the perceptual optimization of space

1.0 IntroductionThroughout the 20th Century, discussions of optimization and performance played themselves out in the service of ever more efficient business models. At the beginning of the 21st Century however, renewed fears over the potentially cat-aclysmic effects of global warming has shifted the conditions of performance and optimization in the service of consuming less. Much has been said about the rule-based, goal orientat-ed structure of sustainable practices and yet notions of user comfort and user productivity have largely been dictated by the mechanical optimization of space (i.e. solar incidence, CFD analysis, air flow analysis, etc...) and not by the user per-ception of space (i.e. attentive & meditative brain waves).

According to a 2007 study conducted by the Journal of Con-sumer Research at Rice University, it was discovered that variations in ceiling height can prime concepts of freedom or confinement which in turn prompt users’ relational (ab-

AbstractIn discussions of spatial optimization, notions of user comfort and user perception have largely been dictated by the me-chanical optimization of space (i.e. solar incidence, CFD anal-ysis, air flow analysis, etc...) and not by the user perception of space. However, recent developments in BCI (Brain Computer Interface) technology has opened the door for architects and designers to re-evaluate techniques of spatial optimization through methods that directly interface with human percep-tion.

This report is a summary of a series of experiments which seeks to form a link between an occupant’s height in rela-tion to the ceiling height (or the Occupant to Ceiling Ratio) and human thinking, through the effects of spatial priming on the type of processing people use. In particular, through differentiating between relational (or creative) thinking pro-cesses (measured by meditation through the Neurosky BCI) and item-specific processing (measured by attention).

Of the (13) experiments conducted, (8) were successful while (2) were incomplete due to synchronization issues with the Neurosky device. Another (2) had incorrect inputs while (1) did not record any input data.

The findings showed a strikingly visible correlation between the manifestation and intensity of relational processing through the progression of the walkthroughs. 75% (or 7 out of 8) of the final results validated the initial hypothesis. These results also validates the potential of BCI technology as a new tool for architects and designers to think about systems of spatial configuration through perceptual optimization.

Figure 1: Neurosky Brain Computer Interface (BCI) Headset

The Perceptual Optimization of Space

Simon [email protected], 3rd YearColumbia University, GSAPP

Authors:

A report on the effects of Electroencephalography on the perceptual optimization of space.

George [email protected], 3rd YearColumbia University, GSAPP

David [email protected], 3rd YearColumbia University, GSAPP

Figure 2: Ceiling Height Experiment, Rice University

2.0 PurposeThe purpose of this experiment is to test whether a person’s height, or the Ceiling to Height Ratio (henceforth referred to as COR), has an effect on creative thinking. The experiment also tests the validity of Electroencephalography (EEG) as an evaluative tool for spatial priming and the value of human perception as a tool for spatial optimization.

3.0 HypothesisBased on the results of the experiment conducted at Rice University, it can be hypothesized that a greater Ceiling to Height Ratio (COR) will correlate to a relational (creative or abstract) process of thinking while a lower COR will correlate to an item-specific process of thinking. Furthermore, it can be hypothesized that COR does indeed have an effect on think-ing processes.

4.0 MethodologyIn order to create and test scenarios using varying heights of occupants, the virtual realm was chosen as the optimal environment for experimentation due to the ease and flex-ibility of being able to scale up or down figures populating the space and to ensure that the exposure and experience of those spaces for each test subject remained consistent throughout all trials.

Brownies Cafe was chosen as the test space due to both its fa-miliarity to architecture students but also due to its function as a social space with mixed-use activities. It also provides a relatively closed environment to test COR conditions.

Figure 3: Walkthrough diagram for Brownies.

Figure 4: Section showing the 3 walkthrough scenarios.

stract or creative) versus item-specific processing. The report found that higher ceiling heights activated alternative types of elaborative thinking which led to higher recall processing while lower ceiling heights reinforced confinement-related thinking. However, one variable that was not considered in the study was the height of the person itself and the role of space-height proportion rather than simply space itself as a factor of creative versus itemized thinking processes.

