Incentive architeture: Investigating spatial affordances
in architecture using mobi and vr Zakaria Djebbara1*, Lars Brorson Fich1, Laura Petrini2 & Klaus Gramann3,4,5 Department of Architecture, Design, Media and Technology, Aalborg University 2 Department of Communication and Psychology, Aalborg University 3 Biological Psychology and Neuroergonomics, Berlin Institute of Technology, Berlin, Germany 4 School of Software, University of Technology Sydney, Sydney, Australia 5 Center for Advanced Neurological Engineering, University of California, San Diego, CA, USA 1
*Corresponding author:
[email protected]
BACKGROUND
PARADIGM
• Transitions from one space to another are defined by two spaces and a delineating threshold, which can manifest in different architectural forms. • Such transitions have impact on the perception and affective evaluation of the connected spaces(1). • Prior spaces seem to affect later spaces and the threshold itself might have an affective influence. • We investigated transitions in the form of openings, to gain a deeper understanding of the perceived affordance (2) of crossing the openings and how this impacts evaluation of the space. • Embedded in a broader investigation of cognitive predictive mechanisms to better understand architectural transitions, the aim of the current study was to investigate whether the physical passing, referring to affordances and active inference (3-5), co-vary with the motor-related cortical potential as measured with the electroencephalogram (EEG).
METHODS
• Participants wore Windows Mixed Reality and a backpack computer for a MoBI-approach (6-8). • In virtual realty, a black sphere surrounds the participants head, indicating a state of “lights off”. • After 3s (+/-1s) lights were turned on, and participants were introduced with openings that were either too narrow to pass, difficult to pass (yet possible), and easily passable (see figure 1). • After 6s (+/-1) the door, turning either green or red, indicated whether to pass or turn around and answer the virtual SAM-questionnaire. • The task entailed an action-dependent transit (50% of trials), with the final goal to reach a red circle in the successive space. • After each trial participants were asked to go back to start and fill in the virtual SAM-questionnaire. Pulling the trigger of the VR-controller would turn the “lights off” again, indicating a restart.
• 18 healthy participants (9 females), with an average age of 28,1 (sd=6.24) have been analyzed. • EEG data was recorded with 500Hz using ANT-Neuro amplifier with a 10-5 system 64-channel cap. • Filtered with 1Hz to 100Hz and down-sampled to 250Hz, and manually cleaned to prepare for signal decomposition. • Bad channels were detected automatically and replaced by interpolation. Re-referenced to average. • To decompose the signal, AMICA was used. • The resulting weights and spheres were copied to the continuous raw data, preprocessed in an identical fashion save cleaning in the time domain. Filtered with 0.2Hz to 40Hz. • Transferring the ICA-weights from first dataset to the new dataset allows for removing blinking artefacts. • Automatic epoch rejection rejected approximately 20%. • The baseline was calculated from -1000 to 0 ms. • Important electrodes for motor related cortical potentials, namely Fz, FCz, Cz, C1 and C2. Raw data
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Figure 2 - A single trial, and the timeline of a single trial.
Largest ERP components of Epoched_GoNarrow
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Figure 3 - The steps of the preprocessing
Largest ERP components of Epoched_GoWide
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participant, indicating an origin best expressed with the IC15. The dipole of IC15 was then automatically fitted and projected. IC15, it was approximated to right cerebrum, frontal lobe, paracentral lobul [RV: 3.98%]. However, the origin of the negativity slope is uncertain and further analyses is necessary. • From the ERP component image, post NS activity reveal muscle activity from the neck and temporal areas, indicating actual movement of the participant. • For future analysis, the marked area on the timeline in figure 2 indicates the time-domain of interest for further studies. As temporal interval between warning and imperative stimulus varies (6 s +/-1), a time warp and analysis of event related spectral perturbations is beneficial to better understand the frequency domain.
RESULTS
• Presented plots are filtered with 0.2Hz to 8Hz. • Point zero is the Go/NoGo event of the experiment. • Preliminary analysis indicates a slow CNV-like negative slope (NS) occurring approximately 200 ms after the cue. The peak of the NS covaries with the opening width of the doors (no statistical analysis yet) • Dominance of the NS appears in the frontal, FCz and Fz, compared to Cz, C1 and C2. • At the current state of the study, no behavioral data has been assessed, thus no results on emotional data is presented.
DISCUSSION
• With only preliminary analysis, we observe motor-related negativity at the Go-cue with linearly different amplitudes of negativity around the central line and C1/C2. • Participants were waiting for Go/NoGo cue, where movement onset seems to have taken place 1 second after the Go/NoGo cue. As the observed negativity is highly inclined and has a duration of 500 ms, its origin is yet questionable, but most likely due to movement. • Following plots of the study illustrates CNV-like negativity taking place approximately 200 ms after Go/NoGo-cue correlates with the width of the opening, as in earlier study of affordances (9). • As an initial step towards understanding the negativity slope, we generated ERP component images (see figures), of a single
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REFERENCES
Figure 4 - Dipole plot from a single participant for IC15 approximated to right cerebrum, frontal lobe, paracentral lobul. In other words, premotor cortex.[RV: 3.98% X: 11 Y: -15 Z: -46]
• 1. Moretti L, Bucci F, Mulazzani M, DeConciliis M. Luigi Moretti: Works and writings. Princeton Architectural Press; 2002. 232 p. • 2. Gibson J. The Ecological Approach to Visual Perception. Houghton Mifflin- Boston. 1979. • 3. Friston K, Mattout J, Kilner J. Action understanding and active inference. Biol Cybern [Internet]. 2011 Feb 17 [cited 2018 Mar 5];104(1–2):137–60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21327826 • 4. Friston KJ, Kilner J, Harrison L. A free energy principle for the brain. J Physiol [Internet]. 2006 Jul 1 [cited 2017 Dec 9];100(1–3):70–87. Available from: http://www.sciencedirect.com.zorac.aub.aau.dk/science/article/pii/S092842570600060X • 5. Bruineberg J, Kiverstein J, Rietveld E. The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective. Synthese [Internet]. 2016 Oct 21 [cited 2018 Mar 5];1–28. Available from: http://link.springer. com/10.1007/s11229-016-1239-1 • 6. Makeig S, Gramann K, Jung T-P, Sejnowski TJ, Poizner H. Linking brain, mind and behavior. Int J Psychophysiol [Internet]. 2009 Aug [cited 2018 Mar 6];73(2):95–100. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19414039 • 7. Gramann K, Gwin JT, Ferris DP, Oie K, Jung T-P, Lin C-T, et al. Cognition in action: imaging brain/body dynamics in mobile humans. Rev Neurosci [Internet]. 2011 Jan 1 [cited 2018 Mar 6];22(6):593–608. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/22070621 • 8. Gramann K, Jung T-P, Ferris DP, Lin C-T, Makeig S. Toward a new cognitive neuroscience: modeling natural brain dynamics. Front Hum Neurosci [Internet]. 2014 [cited 2018 Mar 6];8:444. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24994978 • 9. Bozzacchi C, Spinelli D, Pitzalis S, Giusti MA, Di Russo F. I know what I will see: action-specific motor preparation activity in a passive observation task. Soc Cogn Affect Neurosci [Internet]. 2015 Jun 1 [cited 2017 Nov 29];10(6):783–9. Available from: https://academic.oup.com/scan/article-lookup/doi/10.1093/scan/nsu115
