As previously described the study described here adopts a neurophenomenological methodology (Varela, 1996; Lutz, 2002; Fazelpour and
Thompson, 2015) in seeking to better understand and measure Mindful and
Mindlessness states during interaction. Here the neuro-phenomenological methodology employed uses phenomenological information (first-person reporting upon subjective experience – as subjective ‘A posteriori’ data) to support and understand the relationship and measurement of empirical physiological information (as objective ‘A priori’ data).
2.4.1: Subjective data
Questionnaire
Following each condition participants were asked to complete a brief questionnaire (informed by the preliminary findings of section 1) based on the condition they had just completed. Answers were given through a five-point Likert- type scale (from “Agree completely” to “Disagree completely”), and had option to provide comment following each statement (participants were informed this was entirely optional and not constrained to a particular form of comment). Participants were informed that they may skip a statement or discuss it following completion of the questionnaire. The statements provided were:
• I was intentionally aware of my thoughts and feelings • My mind wandered off and I was easily distracted
• I knew the correct answer and made my choice quickly without needing to
think too much
• I paid attention to the environment around me
• I was completely absorbed in the display/audio; so that all my attention was focused upon it
• I found myself watching/listening to the display/audio but thinking of something else at the same time
* Space to leave broader additional comments See Appendix 2.3
Open-ended Interview
To gain a broader understanding of the participants subjective experience during each condition a brief open-ended interview was conducted following completion of the questionnaire (described above). This was intended to be a responsive probe to
participant answers and thoughts, and explore themes as they arise and provide deeper enquiry into topics of discussion that were unpredictable prior to study. There were however several prompting questions to initiate conversation with focus upon the participants attention and awareness, perception of time, thoughts and feelings, reaction to pop-ups, perception of challenge (ease of completion of test), mind wandering, and any strategies they employed or developed through the condition. As this line of enquiry varied between participants (being responsive to their answers and points raised) the specific prompts and questions also varied yet followed the previously outlined themes.
2.4.2: Objective data
Interaction metrics
As the interaction of the broader study occurs with digital technologies, objective data can be easily gathered that relates specifically to metrics of the interaction occurring. Such metrics include; time between a condition stimulus being shown and answer, correct v’s wrong answers, when a pop-up stimulus is shown (and time to answer) and if the answer provided changed.
Video Analysis
Participants consented to video recording of the study. While video analysis facilitates analysis following a condition i.e. of the open-ended interview, it additionally allows for analysis of the participant during interaction. In this study video analysis of the participant during interaction provided a metric of hesitation. A hesitation here is understood as an act of altering an action prior to a final choice in action. Here a hesitation was regarded as a self-induced (i.e. by the participant) interruption to prevent an action (i.e. provide answer to stimulus) or change an answer. While such events cannot be linked to specific time points (such as with the digitally produced or recorded events) they offer additional insight to the subjective aspects of the interaction and provide stimulus test numbers where an event of reflection might have occurred.
EEG (Electroencephalography – Brain Activity)
While (as previously stated) this thesis does not claim consciousness to be a product of brain activity; there is strong evidence and accounts of correlation between conscious and/or sub-conscious events and brain activity (e.g Baars, 1993, 1996, 1997, 2003; Chalmers, 1995; Varela, 1996; Århem and Liljenström, 1997; Saling and Phillips, 2007; Cosmelli, Lachaux and Thompson, 2007; Shanahan, 2010;
recording brain activity as a “non-invasive” measurement in that it does not break the skin of the subject and does not enter the body in anyway.
To understand how EEG works and what it is measuring it is important to understand some fundamental aspects of the brains physiology; this can be found in appendix 2.4 Brain Activity, EEG and QEEG.
The EEG data was captured using an Emotiv EPOC EEG neuroheadset 10. The EEG headset provides 14 channels (electrodes) with a sampling rate of 128Hz per channel; and so a range of 1-64Hz measurement following FFT. As noted by Debener et al. (2012) the Emotiv EPOC provides an affordable and accessible EEG hardware device, though it should be noted that the standard saline pads (for conductivity of electrical current from the scalp to the electrodes) reduces signal quality, and the placement of electrodes is approximate (i.e. does not adjust to compensate for head shape and size). As with Debener et al. (2012) these limitations were overcome through the use of a third-party stretchable EEG cap (Mitsar medical MCSCap11) that when placed over a participant head provides correct 10/20 placements of electrodes. To allow for mating the EPOC to the MCSCap sintered Ag\AgCl electrodes were connected to the EPOC electrode fittings using silver solder (for reduced signal noise). EEG signals were validated through comparison of the adapted headcap and original saline electrodes, assessing signal amplitude, noise, and original software signal analysis. Through the use of this cap custom electrode positioning was allowed that moved beyond the EPOC standard position, in addition to a reduced sensitivity to artifacts in the EEG signal from body movements. The final 1212 electrode positions can be found in figure 2.10.