This study attempts to bridge this research gap by show-ing that ceiling height, a virtually omnipresent atmospheric variable in the architecture and consumer setting can affect the manner in which individuals process information and that this correlation can be described using the proportional height relationship of users and their spaces.

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4.1 Walkthrough & PromptThree walkthrough videos were created for the experiment procedure, each using the same space, lighting, rendering resolution, and camera path. The only changing variable was the height of the scale figures populating the space and in-evitably, the height of camera and the Ceiling to Occupant Ratio (COR). The height of the figures was categorized accord-ing to national averages, but exaggerated in order to register difference. They are: (1) 4’ (2) 5’7 (average height in the U.S.) and (3) 7 (Figure 4).

In order to test for relational versus item-specific thinking, the space of the walkthrough was filled with 756 suspended vox-els. Prior to viewing the walkthroughs, each test subject was briefed and given the prompt: “how many boxes are there in

Each video walkthrough was approximately 1 minute long. Participants were asked to input their responses at any time during the experiment with no limit to how many answers they may input per walkthrough. The participant was then given a 10 second pause before the next walkthrough began. The entire experiment takes approximately 5 minutes in total to complete and is video recorded so that responses can be matched to the time stamped EEG readings.

4.2 Tools & WorkflowFigure 5. Highlights the entire workflow process for the ex-periment itself. EEG data is collected through the Neurosky Mindset device, which occurs during the entire duration of the experiment. Collected data is streamed via Processing and collated into an editable text file. This text file is then read

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01. Walkthrough experiment utilizing 3 scenarios varying the height of people within brownies, presented in video form.

02. Cognitive functions of the brain are utilized throughout each senario to address questions/ prompts.

03. Electroencephalography (EEG) information is read by the Neurosky headset.

04. EEG data is collected and collated into a text file.

05. accesses and parses information from text file and runs analysis.

06. visualizes and exports data in geometry.

01a. Responses to prompts after each walkthrough is recorded.

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Figure 6: Stills showing 3 walkthrough scenarios.

and parsed by Grasshopper. The definition allows for the col-lected data to be analyzed which is then visualized and ex-ported using Rhinoceros. This process is repeated for every experiment conducted.

5.0 Interpreting the DataThe Neurosky Mindset Brainwave Sensing Headset is a Brain Computer Interface (BCI) capable of sensing, interpreting and translating electroencephalography activity within the brain.

In particular, the Neurosky technology has a proprietary al-gorithm called “eSense” which is able to characterize mental states. These mental states, known as “attention”, and “medi-tation”, are derived from the frequencies of electrical signals emitted by firing neurons in the brain.

Neurosky is able to read a range of these frequencies, ranging from Delta waves up to High Beta waves. Each wave type can furthermore be associated with a particular state of activity

or state of being within the brain. Figure 6 provides the full descriptive summary of the wave lengths and effects.

It is important here to note that both attention and medi-tation can more specifically be defined through the range of frequencies read by the Mindset. Meditation in this case refers to the lower range of frequencies comprising of the Delta, Theta, and Alpha waves. Attention on the other hand reflects the higher range wavelengths of the low, mid, and high Beta waves.

As the scope of the project is interested in the relationship between relational (creative) versus item-specific thinking through the ceiling to occupant ratio, analysis was focused on meditation or the theta and alpha waves as a correlator to relational processing while attention or, the low to high beta waves as a correlator to item-specific processing.

01. Delta Waves0.1 Hz to 3 HzDeep, dreamless sleep, non-REM sleep or unconsciousness.

02. Theta Waves4 Hz to 7 HzIntuition, creativity, recall, fantasizing, imagining, dreaming.

03. Alpha Waves8 Hz to 12 HzRelaxed state, feeling tranquil, conscious.

04. Low Beta12 Hz to 15 HzRelaxed yet focused state.

05. Mid Beta16 Hz to 20 HzThinking, self-concious and aware of surround-ings.

06. High Beta21 Hz to 30 HzAlert, agitated.

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Figure 8: Parser and data visualization definition.

Figure 9: Voxel population definition.

Figure 7: Processing code for video and brainwave extraction.

