Anticipatory feelings: Neural correlates and linguistic markers

This review introduces anticipatory feelings (AF) as a new construct related to the process of anticipation and prediction of future events. AF, defined as the state of awareness of physiological and neurocognitive changes that occur within an oganism in the form of a process of adapting to future events, are an important component of anticipation and expectancy. They encompass bodily-related interoceptive and affective components and are influenced by intrapersonal and dispositional factors, such as optimism, hope, pessimism, or worry. In the present review, we consider evidence from animal and human research, including neuroimaging studies, to characterize the brain structures and brain networks involved in AF. The majority of studies reviewed revealed three brain regions involved in future oriented feelings: 1) the insula; 2) the ventromedial prefrontal cortex (vmPFC); and 3) the amygdala. Moreover, these brain regions were confirmed by a meta-analysis, using a platform for large-scale, automated synthesis of fMRI data. Finally, by adopting a neurolinguistic and a big data approach, we illustrate how AF are expressed in language

Predictive modeling of optimism bias using gray matter cortical thickness.

People have been shown to be optimistically biased when their future outcome expectancies are assessed. In fact, we display optimism bias (OB) toward our own success when compared to a rival individual's (personal OB [POB]). Similarly, success expectancies for social groups we like reliably exceed those we mention for a rival group (social OB [SOB]). Recent findings suggest the existence of neural underpinnings for OB. Mostly using structural/functional MRI, these findings rely on voxel-based mass-univariate analyses. While these results remain associative in nature, an open question abides whether MRI information can accurately predict OB. In this study, we hence used predictive modelling to forecast the two OBs. The biases were quantified using a validated soccer paradigm, where personal (self versus rival) and social (in-group versus out-group) forms of OB were extracted at the participant level. Later, using gray matter cortical thickness, we predicted POB and SOB via machine-learning. Our model explained 17% variance (R2 = 0.17) in individual variability for POB (but not SOB). Key predictors involved the rostral-caudal anterior cingulate cortex, pars orbitalis and entorhinal cortex-areas that have been associated with OB before. We need such predictive models on a larger scale, to help us better understand positive psychology and individual well-being

Brain structure and optimism bias: A voxel-based morphometry approach

Individuals often anticipate an unrealistically favorable future for themselves (personal optimism bias) or others (social optimism bias). While such biases are well established, little is known about their neuroanatomy. In this study, participants engaged in a soccer task and estimated the likelihood of successful passes in personal and social scenarios. Voxel-based morphometry revealed that personal optimism bias varied as a positive function of gray matter volume (GMV) in the putamen, frontal pole, hippocampus, temporal pole, inferior temporal gyrus, visual association areas, and mid-superior temporal gyrus. Social optimism bias correlated positively with GMV in the temporoparietal junction and negatively with GMV in the inferior temporal gyrus and presupplementary motor areas. Together, these findings suggest that parts of our optimistic outlook are biologically rooted. Moreover, while the two biases looked similar at the behavioral level, they were related to distinct gray matter structures, proposing that their underlying mechanisms are not identical

Whole-brain white matter correlates of personality profiles predictive of subjective well-being.

We investigated the white matter correlates of personality profiles predictive of subjective well-being. Using principal component analysis to first determine the possible personality profiles onto which core personality measures would load, we subsequently searched for whole-brain white matter correlations with these profiles. We found three personality profiles that correlated with the integrity of white matter tracts. The correlates of an "optimistic" personality profile suggest (a) an intricate network for self-referential processing that helps regulate negative affect and maintain a positive outlook on life, (b) a sustained capacity for visually tracking rewards in the environment and (c) a motor readiness to act upon the conviction that desired rewards are imminent. The correlates of a "short-term approach behavior" profile was indicative of minimal loss of integrity in white matter tracts supportive of lifting certain behavioral barriers, possibly allowing individuals to act more outgoing and carefree in approaching people and rewards. Lastly, a "long-term approach behavior" profile's association with white matter tracts suggests lowered sensitivity to transient updates of stimulus-based associations of rewards and setbacks, thus facilitating the successful long-term pursuit of goals. Together, our findings yield convincing evidence that subjective well-being has its manifestations in the brain

