pretask

Pretask is a term used across different disciplines to describe activities that occur before a main task, with the purpose of preparing participants, settings, or systems for the upcoming work. It can refer to cognitive, educational, experimental, or operational steps that prime performance, elicit prior knowledge, or ensure appropriate conditions for measurement or execution.

In research, education, and usability contexts, pretasks serve to prime relevant concepts, activate prior knowledge, or establish baseline conditions. Examples include warm-up problems, briefings, practice trials, or simple introductory tasks designed to reduce novelty effects. Pretasks help control for variability in performance and can improve the interpretability of results by separating the main task from learning or acclimation effects.

In psychology and human–computer interaction, pretasks are used to calibrate participants’ expectations, surface prior experiences, or screen for differences that could confound data. They may also be employed to familiarize users with interfaces or procedures before the primary assessment or usability session, ensuring that measured outcomes reflect the task itself rather than unfamiliarity.

In artificial intelligence and machine learning, pretasks or auxiliary tasks are used during pretraining or curriculum learning to shape representations and improve sample efficiency. Such pretasks can be simpler or related tasks that encourage beneficial feature learning before tackling the main objective, potentially accelerating convergence and robustness.

In project management and industrial settings, pretasks denote preparatory steps performed before a core operation, such as scoping, safety checks, resource gathering, or setting up infrastructure. These tasks help reduce risk, clarify requirements, and ensure smooth execution of the primary work.