Humans use inferred statistical properties of sequential events to smoothen subsequent actions by anticipatory movements. These anticipatory movements have been studied in the serial reaction time ...Show moreHumans use inferred statistical properties of sequential events to smoothen subsequent actions by anticipatory movements. These anticipatory movements have been studied in the serial reaction time (SRT) task, in which participants anticipate the target stimuli in learned sequences, however, under uncertainty, the participants seem to adhere to a centering strategy. It remains unclear whether this centering behavior is a statistically inferred way to compensate for the absence of sequence knowledge, using the center as an optimal anticipatory position. In this study, two state-of-the-art Deep Reinforcement Learning (Deep RL) algorithms (Proximal Policy Optimization (PPO) & Soft Actor-Critic (SAC)) are compared and employed to train artificial agents to investigate the scope of centering behavior, by manipulating the frequency distributions of target stimuli. While SAC evidently outperformed PPO in terms of performance and stability, both algorithms displayed an effect of frequency distribution on centering position. Specifically, a proportional shift toward more probable target stimuli, suggesting that centering behavior is indeed anticipatory behavior as a way to compensate for the absence of explicit sequence knowledge.Show less
Low self-esteem is a risk factor for several mental health issues, and it can be formed because of negative social feedback. Adolescents are particularly at risk, since they may be more influenced...Show moreLow self-esteem is a risk factor for several mental health issues, and it can be formed because of negative social feedback. Adolescents are particularly at risk, since they may be more influenced by such feedback than adults and have been shown to adjust their self-feelings more in reaction to negative compared to positive feedback. However, both the age bias and the negative learning bias have not been fully supported by previous research as evidence is contradictory. This study aimed to fill this gap by measuring the degree of change in feelings about the self in response to social performance feedback. In this research, a sample of 75 adolescents (12 to 17 years old) and 145 young adults (18 to 25 years old) underwent a task in which they spoke in front of judges. They then had to evaluate statements regarding their performance and subsequently saw the evaluations of judges on the same statements. A reinforcement learning model was adapted to create affective learning rates (ALRs), which were compared between adolescents and adults and between positive and negative feedback. Additionally, EEG data was gathered and frontal-midline theta (FMT) activity following feedback was compared between groups. This allowed us to assess to what extent such feedback is processed and integrated as a cue for future behavioral performance. This study found no differences in ALR between age groups, but a significantly higher ALR in response to positive feedback for both age groups. This is inconsistent with previous results, and we suggest that might depend on task structure. No significant difference was found in FMT, and we suggest that may be because FMT is more related to expectancy of feedback rather than to its valence. Together, this study indicates that adjustments in self-esteem following social performance feedback may depend more on environmental demands than developmental differences, and that the way such feedback is processed may rely on top-down expectations.Show less
Background: Humans do not only act to benefit themselves but also others, i.e. they engage in prosocial behavior. This is especially true for people high in empathy. A prerequisite for prosocial...Show moreBackground: Humans do not only act to benefit themselves but also others, i.e. they engage in prosocial behavior. This is especially true for people high in empathy. A prerequisite for prosocial behavior is that people learn how to obtain benefits for others (prosocial reinforcement learning). Studies indicate enhancing effects of oxytocin on prosocial behavior; however, little is known about the relationship between oxytocin, learning to benefit others, and empathy. This study investigated the effects of oxytocin on prosocial reinforcement learning and whether these effects differ based on empathic abilities. Method: A double-blind placebo-controlled cross-sectional study was conducted. Healthy male participants (N=28) were administered 24 international units of intranasal oxytocin or a placebo and performed a prosocial learning task in which they could earn a monetary reward for oneself, another person, or no one. Empathy was measured with the online simulation subscale (Reniers et al., 2011). Results: Results revealed no significant difference in prosocial learning when participants received oxytocin or placebo. Further, the effects of oxytocin did not significantly differ when empathy was taken into account. Conclusions: Findings suggest that oxytocin does not facilitate prosocial learning. Further, empathy did not have an influence on the effects of oxytocin on prosocial learning. Although the findings did not provide supportive evidence for the Social Salience Hypothesis (ShamayTsoory & Abu-Akel, 2016), the current study revealed new insights on potential effects of oxytocin on reinforcement learning in a prosocial context considering empathy.Show less
