@misc{Blum2021,
  type      = {Master Thesis},
  author    = {Blum, Franziska},
  title     = {I see you smile, you must be happy! Social-emotional gains and usability evaluation of the new training tool E.V.A.: A pilot study},
  doi       = {10.25932/publishup-50550},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-505509},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIX, 80},
  year      = {2021},
  abstract  = {Emotions are a complex concept and they are present in our everyday life. Persons on the autism spectrum are said to have difficulties in social interactions, showing deficits in emotion recognition in comparison to neurotypically developed persons. But social-emotional skills are believed to be positively augmented by training. A new adaptive social cognition training tool "E.V.A." is introduced which teaches emotion recognition from face, voice and body language. One cross-sectional and one longitudinal study with adult neurotypical and autistic participants were conducted. The aim of the cross-sectional study was to characterize the two groups and see if differences in their social-emotional skills exist. The longitudinal study, on the other hand, aimed for detecting possible training effects following training with the new training tool. In addition, in both studies usability assessments were conducted to investigate the perceived usability of the new tool for neurotypical as well as autistic participants. Differences were found between autistic and neurotypical participants in their social-emotional and emotion recognition abilities. Training effects for neurotypical participants in an emotion recognition task were found after two weeks of home training. Similar perceived usability was found for the neurotypical and autistic participants. The current findings suggest that persons with ASC do not have a general deficit in emotion recognition, but are in need for more time to correctly recognize emotions. In addition, findings suggest that training emotion recognition abilities is possible. Further studies are needed to verify if the training effects found for neurotypical participants also manifest in a larger ASC sample.},
  language  = {en}
}
@misc{Galetzka2018,
  type      = {Master Thesis},
  author    = {Galetzka, Cedric},
  title     = {Reward and prediction errors in Bayesian sensorimotor control},
  doi       = {10.25932/publishup-50350},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-503507},
  school      = {Universit{\"a}t Potsdam},
  pages     = {53},
  year      = {2018},
  abstract  = {Midbrain dopamine neurons invigorate responses by signaling opportunity costs (tonic dopamine) and promote associative learning by encoding a reward prediction error signal (phasic dopamine). Recent studies on Bayesian sensorimotor control have implicated midbrain dopamine concentration in the integration of prior knowledge and current sensory information. The present behavioral study addressed the contributions of tonic and phasic dopamine in a Bayesian decision-making task by alternating reward magnitude and inferring reward prediction errors. Twenty-four participants were asked to indicate the position of a hidden target stimulus under varying prior and likelihood uncertainty. Trial-by-trial rewards were allocated based on performance and two different reward maxima. Overall, participants' behavior agreed with Bayesian decision theory, but indicated excessive reliance on likelihood information. These results thus oppose accounts of statistically optimal integration in sensorimotor control, and suggest that the sensorimotor system is subject to additional decision heuristics. Moreover, higher reward magnitude was not observed to induce enhanced response vigor, and was associated with less Bayes-like integration. In addition, the weighting of prior knowledge and current sensory information proceeded independently of reward prediction errors. Taken together, these findings suggest that the process of combining prior and likelihood uncertainties in sensorimotor control is largely robust to variations in reward.},
  language  = {en}
}