@article{SchadJuengerSeboldetal.2014, author = {Schad, Daniel and Juenger, Elisabeth and Sebold, Miriam Hannah and Garbusow, Maria and Bernhardt, Nadine and Javadi, Amir-Homayoun and Zimmermann, Ulrich S. and Smolka, Michael N. and Heinz, Andreas and Rapp, Michael Armin and Huys, Quentin J. M.}, title = {Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning}, series = {Frontiers in psychology}, volume = {5}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2014.01450}, pages = {10}, year = {2014}, abstract = {Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation.}, language = {en} } @article{TrillaDrimallaBajboujetal.2020, author = {Trilla, Irene and Drimalla, Hanna and Bajbouj, Malek and Dziobek, Isabel}, title = {The influence of reward on facial mimicry}, series = {Frontiers in behavioral neuroscience}, volume = {14}, journal = {Frontiers in behavioral neuroscience}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1662-5153}, doi = {10.3389/fnbeh.2020.00088}, pages = {12}, year = {2020}, abstract = {Recent findings suggest a role of oxytocin on the tendency to spontaneously mimic the emotional facial expressions of others. Oxytocin-related increases of facial mimicry, however, seem to be dependent on contextual factors. Given previous literature showing that people preferentially mimic emotional expressions of individuals associated with high (vs. low) rewards, we examined whether the reward value of the mimicked agent is one factor influencing the oxytocin effects on facial mimicry. To test this hypothesis, 60 male adults received 24 IU of either intranasal oxytocin or placebo in a double-blind, between-subject experiment. Next, the value of male neutral faces was manipulated using an associative learning task with monetary rewards. After the reward associations were learned, participants watched videos of the same faces displaying happy and angry expressions. Facial reactions to the emotional expressions were measured with electromyography. We found that participants judged as more pleasant the face identities associated with high reward values than with low reward values. However, happy expressions by low rewarding faces were more spontaneously mimicked than high rewarding faces. Contrary to our expectations, we did not find a significant direct effect of intranasal oxytocin on facial mimicry, nor on the reward-driven modulation of mimicry. Our results support the notion that mimicry is a complex process that depends on contextual factors, but failed to provide conclusive evidence of a role of oxytocin on the modulation of facial mimicry.}, language = {en} } @article{GhahremaniGieseVogel2020, author = {Ghahremani, Sona and Giese, Holger and Vogel, Thomas}, title = {Improving scalability and reward of utility-driven self-healing for large dynamic architectures}, series = {ACM transactions on autonomous and adaptive systems}, volume = {14}, journal = {ACM transactions on autonomous and adaptive systems}, number = {3}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {1556-4665}, doi = {10.1145/3380965}, pages = {41}, year = {2020}, abstract = {Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.}, language = {en} }