Browsing by Author "Hall, Siobhan"

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    Determining the nature of free will using machine learning
    (Stellenbosch : Stellenbosch University, 2020-03) Hall, Siobhan; Morris, L. D.; Van den Heever, David Jacobus; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Health & Rehabilitation Sciences. Physiotherapy.
    ENGLISH ABSTRACT : Background: The debate around free will has been topical for millennia. The question around free will is important in assigning agency to our decisions and actions. The definition of free will used in this research is the ability for a person to do otherwise, should the exact circumstances be created. In 1983, the Libet paradigm was developed as a means to empirically investigate the nature of free will. The Libet paradigm resulted in the presentation of a rise in neural activity 350 ms before conscious awareness of a decision to act. This rise in neural activity (known as the readiness potential) was prematurely and incorrectly taken as proof that the subconscious having a prominent role in our decision-making processes and therefore the conscious self has no free will. This result has subsequently faced criticism, particularly its method of averaging out EEG data over all the trials and the readiness potential not being present on an individual trial basis. Another major criticism is the method of retrospectively and subjectively reporting the moment of conscious awareness, termed “W”. Objectives: The aim of this research is to determine the role of the subconscious in our decision-making processes using machine learning. A secondary aim is to determine if eye tracking can be used to objectively mark the moment of conscious awareness of a decision to move. Investigating the role of the subconscious in our decision-making processes not only contributes to the fundamental understanding of our brains’ processes and the nature of free will, but also early detection of intentions to move can aid in the earlier identification of features to classify actions in brain-computer interface (BCI) systems. This earlier classification can improve the real-time nature of thought and then action. This can help improve the functionality of people living with disabilities. Methodology: The data collection involved the recreation of the Libet experiment, with electroencephalography (EEG) data being collected in conjunction with eye tracking. Another addition to the Libet paradigm was the choice between “left” and “right”. 21 participants were included (4 females, all right-handed). The participants were asked to make a decision between moving “left” and moving “right” while observing the Libet clock to subjectively mark the moment of subconscious awareness. Deep learning, a branch of machine learning was used for the EEG data analysis. The deep learning model used is known as a convolutional neural network (CNN). The eye tracking data was used to identify any eye movements (saccades) that occurred 500 ms before the action. Results: The CNN model was able to predict the decision “left” or “right” as early as 1.3 seconds before the action with a test accuracy of 99%. The eye tracking data was analysed and no correlations between an eye movement and the moment of conscious awareness was found. Conclusion: This research has provided evidence to support the hypothesis that there is no free will. Further research is needed to investigate earlier predictions using deep learning as well as research focused on using eye tracking as a means to objectively time-lock the moment of conscious awareness.