Computer-implemented method for quantitatively measuring the mental workload of an individual user interacting with a technical system via a user interface, comprising: receiving individual parameter signals for a plurality of gaze-based workload parameters (P1 to Pn) of the individual user which are detected while multiple calibration tasks are being processed by the user over two or more calibration time intervals, wherein the calibration tasks comprise two or more different types of tasks, each having two or more different requirement levels; ascertaining normalized performance data as individual reference signals for the various tasks from the individual parameter signals, wherein the performance data are a measure of the mental workload during processing of the calibration tasks; generating an overall reference signal from the individual reference signals and the overall parameter signals from the individual parameter signals for each detected workload parameter by juxtaposing the respective measurement results for all subtasks of the calibration tasks; selecting the workload parameters of which the overall parameter signals have a greater similarity to the overall reference signal than the similarity between the individual parameter signals thereof and the respective individual reference signals for the individual characteristics of the task type, requirement level, and time interval, wherein the respective similarities are determined via a Procrustes analysis; ascertaining at least one drift-free workload parameter (Pi) having the highest adaptation latency among the selected workload parameters; generating an individual workload function (f(P(i)) for the user by translating at least one of the ascertained drift-free workload parameters (Pi) into an individual linear function by means of linear regression from the performance data detected during the calibration task for the at least one ascertained drift-free workload parameter; detecting the at least one ascertained drift-free workload parameter during the interaction of the individual user with the technical system over a plurality of measurement time intervals; and deriving the performance data per measurement time interval as mental workload by means of the individual workload function. receiving individual parameter signals for a plurality of gaze-based workload parameters (P1 to Pn) of the individual user which are detected while multiple calibration tasks are being processed by the user over two or more calibration time intervals, wherein the calibration tasks comprise two or more different types of tasks, each having two or more different requirement levels; ascertaining normalized performance data as individual reference signals for the various tasks from the individual parameter signals, wherein the performance data are a measure of the mental workload during processing of the calibration tasks; generating an overall reference signal from the individual reference signals and the overall parameter signals from the individual parameter signals for each detected workload parameter by juxtaposing the respective measurement results for all subtasks of the calibration tasks; selecting the workload parameters of which the overall parameter signals have a greater similarity to the overall reference signal than the similarity between the individual parameter signals thereof and the respective individual reference signals for the individual characteristics of the task type, requirement level, and time interval, wherein the respective similarities are determined via a Procrustes analysis; ascertaining at least one drift-free workload parameter (Pi) having the highest adaptation latency among the selected workload parameters; generating an individual workload function (f(P(i)) for the user by translating at least one of the ascertained drift-free workload parameters (Pi) into an individual linear function by means of linear regression from the performance data detected during the calibration task for the at least one ascertained drift-free workload parameter; detecting the at least one ascertained drift-free workload parameter during the interaction of the individual user with the technical system over a plurality of measurement time intervals; and deriving the performance data per measurement time interval as mental workload by means of the individual workload function.