended over time. Specifically, the current study hypothesizes that delta-rp, but not theta-fn, is sensitive to reward history, which are more distal reward sensitive context characteristics (i.e. reward separated in time). 1.2 Feedback Negativity Background The FN is a negative-going deflection that is maximal at frontocentral scalp sites, occurring approximately 250 ms post-feedback presentation. In conventional time-domain measures, time-domain FN overlaps with time-domain P300, a positive-going deflection maximal at more parietal scalp sites that appears around 300 ms post-feedback. Within the context of performance feedback processing, the functional significance of these two deflections can be obscured by the temporal overlap (Gehring & Willoughby, 2002; Holroyd & Coles, 2002; Miltner et al., 1997). The FN, when measured in time-domain, has been demonstrated to reflect the most salient feature of the displayed feedback and may be viewed as selectively sensitive to loss feedback (Nieuwenhuis et al., 2004). Using the Gehring gambling feedback task, Nieuwenhuis and colleagues (2004) exhibited that the time-domain FN was sensitive to either feedback displaying basic reward valence (gain vs. loss) or feedback displaying relative performance (correct vs. incorrect). A number of studies have now revealed that the time-domain FN is sensitive to losses in simulated gambling tasks, such that the time-domain FN magnitude is 2

10 larger (more negative) for losses than gains (Gehring & Willoughby, 2002; Hajcak et al., 2006; Holroyd et al., 2006; Yeung & Sanfey, 2004). In terms of relative performance, a choice can be defined as incorrect if the alternate outcome would have resulted in a more favorable outcome (i.e. subject made a selection that resulted in +5, while the alternate selection would have resulted in +25). Though feedback on each trial contained the same information, Nieuwenhuis and colleagues (2004) used color in order to emphasize either valence or performance, and results indicated that time-domain FN responded to the most salient stimulus feature, such that losses or incorrect responses produced the largest time-domain FN. Theoretical and empirical work has suggested that the time-domain FN reflects reinforcement learning system activity (Holroyd & Coles, 2002). Reinforcement learning theory is rooted in the idea that actions and behaviors, when followed by positive feelings, are more likely to reoccur, while negative outcomes lead to a decreased likelihood of a given action (Cohen & Ranganath, 2007; Holroyd & Coles, 2002). Thus, reward prediction errors or losses are especially notable since individuals must adjust their subsequent responses to maximize reward. According to the theory, the basal ganglia causes a phasic increase in midbrain dopamine neural activity when outcomes are better than expected, whereas worse than expected events result in a phasic decrease in midbrain dopamine neural activity (Hajcak et al., 2007; Schultz, 2002). In one reinforcement learning experiment, various stimuli had different optimal selection mappings, and participants had to learn whether selecting the left or right button for a given stimuli would result in feedback indicating monetary reward (Holroyd & Coles, 2002). The largest time-domain FNs were produced when feedback indicated a loss when reward was most unpredictable, and when the subject, after having learned the mapping, made an incorrect selection when reward was guaranteed for a correct response. Once reward is deemed likely, the FN response to loss feedback is larger (more negative), whereas the FN produced after losses is smaller when reward is deemed unlikely (Cohen, Elger, & Ranganath, 2007). In accordance with reinforcement learning theory, individuals adjust behavior in response to negative feedback, a critical component of reward learning. One study showed that in response to losses, the timedomain FN is more negative after behavioral adjustment than when individuals maintained the target behavior (Cohen & Ranganath, 2007). Together these findings bolster the idea that the FN is sensitive to principles emphasized by reinforcement learning. 3

11 Several studies have confirmed that unexpected negative feedback produces a larger time-domain FN than unexpected positive feedback; however, the FN appears to be insensitive to more complex, or secondary, feedback aspects (Gibson, Krigolson, & Holroyd, 2006; Nieuwenhuis, Nielen, Mol, Hajcak, & Veltman, 2005). Research assessing the impact of loss magnitude on the time-domain FN has determined that the amount of loss did not impact the magnitude of the negativity (Hajcak et al., 2006). The time-domain FN appears to show a similar-sized negative deflection for smaller and larger losses (i.e. reward magnitude); therefore, it has been proposed to be a basic, binary good versus bad feedback evaluation index (Hajcak et al., 2006). Overall, the FN appears be a loss sensitive measure that is a simple index of the most salient aspects of feedback; however, recent work reveals that the FN does not capture more complex aspects of feedback processing (Bernat, et al., in review) Neural Sources Previous research has indicated that neural sources underlying time-domain FN measures lie in medial frontal brain region, with primary contributions from the anterior cingulate cortex (ACC) (Gehring & Willoughby, 2002; Hajcak et al., 2006; Holroyd & Coles, 2008). Thus, the FN appears to be a rudimentary reflection of early ACC activation that differentiates between primary stimulus information - favorable versus unfavorable outcomes (Yeung & Sanfey, 2004). Furthermore, the mesencephalic dopamine system is believed to convey error signals to the basal ganglia, which subsequently attempts to improve predictions, and then responses in the ACC motor areas are altered to improve task performance (Holroyd, Larsen, & Cohen, 2004). Activity in the mesocortico limbic reward system, which includes the ventral striatum and medial prefrontal cortex (mpfc), has also been associated with the time-domain FN measure. Specifically, in a somewhat controversial report, the dorsal striuatum was associated with increased time-domain FN for gain versus loss outcomes (Foti, Weinberg, Dien, & Hajcak, 2011). Importantly, other studies have also implicated the mpfc, ventral striatum, amydala, and orbitofrontal cortex (Carlson, Foti, Mujica-Parodi, Harmon-Jones, & Hajcak, 2011). With regard to functional mechanisms driving the FN, worse than expected outcomes have been understood to produce phasic decreases in the ventral tegmental dopaminergic input,leading to ACC neurons disinhibition and greater neural activity observed for losses (Holroyd & Coles, 2002; Holroyd et al. 2004). 4

