In Human factors
OBJECTIVE : Human problem solvers possess the ability to outsource parts of their mental processing onto cognitive "helpers" (cognitive offloading). However, suboptimal decisions regarding which helper to recruit for which task occur frequently. Here, we investigate if understanding and adjusting a specific subcomponent of mental models-beliefs about task-specific expertise-regarding these helpers could provide a comparatively easy way to improve offloading decisions.
BACKGROUND : Mental models afford the storage of beliefs about a helper that can be retrieved when needed.
METHODS : Arithmetic and social problems were solved by 192 participants. Participants could, in addition to solving a task on their own, offload cognitive processing onto a human, a robot, or one of two smartphone apps. These helpers were introduced with either task-specific (e.g., stating that an app would use machine learning to "recognize faces" and "read emotions") or task-unspecific (e.g., stating that an app was built for solving "complex cognitive tasks") descriptions of their expertise.
RESULTS : Providing task-specific expertise information heavily altered offloading behavior for apps but much less so for humans or robots. This suggests (1) strong preexisting mental models of human and robot helpers and (2) a strong impact of mental model adjustment for novel helpers like unfamiliar smartphone apps.
CONCLUSION : Creating and refining mental models is an easy approach to adjust offloading preferences and thus improve interactions with cognitive environments.
APPLICATION : To efficiently work in environments in which problem-solving includes consulting other people or cognitive tools ("helpers"), accurate mental models-especially regarding task-relevant expertise-are a crucial prerequisite.
Weis Patrick P, Wiese Eva
cognitive offloading, distributed cognition, extended cognition, mental models, metacognition, strategy selection