Rejection sensitivity (RS) is defined as the tendency to anxiously anticipate, perceive, and overreact to real or perceived rejection and can have significant effects on interpersonal relationships. Previous research has shown the negative social effects of RS, particularly in the context of romantic relationships, but less is known about the cognitive implications of having high levels of RS. Specifically, it is unclear whether a heightened sensitivity to rejection is associated with enhanced error processing or feedback evaluation. The current study used EEG to examine the effect of RS on two event-related potential (ERP) components associated with error monitoring and feedback evaluation, error-related negativity (ERN) and feedback-related negativity (FRN), respectively. Participants completed a Flanker task during which they received either social (faces) or nonsocial (symbols) feedback about their performance. Results showed an increased ERN on error trials for individuals with higher RS. Although the FRN was not influenced by RS, there was an expectancy-valence interaction. FRN amplitude was also sensitive to condition, with correct feedback eliciting significantly more negative FRN in the social condition compared to the nonsocial condition; FRN for unexpected feedback was also greater in the social condition. Overall, the results suggest a relationship between error monitoring and RS, as well as a relationship between social information and feedback processing. Future research should further explore the relationship between rejection sensitivity, attention, and social feedback processing.

Master of Science / Department of Psychological Sciences / Michael Young / When making decisions, the probability and magnitude of errors can play a major role in changing preferences. Electroencephalography (EEG) research examining the error-related negativity (ERN) and the associated feedback-related negativity (FRN) has indicated that the amplitude of each component may predict subsequent behavioral change. The current study used a version of the Balloon Analogue Risk Task (BART) that involves outcomes that are dynamically changing over time. As the balloon grows, more points are available but the probability of the balloon popping (netting zero points) is higher; the participant decides when to stop the balloon’s expansion to maximize points. The BART was adapted to facilitate the study of the FRN in dynamic environments. The purpose of Experiment 1 was to determine the effect of error magnitude on FRN amplitude during popped (incorrect) trials, whereas Experiment 2 was aimed at determining the effect of error magnitude on FRN amplitude during cashed-in (correct) trials. It was hypothesized that larger errors (i.e., the balloon popping after waiting a long time to cash-in) would result in a larger FRN than smaller errors. In Experiment 1, error magnitude did not contribute to the amplitude of the FRN. In Experiment 2, the masked points possible condition was a replication of Experiment 1. In the unmasked points possible condition, the number of points that could have been earned for each balloon was presented before participants found out how many points were earned. It was expected that there would be a larger FRN magnitude after cashed-in trials in the unmasked points possible condition compared to the masked points possible condition based on the magnitude of the error. In Experiment 2, the amplitude of the FRN was affected by the magnitude of the error on cashed-in trials in the unmasked condition, but not the masked condition. These results are seemingly at odds, and cannot be assimilated into any currently extant model of the FRN. An explanation relying on the motivational importance of errors is discussed.

Optimal decision-making entails outcome evaluation, comparing received costs and benefits with predicted costs and benefits. The anterior cingulate cortex (ACC), a brain area with major connections to the appetitive and aversive motivation systems, may provide the neural substrate of this evaluation process. One way to measure the relative contribution of these systems on decision-making is to measure individual differences in risk-taking behaviors. For individuals who make risky choices, this evaluation step may be biased: some show a preference for immediate, short-term rewards (increased appetitive system), while devaluing the long-term consequences of their choices (decreased aversive system). However, most studies supporting this theory have utilized monetary loss as the punishment. Other punishments that represent the presence of an aversive outcome, such as delivery of a painful stimulus, may be processed in a separate brain area and thus, may have differing effects on decision-making. The current study aimed to answer two main questions. First, we asked: is the ACC engaged by both appetitive stimuli and aversive stimuli? To answer this question, we recorded the Feedback-Related Negativity (FRN) response, a component thought to represent activity of the ACC, during a passive reward and punishment prediction task. Results indicated that the FRN responded to whether the outcome was A) unexpected and B) delivered or withheld, but not to the valence of the outcome. Second, we asked: do individual differences in these two systems have a differential impact on decision-making? To answer this question, participants completed a gambling task where they had to choose between large and small bets based on a probability of winning while we recorded their FRN response. They also completed self-report questionnaires indicating their sensitivity to reward/punishment and risk-taking behaviors. Results indicated that increased sensitivity of the appetitive system and decreased sensitivity of the aversive system (measured by both self-report and ERPs) predicted risky choice on the self-report measure and less so on the behavioral measure. Taken together, these results complement those that suggest the ACC is involved in evaluating both costs and benefits and may be influenced by both appetitive and aversive motivational systems.