12 The time-domain FN has been associated with other similar medial-frontal negative polarity deflections that can be characterized by theta activity, and theta activity has been demonstrated to have sources in the ACC (Başar, Schürmann, & Sakowitz, 2001; Ishii et al., 1999). For example, mid-frontal negative deflections (N200 or N2 ERP component) related to action monitoring have been observed for stimulus-locked and response-locked recordings (Folstein & Van Petten, 2008). For stimulus-locked recordings, there appear to be two classes of this N2 ERP component. One type of N deals with action selection (control N2), whereas the other is associated with attention (mismatch N2) (Folstein & Van Petten, 2008). The FN exhibits features apparent in both classes of N2 (Holroyd & Coles, 2002). With respect to responselocked components, the error-related negativity (ERN), a post-response self-performance monitoring negativity, shares some similarities with the time-domain FN and has been effectively represented by theta-band activity (Gehring & Willoughby, 2002). Research has demonstrated that theta activity is associated with multiple cognitive processes, including action selection, attention, learning, and memory (Cavanagh, Zambrano-Vazquez, & Allen, 2012; Wang, Ulbert, Schomer, Marinkovic, & Halgren, 2005). Prominent theta band features within this time range have been identified in cognitive tasks that relate to novelty, conflict detection, punishment, and error (Cavanagh, Frank, Klein, & Allen, 2010a). Time-frequency research, using theta as a better representation of FN (i.e. theta-fn), suggests that theta activity is a lowlevel response to negative feedback, and theta-fn appears to be uniquely sensitive to loss feedback (Bernat et al., 2011). Thus, for localizing the neural sources involved, theta activity may be an improved index of FN activity. Research has demonstrated that theta activity has primary sources in the ACC (Cavanagh, Frank, Klein, & Allen, 2010b; Tsujimoto, Shimazu, & Isomura, 2006; Wang et al. 2005). The ACC has been associated with response conflict detection, which is a function associated with the N2 ERP component (Botvinick, Cohen, & Carter, 2004; Yeung & Sanfey, 2004). Subsequent research has asserted that time-domain FN and N2 may be representations of the same neural phenomenon (Holroyd et al. 2008). This has promoted the proposal that a positive-going deflection may be responsible for the amplitude differences apparent between reward and error feedback (Holroyd et al., 2008). Both unexpected positive and negative feedback are thought to generate an N200, while only unexpected postive feedback results in the production of reward 5

13 positivity, which cancels out or weakens the N2 amplitude (Baker & Holroyd, 2011b; Foti et al. 2011; Holroyd et al. 2008). Functional mangetic resonance imaging (fmri) research suggests that a salience network integrates the centers responsible for conflict detection, interoceptive-autonomic processing, and reward processing (Seeley et al., 2007). With connections to subcortical and limbic structures, the dorsal ACC and orbital frontoinsular cortices are believed to be central to this salience network, which identifies the most salient internal and external stimuli in order to alter behavior (Cavanaugh et al. 2010a; Menon & Uddin, 2010; Seeley et al., 2007). Therefore, theta-fn may be a reflection of activity generated by the ACC as part of a salience network that may be critical to reward processing and learning. 1.3 Reward Positivity Background Basic RP Findings. As suggested above, several recent studies now indicate that a reward positivity (RP), a separable positive-going deflection occurring at the same time as the FN, contributes to the differential neural response to gain versus loss feedback that is indexed in the time-domain FN (Baker & Holroyd, 2011b; Bernat et al., 2011; Carlson et al. 2011; Foti et al. 2011; Holroyd et al. 2008; Holroyd, Krigolson, & Lee, 2011; Kreussel et al., 2012). The RP is hypothesized to be sensitive to unexpected and infrequent feedback (Holroyd et al. 2008) such that rewarding unexpected events may elicit more positivity than non-rewarding unexpected events (Holroyd et al. 2011). Unlike theta-fn, which is produced for rewarded and non-rewarded unexpected events, some studies propose that reward is necessary for the RP to occur (Baker & Holroyd, 2011a; Oliveira, McDonald, & Goodman, 2007). Evidence suggests that the RP reflects the initial assessment of reward prediction as well as the reappraisal of that initial information after feedback is delivered (Holroyd et al. 2011). Time-frequency signal processing has revealed that RP is maximal in the delta frequency band (0-4 Hz) and using delta as a measure of RP (i.e. delta-rp) is an effective approach to isolating reward processing (Bernat et al., 2011). Broadly, delta activity has been associated with more elaborative cognitive processing and has traditionally been linked to activity in the P300 time-range (Knyazev, 2012). However, recent research posits that separable delta activity occurs during the entire P2-N2-P3 range (Bernat et al. 2007; Gilmore, Malone, Bernat, & Iacono, 2010) 6

14 and accounts for the RP activity occuring throughout the FN-P300 time window (Bernat et al., 2011). This supports the idea that the ERP measured in time-domain is a mixture of theta and delta activity representing distinct cognitive processeses. As previously reviewed, theta-fn appears to beuniquely sensitive to losses, while delta-rp exhibits unique sensitivity to gain feedback Recent research supports the idea that delta-rp is sensitive to both primary feedback (gain vs. loss), as well as more complex, secondary feedback, while theta-fn is sensitive primarily to primary loss/gain feedack (Bernat et al., in review). This bolsters the expectation that more complex reward effects may be indexed using delta-rp. These findings suggest that delta-rp indexes multiple cognitve processes and may also be sensitive to other secondary feedback attributes, such as previous reward history or reward context. The current study will assess the relationship between delta-rp and reward context/history operationalized using sequential rewards. According to reinforcement learning theory, past rewards dictate future behavior; therefore, the feedback from the immediately previous trial may influence how gains are perceived on the current trial. The current study provides an assessment of delta-rp as an index of reward history, which can be tested by considering trial sequence context: whether previous trials were gains or losses. Considering delta activity s association with complex elaborative processing, delta-rp is proposed to be sensitive to a full array of secondary stimulus attributes that affect reward context (Basar, Basar-Eroglu, Karakas, & Schurmann, 1999; Bernat et al., 2011; Knyazev, 2012) Neural Sources Although there is a dearth of research on the neural sources underlying the delta-rp, some suggestions are emerging from the literature about the underlying neural sources underlying. As noted earlier, some studies in this area suggest that the neural generators for the RP observed in gambling tasks includethe striatum (Carlson et al. 2011; Foti et al. 2011). One recent study using source localization techniques has indicated that the putamen, a component of the striatum, may help differentiate reward versus nonreward (Foti et al. 2011). These sources are consistent with current models, suggesting that better than expected outcomes produce phasic increases in dopamine neuron activity, and these signals inhibit apical dendrites of motor neurons in the dorsal anterior cingulate cortex (dacc), which results in a reduced ERN amplitude 7

