The Pennsylvania State University
The Graduate School
College of Engineering
REALLOCATION OF NARROW BANDS OF SPECTRAL ENERGY:
THE EFFECT ON BRIGHTNESS PERCEPTION AND COLOR PREFERENCE
A Dissertation in
Architectural Engineering
by
Andrea M. Wilkerson
 2013 Andrea M. Wilkerson
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
August 2013
The dissertation of Andrea M. Wilkerson was reviewed and approved* by the following:
Kevin W. Houser
Professor of Architectural Engineering
Dissertation Adviser
Chair of Committee
Richard G. Mistrick
Associate Professor of Architectural Engineering
Jelena Srebric
Professor of Architectural Engineering
Professor of Mechanical Engineering
Eric Loken
Research Associate Professor of Human Development and Family Studies
Robert Davis
Special Member
Chinemelu J. Anumba
Professor of Architectural Engineering
Head of the Department of Architectural Engineering
*Signatures are on file in the Graduate School
ABSTRACT
The research furthers the understanding of human psychophysical response to spectra and the role of
spectral tuning in source optimization by examining the effect of spectral modification on the
perception of brightness and color preference, building upon the work of William Thornton, Kevin
Houser, and their associates. Thornton first theorized over forty years ago that the perception of
brightness could be increased by placing energy in the spectral regions at and near 450, 530 and 610
nm, which he called the prime color spectral regions. He further identified the areas at and near 495
and 575 nm as having deleterious effects on color preference and termed these anti-prime color
regions. Hurvich’s opponent signal sensitivity curves have a relative sensitivity of zero where the
sensitivity switches between blue and yellow at 500 nm and switches between red and green at 480
and 580 nm. This dissertation focused on removal of optical radiation near anti-prime spectral
regions, where the opponent signal relative sensitivity is zero, and resulting perceptions of brightness
and color preference.
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching. Energy was removed from the regions of 500 and 580
nm, with some energy added to other regions of the visible spectrum as necessary to maintain
consistent chromaticity. Brightness discrimination and brightness matching experimental
methodologies were both employed in order to examine if these methodologies would produce
comparable results.
The research aimed to answer the following questions:
1. Do spectra with energy reallocated from the regions of either 500, 580, or 500 and 580 nm
appear brighter than a nearly continuous reference spectrum?
2. Do the experimental methodologies side-by-side brightness matching and side-by-side
brightness discrimination (forced choice) produce comparable results?
3. Does the reallocation of energy from the regions of 500, 580, or 500 and 580 nm change
preference for a person’s own skin tone?
The test spectrum with energy reallocated near the spectral region of 500 nm (SPD500) and the test
spectrum with energy reallocated near the spectral region of 500 and 580 nm (SPD500,580) appeared
brighter than the reference spectrum (SPDRef) at equal illuminance, reaching statistical significance
for both the brightness matching and brightness discrimination experiments. No statistical difference
in brightness was found between the test spectrum with energy reallocated near the spectral region of
580 nm (SPD580) and SPDRef.
The side-by-side brightness matching experimental methodology and the side-by-side brightness
discrimination experimental methodology produced comparable results. The rank order of the
reference and test spectra was similar for both experimental methodologies. The illuminance ratio of
SPD500,580 versus SPDRef at equal perceived brightness was 94.2% for the brightness discrimination
iii
experiment and 95.6% for the brightness matching experiment. The ratio of SPD500 versus SPDRef
was 95.5% and 98.1% for the discrimination and matching experiments, respectively. The ratios from
the two experiments were both found to be significantly different from each other. The SPD580 versus
SPDRef ratios resulting from the two experiments were not significantly different. The brightness
matching ratios were closer to unity, unity indicating no difference, for every spectrum comparison
versus the brightness discrimination ratios.
SPD580 was preferred versus SPDRef for 72.8% of the comparisons between the two spectra. Similarly,
SPD500,580was preferred versus SPDRef for 70.0% of the comparisons between the two spectra.
SPD500,580 and SPD580 were both significantly different than SPDRef at an alpha level of 0.01. The
rank order resulting from the preference experiment matched the rank order of the following metrics:
Gamut Area Index, Color Quality Scale 9.0 Qg, Farnsworth Munsell Gamut, Color Preference Index,
Color Discrimination Index and Feeling of Contrast Index. SPD580 had the highest preference rank,
however it had the lowest CIE Color Rendering Index and R9 rank.
The results of all three experiments show that SPD500,580 is the superior spectrum in comparison to
SPDRef, SPD500 and SPD580. SPD500,580 appeared brighter at equal illuminance than SPD500 and SPD580
when compared to SPDRef. SPD500,580 was also preferred versus SPD500 and SPDRef, and was not
significantly different than SPD580. The LER of SPD500,580 was 8.9% greater than SPDRef and
SPD500,580 required an average of 5.1% less lumens than SPDRef at equal brightness appearance. When
the difference in LER is combined with the results of the brightness experiments, SPD500,580 provides
a 14% improvement in efficacy versus SPDRef at equal perceived brightness.
The findings of this research clearly demonstrate, with comparable results from brightness matching
and discrimination methodologies, that the spectrum with spectral energy reallocated in the regions of
500 and 580 nm was perceived as brighter at equal illuminance and was preferred for rendering a
person’s own hand.
