The feedback-related negativity (FRN) is a negative event-related potential (ERP) component associated with the presentation of task feedback. The possibility that the FRN may habituate has been briefly mentioned in previous research (Garrido Chaves et al., 2020), but not yet been actively investigated. Thus, the current study is the first one to explicity investigate the possibility of short-term (across trials) and long-term (across blocks) habituation effects on the FRN. This was done by using electroencephalography (EEG) and a time-estimation paradigm during which participants were tasked with guessing the duration of one second. Following each estimate, participants were presented with either positive or negative visual feedback (however, only trials with negative feedback were included in the subsequent statistical analysis). It was hypothesized that mean FRN amplitude would decrease, i.e. habituate, upon increased exposure to negative feedback. Contrary to the expected effect, repeated-measures analyses of variance (ANOVA) and t-tests revealed a stong, significant sensitization effect of FRN mean amplitude in the short-term comparison. However, there appeared to be multiple confounds involved, which made these results ambigous and difficult to interpret. No significant results were found for the long-term comparison, although the ERP waveforms suggested that there might be a (non-significant) habituation effect. This effect may become significant provided a greater sample size. Replication with a greater sample size is thus required before any firm conclusions can be drawn.

The current study used event-related potentials (ERPs) to investigate how motivational salience in the form of expectation violation influences spatial attention. The medial frontal negativity (MFN) ERP indexes expected value, being negative to unexpected punishments and positive to unexpected rewards. The P1 and N1 ERPs index spatial attention, being larger to stimuli in attended locations. This design attached motivational value to locations by making one visual hemifield economically rewarding (greater probability of a rewarding outcome) and the other punishing (greater probability of a punishing outcome). Keypresses to a dot probe following a reward-signifying stimulus were awarded money if correct, and penalized following a punishment-signifying stimulus if incorrect. We predicted that salience would be attached to visual hemifield, thus the MFN would be most negative to punishing outcomes in the rewarding hemifield and most positive to rewarding outcomes in the punishing hemifield. We also predicted that attention would be allocated to a location where expectation was violated, thus the P1 and N1 ERPs would be larger and RTs (reaction times) faster to dot probes appearing in the same side as an outcome that violated expected value. In a sample of 36 participants, there were no significant effects on the MFN, although the means were in the predicted direction, suggesting a lack of power. Contrary to our hypothesis, keypresses were slower, P1 smaller, and N1 larger to probes opposite the location where an expectation violation occurred. This suggested that expectation violation did not direct attention to a particular location, but produced general interference.

Reward-based learning is the ability to alter our future behavior following a novel reward. Dysregulation in this system has been linked to different forms of adult and pediatric psychopathologies such as mood disorders, for which it has proved to be an important treatment target given its link to broader health outcomes for these disorders. However, more research is needed to better understand its underlying mechanisms in the pediatric population. The current study examined how probabilistic reward learning, feedback-related negativity (FRN; an event-related potential from EEG), and dimensional ADHD symptom severity relate in adolescents with prominent mood symptoms. The final sample included 36 adolescents (72.2% female; aged 13 to 17) who completed a Probabilistic Reward Task (PRT) during EEG recording. Results revealed an inverse relationship between FRN mean amplitude and the reward learning score, independent of mood and ADHD symptoms. We also found that teens with increased overall ADHD symptom severity showed a larger (i.e., more negative voltage) FRN amplitude and a lower reward learning score. Exploratory analyses also showed that as ADHD inattentive symptom severity increased, FRN mean amplitude became more negative and reward learning score decreased. No significant relationship was found with ADHD hyperactive/impulsive symptom severity. In conclusion, our results showed that a poor modulation of behavior based on prior reward contingencies was related to an increase in dimensional ADHD symptom severity in a sample of adolescents with prominent mood difficulties. This behavioral dysfunction was also reflected by a blunted neural habituation to reward feedback as evidenced by a larger FRN mean amplitude. Additionally, exploratory analyses highlighted how the relationship between overall ADHD symptom severity and FRN may be driven especially by symptoms of inattention vs. hyperactivity/impulsivity. Results suggest deficits in reward learning that could impact response to behavioral therapies in youth with mood disorders and comorbid inattentive ADHD.