15 (Holroyd, 2004; Holroyd et al. 2008). The dacc has been hypothesized to integrate recent reward information and guide subsequent choice behavior (Holroyd & Coles, 2008) Problems with Time-Domain Measures Previous research indicates that the RP and FN processes are overlapping in time, and additional methods may be required to disentangle them (Bernat et al., 2011; Carlson et al. 2011; Foti et al. 2011; Holroyd et al. 2008). For example, during loss feedback, the negative-going time-domain FN may be responsive to losses, and the positive-going RP may be decreased or absent. The presence of the time-domain FN, without the RP to suppress the negative-going waveform, could result in the extreme negativity that is observed during loss feedback processing. Conversely, on gain trials, the RP activity may obscure the time-domain FN or make the whole ERP positive during the relevant time period (Holroyd et al. 2011). Analyzing reward processes during the period that includes the traditonal time-domain FN and P300 is difficult since the two time-domain components overlap temporally (Bernat et al., 2011; Miltner et al., 1997). Research on the time-domain feedback-p300 has been sparse, and most studies have failed to find a relationship to primary stimulus characteristics (loss vs. gain) (Foti et al. 2011; Hajcak et al., 2006; Yeung & Sanfey, 2004). However, P300 has been sensitive to positive valence in some studies (Hajcak et al., 2007). Other empirical work suggests that the P300 indexes secondary stimulus attributes, such as reward magnitude and alternative outcome valence (Gu, Wu, Jiang, & Luo, 2011; Sato et al., 2005; Yeung & Sanfey, 2004). This is consistant with the idea that activity underlying P300 reflects elaborative, post-perceptual processsing that uses environment or context dependent information to update working memory (Dien, Spencer, & Donchin, 2004; Donchin, 1981). Time-frequency (TF) analysis is an effective method to overcome these shortcomings of time-domain measures, and isolating co-ocurring theta and delta activity can elucidate how each frequency band may be indexing different aspects of reward processing (Bernat et al., in review). Though delta-rp may simply be sensitive to primary feedback attributes, recent studies have demonstrated that delta-rp is senstive to reward magnitude and alternative outcomes (Bernat et al., in review). However, the level of complex reward information that delta-rp may index is unknown, and delta-rp may be sensitive to more complex feedback information, such as time and context. Since delta activity has been associated with context dependence, working memory, and complex, elaborative processing, delta-rp appears to be a promising measure of complex 8

16 reward information that may involve multiple cognitive processes. Considering reinforcement learning theory and the proposed incentive salience network, understanding how individuals engage with different kinds of reward information is beneficial. Sequence context is an example of contextual information that impacts reinforcement learning, and is thus a potentially significant aspect of reward processing. Sequence context considers the outcomes from previous trialswhich may impact gain processing on the current trial. Behavioral studies have already demonstrated that previous trial outcomes in a feedback task can influence current trial risk taking and expectation (Gehring & Willoughby, 2002; Nelson, Patrick, Collins, Lang, & Bernat, 2011). Moreover, research has demonstrated that preceding events influence behavioral correlates such as the reaction time (Remington, 1969; Masaki et al., 2006). For example, individuals tend to make selections faster after losing, rather than gaining, money on the previous trial (Osinsky, Mussel, & Hewig, 2012). In addition to behavioral measures, Osinsky and colleagues employed conventional time-domain FN and P300 measures to assess neurophysiological processing associated with sequence context. Findings from this work suggest that the time-domain FN and P300 are sensitive to sequence context. However, given the highlighted problems associated with time-domain measures, time-frequency representations of FN and P300 activity may be more effective for separating the sequencesensitive processes underlying reward feedback. 1.4 Phase Dynamics Understanding the phase dynamics of theta and delta elucidates how delta-rp and theta- FN drive the neural responses generated by gambling feedback and reveals why common timedomain measures produce confusing results. Bernat et al. (2011) demonstrated how theta, due to the phase of the oscillation, produces an increased negative-going deflection for loss outcomes, relative to gain outcomes, during the time-domain FN window ( ms after feedback stimulus presentation), but an increased positive-going deflection during the P300 window ( ms). Figure 1 (derived from the results of current study) provides an illustration of delta and theta phase dynamics that affect gambling feedback. Delta, on the other hand, only produces increased positivity throughout the time-domain FN-P300 period ( ms). Thus, in timedomain measures, due to the change of phase for theta, these processes sum into increased gainloss differences at FN, but muted differences at P300. Overall, understanding the ERP as a 9

17 mixture of theta and delta has improved prospects for parsing multiple cognitive processes occuring during the overlapping time domain FN-P300 window. 1.5 Reward Processing: Implications for Understanding Behavior Reward processing underlies both normative and pathological behavior, and development of a valid index of this kind has the potential for broad application in cognitive neuroscience. Delta-RP, as described above, represents such an index. While indices of error and loss processing have seen substantial development in recent years, separable indices of reward processing have been more difficult to establish with electrophysiological measures. Such electrophysiological measures are particularly important due to the high time resolution available. High time resolution offers the possibility of disentangling the unfolding of complex reward processing in real time. Thus, the current study aims to further assess and validate this new reward positivity measure, which has the potential to provide new avenues for both basic and clinical research related to motivated and dysregulated behavior. Some of the possible areas of application for delta-rp are detailed below Mechanism of Reward Processing Hedonic impact ( liking ), learning, and incentive salience ( wanting ) are three aspects critical to reward processing (Berridge, 2007). Incentive salience, thought to be mediated by anticipatory mesolimbic dopamine neurotransmission, underlies implicit motivational processes, which can make learned and unconditioned rewards more desired (Berridge & Robinson, 1998; Everitt & Robbins, 2005; Hyman & Malenka, 2001; Ikemoto & Panksepp, 1999; Robinson & Berridge, 1993; Volkow, Fowler, & Wang, 2002). As a result, incentive salience is a critical aspect of both normative functioning and clinical problems. Problem behaviors associated with addiction have been linked to reward system dysregulation, which is thought to stem from imbalance in incentive salience and control networks (Hutchison, 2010). Consistent with reinforcement learning theory, the saliency value ascribed to given reinforcing stimulus influences behavioral action, and increased salience attributed to a drug and associated drug cues impacts substance abuse (Baler & Volkow, 2006). Studies have suggested that addictive drugs not only take on increased incentive salience, but also block negative reinforcement experienced during abstinence (Ahmed & Koob, 2005). Further, weakened inhibitory control is a significant factor believed to contribute to substance abuse problems (Baler & Volkow, 2006; Koob & Le Moal, 1997). When reward salience becomes too great for the self-control network to manage, 10