iv
TABLE OF CONTENTS
LIST OF FIGURES ............................................................................................................vii
LIST OF TABLES ..............................................................................................................ix
LIST OF ABREVIATIONS ...............................................................................................xii
ACKNOWLEDGEMENTS ................................................................................................xiii
Chapter 1 INTRODUCTION ..............................................................................................1
Chapter 2 BACKGROUND ................................................................................................2
Chapter 3 LITERATURE REVIEW ...................................................................................4
3.1 COLOR VISION...................................................................................................4
Human Visual System .........................................................................................4
Trichromatic Theory ...........................................................................................5
Opponent-Process Theory ...................................................................................5
Dual Processes Theory ........................................................................................5
3.2 SPECTRAL SENSITIVITY .................................................................................6
Color Matching Functions ...................................................................................7
Opponent Signals ................................................................................................8
3.3 ILLUMINATION METRICS ...............................................................................9
Photometry ..........................................................................................................9
Colorimetry .........................................................................................................10
3.4 PSYCHOPHYSICAL RESPONSES AND METRICS ........................................11
Brightness ............................................................................................................11
Clarity..................................................................................................................12
Brightness and Clarity .........................................................................................12
Color Appearance ................................................................................................12
Color Preference..................................................................................................14
Color Discrimination ...........................................................................................14
3.5 SPECTRAL TUNING ..........................................................................................15
Technology ..........................................................................................................15
Prime and Anti-Prime Spectral Regions .............................................................16
3.6 RESEARCH METHODS......................................................................................18
Brightness ............................................................................................................18
Color Preference..................................................................................................19
Additional Considerations ...................................................................................20
Chapter 4 METHODOLOGY .............................................................................................22
4.1 INDEPENDENT VARIABLE ..............................................................................23
Reference Spectrum ............................................................................................25
Test Spectra .........................................................................................................26
4.3 EXPERIMENTAL APPARATUS ........................................................................30
v
4.5 EXPERIMENTAL TRIALS .................................................................................32
Experimental Procedure ......................................................................................32
Experimental Statistical Design ..........................................................................34
Chapter 5 RESULTS AND ANALYSIS ............................................................................37
5.1 BRIGHTNESS MATCHING................................................................................39
Results .................................................................................................................39
Analysis ...............................................................................................................40
5.2 BRIGHTNESS DISCRIMINATION ....................................................................41
Results .................................................................................................................42
Analysis ...............................................................................................................44
5.3 PREFERENCE DISCRIMINATION ...................................................................51
Results .................................................................................................................52
Analysis ...............................................................................................................52
5.4 DISCUSSION .......................................................................................................53
5.5 LIMITATIONS AND FUTURE RESEARCH .....................................................60
Chapter 6 CONCLUSION ..................................................................................................61
Appendix A Reference and Test SPDs .......................................................................63
Appendix B Experimental Designs .............................................................................65
REFERENCES ...................................................................................................................68
vi
LIST OF FIGURES
Figure 2-1 Progress in Lighting Efficacy [US DOE 2012]. Luminous efficacy of SSL is
projected to surpass the efficacy of traditional sources by 2020. .....................2
Figure 3-1 Possible Neural Networks for Dual Process Theory [left: Goldstein 2007,
right: Palmer 1999]. The responses of the opponent neurons for both
theories depend on the excitatory or inhibitory inputs of two or more cone
receptors............................................................................................................5
Figure 3-2 Mean Absorbance Data for the Four Classes of Human Photoreceptors
[Dartnall et al. 1983]. Blue, green and red represent short, medium and long
wavelength cones, respectively. The peaks are at 420, 530, and 560 nm.
The dashed gray line represents rod sensitivity, peaking at 500 nm. ...............6
Figure 3-3 Quantitative Distributions of Rods and Medium (green) and Long (red)
Wavelength Cones Along the Visible Portion of the Electromagnetic
Spectrum. The dashed-line represents expected normal distributions, and
the solid line for bi-modal distribution [Dartnall et al. 1983]. ..........................7
Figure 3-4 CIE 1931 2°
Color Matching Functions [left, Wyszecki and Stiles
2000] and
Color Matching Functions [right, Berns 2000]. The
CMFs are a transformation of the
CMFs. The positive
peaks of the
CMFs are 450, 545, and 605 nm with the negative
peak at 515 nm. The positive peaks of the
CMFs are 440, 445,
555, and 590 nm. ..............................................................................................8
Figure 3-5 Estimated Opponent Signals [Hurvich 1981]. The opponent signals of the
red-green, yellow-blue, and black-white channels are plotted and are
transformations of 1931 2°
CMFs. The relative sensitivity of the
signals is zero at 475, 500, and 580 nm. ...........................................................9
Figure 3-6 The SPD of an Incandescent Lamp with Neodymium Glass [Ohno 2005]. ......17
Figure 3-7 Plots of 14 CIE Test Color Samples Used on CIE L*a*b* Space Illuminated
by Test Neodymium Lamp Compared to Reference Blackbody Radiator
[Ohno 2005]......................................................................................................17
Figure 4-1 User Interface of Light Replicator Software Used to Control the Telelumen
Luminaires. The interface allows for the modification of the SPD of the
Telelumen by adjusting the 16 different LED channels. ..................................24
Figure 4-2 Reference Spectral Power Distribution. ............................................................26
Figure 4-3 Transmission of Notch Filters. ..........................................................................26
Figure 4-4 Reference and Test Spectral Power Distributions. ............................................27
vii
Figure 4-5 Plot of Reference and Test SPDs on 1931 2° xy Chromaticity Diagram and a
one- step MacAdam Ellipse centered at (x,y) = (0.380, 0.378). The
chromaticity coordinates of all SPDs are within a one-step MacAdam
ellipse except for SPDRef of the left booth.........................................................29
Figure 4-6 Experimental Apparatus: Side-by-Side Viewing Booths. .................................30
Figure 5-1 Brightness Discrimination: Linear Relationship of SPD500 versus SPDRef and
SPD500,580 versus SPDRef. ..................................................................................45
Figure 5-2 Brightness Discrimination: Linear Relationship of SPDRef versus SPD580........45
Figure 5-3 Brightness Discrimination: Linear Relationship of SPD500 versus SPD580 and
SPD500,580 versus SPD580. ..................................................................................46
Figure 5-4 Brightness Discrimination: Linear Relationship of SPD500 versus SPD500,580. ..46
Figure 5-5 Null Condition HDR Radiance Falsecolor Luminance Images (Top- Both
booths illuminated by SPDRef ; Middle- Both booths illuminated by
SPD500,580; Bottom- Luminance difference between the two booths). The
Falsecolor HDR images of the luminance distributions for the test and
reference SPDs are comparable. .......................................................................51
Figure 5-6 Plot of the Reference and Test SPDs Weighted By Hurvich Opponent Signal
Sensitivity. ........................................................................................................56
Figure 5-7 Color of the 14 CQS Samples in CIELAB under Planckian Illumination
(blue) and the Reference and Test SPDs (red). The points plotted on the two
dimensional a*b* CIELAB plot represent the hue and saturation of the CQS
reflective samples illuminated by different spectra [Davis, Ohno 2010]. The
origin represent neutral gray and the distance from the origin represent
chroma and the angle represents hue. ...............................................................59
viii
LIST OF TABLES
Table 3-1 Percentage Sequence Homology and Identity in Pairwise Comparisons of
Visual Pigments. The values below the 100 percent diagonal are percentage
of identical amino acids and above the diagonal are percentage of indentical
or homologous amino acids [Nathans et al. 1986]............................................4
Table 3-2 Modifications of V(λ) Recognized by the CIE [CIE 1990]. ...............................10
Table 4-1 Descriptive Metrics of Reference and Test Spectra: Primary.............................28
Table 4-2 Descriptive Metrics of Reference and Test Spectra: Secondary.........................29
Table 4-3 Chromaticity Coordinates at Varying Dimmer and Reference Settings. The
measurements were taken over a span of 35 minutes due to time necessary
for changing between SPDs. Measurements were taken in the same order of
the SPD list. ......................................................................................................31
Table 4-4 Categorical Break Down of Experimental Trials. ..............................................34
Table 4-5 Brightness Matching: Experimental Design for SPD500 versus SPDRef. All
comparisons followed a similar pattern. ...........................................................35
Table 4-6 Brightness Discrimination: Experimental Design for SPD500 versus SPDRef.
All comparisons followed a similar pattern. .....................................................35
Table 4-7 Preference Discrimination: Experimental Design for SPD500 versus SPDRef.
