Reduced effects of social feedback on learning in Turner syndrome.

Turner syndrome is a genetic condition caused by a complete or partial loss of one of the X chromosomes. Previous studies indicate that Turner syndrome is associated with challenges in social skills, but the underlying mechanisms remain largely unexplored. A possible mechanism is a reduced social influence on learning. The current study examined the impact of social and non-social feedback on learning in women with Turner syndrome (n = 35) and a sex- and age-matched control group (n = 37). Participants were instructed to earn points by repeatedly choosing between two stimuli with unequal probabilities of resulting in a reward. Mastering the task therefore required participants to learn through feedback which of the two stimuli was more likely to be rewarded. Data were analyzed using computational modeling and analyses of choice behavior. Social feedback led to a more explorative choice behavior in the control group, resulting in reduced learning compared to non-social feedback. No effects of social feedback on learning were found in Turner syndrome. The current study thus indicates that women with Turner syndrome may be less sensitive to social influences on reinforcement learning, than the general population.

Social feedback enhances learning in Williams syndrome.

Williams syndrome (WS) is a rare genetic condition characterized by high social interest and approach motivation as well as intellectual disability and anxiety. Despite the fact that social stimuli are believed to have an increased intrinsic reward value in WS, it is not known whether this translates to learning and decision making. Genes homozygously deleted in WS are linked to sociability in the general population, making it a potential model condition for understanding the social brain. Probabilistic reinforcement learning was studied with either social or non-social rewards for correct choices. Social feedback improved learning in individuals with Williams syndrome but not in typically developing controls or individuals with other intellectual disabilities. Computational modeling indicated that these effects on social feedback were mediated by a shift towards higher weight given to rewards relative to punishments and increased choice consistency. We conclude that reward learning in WS is characterized by high volatility and a tendency to learn how to avoid punishment rather than how to gain rewards. Social feedback can partly normalize this pattern and promote adaptive reward learning.

Pupillary response in reward processing in adults with major depressive disorder in remission.

OBJECTIVE: Major depressive disorder (MDD) is associated with impaired reward processing and reward learning. The literature is inconclusive regarding whether these impairments persist after remission. The current study examined reward processing during a probabilistic learning task in individuals in remission from MDD (n = 19) and never depressed healthy controls (n = 31) matched for age and sex. The outcome measures were pupil dilation (an indirect index of noradrenergic activity and arousal) and computational modeling parameters. METHOD: Participants completed two versions (facial/nonfacial feedback) of probabilistic reward learning task with changing contingencies. Pupil dilation was measured with a corneal reflection eye tracker. The hypotheses and analysis plan were preregistered. RESULT: Healthy controls had larger pupil dilation following losses than gains (p <.001), whereas no significant difference between outcomes was found in individuals with a history of MDD, resulting in an interaction between group and outcome (ß = 0.81, SE = 0.34, t = 2.37, p = .018). The rMDD group also achieved lower mean score at the last trial (t[46.77] = 2.12, p = .040) as well as a smaller proportion of correct choices (t[46.70] = 2.09, p = .041) compared with healthy controls. CONCLUSION: Impaired reward processing may persist after remission from MDD and could constitute a latent risk factor for relapse. Measuring pupil dilation in a reward learning task is a promising method for identifying reward processing abnormalities linked to MDD. The task is simple and noninvasive, which makes it feasible for clinical research.

Pupil dilation during negative prediction errors is related to brain choline concentration and depressive symptoms in adolescents.

Depressive symptoms are associated with altered pupillary responses during learning and reward prediction as well as with changes in neurometabolite levels, including brain concentrations of choline, glutamate and gamma-aminobutyric acid (GABA). However, the full link between depressive symptoms, reward-learning-related pupillary responses and neurometabolites is yet to be established as these constructs have not been assessed in the same individuals. The present pilot study, investigated these relations in a sample of 24 adolescents aged 13 years. Participants completed the Revised Child Anxiety and Depression Scale (RCADS) and underwent a reward learning task while measuring pupil dilation and a single voxel dorsal anterior cingulate cortex (dACC) MEGA-PRESS magnetic resonance spectroscopy scan assessing choline, glutamate and GABA concentrations. Pupil dilation was related to prediction errors (PE) during learning, which was captured by a prediction error-weighted pupil dilation response index (PE-PDR) for each individual. Higher PE-PDR scores, indicating larger pupil dilations to negative prediction errors, were related to lower depressive symptoms and lower dACC choline concentrations. Dorsal ACC choline was positively associated with depressive symptoms, whereas glutamate and GABA were not related to PE-PDR or depressive symptoms. The findings support notions of cholinergic involvement in depressive symptoms and cholinergic influence on reward-related pupillary response, suggesting that pupillary responses to negative prediction errors may hold promise as a biomarker of depressive states.

Memory and decision making: Effects of sequential presentation of probabilities and outcomes in risky prospects.

The rationality of decision making under risk is of central concern in psychology and other behavioral sciences. In real-life, the information relevant to a decision often arrives sequentially or changes over time, implying nontrivial demands on memory. Yet, little is known about how this affects the ability to make rational decisions and a default assumption is rather that information about outcomes and probabilities are simultaneously available at the time of the decision. In 4 experiments, we show that participants receiving probability- and outcome information sequentially report substantially (29 to 83%) higher certainty equivalents than participants with simultaneous presentation. This holds also for monetary-incentivized participants with perfect recall of the information. Participants in the sequential conditions often violate stochastic dominance in the sense that they pay more for a lottery with low probability of an outcome than participants in the simultaneous condition pay for a high probability of the same outcome. Computational modeling demonstrates that Cumulative Prospect Theory (Tversky & Kahneman, 1992) fails to account for the effects of sequential presentation, but a model assuming anchoring-and adjustment constrained by memory can account for the data. By implication, established assumptions of rationality may need to be reconsidered to account for the effects of memory in many real-life tasks. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Optimizing electricity consumption: A case of function learning.

A popular way to improve consumers' control over their electricity consumption is by providing outcome feedback on the cost with in-home displays. Research on function learning, however, suggests that outcome feedback may not always be ideal for learning, especially if the feedback signal is noisy. In this study, we relate research on function learning to in-home displays and use a laboratory task simulating a household to investigate the role of outcome feedback and function learning on electricity optimization. Three function training schemes (FTSs) are presented that convey specific properties of the functions that relate the electricity consumption to the utility and cost. In Experiment 1, we compared learning from outcome feedback with 3 FTSs, 1 of which allowed maximization of the utility while keeping the budget, despite no feedback about the total monthly cost. In Experiment 2, we explored the combination of this FTS and outcome feedback. The results suggested that electricity optimization may be facilitated if feedback learning is preceded by a brief period of function training.