18 these factors may lead to an unrestrained cycle of substance abuse (Hutchison, 2010). In comparison to control mice, hyperdopaminergic mice have demonstrated greater incentive salience, such that they are more devoted and willing to work in order to receive rewards (Cagniard, Balsam, Brunner, & Zhuang, 2005; Yin, Zhuang, & Balleine, 2006). Independent of reward learning ability, these findings suggest that incentive salience plays a critical role in reward processing. Being able to more fully characterize delta-rp as an index of reward processing may significantly enhance the ability to understand the aforementioned systems thought to underlie psychopathology Individual Differences In addition to addiction, reward processing and incentive salience has also been assessed relative to broad individual differences constructs. Reinforcement Sensitivity Theory (RST), a biologically-based model of personality, is a framework to better understand how individual differences may affect reward processing (Gray, 1991). RST proposes that the brain has three systems: the Behavioral Approach System (BAS, the Behavioral Inhibition System (BIS), and the Fight-Flight-Freeze-System (FFFS). The BAS, or reward sensitivity, is an approach-related neural system activated for reward and has been linked to reward-reactivity and trait impulsivity measures (Carver & White, 1994; Smillie, Jackson, & Dalgleish, 2006). The BIS relates to inhibitory behaviors activated in response to goal conflict detection and has been viewed as a trait anxiety measure with high BIS activation increasing attention, arousal, and negative affect (Corr, 2004). Finally, the FFFS has been added to the revised RST model to account for avoidance behaviors, and some research posits a close relationship to BIS. In accordance with reinforcement learning theory, the cholinergic BIS system is thought to inhibit the dopaminergic BAS system (Boksem, Tops, Wester, Meijman, & Lorist, 2006; Holroyd & Coles, 2002). Together, the BAS and BIS have been associated with psychological disorders including psychopathy (Newman, Wallace, Schmitt, & Arnett, 1997) substance abuse disorders (Franken, 2002; Franken, Muris, & Georgieva, 2006; Johnson, Turner, & Iwata, 2003). Attention Deficit/Hyperactivity Disorder (Matthys, Goozen, Vries, Cohen-Kettenis, & Engeland, 1998) depression (Henriques, Glowacki, & Davidson, 1994a; Johnson, Turner, & Iwata, 2003) and anxiety disorders (Johnson, Turner, & Iwata, 2003). The BAS may be critical to aberrant reward processing since high trait-bas individuals are believed to be more motivated by and hypersensitive to positive reward stimuli (Lange, Leue, & Beauducel, 2012). Factor analysis of a 11

19 commonly used BAS scale reveals an impulsive sensation-seeking component (BAS-Fun Seeking) and two reward sensitivity components (BAS-Drive and BAS-Reward Responsiveness) (Carver & White, 1994; Dawe, Gullo, & Loxton, 2004; Van, Franken, & Muris, 2010). Research demonstrates that the BAS-Reward subscale, along with more recent measures designed to tap into reward responsiveness, has predictive power with respect to P300 amplitude (Van, Franken, & Muris, 2011). In response to positive feedback, individuals with high BAS scores tend to have larger P300 amplitudes, which may reflect a post-evaluative process in place to ensure future behavior leads to increased reward (Balconi & Crivelli, 2010). Other research comparing high and low impulsivity individuals have noted a similar split, with high impulsive individuals having a larger P300 response (Martin & Potts, 2009). Taking into account that P300 is primarily composed of delta activity, these findings highlight how a better classification of delta-rp as an index of reward could strengthen connections to psychopathology Psychopathology Reward processing is key mechanism involved in psychopathology on both the internalizing and externalizing spectrum of disorders (Chau, Roth, & Green, 2004; Comings & Blum, 2000; Fowles, 1988). Research supports the critical role of reward processing in externalizing disorders such as ADHD (Plichta et al., 2009; Scheres, Tontsch, Thoeny, & Kaczkurkin, 2010; Ströhle et al., 2008) problem gambling (Hewig et al., 2010), substance abuse (Kreek, Nielsen, Butelman, & LaForge, 2005; Loxton & Dawe, 2001; Noble, 2000) conduct disorder (Tranah, Harnett, & Yule, 1998) and Anti-Social Personality Disorder (ASPD) (Blair, Morton, Leonard, & Blair, 2006; Blair et al., 2004). ERP studies suggests that P300, a timedomain component composed primarily of delta, is reduced in amplitude in individuals with ADHD (Brandeis, van Leeuwen, Steger, Imhof, & Steinhausen, 2002; Bresnahan & Barry, 2002), conduct disorder (Bauer & Hesselbrock, 1999), ASPD (Costa et al., 2000), and substance abuse disorders (Bauer, 2001; Anokhin et al., 2000; Iacono, Malone, & McGue, 2003). In addition, binge eating disorder (Davis et al., 2008), obesity risk (Davis et al., 2007), and bipolar disorder (Mason, O'Sullivan, Bentall, & El-Deredy, 2012) have been associated with reward hypersensitivity. With respect to internalizing disorders, research demonstrates that depressed adults and children exhibit less reward sensitivity (Henriques, Glowacki, & Davidson, 1994b; Nestler & Carlezon Jr., 2006; Drevets, 2001; Forbes & Dahl, 2005; Shankman, Klein, Tenke, & Bruder, 2007). Further, depressed individuals express less preference toward reward, and reward 12

20 magnitude may not influence their response tendencies (Henriques & Davidson, 2000; Pizzagalli, Iosifescu, Hallett, Ratner, & Fava, 2008; Forbes, Shaw, & Dahl, 2007). Due to this insensitivity to reward, studies suggest that depressed individuals may be more sensitive to negative stimuli and less sensitive to positive stimuli (Foti et al. 2011; Foti & Hajcak, 2009). 1.6 Current Study Aims The current study aimed to better characterize reward processing during a common gambling feedback task by using time-frequency analysis to resolve measurement problems associated with traditional time-domain ERP studies. In support of previous findings, delta-rp and theta-fn were expected to be sensitive to primary feedback characteristics. Specifically, theta-fn is uniquely sensitive to losses, and delta-rp is uniquely sensitive to gains. Moreover, recent research posits that delta-rp reflects multiple cognitive processes, including sensitivity to secondary feedback attributes such as ability to win money, reward magnitude, and alternative outcomes (Bernat et al., in review). The current study expanded understanding of delta-rp by testing whether it is sensitive to more complex feedback attributes. Specifically, we tested the hypothesis that delta-rp is sensitive to more complex reward context information (i.e. reward history), as formed by the outcome on the previous trial (i.e. previous outcome-current outcome: gain-gain, loss-gain, gain-loss, loss-loss). Delta-RP was expected to index reward accumulated over the course of two trials, such that greater reward resulted in greater delta-rp activity. In contrast, theta-fn was expected to be sensitivity to the current outcome, which is defined as whether or not the current trial outcome was a loss or a gain. Additionally, the current study evaluated behavioral correlates of reward context by testing how previous gains and losses influence reaction time on the subsequent trial. The current study hypothesized that consecutive gains, in comparison to consecutive losses, lead to increased reaction time on the subsequent trial (Osinsky, Mussel, & Hewig, 2012). Specifically, delta-rp, but not theta-rp, was proposed to modulate individual differences in post-reward reaction time since an increase in reaction time after reward is a reflection of complex cognitive processing. Trial sequence context, which is an account of reward history over the, is an example of complex, contextual information that was expected to influence how reward feedback is processed, and the current study aimed to identify delta-rp as a novel index for measuring this aspect of reward processing. 13