Event-related potentials play a significant role in error processing and attentional processes. Specifically, event-related negativity (ERN), feedback-related negativity (FRN), and the P300 are related to performance monitoring. The current study examined these components in relation to subjective probability, or confidence, regarding response accuracy on a complicated learning task. Results indicated that confidence ratings were not associated with any changes in ERN, FRN, or P300 amplitude. P300 amplitude did not vary according to participants’ subjective probabilities. ERN amplitude and FRN amplitude did not change throughout the task as participants learned. Future studies should consider the relationship between ERN and FRN using a learning task that is less difficult than the one employed in this study.

Rejection sensitivity (RS) can have significant effects on interpersonal relationships. Previous research has shown the negative social effects of RS, but less is known about the cognitive implications of having high levels of RS. The current study examined the effect of RS on various event-related potential (ERP) components associated with performance monitoring (error-related negativity, ERN; feedback-related negativity, FRN) and attention (P300; late positive potential, LPP). Participants completed a social or nonsocial Flanker task and an emotional Stroop task. Results showed an increased ERN on error trials for individuals with higher RS. Although the FRN, P300, and LPP were not influenced by RS, FRN was influenced by an expectancy-valence interaction. FRN amplitude was also sensitive to condition, with correct feedback eliciting significantly more negative FRN in the social condition compared to the nonsocial condition; FRN for unexpected feedback was also greater in the social condition. Overall, the results suggest a relationship between error monitoring and RS, as well as a relationship between social information and feedback processing. Future research should further explore the potential relationship between rejection sensitivity and attention throughout goal-directed tasks.

One of the key features of bipolar disorder (BD) is risky and impulsive decision-making, behaviours theorised to arise from dysregulation in a biobehavioural system governing approach of rewards. However the neural mechanisms of this conceptual model have not been well specified, and there remains a gap between this model and key clinical phenomena such as mixed episodes. This thesis takes a neuroeconomics and reinforcement learning approach to characterise the neural mechanisms of motivational decision-making in BD. A review of the neurobiological evidence for reward dysregulation in BD (Chapter 1) arrives at a model in which striatal hypersensitivity is exacerbated by reduced dorsolateral prefrontal cortical (dlPFC) control. This model is tested by four studies using electrophysiology, source analysis and functional neuroimaging. Chapters 3 and 4 employ EEG to explore how hypomanic traits modulate motivational processing in contexts requiring learning and trade-offs between risk and between immediate and delayed reward. In Chapter 3, high trait hypomania was associated with impaired loss learning and a neural evaluation of rewards and losses more favourably, relative to low hypomania. This “rose-tinted” bias may reinforce risky behaviours that pay off and reduce learning from aversive repercussions. Chapter 4 reports an attentional bias towards immediate reward which may drive a steeper delay discounting trajectory and an inability to delay gratification. In Chapters 5 and 6 simultaneous electrophysiological and functional neuroimaging was utilised to characterise spatial and temporal perturbations to the mesocorticolimbic reward network in a clinical sample of BD. Patients showed a poorer ventromedial prefrontal cortical representation of the objective value of outcomes as well as a heightened striatal reward response. The latter finding was related to decreased dlPFC activation, which also interacted with residual manic symptoms. This is interpreted in terms of reduced top-down executive control that is exacerbated by residual manic symptoms, suggesting a potential mechanism underlying relapse and extremely high levels of reward-seeking seen during mania. EEG source imaging localised differences during reward outcome evaluation to early sensory-attentional (N1), reward evaluation (FRN) and cognitive (P300) stages of processing. For rewards, patients exhibited greater activity in precuneus, frontal eye fields (N1) and ventral anterior cingulate (FRN), consistent with an attentional bias to reward that drives hyperactivity in reward circuitry. Collectively the results provide evidence of reward dysfunction from behavioural measures and two neuroimaging modalities. The results support a model in which a core hypersensitivity to reward and a “rose-tinted” evaluation bias act to 1) potentiate the impact of rewarding outcomes and 2) attenuate aversive ones maintains a distorted representation of objective likelihood and value associated with actions. This is exacerbated by reduced prefrontal control – which may be particularly associated with mania – highlighting a potential target for novel pharmacological and psychological interventions.

