Human brain dynamics in naturalistic conditions has seen a gradual advancement in the recent years. This advancement is partly because of the embodied cognition paradigm which claims that the cognitive processes are manifested in body’s interactions with the world. (Wilson, 2002). This view has become more significant with recent research that demonstrates the way we process the environment is influenced by the experience of the human body (Reference). Gibson (1979), in his theory of affordances, emphasized on the idea of varying interdependence between perception, action and the environment. The theory views the available external information in act of providing opportunities for action.
Other reason is the advancement in techniques to record brain and movement activities in real environments while at the same time removing artifacts that could affect the signals of interest. Once such methodology is Mobile brain/body imaging (MoBI). MoBI employs electroencephalography (EEG) integrated with motion, motor, visual capture and other data streams to investigate brain and movement activity while participants actively explore and interact within a natural environment. Therefore, mobile cognition paradigm has made it possible to increase the ecological validity of cognitive science research (Ladouce et al., 2017). (Gramann et al., 2014).
Experimental paradigms sprouting from traditional setup-based research that contains a highly controllable and standardised environment has put restrictions on the investigation of movement related cognitive functions that are essential in a real-world environment. (Makeig et al., 2009; Gramann et al., 2011, 2014). These restrictions restrain information, for example, the integration of visual, motor and vestibular information which is perceived and processed by the human in day-to-day tasks. Employing the embodied cognition paradigm, one needs to design experiments and adopt methodologies that integrates information from the brain, body and environment to understand human cognition in naturalistic conditions (Chiel and Beer (1997)).
Problem Statement and Justification of the Research Project
Humans behave actively with the environment. Day-to-day life situations, involving perceptual processing and allocation of attention to moving stimuli, such as crossing a street with traffic or driving across billboards with dynamic display. Real-world situations require an efficient and adapte performance for an accurate estimation of variables such as speed (Conchillo, Nunes, Ruiz, & Recarte, 1999), acceleration (López-Moliner, Maiche, & Estaún, 2003), location and the temporal duration of different environmental stimuli (Aubry, Guillaume, Mogicato, Bergeret, & Celsis, 2008). The efficiency and adaptation become more difficult when the moving stimuli is perceived and attended to whilst a certain bodily movement.
Mobile cognition approach has implications in research questions that can be addressed in real-world contexts. For example, clinical implications, Parkinson patients have an impaired visuospatial processing, visuo-constructional abilities and motion perception with impaired motor-abilities. Understanding the mechanism of interaction of self-generated motion and external motion will lead in designing physiological interventions which would assist PD patients in faster recovery of the cognitive-motor functions.
Another implication is in the field of Neuroergonomics. Neuroergonomics is the scientific study of the human brain activity in relation to performance at work and everyday settings (Parasuraman, 2003). Insights into brain activity during physical human-machine interaction allow for the improvement of systems to adapt to the operators’ physical and cognitive resources (see e.g., Wascher et al., 2016; Mijovic et al., 2016). Whereas traditional brain imaging approaches do not allow for any kind of movement (Makeig et al., 2009; Gramann et al., 2011) mobile brain/body imaging (MoBI) employs a variety of hardware and software tools to record and analyze brain activity in participants behaving actively. To that extent, mobile-brain body imaging is significantly getting deployed to understand the dynamic relationship of human body experience in a natural environment (Gramann et al., 2014; Ladouce et al., 2017)
Hypothesis and Objectives of the Study
To navigate in a complex and dynamic environment, a wide range of motor and cognitive abilities are required. However, the motor and cognitive abilities interact differently in dynamic situations in order to calibrate with the environment. This calibration is a dynamic process that includes visual, auditory, tactile, olfactory, motor, vestibular, kinesthetic and proprioceptive inputs. For example, crossing a road requires visual processing of objects in motion (cars, other walkers, cycles etc.) and static objects (trees, parked cars, buildings etc). Similarly, the auditory inputs are processed with respect to the location of the sound source. Concurrently, humans process kinesthetic, proprioceptive and vestibular inputs with respect to the body which are necessary to locate in space and to perform a self-generated action in order to cross the road.
Attention plays a crucial role in guiding humans and is closely entwined with human motor abilities. This fact could be further illustrated by means of dual-task paradigms. Dual-task paradigm have been employed to investigate different aspects of attentional processing in tasks which are performed simultaneously.
