The temporal representation of experience in subjective mood

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

The spread of affective and semantic valence representations across states

In many decision problems, outcomes are not reached after a single action but rather after a series of events or states. To optimize decisions over multiple states, representations of how good or bad the outcomes are, that is, the outcomes' valence, should spread across states. One mechanism for valence spreading is a temporal, state-independent process in which a single valence representation is updated when an outcome is experienced and fades away afterwards. Each state's valence is based on its temporal proximity to the experienced outcome. An alternative, state-dependent mechanism relies on the structure of transitions between states, updating a separate valence representation for each state according to its spatial distance from the outcomes. We examined how these mechanistic accounts shape the spread of two formats of valence representation, feelings (affective valence) and knowledge (semantic valence), between states. In two pre-registered experiments (N = 585), we used a novel task in which participants move in a four-state maze, one of which contains an outcome. The participants provide self-reports of affective and semantic valence throughout the maze and after finishing it. Results show that the affective representation of negative valence is more localized in state-space than the semantic representation. We also found evidence for the relative reliance of the affective valence on a temporal, state-independent mechanism and of the semantic valence on a structured, state-dependent mechanism. Our findings provide mechanistic accounts for the differences between affective and semantic valence representations and indicate how such representations may play a role in associative learning and decision-making.

Acute mountain sickness predicts the emotional state of amateur mountaineers

Research on amateur mountaineers is scarce, and this study aims to delve into the emotional experiences of ten amateur mountaineers during their ascent using the "Befindlichkeitsskala" (BFS) and Lake Louise Acute Mountain Sickness scoring system (LLS). These subjects were exposed to altitudes of 3140 m, 4300 m, and 5276 m, respectively. We found that LLS scores were negatively correlated with positive emotions (β = -27.54, p < 0.05) and positively correlated with negative emotions (β = 21.97, p < 0.05). At an altitude of 4300 m, individuals with AMS exhibited significant differences in depression, anger, excitement, and inactivity compared to climbers without AMS. Upon returning to 3140 m after completing the climb, significant differences were observed in emotions such as happiness, calmness, anger, excitement, and depression. Throughout the three-day climb, noteworthy differences emerged in activity, happiness, calmness, inactivity, positive emotions (p < 0.01), negative emotions, and overall emotional scores (p < 0.05). Our study suggests a decline in the emotional well-being of amateur climbers with increasing altitude, highlighting AMS as a pivotal predictive factor for emotional experiences while climbing.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Pramipexole for the Treatment of Depression: Efficacy and Mechanisms

Dopaminergic mechanisms are a plausible treatment target for patients with clinical depression but are relatively underexplored in conventional antidepressant medications. There is continuing interest in the potential antidepressant effects of the dopamine receptor agonist, pramipexole, with data from both case series and controlled trials indicating that this agent may produce benefit for patients with difficult-to-treat depression. Pramipexole's therapeutic utility in depression is likely to be expressed through alterations in reward mechanisms which are strongly influenced by dopamine pathways and are known to function abnormally in depressed patients. Our work in healthy participants using brain imaging in conjunction with computational modelling suggests that repeated pramipexole facilitates reward learning by inhibiting value decay. This mechanism needs to be confirmed in larger clinical trials in depressed patients. Such studies will also allow assessment of whether baseline performance in reward learning in depression predicts therapeutic response to pramipexole treatment.

An experimental approach to training mood for resilience

According to influential theories about mood, exposure to environments characterized by specific patterns of punishments and rewards could shape mood response to future stimuli. This raises the intriguing possibility that mood could be trained by exposure to controlled environments. The aim of the present study is to investigate experimental settings that increase resilience of mood to negative stimuli. For this study, a new task was developed where participants register their mood when rewards are added or subtracted from their score. The study was conducted online, using Amazon MTurk, and a total of N = 1287 participants were recruited for all three sets of experiments. In an exploratory experiment, sixteen different experimental task environments which are characterized by different mood-reward relationships, were tested. We identified six task environments that produce the greatest improvements in mood resilience to negative stimuli, as measured by decreased sensitivity to loss. In a next step, we isolated the two most effective task environments, from the previous set of experiments, and we replicated our results and tested mood's resilience to negative stimuli over time, in a novel sample. We found that the effects of the task environments on mood are detectable and remain significant after multiple task rounds (approximately two minutes) for an environment where good mood yielded maximum reward. These findings are a first step in our effort to better understand the mechanisms behind mood training and its potential clinical utility.