5.1 ProcessingThe base code for the Processing script was initially devel-oped as a part of Toru Hasegawa and Mark Collin’s Brain Hacking studio at Columbia University but was modified to suit the purposes of this experiment. The initial code allowed a connection to occur between Neurosky’s Thinkgear Con-nector and the Mindset device. It also allowed for the stream-ing data from the headset to be written into a text file.

For the purposes of this experiment, 3 modifications were made to the existing code: (1) a timestamp was added to each data entry so that correlations could be made between the video, the test subject’s responses and the fluctuations in the readings themselves, (2) a code was added using the GSVideo library that allowed the walkthrough video to run directly from processing while synchronizing with the data collection of Neurosky, activated with the press of a button. This code was necessary so that the test subjects could run through the experiment independently and not have to re-quire assistance or supervision; and finally (3), direct input functions were embedded so that participants could input their answers and have them recorded and timestamped by Processing.

5.2 GrasshopperGrasshopper was utilized as the main tool for analysis. Figure 8 highlights the definition which enables the recorded brain-wave information to be parsed and then visualized as chang-ing topography in Rhino. A series of sliders allow for quick shifting through of the information with respect to time al-lowing for a visual comparative analysis and animation of re-corded brainwave data.

While the base model for Brownies was made in Rhino, Grass-hopper was also used to generate and populate the space with voxels. Figure 9 depicts the definition which generates voxels based on a subdivided ceiling surface allowing control over the number of voxels and the their respective heights.

6.0 Findings & ConclusionsThirteen test subjects were selected to participate in the ex-periment with each participant being subject to the same testing conditions. A table with a laptop was a set up in an empty room in Avery accompanied by a video camera. Par-ticipants were first briefed about the experiment and were left alone for the duration of the walkthroughs. This was then followed by a debriefing session where each participant was asked to explain their thought process in answering the ex-periment prompt during each of the 3 walkthroughs, and the rationale leading up to each hardcoded response. The entire process from brief to debrief was captured on film and the EEG readings from the walkthrough experiment recorded through the Neurosky Mindset.

Of the (13) experiments conducted, (8) were successful while (2) were incomplete due to synchronization issues with the Neurosky device. Another (2) had incorrect inputs while (1) did not record any input data.

6.1 Data and Results

Participant 01 initially displayed moderate levels of gamma and beta values (henceforth known as attention), and alpha and theta waves (henceforth known as meditation). Experi-ment two saw an increase in meditation and also an increase in attention, followed by a large spike in meditation in experi-ment three.

During the first pass of the experiment, the participant ap-proximated the number of boxes within her view, not ac-counting for the total space. She took this step as a means of itemizing generally the boxes within the given frame of her view. Within the second and third pass she continued this method but extended it to the total space of the experiment.

Below are visualizations of the successful experiments. Each of the 3 diagrams in a participant series is taken across a 2 second time span in each walkthrough when the participant has entered a response to the prompt.

Data represented in the graphs are broken down into 8 brain waves: high and low gamma, high and low beta, high and low alpha, theta, and delta waves.

These waves are visualized as a continuous topographic land-scape. “Mountains and valleys” in the graph represent intensi-ties of their respective brain waves while their associations either to relational (meditation) or item-specific (attention) processing is noted as callouts.

Participant: 01Walkthrough Number: 01Average Meditation Value: 24Average Attention Value: 50



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Participant Number: 01Gener: FemaleAge: 25Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: 28 ft: 310 ft: 712 ft: 814 ft: 10

Participant 03 showed an initial spike in attention followed by a gradual drop off and a moderate increase in meditation.

In the first pass, the participant tried counting the voxels but reocgnized Brownies during the second which distracted him from the prompt and he associated with the space instead.

Participant 04 showed strong levels of attention and medita-tion throughout all 3 walkthroughs but had a spike in medita-tion during walkthrough 3.

During the first pass of the experiment, the participant counted as many boxes as she could. By the second pass, the participant recounted a number of boxes within an area and multiplied that area throughout the entire experiment space.