21 CHAPTER TWO METHOD 2.1 Participants The current sample includes 43 subjects (20 female, age: M = 20, SD = 4) Florida State University undergraduate students who were recruited primarily from introductory psychology classes, and received either course credit or financial compensation ($10/hour). Participants were all over the age of 18, and preliminary screening was conducted to ensure that major neurological conditions, visual impairments, or past traumatic brain injuries would not interfere with experiment participation. 2.2 Procedure Testing was conducted in a sound-attenuated, dimly lit room. Participants completed mood and personality inventories before performing 4 tasks, and the Gehring gambling task was the second task for each participant. Experimental stimuli were presented centrally on a 21 inch Iiyama Vision Master Pro CRT color monitor, placed to ensure a standard 60 cm viewing distance. Experiment Builder (SR Research) software was used to present experimental stimuli and a Microsoft Sidewinder controller was used to make behavioral responses. 2.3 Gambling Task The experimental task was a modified version of a task originally developed by Gehring and Willoughby (2002). Participants choose between two monetary options on each trial and then received feedback that indicated whether the choice resulted in a monetary gain or loss on that trial. Feedback was presented 100 ms after the button press, and the target stimuli consisted of two adjacent squares, each enclosing a number (5 or 25) representing a monetary value in cents (see Figure 2). Until a choice was made between the left or right square, the two choices remained on the screen and then a blank screen appeared for 100 ms. Each response was followed by a feedback stimulus that indicated the outcome of their decision. The chosen box became either red or green to indicate either a win or a loss (with red or green as the winning color counterbalanced across participants), and the unselected box became the opposite color (either green or red) to indicate that the unselected box would have resulted in the opposite result. The feedback stimulus appeared for 1,000 ms, followed by a blank screen for 1,500 ms prior to the onset of the next trial. The four possible combinations of 5 and 25 (i.e., 5 5, 5 25, 14

22 25 5, and 25 25) were crossed such that there are eight trial types; thus, although the participant s choice produces a designated outcome on each trial, signaled by the feedback, outcomes on future trials were not predictable from outcomes associated with prior choices (analogous to a roulette wheel or slot machine). Figure 2 depicts the eight trial types. After completion of each block, participants received information regarding their monetary performance on the current block as well as overall performance up to that point. In this experiment, participants completed a total of six blocks of 32 trials for a total of 192 trials. 2.4 Psychophysiological Data Acquisition EEG activity was recorded using 40-channel Neuroscan NuAmp amplifier containing sintered Ag-AgCl electrodes positioned in accordance with the International System (Jasper, 1958). In addition to HEOG and VEOG channels, 32 scalp sensors were recorded: FP1, FP2, F7, F3, FZ, F4, F8, FT7, FC3, FCZ, FC4, FT8, T7, C3, CZ, C4, T8, TP7, CP3, CPZ, CP4, TP8, A1, T5, P3, PZ, P4, T6, A2, 01, 0Z, 02. Impedances were kept below 10 kω, and ocular activity was monitored using electrodes placed above and below the left eye (VEOG) and on the outer canthus of each eye (HEOG). All EEG signals were referenced to CPZ and digitized online at 1000 Hz. Additionally, eye movements and pupil diameter were recorded with an Eyelink CL (SR Research) eye tracker sampling from the right eye at 1000 Hz and a spatial resolution of 0.1. Although eye tracking data was measured, it is not within the scope of this report. 2.5 Data Preprocessing EEG signals were epoched off-line from 1000 ms pre-stimulus and 2000 ms poststimulus and re-referenced to linked mastoids. Trial-level EEG data was corrected for ocular and movement artifacts using an algorithm developed by Semlitsch, Anderer, Schuster, & Presslich (1986), as implemented in the Neuroscan Edit software (version 4.3). Subsequently, the processed data was re-sampled off-line to 128 Hz. The data was averaged separately for each trial sequence context category (i.e. previous trial gain-current trial gain, previous trial losscurrent trial gain, previous trial gain-current trial loss previous trial loss-current trial loss), and epochs were baseline-corrected for the 150 ms period prior to stimulus onset. In order to identify and exclude movement and other artifacts, a careful visual inspection of the data was undertaken, and the following exclusionary criteria was utilized. To exclude ocular artifacts remaining after ocular correction, trials on which activity at frontal electrode sites F1 or F2 exceed 75 µv within a 2000 millisecond post-stimulus window (relative to median 15

23 activity within a 1000 ms window immediately preceding the stimulus) were excluded from further processing. Then, within each trial, individual electrode sites at which activity exceed ± 75 µv in either the pre (-1000 to 0 ms) or post-stimulus (0 to 2000 ms) time regions (relative to one another) were omitted from the analysis. Equipment problems necessitated the removal of 2 channels (O1 and O3) from the current analysis because most subjects did not have data from these channels. Further, subjects were excluded if greater than 25% of electrodes across all trials were artifact tagged based on the noted criteria. 2.6 Data Reduction Time-Domain (TD) Components Both TD components were identified via visual inspection of unfiltered, grand average ERP waveforms. The TD FN component was defined as the maximum negative deflection occurring in the range of 200 and 350 ms after stimulus onset. The TD P300 component was determined based on the maximum positive deflection occurring between 250 and 600 ms post stimulus onset. TD FN and TD P300 were measured based on activity from electrode sites FCZ and CZ, respectively. Visual inspection of topographic maps confirmed that these electrode sites represented maximal activity for these components Time-Frequency (TF) Components TF analysis was used to measure the spectral quality of the ERP signals across time. TF surfaces were computed for the gambling task s feedback stimulus-locked ERP for the time span of 0 to 1000 ms and bandpass filtering was utilized in order to represent delta (<4 Hz) and theta (>2 Hz) activity. TF-PCA was applied to the TF surfaces (Bernat et al, 2005), and a scree plot was used to determine the potential solutions for both delta and theta. For both delta and theta, a five PC solution was found to be most appropriate as each contained one PC that fully captured the FN-P300 time region of interest. PC4 was selected to represent theta-fn, and PC5 was selected for delta-rp (see Figure 1). Similar to the TD component electrode selection methodology, FCZ was selected for theta-fn and CZ for delta-rp. This was based on the site with maximal activity as indicated by visual inspection of topographic maps. To manage outliers, TF theta-fn and delta-rp values that were four standard deviations from the mean were Windsorized such that these outlying values were adjusted to the value four standard deviations above or below the mean. This was completed separately for each trial sequence context. The same methodology was used to control for outliers in the behavioral reaction time data. 16

