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Predicting and understanding human action decisions during skillful joint-action using supervised machine learning

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Supervised machine learning accurately predicted human decisions in a multiagent task, even before conscious intent. Explainable AI revealed experts use different information than novices.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Understanding human decision-making in complex environments is crucial.
  • Supervised machine learning (SML) and explainable AI (AI) offer novel approaches to model cognitive processes.

Purpose of the Study:

  • To investigate the utility of SML and explainable AI for modeling human decision-making in multiagent tasks.
  • To predict and understand expertise-based differences in decision-making strategies.

Main Methods:

  • Trained Long Short-Term Memory (LSTM) networks to predict player decisions in a multiagent herding task.
  • Utilized SHapley Additive explanation (SHAP) to identify key features influencing model predictions.

Main Results:

  • LSTM models accurately predicted expert and novice player decisions at timescales preceding conscious intent.
  • Models demonstrated expertise specificity, failing to generalize between expert and novice predictions.
  • SHAP analysis indicated experts rely more on target direction and coherder location than novices.

Conclusions:

  • SML and explainable AI are effective tools for modeling and understanding human decision-making in multiagent systems.
  • Expertise significantly influences information processing and decision strategies.
  • These methods offer insights into the cognitive mechanisms underlying skilled performance.