La vengeance réfère à la tentative de blesser ou de faire du mal à celui qui nous a causé du tort par sa faute. Alors que la vengeance se rapporte à l’action, le désir de vengeance réfère à l’émotion qui motive à la vengeance. La colère est une émotion ressentie lorsque nous subissons un dommage (interférence subite à la poursuite d’un but que nous tenons à coeur), alors que la rancune est une émotion qui est suscitée par la perception d’avoir ou le fait d’avoir réellement souffert d’une faute (préjudice qui est infligé de manière responsable d’un individu à une victime). Ces définitions de la colère et de la rancune peuvent se traduire de manière opérationnelle par le fait de provoquer des participants de façon qu’ils perçoivent cela comme accidentelle (sans faute) ou personnelle (avec faute) ; la première provocation induirait un état émotionnel de colère alors que la seconde de rancune. Les études passées ont démontré l’effet de la colère sur le taux de rejets d’offres monétaires très injustes et moyennement injustes comparativement aux offres justes lors d’une tâche de prise de décision économique tel que la tâche Ultimatum Game (UG) ainsi que sur l’amplitude de la Feedback-Related Negativity (FRN), une composante de potentiel reliés aux évènements qui devient plus prononcée lors d’une rétroaction négative associée à des résultats défavorable (ex., réponses incorrectes ou pertes monétaires). Ces données suggèrent que la colère augmenterait l’évaluation affective négative associée aux offres très injustes et moyennement injustes et les comportements de vengeance associés aux taux de rejet. Le rôle des émotions dans la vengeance pose la question de savoir si leur influence se transmet directement dans les comportements de vengeance. Le trait de vengeance réfère à la tendance dispositionnelle à entretenir des attitudes positives envers la vengeance et à la rechercher en réponse à des provocations. Les études antérieures ont démontré que le trait d’affects négatifs modérait la relation entre l'état affects négatifs et l’ampleur de la FRN. Il y a un manque dans nos connaissances sur le rôle du trait de vengeance sur la relation entre l’amplitude de la FRN et le taux de rejet d’offres monétaires. Le premier objectif de la présente étude est de comparer les effets de la colère à ceux de la rancune sur le taux de rejets d’offres monétaires justes, moyennement injustes et très injustes ainsi que sur la FRN durant la tâche UG. Le second objectif est de vérifier le rôle de modérateur du trait de vengeance sur la relation entre la FRN et le taux de rejet des offres, et ce en réponse à des offres très injustes et moyennement injustes. Deux groupes expérimentaux ont été créés : le groupe Colère (GrC) et le groupe Rancune (GrR). Le premier groupe a subi une induction de colère par le biais d'une provocation humaine sans faute pendant la réalisation d'une tâche de créativité. Au cours de la même tâche de créativité, les participants du second groupe ont été soumis à une induction de rancune par le biais d'une provocation humaine avec faute. Ensuite, il a été demandé aux participants de participer à la tâche UG, dans lequel ils devaient accepter ou rejeter des offres monétaires très injustes, moyennement injustes et justes proposées par un participant fictif. En plus de l’analyse du taux de rejet, le signal électroencéphalographique (EEG) a été quantifié puis analysé sur la FRN dans le but de servir de proxy de la perception d’injustice et du niveau de complexité de la prise de décision en fonction du groupe de participants et du type d’offres. Contrairement à ce qui était attendu, les résultats indiquent que le taux de rejet des participants du GrC devant les offres moyennement injustes est significativement plus élevés que celui des participants GrR. De plus, les résultats ne démontrent pas une amplitude FRN significativement plus grande chez les participants GrR comparée à celle chez les participants GrC, et ceci peu importe le type d’offres. De manière congruente avec la littérature, la FRN associée aux offres très injustes et moyennement injustes est plus négative que celle associée aux offres justes. Toutefois, dans le cadre de la présente étude, ce résultat sur la FRN a été observé uniquement pour le GrC. Enfin, que ce soit en réponse à des offres très injustes ou moyennement injustes, les résultats ne démontrent pas le rôle modérateur du trait de vengeance dans la relation entre l’amplitude de la FRN et le taux de rejet d’offres monétaires. L’effet de groupe observé sur le taux de rejet des offres moyennement injustes suggère que les personnes en colère résolvent leur conflit cognitif davantage en outre-passant leurs intérêts personnels monétaires comparativement aux personnes qui vivent de la rancune. Ces résultats suggèrent aussi que, contrairement aux individus en état de colère qui perçoivent les offres justes d’une façon différente des autres types d’offres, les individus vivant de la rancune perçoivent les offres justes, moyennement injustes et très injustes de la même façon. Il est possible de croire que l’état de rancune augmente la sensibilité à