Ulbrich, Philipp

[["dc.bibliographiccitation.firstpage","135"],["dc.bibliographiccitation.journal","Gondwana Research"],["dc.bibliographiccitation.lastpage","146"],["dc.bibliographiccitation.volume","56"],["dc.contributor.author","Schmidt, Alexander R."],["dc.contributor.author","Kaulfuss, Uwe"],["dc.contributor.author","Bannister, Jennifer M."],["dc.contributor.author","Baranov, Viktor"],["dc.contributor.author","Beimforde, Christina"],["dc.contributor.author","Bleile, Natalie"],["dc.contributor.author","Borkent, Art"],["dc.contributor.author","Busch, Ariane"],["dc.contributor.author","Conran, John G."],["dc.contributor.author","Engel, Michael S."],["dc.contributor.author","Harvey, Mark"],["dc.contributor.author","Kennedy, Elizabeth M."],["dc.contributor.author","Kerr, Peter H."],["dc.contributor.author","Kettunen, Elina"],["dc.contributor.author","Kiecksee, Anna Philie"],["dc.contributor.author","Lengeling, Franziska"],["dc.contributor.author","Lindqvist, Jon K."],["dc.contributor.author","Maraun, Mark"],["dc.contributor.author","Mildenhall, Dallas C."],["dc.contributor.author","Perrichot, Vincent"],["dc.contributor.author","Rikkinen, Jouko"],["dc.contributor.author","Sadowski, Eva-Maria"],["dc.contributor.author","Seyfullah, Leyla J."],["dc.contributor.author","Stebner, Frauke"],["dc.contributor.author","Szwedo, Jacek"],["dc.contributor.author","Ulbrich, Philipp"],["dc.contributor.author","Lee, Daphne E."],["dc.date.accessioned","2020-12-10T14:24:23Z"],["dc.date.available","2020-12-10T14:24:23Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1016/j.gr.2017.12.003"],["dc.identifier.issn","1342-937X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72232"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Amber inclusions from New Zealand"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Behavior Research Methods"],["dc.contributor.author","Ulbrich, Philipp"],["dc.contributor.author","Gail, Alexander"],["dc.date.accessioned","2021-06-01T09:42:43Z"],["dc.date.available","2021-06-01T09:42:43Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Ongoing goal-directed movements can be rapidly adjusted following new environmental information, e.g., when chasing pray or foraging. This makes movement trajectories in go-before-you-know decision-making a suitable behavioral readout of the ongoing decision process. Yet, existing methods of movement analysis are often based on statistically comparing two groups of trial-averaged trajectories and are not easily applied to three-dimensional data, preventing them from being applicable to natural free behavior. We developed and tested the cone method to estimate the point of overt commitment (POC) along a single two- or three-dimensional trajectory, i.e., the position where the movement is adjusted towards a newly selected spatial target. In Experiment 1, we established a “ground truth” data set in which the cone method successfully identified the experimentally constrained POCs across a wide range of all but the shallowest adjustment angles. In Experiment 2, we demonstrate the power of the method in a typical decision-making task with expected decision time differences known from previous findings. The POCs identified by cone method matched these expected effects. In both experiments, we compared the cone method\\’s single trial performance with a trial-averaging method and obtained comparable results. We discuss the advantages of the single-trajectory cone method over trial-averaging methods and possible applications beyond the examples presented in this study. The cone method provides a distinct addition to existing tools used to study decisions during ongoing movement behavior, which we consider particularly promising towards studies of non-repetitive free behavior."],["dc.identifier.doi","10.3758/s13428-021-01579-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85332"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","1554-3528"],["dc.title","The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e2001323"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Biology"],["dc.bibliographiccitation.lastpage","23"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Morel, Pierre"],["dc.contributor.author","Ulbrich, Philipp"],["dc.contributor.author","Gail, Alexander"],["dc.date.accessioned","2017-09-07T11:47:47Z"],["dc.date.available","2017-09-07T11:47:47Z"],["dc.date.issued","2017"],["dc.description.abstract","When deciding between alternative options, a rational agent chooses on the basis of the desirability of each outcome, including associated costs. As different options typically result in different actions, the effort associated with each action is an essential cost parameter. How do humans discount physical effort when deciding between movements? We used an action-selection task to characterize how subjective effort depends on the parameters of arm transport movements and controlled for potential confounding factors such as delay discounting and performance. First, by repeatedly asking subjects to choose between 2 arm movements of different amplitudes or durations, performed against different levels of force, we identified parameter combinations that subjects experienced as identical in effort (isoeffort curves). Movements with a long duration were judged more effortful than short-duration movements against the same force, while movement amplitudes did not influence effort. Biomechanics of the movements also affected effort, as movements towards the body midline were preferred to movements away from it. Second, by introducing movement repetitions, we further determined that the cost function for choosing between effortful movements had a quadratic relationship with force, while choices were made on the basis of the logarithm of these costs. Our results show that effort-based action selection during reaching cannot easily be explained by metabolic costs. Instead, force-loaded reaches, a widely occurring natural behavior, imposed an effort cost for decision making similar to cost functions in motor control. Our results thereby support the idea that motor control and economic choice are governed by partly overlapping optimization principles."],["dc.identifier.doi","10.1371/journal.pbio.2001323"],["dc.identifier.gro","3150720"],["dc.identifier.pmid","28586347"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14559"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/7507"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.relation.issn","1545-7885"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]