24 2.7 Data Analysis Plan Analyses were planned to replicate previous findings regarding the nature of delta-rp and theta-fn, and to expand understanding of delta-rp by testing whether it was sensitive to reward history (i.e. trial sequence context), an example of complex reward context information. Replication: Delta-RP is uniquely sensitive to gains, while theta-fn is uniquely sensitive to losses. Regression analysis was conducted to establish that TD FN and TD P300 should be viewed as a mixture of delta-rp and theta-fn activity. In the regression, relevant TF delta-rp and theta-fn gain-loss differences were independent variables used to predict TD FN and TD P300 gain-loss differences. Then, t-tests comparing gain versus loss were performed to replicate the finding that theta-fn is loss sensitive and delta-rp is gain sensitive. Aim #1a: Delta-RP is sensitive to reward history (i.e. trial sequence context). Aim #1b: Theta-FN will be primarily sensitive to current trial feedback. A one-way repeated measures ANOVA was used to test the primary hypothesis that current trial delta-rp increases based on greater accumulated reward over the course of two trials. Figure 3 depicts the four possible combinations when accounting for the previous trial outcome. The four reward categories (previous gain-current gain, loss-gain, gain-loss, loss-loss) were arranged relative to the current trial outcome. Thus, a current gain following a loss was categorized as representative of more reward than a current loss following a gain. Multiple statistical methods were used to evaluate this hypothesis. Since we hypothesized that delta-rp would be a reward sensitive measure of trial sequence context, delta-rp activity should decrease, in a linear manner, across the four decreasing reward levels. An exclusively linear relationship would help support the idea that delta-rp is not simply indexing current gain processing, but is also sensitive to reward outcomes on the previous trial. Further, t-tests comparing adjacent reward levels were conducted to evaluate the extent to which delta-rp was sensitive to declining levels of overall reward during the two trials. Additionally, a 2 (current reward: gain, loss) x 2 (previous reward: gain, loss) ANOVA was conducted to describe the results within a current versus previous reward framework. This framework was also employed to provide a contrast to theta-fn results. Based on the hypothesis that delta-rp is sensitive to reward history, significant main effects were expected for current reward and previous reward. 17

25 The current study also aimed to evaluate whether theta-fn is a loss sensitive index that is primarily sensitive to current trial feedback. A one-way repeated measures ANOVA, identical to the one conducted for delta-rp, was utilized. T-tests comparing subsequent reward levels were conducted to highlight that the main theta difference occurred between the loss-gain and gainloss sequence contexts. A theta-fn 2 (current reward: gain, loss) x 2 (previous reward: gain, loss) ANOVA was then performed to test whether only current reward would result in a significant main effect. Post-hoc analysis of all pairwise comparisons, Tukey corrected, were completed based on the findings. ANOVA results are Greenhouse-Geiser adjusted as appropriate and the reported p values for planned comparisons were Bonferroni corrected. Partial eta squared (η 2 p ) was used as a measure of effect size. Aim #2a: Assess the effect of consecutive gains and losses on reaction time during the subsequent decision. Aim #2b: Individual differences in post-reward reaction time will be modulated by delta-rp. In order to evaluate how reaction time was affected by reward history, t-tests and a four level one-way repeated measures ANOVA were employed. First, a t-test was performed to compare reaction times following any gain and loss feedback. Due to exploratory nature of this aim, all pairwise comparisons, Tukey corrected, were calculated, and an emphasis was placed on understanding the nature of the behavioral differences between consecutive gains and consecutive losses. To evaluate aim #2b, bivariate correlations between reaction time and delta- RP were conducted for each trial sequence category. Theta-FN was also substituted for delta-rp, but this was beyond the scope of the study proposal. 18

26 CHAPTER THREE RESULTS Replication: Delta-RP is uniquely sensitive to gains, while theta-fn is uniquely sensitive to losses. Multiple regression analysis confirmed that both TD FN and TD P300 can be viewed as a mixture of delta-rp and theta-fn. Two separate multiple regressions were conducted with delta- RP and theta-fn gain-loss differences used as the predictor variables, and TD FN and TD P300 gain-loss differences alternatively serving as the dependent variable. Delta-RP and theta-fn accounted for 59% of the variance in TD FN and 56% of the variance in TD P300. With respect to TD FN, independent effects were found for delta-rp (β =.26, p <.03) and theta-fn (β = -.63, p<.001). Likewise, delta-rp (β =.78, p <.001) and theta-fn (β =.51, p <.001) uniquely predicted TD P300 gain-loss differences. Further, paired samples t-tests were conducted to replicate the finding that theta-fn is sensitive to loss feedback (t(36) = -5.01, p <.001) and delta-rp is sensitive to gain feedback (t(36) = 3.63, p <.001) (see Table 1). Aim #1a: Delta-RP is sensitive to trial sequence context. Aim #1b: Theta-FN will be primarily sensitive to current trial feedback. Due to low trial counts for three or more consecutive gains, multiple consecutive gains were grouped such that the gain-gain sequence context may have been preceded by gain or loss outcomes. This was also the case for consecutive losses, and all consecutive loss variations were grouped. A one-way repeated measures ANOVA with four reward sequence context categories (previous outcome-current outcome: gain-gain, loss-gain, gain-loss, loss-loss) was performed order to test for a linear pattern in delta-rp across reward sequence context. First, a significant 2 main effect for sequence context, F(3,126) = 18.54, p <.001, η p =.31, was found. Subsequent tests of polynomial contrasts revealed that delta-rp, across the four reward sequence contexts, was best described by a linear term, F(1,42) = 27.61, p <.001, η 2 p =.40. Polynomial contrasts were not significant for quadratic and cubic terms (see Table 2). This indicated that only a linear pattern best described delta-rp across reward sequence context categories. Figure 4 illustrates the four reward sequence contexts arranged from most to least reward, relative to the current trial. Pairwise comparisons, using a Tukey correction, were conducted between each adjacent 19

27 reward sequence context categories. Consecutive gains (gain-gain) resulted in significantly greater delta-rp activity than single current gains (loss-gain), t(42) = 2.77, p <.01, which in turn resulted in more delta-rp activity than single current losses (gain-loss), t(42) = 3.18, p <.01. The latter comparison was compatible with the t-test conducted as part of the replication, which indicated significant gain and loss outcome differences without accounting for reward history. Finally, single current losses (gain-loss) resulted in significantly greater delta-rp activity than consecutive losses (loss-loss), t(42) = 2.64, p<.001. Overall, this indicated that current delta-rp is sensitive to reward history and linearly scales with reward sequence context. In order to determine the polynomial pattern for theta-fn, a repeated measures ANOVA with the same four reward sequence context categories was conducted. Indicating that mean theta-fn varied across the reward categories, the omnibus test was significant, F(3,126) = 20.02, p <.001, 2 η p =.32. Unlike delta-rp, which was only characterized by a linear pattern, polynomial contrast tests indicated that theta-fn had statistically significant linear, quadratic, and cubic terms (see Table 2). Along with visual inspection (Figure 5), three t-tests comparing theta-fn differences between adjacent reward sequence contexts were conducted to better understand why all three of the tested polynomial contrasts were significant. In contrast to delta-rp, consecutive gains (gaingain) and single current gains (loss-gain) did not result in a significantly different amount of theta-fn activity, t(42) =.268, ns. There were significant differences in theta-fn between the current gain contexts and both of the current loss contexts. For example, single current gains (loss-gain) resulted in significantly less theta-fn activity than single current losses (gain-loss), t(42) = -5.21, p <.001 (Table 2). However, in contrast to delta-rp, consecutive gains (gain-gain) and single current gains (loss-gain) did not result in a significantly different amount of theta-fn activity, t(42) =.268, ns. Also, significant differences were found between both current loss sequences, such that single current losses (gain-loss) had significantly greater theta-fn activity than consecutive losses (loss-loss), t(42) = 3.11, p <.003. Subsequently, two-way repeated measures ANOVA (2 x 2) was conducted for each TF component in order to assess the relative effects of reward history on delta-rp and theta-fn, and test the hypothesis that theta-fn is primarily driven by the current trial outcome. With respect to delta-rp, a two-way ANOVA analysis found significant main effects for Current Reward (yes/gain, no/loss), F(1,42) = 24.50***, η 2 p =.37, and Previous Reward (yes/gain, no/loss), F(1,42) = 13.50***, η 2 p =.24. However, there was no significant interaction term (see Table 4 20