Reward prediction errors are quantitative signed terms that express the difference between the value of an obtained outcome and the expected value that was placed on it prior to its receipt. Positive reward prediction errors constitute reward, negative reward prediction errors constitute punishment. Reward prediction errors have been shown to be powerful drivers of reinforcement learning in formal models and there is thus a strong reason to believe they are used in the brain. Isolating such neural signals stands to help elucidate how reinforcement learning is implemented in the brain, and may ultimately shed light on individual differences, psychopathologies of reward such as addiction and depression, and the apparently non-normative behaviour under risk described by behavioural economics. In the present thesis, I used the event related potential technique to isolate and study electrophysiological components whose behaviour resembled reward prediction errors. I demonstrated that a candidate component, “feedback related negativity”, occurring 250 to 350 ms after receipt of reward or punishment, showed such behaviour. A meta-analysis of the existing literature on this component, using a novel technique of “great grand averaging”, supported this view. The component showed marked asymmetries however, being more responsive to reward than punishment and more responsive to appetitive rather than aversive outcomes. I also used novel data-driven techniques to examine activity outside the temporal interval associated with the feedback related negativity. This revealed a later component responding solely to punishments incurred in a Pavlovian learning task. It also revealed numerous salience-encoding components which were sensitive to a prediction error’s size but not its sign.

人們對於錯誤訊息處理經由自我覺察或外在回饋之管道，可藉由事件關聯電位分別測得ERN (error-related negativity) 及FRN (feedback-related negativity)。過去研究曾指出雙側作業(Flanker task)中錯誤所引發的ERN會受到以圖片呈現的短期情緒所調節，然而對於回饋誘發的FRN與個體情緒調節的關係則未曾被探討過。過去FRN的研究認為唯有當受試者所進行的作業為增強學習作業時，受試者對於回饋結果的預期狀態才能反映於FRN的反應強度。本研究利用兩個實驗分別採用非增強學習作業及增強學習作業，其中並以IAPS情緒圖片進行短期情緒的引發，在受試者於實驗中對其反應結果的不同預期狀態，探測受試者FRN受短期情緒調節的效果。 實驗一利用非增強學習作業，結果顯示FRN的強度可以反映受試者對於回饋結果的預期狀態，其中以非預期時FRN的強度為最大，預期時FRN的強度為最小；另外，正向情緒圖片對於FRN具有調節效果，正向情緒下FRN反應強度小於中性以及負向情緒下FRN反應強度。實驗二利用增強學習作業，前述的FRN強度反映受試者對回饋結果的預期效果，只有在實驗前半段的嘗試次中被觀測到，此效果未見於全部嘗試次納入分析；另外，實驗二中沒有觀察到情緒對於FRN的調節效果。 綜合而言，本研究發現受試者唯有持續處於學習的情形下，FRN才能反映受試者對於回饋結果的預期狀態，情緒對FRN的調節效果也僅於此情況下才能被觀測到。 / Error-related information in human can be processed via self-awareness and/or feedback given externally, which are measurable by the use of event-related potential (ERP) and termed error-related negativity (ERN) and feedback-related negativity (FRN) respectively. Previous studies showed that short-term affective stimuli would modulate the magnitude of ERN elicited by Flanker task. However, such modulation effect has not been tested on FRN. Furthermore, the magnitude of FRN is indicated to be related to the expectancy states toward feedback when the subject is undergoing a reinforcement learning task. Present study, thus, was designed to test the affective modulation effect on FRN in two separate tasks. In which, emotional pictures adopted from IAPS were used as the short-term affective stimuli, and different expectancy states in both non-reinforcement learning task (Experiment1) and reinforcement learning task (Experiment 2) were manipulated. In the results of Experiment 1, the magnitude of FRN was larger under the unexpected condition in comparing to the expected one. Modulation effect of short-term affective stimuli on FRN was obtained when positive emotion pictures were presented in non-reinforcement learning task, which FRN amplitude was significantly smaller in comparing to those measured after the presentation of neutral and negative pictures. In the results of Experiment 2, FRN elicited in the unexpected condition was only obtained from analyzing the dada collected in the first half of trails. Such effect was not confirmed when the data from all trials were analyzed. A lack of modulation effect of short-term affective stimuli on FRN appeared in Experiment 2. In conclusion, it is indicated that the expectancy depended FRN is most apparent when the subject is undergoing a continuous learning-demanded process. Meanwhile, short-term affective stimuli can modulate such FRN.