All comparisons followed a similar pattern. .....................................................36
Table 5-1 Participant Demographics (n = 35).....................................................................37
Table 5-2 Chromaticity Coordinates of Reference Spectra Before and After
Experimental Sessions. .....................................................................................38
Table 5-3 Chromaticity Coordinates of Reference Spectra Before and After
Experimental Sessions: Matching and Discrimination. ....................................38
Table 5-4 Brightness Matching: Average Ratio of Comparisons. The “u” and “d” next
to the spectra indicates the variable spectra and if the spectra was expected
to be adjusted up or down respectively. The ratios listed are ratios of the
illuminance in the booths when the participant set the booths to equal
brightness appearance. The ratio is calculated as indicated in the Final Ratio
column. The final ratio for each comparison is the result of 140 brightness
matches. The asterisk (*) indicates the trial has a statistically significant
positional bias. Positional bias was calculated using a two-sample t-test.
One asterisk denotes significance at an alpha level of 0.05 and two asterisks
denote an alpha level of 0.01. The following notation is used: A = SPDRef;
B = SPD500; C = SPD580; D= SPD500,580. ...........................................................40
ix
Table 5-5 Brightness Matching: Statistical Significance of Brightness Matching Ratios. .41
Table 5-6 Brightness Matching: Null Condition Trials. The “u” and “d” next to the
spectra indicates the variable spectra and if the spectra were expected to be
adjusted up or down respectively. The ratios were calculated by dividing the
illuminance of the left booth by the illuminance of the right booth. The
following notation is used: A = SPDRef; B = SPD500; C = SPD580; D=
SPD500,580...........................................................................................................41
Table 5-7 Brightness Discrimination: Percentage of Trials Spectra Selected as Brighter.
Each participant viewed each trial listed. The percentage is calculated based
on the total number of trials that the SPD was selected as appearing brighter
divided by the total judgments for each trial, 35. The trial number at which
the two spectra were compared at the same illuminance is highlighted in
gray. ..................................................................................................................43
Table 5-8 Brightness Discrimination: Dunn-Rankin VSRS Matrix of Rank Differences
and Critical Ranges. Ri is the scaled rank score, and the difference in the
scaled rank scores is the critical range. .............................................................44
Table 5-9 Brightness Discrimination: Linear Regression Model of Spectrum
Comparisons .....................................................................................................47
Table 5-10 Brightness Discrimination: Binary Logistic Regression Model of Spectrum
Comparisons .....................................................................................................47
Table 5-11 Comparison of Brightness Matching and Brightness Discrimination Results. 48
Table 5-12 Comparison of Brightness Matching and Brightness Discrimination Results
: One-Sample t-Test 95% Confidence Intervals. ..............................................48
Table 5-13 Brightness Discrimination: Selection Count Comparison between Left and
Right Booths. The trials highlighted in gray are the trials in which the count
difference between Group 1 Left and Group 2 Right is greater than 5.............49
Table 5-14 Comparison of the Luminous Flux of the Left and Right Booths. ...................50
Table 5-15 Preference Discrimination: Percentage of Trials Spectra Selected as
Preferred. Each participant viewed each trial listed below once, with the
final calculated percent of the non-null condition trials calculated from 70
judgments..........................................................................................................52
Table 5-16 Preference Discrimination: Dunn-Rankin VSRS Matrix of Rank
Differences and Critical Ranges. Ri is the scaled rank score, and the
difference in the scaled rank scores is the critical range. The trials
comparing the same SPDs, only varying in position, were combined
because the observations of the pairs are not independent. ..............................53
Table 5-17 Luminous Efficacy of Radiation Comparison of Reference and Test SPDs. ...54
x
Table 5-18 Experimental Brightness Rank Compared to Brightness Appearance
Prediction Metrics. The brightness rank resulting from the matching and
discrimination metrics is compared to the brightness rank according to CRI,
CDI, S/P Ratio and GAI. The computed value of the metrics is listed, with
the color indicating the rank. The rank values in the first row, Brightness
Rank, indicate the color assigned to a given rank.............................................55
Table 5-19 Computed Values for Reference and Test SPDs Weighted by Hurvich
Opponent Signal Sensitivity. The values shown are a summation of the
absolute values of the signals of the weighted SPDs. .......................................57
Table 5-20 Experimental Preference Rank Compared to Preference Prediction Metrics.
The computed value of the metrics is listed, with the color indicating the
rank. The rank values in the first row, Preference Rank, indicate the color
assigned to a given rank....................................................................................58
xi
LIST OF ABREVIATIONS
CAM
Color Appearance Model
CCT
Correlated Color Temperature
CDI
Color Discrimination Index
CFI
Color Fidelity Index
CIE
Commission Internationale de L’Eclairage
CPI
Color Preference Index
CQS
Color Quality Scale
CSI
Color Saturation Index
CMF
Color Matching Function
CRI
Color Rendering Index
DOE
Department of Energy
FCI
Feeling of Contrast Index
FM Gamut
Farnsworth Munsell Gamut Area
GAI
Gamut Area Index
GLM
General Linear Model
HDI
Hue Distortion Index
HDR
High Dynamic Range
HID
High Intensity Discharge
IES
Illuminating Engineering Society
ipRGC
Intrinsically Photosensitive Retinal Ganglion Cells
LED
Light Emitting Diode
LER
Luminous Efficacy of Radiation
NIST
National Institute of Standards
RGB
Red Green Blue
SPD
Spectral Power Distribution
SRD
Spectral Reflectance Distribution
VSRS
Variance Stable Rank Sums
xii
ACKNOWLEDGEMENTS
I am grateful for the professional and personal support and advice of my advsior, Dr. Kevin Houser,
whom I highly respect. I am also grateful for the investment of time and valuable advice of my
committee members: Dr. Richard Mistrick, Dr. Jelena Srebric, Dr. Robert Davis and Dr. Eric Loken.
I am grateful for the support and encouragement of numerous lighting industry professionals. A
special note of appreciation is due to the International Association of Lighting Designers (IALD), the
IALD Education Trust and Project CANDLE members for their financial support of my dissertation
research.
I am grateful for my Penn State lighting colleagues Craig Casey, Mike Royer, and Tommy Wei for
their friendship, support, technical assistance and for being a sounding board through the highs and
lows. Thank you to the Penn State graduate students who also provided support and encouragement.
I am grateful for the continuous and sustaining love and support of my friends and family.
I thank God for surrounding me with phenomenal people and for providing unimaginable
opportunities and experiences.
xiii
Chapter 1
INTRODUCTION
The human visual system is an intricate network of neurons transforming environmental stimuli into
electrical signals with interpretation by the visual cortex. Researchers attempt to understand and
explain this intricate system with models and functions. Many of these measures and models are
derived from human experiments in the field of illuminating engineering, and are not always as
accurate as desired. Models and functions predicting human response to light stimuli have known
shortcomings, yet some remain the standard for light measurements. A more accurate understanding
of the human psychophysical response to spectra is needed to improve current models and measures.
Improving the accuracy of current models and measures is difficult because visual perception is also
not accurate, but as stated by vision scientist Stephen E. Palmer, it is “as accurate as it needs to be”
[Palmer 1999]. A comprehensive understanding of the human visual system can potentially improve
spectral tuning of sources, concurrently optimizing efficiency and psychophysical responses,
including brightness and preference.
