Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving

TL;DR

This study demonstrates that combining eye-gaze, posture, and emotion signals improves the accuracy of modeling chess players' expertise and cognitive states during problem solving, validating a multimodal approach.

Contribution

The paper introduces a multimodal sensor system for analyzing human cognitive and emotional states during complex problem solving, showing improved accuracy over unimodal methods.

Findings

Multimodal approach achieves up to 93% accuracy in classifying chess expertise.

Combining multiple signals outperforms single-signal methods.

The equipment is validated as a reliable tool for studying screen-based problem solving.

Abstract

In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects, and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion. We observed chess players engaged in problems of increasing difficulty while recording their behavior. Such recordings can be used to estimate a participant's awareness of the current situation and to predict ability to respond effectively to challenging situations. Results show that a multimodal approach is more accurate than a unimodal one. By combining body posture, visual attention and emotion, the multimodal approach can reach up to 93% of accuracy when determining player's chess expertise while unimodal approach reaches 86%. Finally this experiment validates the use of our equipment as a general and reproducible tool for the study of participants engaged in screen-based interaction and/or problem solving.