Damasio(1994)提出軀體標記假說（Somatic Marker Hypothesis）來解釋情緒如何影響行為決策，認為人們在決策前，與過去的情緒經驗關聯的生理反應會再現，幫助人們做出較好的決策判斷。並利用愛荷華賭博作業（Iowa Gambling Task）來模擬日常生活決策情境，並同時紀錄膚電反應(Skin conductance respons)，量測受試者決策前的預期膚電反應（Anticipatory SCR）和結果呈現後的回饋膚電反應(Feedback SCR)，加以佐證其神經生理機制。本研究將從三個方向進一步驗證其假說，分別是風險因素、生理證據和個別差異。在愛荷華賭博作業中，期望值負的牌也是高風險程度的牌，使結果無法清楚解釋是期望值或者風險程度造成的影響。而過去雖然有許多研究也使用膚電反應當做生理指標，但有許多相異的研究結果，並且較少研究利用事件關聯電位瞭解其中樞歷程。再者，過去相關研究發現個別差異的存在，但是缺乏一致的解釋。因此，本研究以修改版愛荷華賭博作業，控制期望值皆為零的狀況下，操弄風險程度，並且利用膚電反應和事件關聯電位當作周邊和和中樞的生理反應指標，探討受試者在單純風險情境，是否也會受到情緒軀體標記影響風險行為偏好，以及各項生理指標和風險行為偏好間的關聯，並瞭解不同風險偏好的受試者生理指標是否有所差異。結果發現，從行為上顯示有風險追逐和風險趨避兩組受試者，不同風險程度的牌損失回饋對受試者的歷程影響也不一樣，額葉的腦部回饋相關負波(Feedback-related Negativity，FRN)結果顯示，風險追逐的受試者對高低風險損失時的FRN沒有差異，風險趨避的受試者看到低風險損失時的FRN大於看到高風險損失時的FRN。此外，看到高風險酬賞比起低風險酬有較大回饋膚電反應的受試者，和看到高風險損失比起低風險損失有較小回饋膚電反應的受試者，接受高風險牌的比率也較高，其它生理變項對風險行為偏好沒有顯著的預測力。並且預期膚電反應並非過去研究認為單純扮演警訊或者誘因，而有更複雜的機制存在，受試者在接受非偏好的牌和拒絕偏好的牌前有較大的預期膚電反應。預期階段N170的結果顯示，受試者看到刺激之後會拒絕的N170會大於之後會接受的N170，顯示接受或拒絕兩種不同情境時受試者對刺激的處理歷程亦相異。 / Somatic Marker Hypothesis was proposed to explain the influence of emotion on decision making. To examine this hypothesis, Damasio and his colleagues designed the Iowa Gambling Task (IGT) and found that the “anticipatory skin conductance responses (SCR)”, i.e. somatic markers, was elevated before selecting from bad decks to serve as alarms and it warned participants not to select “bad deck” which was negative expected value. However, there are three unsolved problem in these IGT researches: the risk factor, inconsistent physiological evidences, and individual differences. In the original IGT, the bad decks are also more risky and that confounds the interpretations of participants’ choice behaviors and related physiological evidences. There are inconsistent evidences of how the anticipatory SCR and feedback SCR related with choice behaviors. Moreover, there are little event-related potential IGT studies. To solve these issues, the primary aim of the present study is to clarify whether decision making is influenced by risk level even when all options have the same expected value. A modified IGT with high risk deck and low risk deck was used and the expected values of two decks were all zero. Moreover, the procedure was different from original IGT. Participants saw a deck with mark first and then decided to accept or reject this deck. Thus, the role of anticipatory SCR could be clarified more clearly. In addition to SCR, ERP was also recorded for further physiological evidences. To elaborately clarify individual differences of choice behavior and physiological evidences, participants would group to risk-seeking (i.e., accepting more high risk deck and rejecting more low risk deck) and risk-aversion (i.e., accepting more low risk deck and rejecting more high risk deck) according their choice behaviors. The result revealed that the participant who accepted more high risk deck, their reward SCR was higher from high risk deck than from low risk decks, and induced lower punishment SCR from high risk deck than from low risk decks. Moreover, the anticipatory SCR was higher both before they decided to reject the liked deck and before they decided to accept the disliked deck. The results of feedback-related negativity (FRN) from ERP data in frontal region showed that the magnitude of FRN was larger under the conflict punishment (the punishment from low risk decks) condition for risk-aversion participants. The results of N170 from ERP data showed that the magnitude of N170 was larger under the reject condition. These results suggest that the SMH could be explained not only with expected value but also with risk preference. In conclusion, the interpretation of anticipatory SCR by previous study was not completed, and it reflected not merely the negative feeling or positive feeling. This strong anticipatory emotion affects people to change the routine behavior about their risk preference, and there exist individual differences of choice behavior and physiological evidences.