Brightness, as defined by the International Commission on Illumination (CIE), is an “attribute of a
visual perception according to which an area appears to emit, or reflect, more or less light” [CIE
2013]. Brightness determines how we perceive objects and spaces, people and places. Brightness is a
factor in our visual systems determination of reality. Creating a spectrum that elicits the maximal
brightness per watt of optical radiation has the potential to save a significant amount of energy, but
can also produce an undesirable color. Preference and color rendering must be factors in the
development of sources. The goal of this research is to further the understanding of human
psychophysical response to spectra and the role of spectral tuning in source optimization by
examining the effect of spectral modification on the perception of brightness and color preference.
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching. The results of this study can be used in conjunction with
other research to improve general illumination sources.
1
Chapter 2
BACKGROUND
This research seeks a better understanding of psychophysical response to spectra. The research is not
source specific, however research and development expenditures in the lighting industry are focused
almost solely on LED sources. The Department of Energy (DOE) has numerous programs currently
advancing LEDs and believes LEDs have the most potential to save energy compared to any source
currently available [US DOE 2010]. Figure 2-1 illustrates the change in luminous efficacy of
conventional sources and solid-state lighting sources (SSL), including both LEDs and Organic Light
Emitting Diodes (OLEDs), over the past 70 years. Significant changes are projected for the luminous
efficacy of SSL sources versus conventional sources during the next seven years, with efficacy of
SSL predicted to surpass all conventional sources. LEDs could decrease national lighting energy
consumption 29% by 2025, saving 3.5 quadrillion BTUs of primary energy and delaying the creation
of forty 1000 MW power plants [Brodrick 2004]. In order to ensure projected energy savings are
realized, the DOE has made research focusing on increasing energy efficiency of LEDs one of its
main priorities.
Figure 2-1 Progress in Lighting Efficacy [US DOE 2012]. Luminous efficacy of SSL is projected to
surpass the efficacy of traditional sources by 2020.
As of 2009, DOE investment in research aiming to improve the energy efficiency of LEDs was $33.1
million [Navigant et al. 2009]. For example, Yale University is examining multicolor, highefficiency, nanotexture LEDs; Carnegie Mellon University is examining novel heterostructure designs
for increased internal quantum efficiencies in nitride LEDs; and the Research Triangle Institute is
examining photoluminescent nanofibers for high-efficiency solid-state lighting phosphors [US DOE
2
2010]. Further investment continues, and in 2011 Energy Secretary Steven Chu announced another
$15 million to support research and development of solid-state lighting [US DOE 2011]. Research
improving the energy efficiency of LEDs is important, however research examining the
psychophysical responses to tuned spectra is also necessary in the pursuit of more efficient light
sources.
Part of the reason psychophysical responses are overlooked is due to lack of education and knowledge
of the inaccuracies of the current measurement system of photometry. This is prevalent not only in
general society, but also within the lighting industry and lighting education. The knowledge of these
shortcomings is not new. The shortcomings of the system of photometry were noted shortly after the
establishment of the definition of photometry by the International Commission on Illumination (CIE),
the internationally recognized authority on lighting. The CIE regarded V(λ) “as an arbitrary
wavelength function adopted for its convenience and utility rather than because luminance so
evaluated correlates with what the eye sees” [Judd 1955]. The Illuminating Engineering Society (IES)
has established the Visual Effects of Lamp Spectral Distribution-Brightness Committee to identify
evidence that demonstrates the effects of lamp spectrum on spatial brightness and methods for
predicting the magnitude of the lamp spectral power distribution (SPD) effect on spatial brightness
[IES 2011]. The metric not derived from “an arbitrary wavelength function” may provide energy
savings that is currently overlooked.
A better understanding of how spectra are processed by the human visual system is an important part
of providing an energy efficient environment that allows humans to comfortably live and work.
Consideration of human factors must not be forgotten in the pursuit to decrease energy consumption
as quickly as possible, because the purpose of buildings is to enhance human comfort. People adapt to
different environments, but there is a loss of productivity and general well-being if certain conditions,
specifically relating to light intensity and color, are not met [Abbas et al. 2005]. The use of LEDs
provides more opportunities to improve source efficiency and the psychophysical responses to the
source with spectral tuning.
An understanding of the complexities and trade-offs involved in optimizing both psychophysical
response and energy efficiency is necessary. Thornton [1992a] highlights the need for “system
sensitivity as seen from the rear end of the visual system, not of retinal processes.” The research
intends to examine the psychophysical responses from the “rear end” of the system by examining
human response to light stimuli in order to provide a more accurate understanding of human vision.
Lamp color properties can affect perceived brightness, and it is important to consider both color
preference and lamp properties [Fotios 2001]. The characteristics of LEDs and other highly structured
sources are often not accurately portrayed due to inadequate models and measures. Comparing the
results of this research to common preference metrics furthers understanding of the strengths and
weaknesses of the metrics. A thorough understanding of the properties of illumination, the human
visual system and its psychophysical responses to illumination, and the measures and models that
predict the response of the human visual system is critical to realizing the potential of spectral tuning
and source optimization.
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching. The purpose of the research is to further the
understanding of the relationship between psychophysical responses and spectra. It contributes to the
larger body of knowledge on source optimization and spectral tuning, particularly in the area of
brightness and color preference.
3
Chapter 3
LITERATURE REVIEW
“So if the principles we laid down about the appearance of colors are true the rainbow necessarily has
three colours, and these three and no others. The appearance of yellow is due to contrast, for the red is
whitened by its juxtaposition with green.”
-Aristotle [300 B.C.E.]
3.1 COLOR VISION
Scientists have been theorizing about color vision for over 2,000 years, yet a complete understanding
of the human visual system and color vision continues to elude scientists. The time span of color
vision study signifies the complexities of the human visual system and psychophysical research.
Observing psychological response to physical stimuli, such as light, and understanding why the
response occurs is difficult due to the interaction of a number of processes. These processes include,
but are not limited to, the initial passage of light through the cornea, sorting and transformation of
light stimuli into signals by the rods, cones and intrinsically photosensitive retinal ganglion cells
(ipRGCs), and interpretation of signals by the visual cortex.
Human Visual System
Vision begins when a photon is absorbed by a visual pigment, starting a series of processes that create
a neural signal. Visual pigments are light-absorbing molecules consisting of an apoprotein, opsin,
which resides in the plasma and disk membranes of the photoreceptor outer segment [Nathans et al.
1986]. Three differing opsins form the long, medium and short wavelength responses, generally
known as cone responses. The responses are also known as red, green and blue in relation to the
general area of sensitivity along the visible spectrum. Nathans et al. [1986] isolated and sequenced the
opsins in order to analyze their structures, finding a 96 percent mutual identity between the red and
green pigments but only a 43 percent identity with the blue pigment. Further comparisons are detailed
in Table 3-1. The differences in the opsin structure produce differing absorption spectra. The
principle of univariance states that the absorption of a photon by a visual pigment molecule causes the
same effect and is independent of wavelength [Goldstein 2007], therefore more than one receptor type
is necessary for color vision.