Environmental concern as a moderator of information processing: A fMRI study

The psychological processes that predict behaviour can be influenced by the approaches taken in environmental awareness messages. The persuasiveness generated by different approaches depends on the environmental concern of viewers. The study identifies brain regions active while processing positive advertisements. It also studies brain activity in subjects with high (vs low) environmental concern while processing positive ads. In addition, it relates the brain activity evoked in response to positive ads which predicts more significant attitudes toward the ads, in subjects with great environmental concern. The results indicate that positive messages activate regions linked to self-value and societal benefit in those subjects who are more concerned. We found a stronger effect in regions linked to an emotional response in viewers with greater environmental concern. The identified emotional response may precede higher attitudinal ratings

Non-invasive stimulation reveals ventromedial prefrontal cortex function in reward prediction and reward processing

IntroductionStudies suggest an involvement of the ventromedial prefrontal cortex (vmPFC) in reward prediction and processing, with reward-based learning relying on neural activity in response to unpredicted rewards or non-rewards (reward prediction error, RPE). Here, we investigated the causal role of the vmPFC in reward prediction, processing, and RPE signaling by transiently modulating vmPFC excitability using transcranial Direct Current Stimulation (tDCS).MethodsParticipants received excitatory or inhibitory tDCS of the vmPFC before completing a gambling task, in which cues signaled varying reward probabilities and symbols provided feedback on monetary gain or loss. We collected self-reported and evaluative data on reward prediction and processing. In addition, cue-locked and feedback-locked neural activity via magnetoencephalography (MEG) and pupil diameter using eye-tracking were recorded.ResultsRegarding reward prediction (cue-locked analysis), vmPFC excitation (versus inhibition) resulted in increased prefrontal activation preceding loss predictions, increased pupil dilations, and tentatively more optimistic reward predictions. Regarding reward processing (feedback-locked analysis), vmPFC excitation (versus inhibition) resulted in increased pleasantness, increased vmPFC activation, especially for unpredicted gains (i.e., gain RPEs), decreased perseveration in choice behavior after negative feedback, and increased pupil dilations.DiscussionOur results support the pivotal role of the vmPFC in reward prediction and processing. Furthermore, they suggest that transient vmPFC excitation via tDCS induces a positive bias into the reward system that leads to enhanced anticipation and appraisal of positive outcomes and improves reward-based learning, as indicated by greater behavioral flexibility after losses and unpredicted outcomes, which can be seen as an improved reaction to the received feedback

Neural Correlates of Polysubstance Use: Differential and Interactive Effects of Alcohol and Cannabis on the Adolescent Brain

Two of the most commonly used and abused substances by adolescents in the United States are alcohol and cannabis, which are associated with adverse medical and psychiatric outcomes. Alcohol use and cannabis use during adolescence is also associated with an increased risk of alcohol use disorder (AUD) and/or cannabis use disorder (CUD) in adulthood as well as increased likelihood of relapse after successful treatment. Despite this, much of the previous work on the neurobiology of substance use disorders has focused on adult substance use. This work has shown that individuals with AUD and/or CUD show dysfunction within reward processing, emotion processing, and executive functioning neuro-circuitries. In this dissertation, we have utilized the Monetary Incentive Delay (MID), Affective Stroop (aST), and Optimistic Bias (OB) tasks in order to examine dysfunction in these neuro-circuitries related to AUD and CUD symptomatology in a group of adolescents from a residential treatment facility and the surrounding community. The current data indicate that dysfunction in reward processing, emotion processing, and executive functioning neuro-circuitries is associated with AUD symptomatology, primarily within the MID and aST. However, dysfunction in emotion processing and executive functioning neuro-circuitries is associated with CUD neuro-circuitries across all three tasks. Moreover, there are interactive effects of AUD and CUD symptom severity on emotional processing and executive functioning neuro-circuitries within the aST and OB tasks. These data indicate differential and interactive effects of AUD and CUD on various neuro-circuitries within the adolescent brain

