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Projekt Druckansicht

Ist man zu zweit besser als allein? Entwicklung eines Models zur Vorhersage von Gruppenvorteilen in kooperativen räumlich-visuellen Aufgaben

Antragsteller Dr. Basil Wahn
Fachliche Zuordnung Allgemeine, Kognitive und Mathematische Psychologie
Förderung Förderung von 2019 bis 2020
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 417427699
 
Erstellungsjahr 2020

Zusammenfassung der Projektergebnisse

In daily life, humans often perform visual tasks together such as, for instance, solving puzzles or looking for a misplaced key. In such tasks, humans tend to distribute the labor, enabling them to reach a higher performance compared to performing the same task alone – a group benefit. In this project, I extended earlier findings on group benefits in several ways. I devised a model, which accurately can predict group benefits. Moreover, I found that humans have preferences on how they distribute the labor and that theses preferences depend on the task environment. I also found that humans are willing to distribute the labor with an artificial agent but only if that agent is behaving in a human-like way and importantly, is also described to be behaving in a human-like way prior to any interactions. While earlier research primarily investigated dyadic cooperative tasks, I also investigated how labor divisions come about in larger groups (i.e., triads) and found that increasing the group size still leads to group benefits but also additional accuracy costs. I also investigated physiological correlates of group benefits using pupil size measurements and found that the coordination effort required for devising labor divisions correlates with changes in pupil sizes, suggesting that pupil sizes could be used as a measure of coordination effort. Finally, while all of the above studies used visual tasks, I also investigated the benefits of labor divisions in multisensory tasks, in which each participant in a dyad responds to stimuli from a different sensory modality. For such tasks, labor divisions are only beneficial for spatial localization tasks but not for motion, counting, or temporal tasks. Taken together, I find a variety of factors influencing group performance and that group benefits can be accurately predicted using statistical modeling. Given that humans often collaboratively distribute task demands in several professions, these findings could be used to make labor divisions more efficient, assemble more effective teams, and generally reduce the risk of errors.

Projektbezogene Publikationen (Auswahl)

 
 

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