Table 3-1 Percentage Sequence Homology and Identity in Pairwise Comparisons of Visual
Pigments. The values below the 100 percent diagonal are percentage of identical amino acids
and above the diagonal are percentage of indentical or homologous amino acids [Nathans et al.
1986].
Percentage
Rhodopsin
Blue
Red
Green
Rhodopsin
Blue
Red
Green
100
42
40
41
75
100
43
44
73
79
100
96
73
79
99
100
4
Trichromatic Theory
Three light receptors in the eye code light stimuli for interpretation by the brain according to the
Young-Helmholtz trichromatic theory of color vision. Trichromatic theory explains the inherent three
dimensionality of color and certain types of color-blindness. Hemholtz stated “When we speak of
reducing the colors to three fundamental colors, this must be understood in a subjective sense as being
an attempt to trace the color sensations to three fundamental sensations” [MacAdam 1970]. Another
competing theory was being developed at the same time also characterizing color vision with three
fundamental sensations to explain phenomenon, and this theory explained phenomenon that the
trichromatic theory could not explain.
Opponent-Process Theory
Trichromacy does not explain the color yellow, the loss of the ability to see color in pairs instead of
singularly and complementary after-images. Hering was developing the opponent-process theory to
explain these visual phenomenon around the same time that Helmholtz was developing the
trichromactic theory. Hering also theorized that vision contained three channels, but these channels he
believed generated opposing responses. The opposing responses include two chromatic channels, a
yellow-blue and red-green channel, and a third achromatic channel, black-white. Hering’s theory
explains the anomalies of the trichromatic theory mentioned previously, but his theory was not taken
seriously until research in the mid-19th century found evidence of opponent neurons [Goldstein 2007].
Dual Processes Theory
Two originally competing theories of color vision now complement each other to form the dual
processes theory. The trichromacy model explains an initial stage of the visual process, what occurs
when light strikes the retina, and the opponent process model describes visual processes beyond the
retina. The precise interaction of these theories remains unknown. Various researchers estimate how
the two theories work together to describe our visual system [Goldstein 2007, Berns 2000], and there
is not one dominant model. The basis of these models is that three cones send either excitatory or
inhibitory signals to the three opponent neurons. The variance in the theories occurs in describing the
type of signals each of the three cones sends and to which opponent channels the signals are sent.
Figure 3-1 illustrates two of models, detailing the possible interaction between the cones and
opponent neurons.
Figure 3-1 Possible Neural Networks for Dual Process Theory [left: Goldstein 2007, right:
Palmer 1999]. The responses of the opponent neurons for both theories depend on the
excitatory or inhibitory inputs of two or more cone receptors.
5
3.2 SPECTRAL SENSITIVITY
The theory of trichromacy was developed in the first half of the 19th century, however the three
photoreceptors and their sensitivities would not be identified until research in the latter half of the 20th
century [Goldstein 2007]. Several methods were used to derive the sensitivities of the receptors. One
method is the derivation of spectral sensitivity from measurements of cone responses by dissecting
human eyes and measuring cone responses to flashes of mono-chromatic light [Schnapf et al. 1987].
Using this method, Schanpf et al. discovered that the spectral sensitivities of the medium and long
wavelength cones peak around 530 and 560 nm respectively, and concluded that the research
satisfactorily predicts V(λ). These spectral sensitivity measurements support the earlier findings of
Dartnall et al. [1983]. Dartnall and colleagues dissected and examined seven human eyes with a
microspectrophotometer to find the mean absorbance of the photoreceptors, Figure 3-2.
Relative Absorbance
1.0
0.8
0.6
0.4
0.2
0.0
380
480
580
Wavelength (nm)
Figure 3-2 Mean Absorbance Data for the Four Classes of Human Photoreceptors [Dartnall et
al. 1983]. Blue, green and red represent short, medium and long wavelength cones, respectively.
The peaks are at 420, 530, and 560 nm. The dashed gray line represents rod sensitivity, peaking
at 500 nm.
Although these studies closely align and demonstrate curves with the same characteristic shape,
achieving agreement is difficult because of the variance of the visual system between humans.
Research by Dartnall et al. [1983] demonstrates the variability in the spectral position of the green
and red pigments in normal human observers, and suggest that the distributions of the green and red
receptors are bi-modal. The bi-modal distribution, Figure 3-3, provides evidence of two subpopulations within the long and short wavelength sensitivities. This research illustrates one of the
sources of variance that make it difficult to characterize the color vision of the average human
observer. Prior to the ability to dissect human eyes and measure sensitivity, researchers derived
spectral sensitivity curves from psychophysical experiments.
6
Figure 3-3 Quantitative Distributions of Rods and Medium (green) and Long (red) Wavelength
Cones Along the Visible Portion of the Electromagnetic Spectrum. The dashed-line represents
expected normal distributions, and the solid line for bi-modal distribution [Dartnall et al. 1983].
Color Matching Functions
Color matching functions (CMFs) were first developed from experiments by Wright and Guild
around 1930 [Wyszecki and Stiles 2000] to model the tri-chromatic character of the human visual
system. CMFs are typically defined for equal-energy spectral lights, and the three functions that
describe an observer’s color-matching behavior relate to the matching intensities of three primary
lights to the wavelength of the spectral light [Stockman et al 1993]. CMFs vary depending upon the
three primary sources used to derive the functions, the experimental method, and vary from person to
person. CMFs also vary from measured spectral sensitivities due to individual human differences in
the densities of yellow pigments in the macula and lens [Stockman et al. 1993]. The original CMFs
, Figure 3-4, contain both positive and negative values, therefore to make calculations and
measurements easier it was decided to transform the
CMFs to positive values [Berns 2000].
The use of the previously developed V(λ) function replaced the central function for the new set of
CMFs
, Figure 3-4, to provide consistency between the CMFs and V(λ), formally referred
to as the 1931 Standard Colorimetric Observer [Wyszecki and Stiles 2000].
7
Tristimulus Value
Tristimulus Value
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
-0.05
-0.10
-0.15
380
480
580
680
Wavelength (nm)
780
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
380
480
580
680
Wavelength (nm)
780
Figure 3-4 CIE 1931 2°
Color Matching Functions [left, Wyszecki and Stiles 2000]
and
Color Matching Functions [right, Berns 2000]. The
CMFs are a
transformation of the
CMFs. The positive peaks of the
CMFs are 450, 545,
and 605 nm with the negative peak at 515 nm. The positive peaks of the
CMFs are
440, 445, 555, and 590 nm.
Opponent Signals
Hurvich [1981] transformed the 1931 2°
CMFs into opponent signal functions to provide a
more accurate representation of opponent channel interactions within the visual system, building upon
the work of Hering. The transformation was experimentally derived by determining the amount of
chromatic stimuli necessary to cancel the hue of another stimulus [Hurvich 1981, Kuehni 2003].