The impact of motivational and affective context on error-induced learning

I THEORETISCHER HINTERGRUND 1 1. Einleitung 1 2. Literaturübersicht 3 Überblick 3 Theorien zum Verstärkungslernen 3 Begriffsbestimmung Verstärkungslernen 3 Zustände, Handlungen und Verstärkungen 4 Instrumentelle vs. Klassische Konditionierung 5 Motivationale Mechanismen des Instrumentellen Lernens 6 Mathematische Modelle des Verstärkungslernens 9 Zusammenfassung und Implikationen für die vorliegende Studie 14 Neuronale Grundlagen des Verstärkungslernens 14 Die "Dopamine Reward Prediction Error"-Hypothese 15 Unterschiedliche Funktionen dopaminerger Neurotransmission in den Basalganglien und im Präfrontalen Kortex 17 Die Integration von Kognition, Emotion und Handlung im Anterioren Cingulären Kortex 25 Zusammenfassung und Implikationen für die vorliegende Studie 30 Die Bedeutung des affektiven und motivationalen Handlungskontexts bei Lern- und Entscheidungsprozessen 31 Grundlegende Konzepte 31 Persönlichkeitseigenschaften, Motivation und Emotion 35 Unkontrollierbare Misserfolgserfahrungen und Lernen – Sonderfall eines Defizits in der Regulation von Motivation und Affekt? 39 Hirnmechanismen der Interaktion von Motivation, Emotion, und Kognition 42 Die neurophysiologischen Konsequenzen von Misserfolgserfahrungen 44 Zusammenfassung und Implikationen für die vorliegende Studie 45 Elektrophysiologische Korrelate des Verstärkungslernens 46 Die Fehlernegativierung (Error Negativity, Ne) 46 Die Feedback-Related Negativity (FRN) 49 Die Fehlerpositivierung (Error Positivity, Pe) 52 Motivationale und affektive Einflüsse auf Error Negativity, Feedback-Related Negativity und Error Positivity 54 Zusammenfassung und Implikationen für die vorliegende Studie 59 Integrative theoretische Ansätze zur Handlungsüberwachung 59 Die "Reinforcement Learning Theory" von Holroyd and Coles – Ein integrativer theoretischer Ansatz zu Fehlerverarbeitung und Lernen 60 Alternative Ansätze zur Error Negativity und ähnlichen EKP-Komponenten 64 Zusammenfassung und Implikationen für die vorliegende Studie 68 3. Fragestellung and Überblick über die Studien 70 II EMPIRISCHER TEIL 74 4. Studienziele: Experiment 1 und 2 74 5. Experiment 1 76 Studiendesign 76 Hypothesen 76 Lernbedingte Modulationen der Ne, FRN und Pe 77 Effekte von Misserfolg auf die Verhaltens- und EKP-Korrelate des Lernens 79 Die modulierende Rolle der Persönlichekit 82 Methoden 83 Versuchsteilnehmer 83 Überblick über den Versuchsablauf 84 Reizmaterial und Aufgaben 85 EEG-Aufnahme 88 Datenanalyse 88 Ergebnisse 91 Kontrollananlysen 91 Verhaltensdaten 92 EKP-Daten 96 Zusammenfassung Experiment 1 106 6. Experiment 2 108 Hypothesen 108 Methoden 110 Versuchsteilnehmer 110 Reizmaterial, Aufgaben und Ablauf 110 Ergebnisse 110 Kontrollananlysen 110 Verhaltensdaten 112 EKP-Daten 115 Zusammenfassung Experiment 2 124 7. Vorläufige Diskussion Experiment 1 and 2 125 Zusammenfassung der Hauptbefunde 125 Lernbedingte Veränderungen in den antwort- und feedback-bezogenen EKPs 127 Effekte von Misserfolg auf Fehlerverarbeitung und Lernen 132 8. Experiment 3 147 Fragestellung und Untersuchungsziele 147 Studiendesign 152 Hypothesen 152 Methoden 156 Versuchsteilnehmer 156 Reizmaterial und Aufgabe 156 Trialablauf 157 Versuchsablauf 158 EEG-Aufnahme 158 Datenanalyse 159 Ergebnisse 162 Verhaltensdaten 162 EKP-Daten 164 9. Vorläufige Diskussion Experiment 3 176 Zusammenfassung der Hauptbefunde 176 Lernbedingte Veränderungen in Ne, FRN, and Pe 177 Effecte appetitiver und aversiver Motivation auf Fehlerverarbeitung und Lernen 179 Der Beitrag cingulärer Subregionen zur Fehlerverarbeitung 187 10. Gesamtdiskussion 190 Lernbedingte Veränderungen in den EKP-Korrelaten der Fehler- und Feedback- verarbeitung 191 Affektive and motivationale Einflüsse auf Handlungsüberwachung und Lernen 195 Beschränkungen der vorliegenden Studie und Ausblick 205 Schlussfolgerungen 208The aim of this project was to examine the impact of affective-motivational context on the ability to use error signals for behavioural adaptation in feedback-based learning. Evidence