Subject: 03Walkthrough Number: 01Average Meditation Value: 66Average Attention Value: 80

Subject: 03Walkthrough Number: 02Average Meditation Value: 29 Average Attention Value: 20


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Participant Number: 03Gener: MaleAge: 24Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: 28 ft: 810 ft: 812 ft: 614 ft: 4

Participant Number: 04Gener: FemaleAge: 34Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: N/A8 ft: N/A10 ft: N/A12 ft: N/A14 ft: N/A

Participant 05 showed baseline readings of attention during the first walkthrough followed by strong readings of medita-tion in both the second and third walkthroughs.

The participant initially counted as many boxes as he could. By the second and third trial, he looked for patterns in the boxes composition in order to come to an approximate guess.

Participant 07 showed low readings of attention and medita-tion during experiment one, with slight increase in medita-tion in two, and a large spike in three.

In the first pass, the participant approximated the number of boxes within a cluster of his view, not accounting for the total space. By the third pass he began to consider all the boxes as a series of clusters and patterns within the total space.

Subject: 05Walkthrough Number: 01Average Meditation Value: 7 Average Attention Value: 83



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Participant Number: 05Gener: MaleAge: 25Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: N/A8 ft: N/A10 ft: N/A12 ft: N/A14 ft: N/A

Participant Number: 07Gener: MaleAge: 29Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: 78 ft: 810 ft: 612 ft: 714 ft: 6

Participant 08 showed moderate readings of attention and meditation during experiment one, which a decrease in both during two and three.

In the second pass, the participant became aware of the height difference to the first. By the third, she had an impres-sion that there were many more boxes within the space.

Participant 10 showed very low readings of attention initially followed by a moderate increase in attention and meditation and large spikes in both during the third walkthrough.

Participant initially attempted to count boxes but found both the motion of the camera and the incomplete views of the space too difficult to focus. He attempted to focus more in the second pass, and shifted in the third to recognizing pat-terns within a given amount of space multiplied by the total.




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Participant Number: 10Gener: MaleAge: 24Occupation: Architecture StudentQuestionaire: Rate from 1-10 how cre-atively you would think in a space with the following ceiling height:6 ft: 78 ft: 7.510 ft: 812 ft: 8.514 ft: 8

Participant 11 showed low levels of attention initially fol-lowed by low levels of attention and meditation during the second walkthrough and a moderate spike in meditation dur-ing the third.

During the first 15 seconds the participant attempted to count each box within their view. From 15 seconds through the next two passes she began generalizing the number of boxes within the space, associating the each pass with the same number of boxes but in an increasingly larger space.

6.2 Concluding RemarksThe initial hypothesis of the experiment stipulated that a greater Ceiling to Height Ratio (COR) will correlate to a rela-tional (creative or abstract) process of thinking while a lower COR will correlate to an item-specific process of thinking. Fur-thermore, it was hypothesized that COR does indeed have an effect on thinking processes.

In analyzing the data from each of the 8 successful experi-ments, it can be deduced that overall, there was an increase in relational thinking, or the meditative range of brain waves (alpha, theta, delta), across the 3 experiments. Furthermore, it can be concluded that with the exception of 1 participant, all other participants showed the greatest level of relational processing during the third walkthrough when the COR was the greatest (when the height of the scale figure was the smallest).

In 4 instances, the increase in relational processing across the three walkthroughs was followed by a decline in item-specific processing. In 3 instances, the increase in relational process-ing was accompanied by a slightly less increase in item-spe-cific processing as well, and in 1 scenario, there was actually a slight drop in item-specific processing with no increase in either relational or item-specific processing for the following two walkthroughs.

During the debriefing session, many participants acknowl-edged that they had addressed the prompt by initially count-ing the number of voxels in the space, however many real-ized after the first walkthrough that this was essentially an impossible task given the amount of voxels there were and the amount of time given for visual exposure.

Once this was realized, all participants resorted to alternative techniques or rationales that could be classified as relational thinking. Many of these strategies involved abstract associa-tion, estimation and mental visualization.

Despite the apparent success of the experiment, there are several elements that could have contributed to or otherwise influenced these results, such as lag and external or second-ary variables (see 7.0 Discussion).