28 and Figure 6). The same statistical test was applied to theta-fn, and it revealed similarly robust main effects for both factors: Current Reward (yes/gain, no/loss), F(1,42) = 25.55***, η 2 p =.38, and Previous Reward (yes/gain, no/loss), F(1,42) = 12.19***, η 2 p =.23. In support of Aim 1b, a significant Current Reward x Previous Reward interaction was found, F(1,42) = 5.47***, η 2 p =.12 (see Table 4 and Figure 7). This interaction effect describes previous reward status impacts current trial-fn activity only when there is no current reward (loss). The previously addressed contrast between gain-loss and loss-loss conditions found a significant difference such that an accumulation of losses was associated with less theta-fn activity. Moreover, this difference is in the opposite direction of what would be expected if theta-fn was distally sensitive to loss processing. Given a loss on the current trial, more loss was not associated with more theta-fn activity. The Current Reward x Previous Reward interaction and the direction of the theta-fn activity difference in the two current trial loss conditions buttress the claim that theta-fn is primarily sensitive to current reward. Though previous reward had an effect on the strength of current theta-fn activity, it was not in a manner that suggests theta-fn is sensitive to reward sequence context. Aim #2a: Assess the effect of consecutive gains and losses on reaction time during the subsequent decision. Aim #2b: Individual differences in post-reward reaction time will be modulated by delta-rp. To test the hypothesis that trial sequence context impacts subsequent decision-making reaction time, a t-test comparing reactions times after any gain and any loss was first performed. Reaction time after gain feedback (M= ms, SD=328.95) was significant longer than after loss feedback (M= ms, SD=288.58), t(42)=3.69, p<.001. Next, an omnibus test of the four sequence contexts, using one-way repeated measures ANOVA, was conducted and results were 2 significant, F(3,126) = 12.40, p <.001, η p =.23. Subsequent follow-up contrasts between the four sequence contexts revealed that reaction time for three of the six pairs significantly differed, even after using a Tukey correction to account for all pairwise comparisons (see Table 5). Reaction times following consecutive losses (loss-loss) were significantly shorter (on average, by at least 103 ms, SE=27.9) than the other three categories. This partially supports the prediction that consecutive gains would result in greater deliberation than after consecutive losses. Having experienced at least one gain during the previous two trials was associated with more time spent 21

29 prior to making the next selection. Given the notable decline in deliberation time after consecutive losses, the initially determined significant reaction time difference following nonspecific gains and losses was determined to be due to the impact of the loss-loss condition. Furthermore, bivariate correlations between delta-rp and reaction times for the four corresponding trial sequence context categories were not significant. Post-hoc, bivariate correlations between theta-fn and reaction times were not significant for any of the four trial sequence contexts. 22

30 CHAPTER FOUR DISCUSSION The current study evaluated delta-rp and theta-fn in a common gambling task in order to expand understanding about how these indices respond to more complex reward attributes. Trial sequence context is an example of a complex reward attribute that takes into account reward history. We assessed whether sequence context, operationalized as the outcome on previous trials, would be indexed by delta-rp. In contrast, theta-fn was hypothesized to mainly exhibit sensitivity to the primary feedback characteristic, which in this task was whether or not the current outcome was either a loss or a gain. The current study aimed to (1) determine whether delta-rp was sensitive to more distal reward context characteristics, (2) confirm that theta-fn primarily exhibited sensitivity to current trial feedback characteristics, and (3) assess whether reward sequence context impacted subsequent decision-making reaction time. The functional significance of traditional time domain measures is obscured by the temporal overlap of multiple neurocognitive processing underlying the TD FN and TD P300 time period; however, time-frequency analysis has been an effective approach to disentangle these processes (Gehring & Willoughby, 2002; Holroyd & Coles, 2002; Miltner et al., 1997; Bernat et al., 2005; Harper, Malone, & Bernat, 2014). The current study confirmed that TD FN and TD P300 is a mixture of theta and delta activity with both theta and delta providing unique contributions. Previous research has demonstrated that delta-rp and theta-fn, which temporally overlap, are considered uniquely sensitive to gain and loss feedback, respectively (Bernat et al., 2011). The current study confirmed the sensitivity of delta-rp to gain feedback and theta-fn to loss feedback. Moreover, beyond primary feedback characteristics, delta-rp is sensitive to secondary feedback characteristics such as reward magnitude and alternative outcome (Bernat, Nelson, & Baskin-Somers, in review). The current study furthered this idea by demonstrating that reward history, defined as trial sequence context, is an additional secondary feedback characteristic indexed by delta-rp. Reward sequence context was conceptualized as a reward continuum that accounted for overall reward over the course of two trials. Thus, four levels of reward were possible: gain (previous trial)-gain (current trial), gain-loss, loss-gain, and loss-loss. The current study demonstrated that current trial delta-rp linearly scaled with total reward accumulated over 23

31 two trials. Specifically, current trial gain outcomes, when directly preceded by gain feedback, resulted in significantly greater delta-rp activity than current trial gains that were immediately preceded by loss feedback. This pattern continued with current trial gains, preceded by loss feedback, associated with greater delta-rp than current trials losses that were immediately preceded by gain feedback. Previous studies demonstrated that delta-rp was sensitive to gains in a nonspecific manner that did not consider the outcome on the previous trial; therefore, gain-gain and loss-gain outcomes were combined and approached in a nonspecific manner (Bernat et al., 2011). Finally, current trial losses, when followed by gain feedback, resulted in significantly greater delta-rp activity than consecutive losses. Delta-RP activity across these four sequence context categories was uniquely described as a linear relationship such that increases in delta-rp corresponded to greater reward over time. Overall, findings supported the hypothesis that sequence context is a secondary feedback characteristic indexed by delta-rp. This expands the scope of delta-rp as a reward specific measure that may be utilized to better understand mechanisms of reward processing. The current study also aimed to understand how theta-fn responded to sequence context. Theta-FN was primarily sensitive to whether or not the current trial outcome was a gain or a loss. As hypothesized, current trial loss feedback, regardless of whether the previous trial was a gain or a loss, produced significantly greater theta-fn activity than current trial gain feedback. Current trial gains were not significantly affected by the previous outcome, and both gain-gain and loss-gain contexts resulted in attenuated theta-fn activity. However, theta-fn activity on current trial losses was influenced by whether the preceding trial was a gain or loss. Current trial losses, when preceded by gain feedback, were associated with significantly more theta-rp activity than current trial losses preceded by loss feedback. The direction of increased theta-fn activity is counter to what would be expected if theta-fn, as a loss sensitive measure, exhibited reward sensitivity to trial sequence context. If theta-fn was responding to the lack of reward over time, theta-fn activity should be greater for the loss-loss context than for the gain-loss context. The increased theta activity following a gain-loss outcome may be better understood given the characterization of theta as representative of novelty, orienting, and conflict detection (Cavanaugh et al. 2010a). The required orienting and novelty associated with a loss following a gain may explain why this sequence category had greater theta-fn activity than consecutive losses. The current findings are consistent with TD FN studies that have characterized FN as 24

