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Investigating mental workload across task modalities: a multimodal analysis using pupillometry.

Snehal Dhengre1, Ling Rothrock1

  • 1Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, State College, USA.

Ergonomics
|October 16, 2024
PubMed
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This study shows that mental workload measures like accuracy, NASA-TLX, and peak pupil latency are reliable across different information types. Understanding mental workload requires considering task modality and load for accurate assessment.

Area of Science:

  • Cognitive Psychology
  • Human-Computer Interaction
  • Neuroscience

Background:

  • Mental workload is complex, with measures like task performance, perceived workload, and physiological responses showing varied sensitivities.
  • Existing research often struggles to capture the multidimensional nature of mental workload across different information processing modalities.

Purpose of the Study:

  • To investigate how varying task loads affect performance, perceived workload, and pupil-based physiological responses across different information modalities.
  • To identify robust measures of mental workload that are consistent across verbal and visual information processing.

Main Methods:

  • A within-subjects experimental design was employed, featuring three cognitive tasks (digit span, matrix span, dual n-back) at three distinct task load levels.
Keywords:
NASA-TLXTask performanceconfirmatory factor analysispupiltask load

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  • Measures included task performance (accuracy, reaction time), perceived workload (NASA-TLX), and pupillometry (peak pupil diameter, peak pupil latency).

    • Main Results:

    • Consistent associations between task load and workload measures were observed across verbal and visual modalities.
    • Peak pupil latency, accuracy, and NASA-TLX demonstrated robustness across modalities, indicating reliable mental workload assessment.
    • Peak pupil diameter showed less consistent patterns, with significant differences observed between modalities.

    Conclusions:

    • Mental workload assessment benefits from a multivariate approach, integrating performance, subjective, and physiological measures.
    • Peak pupil latency and accuracy are promising objective measures for mental workload, particularly in verbal and visual tasks.
    • Task modality significantly influences certain physiological workload indicators, necessitating careful consideration in experimental design and interpretation.