Offloading under cognitive load: Humans are willing to offload parts of an attentionally demanding task to an algorithm

Basil Wahn, Laura Schmitz, Frauke Nora Gerster and Matthias Weiss

PLOS ONE, 2023, vol. 18, issue 5, 1-15

Abstract: In the near future, humans will increasingly be required to offload tasks to artificial systems to facilitate daily as well as professional activities. Yet, research has shown that humans are often averse to offloading tasks to algorithms (so-called “algorithmic aversion”). In the present study, we asked whether this aversion is also present when humans act under high cognitive load. Participants performed an attentionally demanding task (a multiple object tracking (MOT) task), which required them to track a subset of moving targets among distractors on a computer screen. Participants first performed the MOT task alone (Solo condition) and were then given the option to offload an unlimited number of targets to a computer partner (Joint condition). We found that participants significantly offloaded some (but not all) targets to the computer partner, thereby improving their individual tracking accuracy (Experiment 1). A similar tendency for offloading was observed when participants were informed beforehand that the computer partner’s tracking accuracy was flawless (Experiment 2). The present findings show that humans are willing to (partially) offload task demands to an algorithm to reduce their own cognitive load. We suggest that the cognitive load of a task is an important factor to consider when evaluating human tendencies for offloading cognition onto artificial systems.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0286102

DOI: 10.1371/journal.pone.0286102

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