32 insensitive to secondary feedback characteristics such as reward magnitude (Gibson et al., 2006, Hajcak et al., 2006). The current study supports the idea that FN, characterized by TF theta activity, does not respond to more complex feedback characteristics, and this characterization is extended to past reward. Overall, findings support the hypothesis that theta-fn is primarily sensitive to current trial feedback, and unlike delta-fn, it did not scale with reward history. With respect to behavioral effects of trial sequence context, loss outcomes were followed by significantly faster reaction times on the subsequent decision. However, parsing reaction times into four sequence context categories revealed that this post-loss decrease in reaction time was primarily a function of consecutive losses, the category representing the least amount of reward. Reaction time after a loss-loss were significantly faster that any of the other sequence contexts, while there were no significant differences in reaction times between the gain-loss, loss-gain, and gain-gain. Neither delta-rp nor theta-fn were associated with the apparent decrease in reaction time for the loss-loss trial sequence context. The current findings corresponded to previous research demonstrating faster reaction times after loss outcomes and further specified that this a reflection of faster responses after consecutive losses; however, TF measures were not associated with this behavioral effect (Osinsky, Mussel, & Hewig, 2012). A notable limitation was the extent to which reward history was non-specific. The exact outcome two trials preceding the current trial was not considered due to low trial counts for individual combinations. For example, the gain-gain category included both loss-gain-gain and gain-gain-gain trial sequences. The extent to which more distal reward impacted current reward was not fully accounted for by the current study. Future studies could better characterize the extent to which delta-rp is sensitive to reward history by assessing the impact of more distal reward outcomes. An interesting effect evidenced in the current study was the significant stepwise differences between proximal reward sequence categories. Whether this pattern continues or asymptotes after several trials would elucidate the extent to which reward history can be indexed using TF measures. Furthermore, a lack of decision complexity in the gambling task may have impacted behavioral findings. Though an advantage in obtaining psychophysiological findings, behavior effects may have been obscured by the simplicity of choosing between the same two point amounts on every trial. The task s simple choices may have limited the extent to which subjects became engaged in the post-reward decision-making process. Moreover, the task was simpler 25

33 than the common Gehring & Willoughby (2002) gambling task, which includes more outcomestimulus combinations. Given the current findings regarding delta-rp, increased task complexity could better characterize behavioral correlates and their potential association with TF measures. Also, a greater range of task parameters would allow researchers to further evaluate other secondary reward characteristics that delta-rp may index. For example, expectancy is a means to motivate individuals into making specific choices. A task that provides such cues could impact the manner in which subjects respond to feedback violating their expectancy. How delta-rp and theta-fn are associated with these more complex reward characteristics is another future direction based on the current findings. Based on the role an individual s responsiveness to reward has on reward seeking and previous findings that suggest TD P300 amplitude is greater in those with greater reward sensitivity, the current delta-rp findings could be applied to reward specific individual difference measures (Van et al., 2011, Balconi & Crivelli, 2010). On the other hand, past research suggests that TD P300, a component closely connected to delta activity, is attenuated across various externalizing spectrum disorders (Bresnahan & Barry, 2002; Costa et al., 2000; Iacono et al., 2003). Considering the role of reward hypersensitivity in addiction and BASrelated findings suggesting an exaggerated P300 response, delta-rp may be a more suitable measure to index the electrophysiology of reward processing. Though the current study does not directly address how a more robust index of reward will elucidate these issues, there is a need for psychophysiological measures that better capture different facets of reward processing. Reward processing is a critical aspect of human behavior and learning that takes into account numerous secondary reward characteristics. Response to reward may be influenced by the presence or absence of previous reward. Reward history, defined by trial sequence context, is an example of secondary feedback characteristics that may influence current reward processing. The current study successfully evaluated whether delta-rp, a recently proposed ERP measure sensitive to secondary reward characteristics, indexed reward history. Taking into account feedback from the previous trial, the current study found that delta-rp was sensitive to more distal reward than just the current trial. Moreover, delta-rp scaled in a linear manner with respect to accumulated reward. Furthermore, these findings suggest that theta-fn, a co-occurring TF component sensitive to loss, is primarily sensitive to the current trial outcome. Though delta- 26

34 RP did not exhibit a relationship to behavioral correlates, delta-rp appears to be an effective index of many secondary stimulus characteristics, including reward history. 27

35 APPENDIX A FIGURES Figure 1. Time-Frequency representation of gain vs. loss. Basic gain-loss differences and PCs selected to represent theta-fn and delta-rp in the current study are represented. 28

36 Figure 2. Gehring gambling task trial types. Four stimulus combinations by two outcomes (gain or loss). Circles represent an individual s choice. Figure 3. Trial sequence context. Previous trial and current trial outcomes represented as a reward continuum. Overall reward is relative to the current trial. 29

37 * p-value <.05; ** p-value <.01; *** p-value <.001 (Reported significance levels corrected for multiple comparisons.) Figure 4. Delta-RP reward context line plot. This illustrates the linear trend exhibited by current trial delta-rp activity across four reward sequence contexts. Accounting for reward history, the four reward sequence contexts are ordered from greatest to least possible reward accumulated over the course of two trials. Each subsequent sequence context is associated with significantly less delta-rp activity. 30

38 * p-value <.05; ** p-value <.01; *** p-value <.001 (Reported significance levels corrected for multiple comparisons.) Figure 5. Theta-FN reward context line plot. This illustrates the non-specific polynomial trend exhibited by current trial theta-fn activity across four reward sequence contexts. Accounting for reward history, the four reward sequence contexts are ordered from greatest to least possible reward accumulated over the course of two trials. Current trial gains show no significant difference in theta-fn and the primary statistically significant difference is between current trial gain and current trial loss. Further 31