@article{HoffmannPieczykolanKochetal.2020,
  author    = {Hoffmann, Mareike A. and Pieczykolan, Aleks and Koch, Iring and Huestegge, Lynn},
  title     = {Two sources of task prioritization: The interplay of effector-based and task order-based capacity allocation in the PRP paradigm},
  series = {Attention, Perception, \& Psychophysics},
  volume    = {82},
  journal   = {Attention, Perception, \& Psychophysics},
  issn      = {1943-3921},
  doi       = {10.3758/s13414-020-02071-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235365},
  pages     = {3402-3414},
  year      = {2020},
  abstract  = {When processing of two tasks overlaps, performance is known to suffer. In the well-established psychological refractory period (PRP) paradigm, tasks are triggered by two stimuli with a short temporal delay (stimulus onset asynchrony; SOA), thereby allowing control of the degree of task overlap. A decrease of the SOA reliably yields longer RTs of the task associated with the second stimulus (Task 2) while performance in the other task (Task 1) remains largely unaffected. This Task 2-specific SOA effect is usually interpreted in terms of central capacity limitations. Particularly, it has been assumed that response selection in Task 2 is delayed due to the allocation of less capacity until this process has been completed in Task 1. Recently, another important factor determining task prioritization has been proposed—namely, the particular effector systems associated with tasks. Here, we study both sources of task prioritization simultaneously by systematically combining three different effector systems (pairwise combinations of oculomotor, vocal, and manual responses) in the PRP paradigm. Specifically, we asked whether task order-based task prioritization (SOA effect) is modulated as a function of Task 2 effector system. The results indicate a modulation of SOA effects when the same (oculomotor) Task 1 is combined with a vocal versus a manual Task 2. This is incompatible with the assumption that SOA effects are solely determined by Task 1 response selection duration. Instead, they support the view that dual-task processing bottlenecks are resolved by establishing a capacity allocation scheme fed by multiple input factors, including attentional weights associated with particular effector systems.},
  language  = {en}
}
@article{PoetzschGermanakosHuestegge2020,
  author    = {Poetzsch, Tristan and Germanakos, Panagiotis and Huestegge, Lynn},
  title     = {Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications},
  series = {Frontiers in Artificial Intelligence},
  volume    = {3},
  journal   = {Frontiers in Artificial Intelligence},
  issn      = {2624-8212},
  doi       = {10.3389/frai.2020.00009},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-202074},
  year      = {2020},
  abstract  = {Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.},
  language  = {en}
}