l’injustice envers des offres qui normalement devrait être perçues comme différentes. Des limites méthodologiques peuvent possiblement expliquer l’absence d’effet de modération du trait de vengeance. / Vengeance refers to the attempt to hurt or harm someone who has caused us harm through their wrong. While revenge refers to action, the desire for vengeance refers to the emotion that motivates revenge. Anger is an emotion felt when we suffer from a perceived or actual harm (sudden interference in the pursuit of an important goal for us), while resentment is an emotion that is aroused by the perception of having or the fact of having actually suffered from a wrong (harm that is responsibly inflicted by an individual on a victim). These definitions of anger and resentment can be operationalized by provoking participants in such a way that they perceive it as accidental (without wrong) or personal (with wrong); the first provocation would induce an emotional state of anger while the second would induce a state of resentment. Past studies have demonstrated the effect of anger on the rejection rate of unfair and mid-value offers compared to fair offers during an economic decision-making task such as the Ultimatum Game (UG), as well as on the amplitude of Feedback-Related Negativity (FRN), an event-related potential that becomes more pronounced during negative feedback associated with unfavorable outcomes (e.g., incorrect responses or monetary losses). These prior studies suggest that anger would increase the negative affective evaluation associated with unfair and mid-value offers and vengeance behaviors associated with rejection rates. The role of emotions in vengeance raises the question of whether their influence is transmitted directly into revenge behaviors. Trait vengeance refers to the dispositional tendency to maintain positive attitudes toward revenge and to seek it in response to provocations. Previous studies demonstrated that trait negative affect moderated the relationship between state negative affect and FRN magnitude. There is a gap in our knowledge about the role of trait vengeance on the relationship between the FRN amplitude and the rejection rate of monetary offers. The first objective of the current study is to compare the effects of anger to those of resentment on the rejection rate of fair, mid-value and unfair offers as well as on the FRN amplitude during the UG. The second objective is to verify the moderating role of trait vengeance on the relationship between FRN amplitude and the rejection rate in response to unfair and mid-value offers. Two experimental groups were created: a group primed with a human provocation without wrong (Unwronged) and a second group primed with a human provocation with wrong (Wronged). The first group underwent anger induction through a human provocation without wrong while performing a creativity task. During the same creativity task, participants in the second group were subjected to a resentment induction through a human provocation with wrong. Next, participants were asked to participate in the UG, in which they had to accept or reject unfair, mid-value, and fair offers proposed by a fictitious participant. In addition to the analysis of the rejection rate, the electroencephalographic (EEG) signal was quantified and then analyzed on the FRN amplitude with the aim of serving as a proxy for the perception of injustice and the level of complexity of decision-making based on the experimental groups and the type of offers. Contrary to what was expected, the results indicate that the rejection rate of the Unwronged group in response to mid-value offers is significantly higher than the Wronged group. Furthermore, the results do not demonstrate a significantly greater FRN amplitude in the Wronged group compared to the Unwronged group, regardless of the type of offers. In accordance with the literature, the FRNs associated with unfair and mid-value offers are more negative compared to the FRN associated with fair offers. However, in the context of the present study, this result on FRN was observed only for the Unwronged group. Finally, whether in response to unfair or mid-value offers, the results do not demonstrate the moderating role of the trait vengeance in the relationship between the FRN amplitude and the rejection rate. The group effect observed on the rejection rate of mid-value offers suggests that angry participants resolve their cognitive conflict more by overriding their personal monetary interests compared to participants who feel resentment. These results also suggest that, unlike individuals in a state of anger who perceive fair offers in a different way from other types of offers, individuals feeling resentment perceive fair, mid-value and unfair offers in the same way. It is possible to believe that the state of resentment increases sensitivity to injustice towards offers that should normally be perceived as different. Methodological limitations can possibly explain the lack of moderation effect of the trait vengeance.

No description available.

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.