Figure 3-5 demonstrates the relative sensitivity of the opponent signals. Hurvich’s opponent
sensitivity curves are divided into red-green, black-white, and yellow-blue in agreement with
opponent process theory. Opponent channels have a relative sensitivity of zero where the channels
switch between blue and yellow at 500 nm and at the switches between red and green at 480 and 580
nm. These general areas of the spectrum have been termed the “anti-prime” regions because energy
placed in these regions often produce undesirable psychophysical responses, and will be discussed
later in the paper in greater detail [Thorton1971]. The opponent channels add another layer of
complexity to the development of metrics that more accurately model the human visual system.
8
Relative Sensitivity
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
-1
380
480
580
680
Wavelength (nm)
780
Figure 3-5 Estimated Opponent Signals [Hurvich 1981]. The opponent signals of the red-green,
yellow-blue, and black-white channels are plotted and are transformations of 1931 2°
CMFs. The relative sensitivity of the signals is zero at 475, 500, and 580 nm.
3.3 ILLUMINATION METRICS
Photometric and colorimetric quantities are peculiar because these metrics are based upon the
response of the human visual system, instead of a physically measurable standard. The dependence on
the human visual system complicates photometry and colorimetry considerably, and further
understanding of visual response will improve the ability of these metrics to predict human response
to varying light stimuli.
Photometry
The 1924 Standard Observer, the V(λ) function, models the standard human’s visual response to
radiant energy, and is the basis of all other photometric quantities. V(λ) was developed from the
measurements of 5 independent experiments in 1918 and 1923 with a total of 177 observers
[Wyszecki and Stiles 2000]. Although the current photometric system is based on a relatively small
sample and 1920’s equipment is inferior to equipment currently available, V(λ) is used exclusively by
commerce to communicate the characteristics of lighting products [Rea and Bullough 2007]. V(λ)
remains the standard because it performs surprisingly well, and no other metric demonstrates enough
of an improvement to justify expending the resources necessary to change the base metric of the
lighting industry. However, the limitations of V(λ) remain a concern. Houser [2001] lists four
limitations of V(λ) including: field of view, field luminance, additivity assumption and experimental
methods, suggesting that a brightness function based upon the opponent colors model will improve
upon the current limitations of V(λ).
The application of V(λ) is fundamentally limited to a field of view of two degrees [CIE 1990], yet
V(λ) is currently the basis of fundamental lighting metrics that are daily applied to fields of view
larger than two degrees. A two degree field of view results in the exclusion of the short wavelength
cones and rods that are spaced throughout the periphery of the retina, and is a reason V(λ) aligns so
well with the medium and long wavelength cone sensitivities. One-tenth of a degree of the center
9
fovea contains almost entirely long and medium wavelength cones, with the short wavelength cones
comprising approximately five percent at one degree [Palmer 1999]. Consequently, V(λ)
underestimates the effect of the short wavelength cones when V(λ) is applied to a field larger than
two degrees, and fails to adequately characterize the chromatic qualities of interior lighting [Fotios
2001].
Additional functions account for the limitations of V(λ),Table 3-2, including V10(λ) to account for
fields of view larger than two degrees. The ratio of long to medium to short cones throughout the
retina is approximately 10:5:1 [De Valois and De Valois 1993], so the mitigated effect of the short
wavelength cones in V(λ) is relatively minor. However, the differences between V(λ) and V10(λ)
increase with the amount of short wavelength emission of sources [Schanda 2002]. The limitations of
the single channel system of photometry are not surprising given vision is fundamentally based upon
three channels.
Table 3-2 Modifications of V(λ) Recognized by the CIE [CIE 1990].
Functions
Level
Field Size
Usage and Remarks
CIE Publication
Number
ISO/CIE 10527
(λ)
V(λ)
Photopic
2°
Photometric quantities;
same as (λ)
VM(λ)
Photopic
2°
Physiologically
meaningful photometric
quantities modified from
V(λ)
No. 86 (1990)
V10(λ)
Photopic
10° or off
fovea beyond
2°
Photometric quantities
(physiologically
meaningful) ; same as
10(λ)
ISO/CIE 10527
10(λ)
Vb,p(λ)
Photopic
Point sources
Efficiency for brightness
same as VM(λ)
No. 75 (1988)
Vb,2(λ)
Photopic
2°
Efficiency for brightness
No. 75 (1988)
Vb,10(λ)
Photopic
10° or off
fovea beyond
2°
Efficiency for brightness
No. 75 (1988)
V’(λ)
Scotopic
> 2°
Scotopic photometric
quantities
Proc. 1951
Colorimetry
The system of colorimetry was initially entirely a visual representation, but through the course of time
became mixed with mathematical equations making it difficult to separate what is truly representative
10
of human vision [Thornton 1992a]. As previously mentioned, the 1931 Standard Observer
CMF is considered to characterize normal human color vision. However, it is based upon two
experiments, with a total sample size of 17 human subjects and was mathematically manipulated
[Berns 2000]. Problems with the mathematical construct according to Thornton include the
“…assumption of the transformability of primaries, the conceptualization of the ‘tristimulus value’,
the normalization of CMFs, and the construction of the chromaticity diagram” [Thornton 1992a]. The
current limitations of colorimetry inhibit researchers and the lighting industry from properly
characterizing light sources and predicting consumer response. Thornton [1992a] states “The need for
a colorimetry system has been so urgent that the existing system, with its unresolved difficulties, was
pressed into service and has become embedded in the practices of a number of industries, making it
increasingly hard to make needed changes”.
The chromaticity diagram has been revised numerous times by researchers attempting to develop a
more uniform color space, but the chromaticity diagram continues to not properly predict the
characteristics of sources plotted on the diagram. One fundamental source of error is the significant
reduction in dimension, and therefore information, in the plotting of an illuminant on the chromaticity
diagram. A source with varying power over the wavelengths of 380 to 700 nm is reduced to two
values, x and y coordinates. Thus, it is not surprising that the chromaticity diagram and colorimetry
do not predict psychophysical responses to light stimuli as accurately as desired by researchers and
the lighting industry.
3.4 PSYCHOPHYSICAL RESPONSES AND METRICS
The understanding of the human psychological response to physical stimuli is crucial in the design of
the built environment. Americans now spend 90% of their time inside [US EPA 2010], and are
exposed to a variety of unnatural light sources that elicit psychophysical responses that vary from the
psychophysical responses to daylight. The measurement of psychophysical responses is difficult to
define due to the variance of the human visual system, subjective human assessment of sensory
responses, and the limitations of photometry and colorimetry. The lack of metrics quantifying
psychophysical responses complicates research, although there are several metrics for predicting
brightness.