for a neural error-processing system has been inferred from the error negativity (Ne), a component in the event-related potential (ERP) elicited when participants commit errors on reaction time tasks. According to an influential theoretical account on error processing and learning, the Ne reflects the transmission of a negative reinforcement learning signal from the midbrain dopamine system to the anterior cingulate cortex (ACC) and indicates that the outcome of an action is “worse than expected”. Importantly, the Ne has been suggested to increase with learning, reflecting the development of an internal representation of the correct response. Moreover, it has been shown that the Ne predicts the extent to which individuals learn from their errors. At the same time, there is accumulating evidence indicating that the Ne varies as a function of motivational and affective variables. Consequently, it has been proposed that the Ne might index broader activity of the action-regulation circuitry in the limbic system, including the affective evaluation of an error. Given the critical role of error processing in learning, an important, but thus far neglected question concerns the influence of experimental manipulations of affective and motivational states on action monitoring processes in feedback-based learning. The empirical work includes two ERP studies. The main goal of the first study was to determine the extent to which self-relevant failure influences error monitoring – as reflected in the Ne – and behavioural adaptation during subsequent learning. Therefore, I conducted an experimental design with two phases (pre- and posttest) in which subjects performed a probabilistic learning task. Between pre- and posttest, participants were assigned to one of two groups receiving either failure feedback or no feedback during a visual search task described as diagnostic of intellectual abilities. To disentangle the effects of failure and motivational disengagement due to prolonged task performance, the posttest was linked to intelligence (Experiment 1) or described in neutral terms (Experiment 2). The aim of the second study (Experiment 3) was to examine whether gain and loss anticipation have dissociable effects on behavioural and ERP correlates of error processing in feedback-based learning. To this end, predictive cues indicating the incentive value (gain, loss, or neutral) of the upcoming target were incorporated in the learning task. The incentive value was manipulated on a trial-by-trial basis. Experiments 1 and 2 showed that failure induction resulted in an increase of the Ne from pre- to posttest. Amplitude enhancement was not accompanied by higher posttest accuracy and therefore cannot simply be explained by changes in task performance. Rather than affecting overall performance failure feedback resulted in higher post-error accuracy indicating a higher impact of error signals on behavioural adaptation on a trial-to-trial basis. This suggests a failure-related shift towards a reactive, error-driven mode of adaptive control rather than an overall increase in the recruitment of cognitive control processes. In Study 2, behavioural performance was improved on gain and loss trials compared to neutral trials, suggesting that participants used the incentive value information to optimize performance. Moreover, in the loss condition, participants were more likely to switch responses after errors than in the gain condition (better lose-shift performance). In support of the assumption that the Ne constitutes a teaching signal that reflects the affective-motivational context of maladaptive decisions, larger Ne amplitudes were observed on error trials in the loss condition compared to gain and neutral conditions. In line with a growing body of evidence indicating a close interaction between cognition, emotion, and motivation in executive control, these findings underscore the importance of factors related to affective and motivational state in elucidating the neural mechanisms of action monitoring and behavioural adaptation