@article{EhrenfeldHerbortButz2013,
  author    = {Ehrenfeld, Stephan and Herbort, Oliver and Butz, Martin V.},
  title     = {Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference},
  series = {Frontiers in Computational Neuroscience},
  volume    = {7},
  journal   = {Frontiers in Computational Neuroscience},
  number    = {148},
  doi       = {10.3389/fncom.2013.00148},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-122253},
  year      = {2013},
  abstract  = {This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.},
  language  = {en}
}
@article{KirschHerbortButzetal.2012,
  author    = {Kirsch, Wladimir and Herbort, Oliver and Butz, Martin V. and Kunde, Wilfried},
  title     = {Influence of Motor Planning on Distance Perception within the Peripersonal Space},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-75332},
  year      = {2012},
  abstract  = {We examined whether movement costs as defined by movement magnitude have an impact on distance perception in near space. In Experiment 1, participants were given a numerical cue regarding the amplitude of a hand movement to be carried out. Before the movement execution, the length of a visual distance had to be judged. These visual distances were judged to be larger, the larger the amplitude of the concurrently prepared hand movement was. In Experiment 2, in which numerical cues were merely memorized without concurrent movement planning, this general increase of distance with cue size was not observed. The results of these experiments indicate that visual perception of near space is specifically affected by the costs of planned hand movements.},
  subject      = {Psychologie},
  language  = {en}
}
@article{HerbortButz2012,
  author    = {Herbort, Oliver and Butz, Martin V.},
  title     = {Too good to be true? Ideomotor theory from a computational perspective},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-76383},
  year      = {2012},
  abstract  = {In recent years, Ideomotor Theory has regained widespread attention and sparked the development of a number of theories on goal-directed behavior and learning. However, there are two issues with previous studies' use of Ideomotor Theory. Although Ideomotor Theory is seen as very general, it is often studied in settings that are considerably more simplistic than most natural situations. Moreover, Ideomotor Theory's claim that effect anticipations directly trigger actions and that action-effect learning is based on the formation of direct action-effect associations is hard to address empirically. We address these points from a computational perspective. A simple computational model of Ideomotor Theory was tested in tasks with different degrees of complexity.The model evaluation showed that Ideomotor Theory is a computationally feasible approach for understanding efficient action-effect learning for goal-directed behavior if the following preconditions are met: (1) The range of potential actions and effects has to be restricted. (2) Effects have to follow actions within a short time window. (3) Actions have to be simple and may not require sequencing. The first two preconditions also limit human performance and thus support Ideomotor Theory. The last precondition can be circumvented by extending the model with more complex, indirect action generation processes. In conclusion, we suggest that IdeomotorTheory offers a comprehensive framework to understand action-effect learning. However, we also suggest that additional processes may mediate the conversion of effect anticipations into actions in many situations.},
  subject      = {Psychologie},
  language  = {en}
}