Brightness
Brightness, as defined by the International Commission on Illumination (CIE), is an “attribute of a
visual perception according to which an area appears to emit, or reflect, more or less light” [CIE
2013]. Photometry aims to correlate with perceived brightness [Houser 2001], however photometry
fails to do so and photometric measurements are often incorrectly associated with brightness. Schanda
[2008] warns that V(λ), VM(λ), or V10(λ) should not used to characterize the stimulus for perceived
brightness because they does not obey Abney’s law. Recently developed metrics can add additional
complexities that do not provide enough of an improvement in reliability to justify the additional
effort for common applications, as is the case with the recently developed Color Appearance Model
(CAM) CIECAM02. The metric was adopted by the CIE because it more accurately predicts
attributes of an illuminant, including brightness correlate (Q). The calculation of Q includes V(λ),
however there are numerous other factors including an achromatic response factor that increase the
accuracy of the CIECAM02 [Fairchild 2005] and differentiate CIECAM02 from photometry.
Other singular predictors of brightness have been proposed, but Hu et al. [2006] warn “it is a logical
error to assume simple relationships between the derived quantities and perceived brightness” and
“simplifying complex spectra into one-dimensional measures like CCT is a process that necessarily
discards much meaningful information.” In a review of studies examining the effect of lamp spectrum
11
on brightness studies Fotios [2001] concludes that CCT, CRI, gamut area do not singularly make
good predictiors of brightness and that brightness is affected by lamp spectrum. Houser et al. [2009]
demonstrate that the S/P ratio and CCT are not important predictors of brightness with two different
experimental methods, rapid-sequential and side-by-side. Further research is necessary in the
development and refinement of brightness prediction models.
Clarity
There is currently no metric commonly used to quantify visual clarity. One of the reasons is the
difficulty in defining clarity due the lack of distinction between clarity other psychophysical
responses. One study demonstrated no significant differences in response when subjects are given the
visual objectives equal clarity, brightness, pleasantness, illuminance, satisfaction in visual
appearance, and visual equality in side by side matching tests [Fotios and Gado 2005]. Houser et al.
[2004a] suggest that clarity is dependent upon visual contrast, which is effected by both brightness
and color. Hashimoto et al. [2006] propose the existence of a correlation between clarity and feeling
of contrast. The development of the feeling of contrast (FCI) metric characterizes the color rendering
performance of a light source based on the feeling of contrast, and FCI can additionally be interpreted
as a clarity metric. Hashimoto and the other developers of the metric believed it would be considered
within the CIE, but this metric has failed to gain broad acceptance.
Brightness and Clarity
Brightness and clarity metrics are difficult to define, but research demonstrates that both brightness
and clarity are easily perceptible and can be used to predict preference. Boyce and Cuttle [1990]
instructed test participants to describe “the lighting of a room” and found that the lighting was
described in terms of brightness and clarity. Additionally, Houser et al. [2004a] found subjects tended
to choose as their preferred work environment the same environments that they selected as having
higher visual clarity and appearing brighter. Schanda and Sandor [2002] contend that brightness is
only one dimension of color space and other dimensions are needed to clarify color appearance.
Color Appearance
Color rendition is the psychophysical metric that has garnered the most attention in the lighting
industry, especially with the increase in prevalence of highly structured polychromatic spectra, most
notably LEDs. The ability of sources to accurately render colors to meet appearance expectations is of
utmost importance in the lighting industry, yet a concise metric that accurately conveys the color
rendering ability of a source continues to elude researchers. Numerous metrics have been proposed,
yet each metric continues to fails gain the acceptance of the lighting industry.
The CIE color rendering index (CRI) is the only internationally accepted metric for appraising the
source color rendering performance [Davis and Ohno 2005]. However, the limitations of CRI have
been well documented by researchers, and include the following:






Reference based metric [Davis 2006]
Confines comparison of CRI to sources of same CCT [Guo and Houser 2005]
Average of color shifts [Davis 2006]
Direction of the shift not taken into account [Fotios 2001, Ohno 2005]
Only 8 test color samples [ Davis 2006]
Samples are not representative of a range of colors, including saturated colors [CIE 1999,
Ohno 2005, Davis 2006]
12





Samples are no longer available in original form [CIE 1999, Schanda and Sandor 2002]
Single dimension metric for a multi-dimensional experience [Fotios 2001, Žukauskas et
al. 2009 ]
Outdated non-uniform color space [CIE 1999, Schanda and Sandor 2002, Ohno 2005,
Davis 2006]
Von-Kries chromatic adaptation transform is incorrect [CIE 1999 , Fairchild 2005]
Fails to characterize highly structured spectra [Schanda and Sandor 2002, Ohno 2005,
Davis 2006, Žukauskas et al. 2009, van der Burgt and van Kemenade 2010]
The CIE recognizes the problems associated with CRI and white LED sources, and created a
technical report to review the problem [CIE 2007]. The report concludes “CIE CRI is generally not
applicable to predict the colour rendering rank order of a set of light sources when white LED light
sources are involved in this set” and recommends the creation of a new color rendering index or set of
indices.
Researchers have varying suggestions for the improvement of CRI including:



van der Burgt and van Kemenade [2010] argue that a color rendering metric should be
independent of a set of colors
Guo and Houser [2004] suggest an alternate measure to the blackbody reference currently
used to assess sources below 5000K
Schanda and Sandor [2002] advise that color appearance should be the basis of color
rendering, not on color difference
Additional suggestions for improvement are discussed by Houser et al. [2013] and in Chapter Six of
the IES Handbook, 10th Edition [DiLaura et al. 2010].
Ohno and Davis [Davis 2006, Davis and Ohno 2005] attempt to resolve some of these limitations in
the development of the color quality scale (CQS) while maintaining the fundamental calculation
methodology of CRI. The differences include:







Sample size of 15 with larger range of hues
All samples have high chroma
No penalty for color shifts that increase chroma
1976 CIE L*a*b* color space
Root-mean square of shifts of the 15 samples determines CQS value
Gamut area factor penalizes CQS of sources outside the range of 3500 to 6500K CCT
Overall source quality assessment
Žukauskas et al. [2009] propose a multi-dimensional color quality metric, including CCT, color
fidelity index (CFI), color saturation index (CSI), and hue distortion index (HDI), that can be clearly
communicated with a single icon. This multi-dimensional proposal was a result of statistical analysis
of the color rendition vector fields of 1269 Munsell test color samples illuminated by fluorescent and
LED lamps. The authors conclude by advocating the use of a minimum of two metrics that accurately
characterize color fidelity and saturation when evaluating solid-state sources. Research points to
another caution necessary when using LEDs. The testing of traditional sources and 3-chip and 4-chip
LED sources demonstrates the inability of 3-chip LED sources to render saturated colors [Ohno
2005].
CRI measures the fidelity of a source compared to a reference, and was never intended to account for
preference or color quality. The CIE [1987] definition of color rendering states: “Colour rendering of
13
an illuminant is the effect of the illuminant on the colour appearance of objects by conscious or
subconscious comparison with their colour appearance under a reference illuminant.” One of the
problems of CRI is that it is used beyond its original intent, and other metrics have been developed
that account for other considerations beyond fidelity including color preference.