EEG raw data recording was performed using the Emotiv Test Bench software (v1.5.1.2). Communication between the custom software (controlling stimulus, input, and interaction metrics) and Emotiv Test Bench was facilitated using Eterlogic VSPE13, a virtual serial port emulator. Events (such as start of study, end of study, stimulus shown) were sent form the custom software through the serial port emulator and to the Test Bench software so that markers could be used in data analysis off- line (i.e. after the study).
10 https://www.emotiv.com/product/emotiv-epoc-14-channel-mobile-eeg/ 11 http://www.mitsar-medical.com/eeg-accessories/
12 While the MCSCap provided correct placement of electrodes, due to differing head shapes and
participants hair, several of the participants had intermittent ‘noisy’ EEG signals from the
placement of electrodes at positions O1 and O2. For reasons of cross condition comparability and to prevent erroneous data effecting evaluation, these electrode recordings were removed from the data following recording and prior to final data processing.
Figure 2.10: EEG electrode positioning used in analysis: Fp1&2; F3,z,4; C3,z,4; P3,z,4; Oz. Reference electrodes (CMS and DRL) were connected to the left and right ears respectively via sintered Ag\AgCl electrodes in a spring clamp. Ten20 conductive paste was used for skin-electrode conductivity.
EEG data, following each condition testing, was saved and exported in .EDF format14; a common exchange file format for multi-channel biometric recordings. Due to the nature of EEG and the sensitivity of the electrodes, it is common that artifacts from body movement, eye-blinks, speech etc. produce spikes in the recording. To improve signal quality the EDF recordings were imported to EEGLab15 extension of MATLAB (Version R2013b), and ‘cleaned’ using the clean_rawdata16 toolbox plugin. This plugin utilized Artifact Subspace Reconstruction to reconstruct missing data using a spatial mixing matrix (with assumed volume conduction). To facilitate this, prior to each condition participants were asked to remain still for two minutes to provide a baseline of clean data from which calibration would occur; an example of this processes effect can be seen in figure 2.11. While performing artifact rejection two electrodes (O1 and O2) showed intermittent excessive artifacts (i.e. signal amplitudes excessive of brain activity from muscle and head movement) during several recordings of conditions and so were removed for all participants and all conditions (with a new artifact rejection being performed on the raw/ original data excluding O1 and O2 electrodes). The positions O1 and O2 are typically
14 http://www.edfplus.info 15 https://sccn.ucsd.edu/eeglab/
utilised in the understanding of visual association processing of the secondary visual cortex. The data from those electrodes are excluded from dataset processing and analysis to prevent erroneous data effecting evaluation and allow cross condition/participant comparability. Following artifact rejection the raw EEG files were exported and analysed using MNE package for Python (v2.7) (Gramfort et. al.,
2013; Gramfort et. al., 2014). Specific methods of analysis are discussed in greater
length in the results chapter.
Figure 2.11: Artifact Rejection can be seen in red (large spikes in the raw EEG signal), these deviate from normal (blue) EEG signal. Corrected signal (used for analysis) can be seen in blue (i.e. an EEG signal with large contaminate artifacts caused by bodily movements removed).
Eye Tracking and Pupil Dilation metrics:
To compliment the EEG data, eye tracking and pupil metrics were additionally captured. As noted by Liversedge and Findlay (2000), saccadic eye movements (how the eye move between fixation on different objects) can provide indication toward cognitive processes; likewise, Kahneman (1973) found strong correlation between pupil size and cognitive loading. Eye-gaze and pupilometry were captured using a Tobii TX300 system capturing both eye gaze position and pupil dilation (at 300Hz sampling frequency of both eyes). Eye data was recorded using the Tobii Studio Professional v3.2 software17, which exported eye-tracking data into a CSV format for analysis in Python (v2.7). Participants performed a 9-point calibration of the eye- tracker prior to each condition. Data was exported using the recommended settings of the Tobii Studio Pro software, specifically applying the Tobii Fixation filter and saccade analysis (Olsson, 2007; Komogortsev et al. 2010). Specific methods of analysis are discussed in greater length in the following chapters.