A highly replicable decline in mood during rest and simple tasks

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Hedonism as a motive for information search: biased information-seeking leads to biased beliefs

Confirmation bias in information-search contributes to the formation of polarized echo-chambers of beliefs. However, the role of valence on information source selection remains poorly understood. In Experiment 1, participants won financial rewards depending on the outcomes of a set of lotteries. They were not shown these outcomes, but instead could choose to view a prediction of each lottery outcome made by one of two sources. Before choosing their favoured source, participants were first shown a series of example predictions made by each. The sources systematically varied in the accuracy and positivity (i.e., how often they predicted a win) of their predictions. Hierarchical Bayesian modeling indicated that both source accuracy and positivity impacted participants' choices. Importantly, those that viewed more positively-biased information believed that they had won more often and had higher confidence in those beliefs. In Experiment 2, we directly assessed the effect of positivity on the perceived credibility of a source. In each trial, participants watched a single source making a series of predictions of lottery outcomes and rated the strength of their beliefs in each source. Interestingly, positively-biased sources were not seen as more credible. Together, these findings suggest that positively-biased information is sought partly due to the desirable emotional state it induces rather than having enhanced perceived credibility. Information sought on this basis nevertheless produced consequential biased beliefs about the world-state, highlighting a potentially key role for hedonic preferences in information selection and subsequent belief formation.

Computational models of subjective feelings in psychiatry

Research in computational psychiatry is dominated by models of behavior. Subjective experience during behavioral tasks is not well understood, even though it should be relevant to understanding the symptoms of psychiatric disorders. Here, we bridge this gap and review recent progress in computational models for subjective feelings. For example, happiness reflects not how well people are doing, but whether they are doing better than expected. This dependence on recent reward prediction errors is intact in major depression, although depressive symptoms lower happiness during tasks. Uncertainty predicts subjective feelings of stress in volatile environments. Social prediction errors influence feelings of self-worth more in individuals with low self-esteem despite a reduced willingness to change beliefs due to social feedback. Measuring affective state during behavioral tasks provides a tool for understanding psychiatric symptoms that can be dissociable from behavior. When smartphone tasks are collected longitudinally, subjective feelings provide a potential means to bridge the gap between lab-based behavioral tasks and real-life behavior, emotion, and psychiatric symptoms.

Comparing gratitude and pride: evidence from brain and behavior

Gratitude and pride are both positive emotions. Yet gratitude motivates people to help others and build up relationships, whereas pride motivates people to pursue achievements and build on self-esteem. Although these social outcomes are crucial for humans to be evolutionarily adaptive, no study so far has systematically compared gratitude and pride to understand why and how they can motivate humans differently. In this review, we compared gratitude and pride from their etymologies, cognitive prerequisites, motivational functions, and brain regions involved. By integrating the evidence from brain and behavior, we suggest that gratitude and pride share a common reward basis, yet gratitude is more related to theory of mind, while pride is more related to self-referential processing. Moreover, we proposed a cognitive neuroscientific model to explain the dynamics in gratitude and pride under a reinforcement learning framework.

A longitudinal 18F-FDG PET/MRI study in asymptomatic stage of genetic Creutzfeldt-Jakob disease linked to G114V mutation

BACKGROUND

Pathogenic prion protein may start to deposit in some brain regions and cause functional alterations in the asymptomatic stage in Creutzfeldt-Jakob disease. The study aims to determine the trajectory of the brain metabolic changes for prion protein diseases at the preclinical stage.

METHODS

At baseline, we enrolled five asymptomatic PRNP G114V mutation carriers, six affected genetic PRNP E200K CJD patients and 23 normal controls. All participants completed clinical, diffusion-weighted imaging (DWI) and ¹⁸F fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG-PET) examinations. Longitudinal follow-up was completed in five asymptomatic mutation carriers. We set three-time points to identify the changing trajectory in the asymptomatic carriers group including baseline, 2-year and 4-year follow-up.

RESULTS

At baseline, DWI signals, the cerebral glucose standardized uptake value rate ratio (SUVR) and clinical status in 5 asymptomatic cases were normal. At the follow-up period, mild hypometabolism on PET images was found in asymptomatic carriers without any DWI abnormal signal. Further group quantitatively analysis showed hypometabolic brain regions in the asymptomatic genetic CJD group were in the insula, frontal, parietal, and temporal lobes in 4-year follow-up. The SUVR changing trajectories of all asymptomatic cases were within the range between the normal controls and affected patients. Notably, the SUVR of one asymptomatic individual whose baseline age was older showed a rapid decline at the last follow-up.

CONCLUSIONS

Our study illustrates that the neurodegenerative process associated with genetic CJD may initiate before the clinical presentation of the disease.

Medial and orbital frontal cortex in decision-making and flexible behavior

The medial frontal cortex and adjacent orbitofrontal cortex have been the focus of investigations of decision-making, behavioral flexibility, and social behavior. We review studies conducted in humans, macaques, and rodents and argue that several regions with different functional roles can be identified in the dorsal anterior cingulate cortex, perigenual anterior cingulate cortex, anterior medial frontal cortex, ventromedial prefrontal cortex, and medial and lateral parts of the orbitofrontal cortex. There is increasing evidence that the manner in which these areas represent the value of the environment and specific choices is different from subcortical brain regions and more complex than previously thought. Although activity in some regions reflects distributions of reward and opportunities across the environment, in other cases, activity reflects the structural relationships between features of the environment that animals can use to infer what decision to take even if they have not encountered identical opportunities in the past.

Magnetoencephalographic Correlates of Mood and Reward Dynamics in Human Adolescents

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25–40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.