In a dynamic environment the visual and auditory inputs are perceived as in motion with respect to our body movement. How we process, attend and respond to the moving inputs is what this research aims to demonstrate. Previous studies have showed how the upper-limb respond to a moving visual stimulus with a naturalistic movement rather than an artificial one. For example, Gramann et al., 2016 demonstrated that onsets of hand movements and corresponding reaction times in physical pointing towards a dynamic visual stimulus were significantly faster when compared to the button press.
In order to understand the mechanism behind the integration of processing, attending and responding to visual and auditory inputs in motion with a naturalistic movement (upper and lower limb) in a real-world context we hypothesize;
• Change in visual/auditory processing accompanying self-generated motion will be different from change in visual/auditory processing induced by an external motion.
• Change in visual processing induced by an external motion will be affected differently by biological and non-biological patterns.
• Perception of our own body movement will modulate how we perceive external motion?
• Object (visual/auditory) dimensions such as speed, acceleration and temporal duration will have an effect on the processing of external motion.
• There will be a cross-modal congruency (visual and auditory) effect in processing of external motion.
Literature and Research Review
Research Method(s)
Study One – Is change in visual processing accompanying self-generated motion any different from change in visual processing induced by an external motion?
Participants
Healthy participants will be recruited by means of posters and word of mouth at the premises of the University. On the whole, exclusion factors concerned participants with motor-visual-auditory impairment or a history of psychiatric illness.
Stimuli and cognitive task
The cognitive task will be presented in virtual scene, which will be presented through a virtual reality headset. The virtual scenario will act as a control to limit the perception of self-body movements. Target stimuli will be represented by a ‘moving bee’ in external motion conditions and ‘static bee’ in no external motion condition. The target stimuli will be presented static and moving on all the four quadrants of the visual space. The presentation of visual stimuli will be pseudo-randomise and additionally, will be consistent across all participants and in each condition. The total number of trials will 300; duration of each trial will be one second. The reaction times will be recorded with button-press. Eye-tracking will be used as a control to map the ocular movements in processing static and moving stimuli.
Design and procedure
Participants will be tested on four 5-minutes experimental conditions. In three conditions participants and will be instructed to press the button corresponding to the presentation of visual stimuli location in static condition and when the participants perceive visual stimuli has cease to be in motion in the moving condition as fast as possible.
In order to distinguish the contribution of self-generated motion and external motion on visuo-spatial attentional resources deployed towards the processing of task-related stimuli, these factors will be manipulated through a factorial within-subject design.
Learning phase
The participants will be introduced to the condition. In this case, the participant will be asked to walk and get comfortable while the stimuli will be presented.
Actual Task
After the introduction, the participant will confirm that they are comfortable. Then the task will commence.
Condition One
The participants will be asked to walk and react as soon as they see a bee.
Condition Two
The participants will be asked to stand and react as soon as they see a bee.
Condition Three
The participants will be asked to walk and react as soon as they perceive that the bee has stop moving.
Condition Four
The participants will be asked to stand and react as soon as they perceive that the bee has stop moving.
The standing and static stimuli (condition two) will be used as a baseline condition since the experimental factor, self-generated motion and external motion will be absent. Two intermediate conditions will be used to isolate the respective contribution of the experimental variables investigated. In condition one, participants will effectively be walking but stimuli remained stationary (self-generated motion; no external motion). In condition four, participants will be standing but stimuli will be in motion (no self-generated motion; external motion). Generally, the order of conditions will be counterbalanced, and the task will be performed only once for each condition.
Data Acquisition
Equipment
• 64 and 128 channel ANT eego sports amplifier with passive electrodes and active cable shielding
• PhaseSpace motion caption cameras with up to 70 active LEDs for body imaging
• HTC Vive eyetracking equipment
• HTC Vive Controlles
• Step Sensors
Software
• LabStreamingLayer (LSL, by Chrisitan Kothe) will be used to synchronise events and data streams.
• Worldviz Vizard
• EEGLAB
• MoBILAB
Neural Correlates
Statistical analysis approach
Statistical analyses will be conducted using SPSS Statistics (Version 23.0. Armonk, NY, IBM Corp). Descriptive statistics summarising dataset’s central tendencies (e.g., mean) and their variability (e.g., standard deviation) will be performed. For each EEG feature, a 2×2 factorial repeated measures analysis of variance (ANOVA) will be performed subject to factors “Self-generated motion” and “External motion”. Post hoc analysis will be employed for further testing of main effects. Paired-sample t-tests will be applied to the data, effectively providing probability values (p-values) of the null hypothesis (i.e., datasets compared are not different). These p-values will be considered as reflecting significant difference between datasets at a threshold of 5% (p < .05), under which the null hypothesis will be rejected.