Despite any potential errors or inconsistencies apparent in the 8 successful experiments, 75% (or 7 out of 8) of the fi-nal results validated the initial hypothesis. This is ultimately to say that there is indeed value in something as ubiquitous and seemingly simple as a ceiling and its height in driving the mental state and processes of its occupants. Coincidently, these results and validates the potential of BCI technology as a new tool for architects and designers to think about systems of spatial configuration through perceptual optimization.




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7.0 Discussion

7.1 LagOne of the most challenging aspects of the experiment pro-cess was managing issues of lag. While the equipment itself had an inherent lag in the recording of data, it has a persis-tence in almost all areas of the data collection and analysis process.

The challenge of lag starts at the brain which processes data before it is actually translated into physical action. This means that the keystrokes being entered during the walkthrough already had an inherent first degree lag behind the signals of the brain before it was recorded by Processing. This is also to say that by the time the data was finally recorded by Pro-cessing, the timestamp of that record has had 2 degrees of lag. Now this is of course compounded by the fact that the timestamped EEG recordings also has an embedded lag as well which further skews the data analysis process.

While issues of lag is in part a limitation of the technology, it is also inherently a limitation of the methodology itself. The breakdown of the experiment into several processes and the use of multiple softwares inevitably led to some degree of a misinterpretation of information. It is for this reason, that a wider sample area (or timeframe) was used in the visual anal-ysis process.

More broadly, this also highlights the challenge of current commercial BCI technology as a rigorous analytic tool and the need for both the technology itself, and the available software to reach a point of higher integration.

7.2 VariablesThe human brain is a complex network of information. In or-der to make some sense of all this data, it is necessary to both limit and be precise about the variables in the experiment. While the constant variable in this experiment was the height of the occupants, in the debriefing sessions that followed, it became clear that there were several unforeseen external and secondary variables at play that had affected participants’ an-swers, choices, and rationales.

Some of these variables which participants brought up in discussion included using a familiar space (Brownies), color coding the voxels, movement and framing, speed, and the positioning of the scale models.

While these other variables might have added degrees of complexity to the initial variable of height, they nevertheless in almost all cases, contributed productively to fundamental-ly transforming the subject’s form of thinking in approaching the prompt from item-specific to relational processing.

For instance, in the debriefing session with participant 03, it

was discussed that when the subject discovered that the space was Brownies during the third walkthrough, he im-mediately thought about how the scale or proportion of the space became more reminiscent of a mass dining hall rather than the cafe that he was familiar with. While he indicated that this point of thought distracted him from the prompt itself, it is also a clear indication that the scale of the space in relation to the scale figures, did in fact instilled a level of relational processing in the subject, even if that was initially prompted by the familiarity of the space itself. Interestingly, only two participants did not eventually realize that the space was Brownies.

It is important to note here that these extra variables ulti-mately cumulated in a testing situation where nearly every-one thought the ceiling alone was getting higher, with a few thinking the space was getting larger and no one guessing that they were getting proportionally smaller to the space it-self. This begins to address one of the fundamental difficulties of dealing with a subject’s mind, which is that of individual perception.

7.3 ImplicationsWhereas mechanical optimization seeks to create the most energy efficient buildings, perceptual optimization has the potential to address entire systems of workflow and produc-tivity.

The implications of this study suggest that it might be pos-sible to associate different spatial configurations in relation to a particular kind of task. For example, a meeting space de-signed for creative brainstorming might merit a higher ceil-ing height in order to increase relational processing while a data processing space might merit a lower ceiling height in order to increase item-specific processing.

While this study begins at the ceiling, a ubiquitous spatial condition, it has the potential to affect all configurations and systems of architecture that contribute to work, living, and productivity. Ultimately, the implications of this study sug-gests at a new kind of architectural standard, one not based on the components of systems, but the systems of work and productivity.

While historical architectural discourse codified space through type, program, and now the environment, this study proposes a future discourse based on the codification of space through mental processing.

8.0 References

Meyers-Levy, Joan and Zhu Rui (2007) The Influence of Ceiling Height: The Effect of Priming on the Type of ProcessingThat People Use. Journal of Consumer Research. Vol. 34. http://www.csom.umn.edu/assets/71190.pdf

the perceptual optimization of space

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