Color Preference
Significant color preference research during the latter half of the 20th century continues to influence
research and development of color appearance metrics, including CQS. Bartleson [1960] found
research subjects recalled colors as having a higher saturation and lightness than naturally colors. The
dominant chromatic characteristic of the object was exaggerated, for example the grass was recalled
as greener and bricks as redder than reality. Building upon the work of Bartleson, lighting researchers
began developing color rendering metrics and accounting for the human preference for saturated
colors [Judd 1967; Thornton 1974]. Judd [1967] created a Flattery Index to evaluate the ability of an
artificial illuminant to flatter people and objects. The shortcomings of the index were immediately
realized by Judd, and Jerome [1972] continued the work of Judd, making adjustments to the
computation of the Flattery Index. The work of Judd additionally led to the development of the Color
Preference Index (CPI) by Thornton [1974]. Source comparison and evaluation using CPI is found in
research, but CPI lacks recognition as a standard. Thornton’s research interests extended beyond CPI
and included color discrimination.
Color Discrimination
Thornton [1972a] proposed the color discrimination index (CDI) metric for quantifying the color
discrimination capability of a source, equivalent to the gamut area of the CRI test samples plotted on
the 1960 u, v diagram. Wavelengths of 450, 540, and 610 nm were shown to have a gamut area larger
than daylight by Thornton [1972b], and he predicted that a source at these wavelengths would provide
superior color discrimination capability. Royer et al. [2011] question the use gamut area in
characterizing color discrimination capability, demonstrating the inability of CDI and gamut area
index (GAI) [Rea et al. 2008] to predict color discrimination capability. Additionally the researchers
demonstrate that a three primary red-green-blue (RGB) LED source with energy in the three prime
color regions exhibits significantly worse color discrimination capability, countering the predictions
of Thornton.
Color discrimination is crucial in signaling, perceptual organization, and identification [Goldstein
2007] and remains an active area of research partially due to increase in sources with highly
structured spectra. Research continues to expose the problems of LED sources, leading some
researchers to conclude that “the RGB LED cluster should be avoided” [Vienot et al. 2005].
Stanikunas et al. [2004] found the greatest differences in hue and subjective color discrimination
between a tungsten source and a quadrichromatic LED source correspond to the wide dips in the SPD
of the LED source. These sources were optimized to obtain the highest color rendering index (CRI)
possible, demonstrating the lack of correlation between CRI and color discrimination capability. The
results of Royer et al. [2011] underscore the lack of correlation and also the distortion caused by
highly structured spectra. Peaks in the RGB LED SPD of the 452, 527 and 644 nm that align with
peaks in the spectral reflectance distribution (SRD) of the illuminated object can cause significant
chromaticity shifts that distort color appearance.
It is important to note that not all RGB LED sources are the same, however research does highlight a
concern that must be addressed by the lighting industry and researchers. Sources must illuminate
surfaces in a manner that avoids color distortion and accounts for color preference. The absence of
14
metrics limits the ability of researchers and manufacturers to optimally tune sources for color
discrimination, color appearance and brightness. Further psychophysical experiments examining
spectral tuning are necessary in the absence of these metrics.
3.5 SPECTRAL TUNING
Tuning sources to enhance psychophysical responses and improve efficiency is difficult due to the
incomplete understanding of the human visual system’s multidimensional processing of color,
therefore it is important to refer to previous research. A review of over forty studies examining SPD
and visual performance suggests that the achievement of energy efficient illumination is possible by
tuning the SPD and the illuminance of a source [Fotios & Houser 2007]. The wavelength at which
radiant power is delivered can predict brightness better than the magnitude of radiant power when a
fluorescent spectrum is tuned [Houser et al. 2004a]. One of the obstacles of spectral tuning research is
manipulation of radiant power at particular wavelengths, previously difficult with conventional
sources due the physical and chemical characteristics of the sources compounding with technological
and resource constraints. The increasing prevalence of LEDs, with characteristic narrow Gaussian
SPDs, diminishes obstacles in spectral tuning research and makes the research even more important
as obstacles in commercialization of tuned sources are also diminished.
Technology
Phosphors, filters, metals and tunable LED sources are all used to tune spectra. Phosphors are
commonly used to tune spectra of fluorescent and LED sources. Houser et al. [2004b] blended
phosphors together to create four different fluorescent lamps with intentionally shaped SPDs. One of
the common ways that white optical radiation is created using LEDs is a short-wavelength chip with
phosphors to convert some of the short-wavelength energy to medium- and long-wavelength energy.
Neodymium glass is a filter, absorbing yellow energy, and is used to modify the spectra of general
illumination sources and is also used in other applications including photography. Low-pass, highpass and band-pass filters are also used in research to modify spectra [Houser and Hu 2004a]. Metals
are used in high intensity discharge (HID) sources to modify the SPD, with different combinations of
metals resulting in different SPDs. Phosphors may employed in conjunction with the metals. Tunable
LED sources can be used to tune spectra, with the advantage of spectral tuning in real-time.
Tuning the spectrum of sources in real-time is possible due to recent advances in technology enabling
the control of chromaticity, flux and the location of power while balancing qualitative and
quantitative characteristics. Installing tunable sources is no longer cost-prohibitive for researchers,
and tunable light sources provide drastic improvements in flexibility and controllability compared to
conventional sources. The National Institute of Standards (NIST) commissioned the development of
the Spectrally Tunable Light Source for human psychophysical research [Dowling and Kolsky 2009].
400 channels control the 2400 LEDs integrated in the 1.5 square meter source that produces over
100,000 lumens. NIST also developed another spectrally tunable source to serves as a
spectroradiometric, colorimetric, and photometric standard and potentially reduce colorimeter
calibration error [Fryc et al. 2005]. The Telelumen, LLC has also developed a tunable source with 16
LED channels that is commercially available. The drastic decrease in the cost of LEDs will hopefully
increase access to spectrally tunable sources and provide researchers the opportunity to conduct
psychophysical research in a manner that was previously unfeasible.
15
Prime and Anti-Prime Spectral Regions
Thornton utilized fluorescent sources for his spectral tuning research and his research examining the
prime and anti-prime regions led to the development of the modern tri-phosphor fluorescent lamp.
Sources with energy at wavelengths 450, 530, and 610 nm produce superior CRI and luminous
efficacy versus other regions of the spectrum [Thorton1971]. The wavelengths 450, 530, 610 nm were
referred to by Thornton as prime-color regions. In contrast, energy in two regions of the spectrum,
495 and 575 nm, appear to have deleterious effects on color rendition and poor luminous efficacy,
and Thornton termed these regions anti-prime. Thornton’s additional findings in these regions
include:
Anti-Prime
 Light in the anti-prime regions falsifies the perceived chromaticity of colored objects
illuminated by white light, therefore harming color rendition [1972b]
 CRI increases when energy is removed from the region near 580 nm [1972b]
 Light reflected in these regions can contribute to confusion due to strong metamerism or
large color shift [1992c]
 Errors in the chromaticities of sets of 28 visually-matching lights are particularly large
when the amount of spectral content near 500 and 580 nm is large [1992b]
 Appearance of the Maxwell spot [Thornton1992a]
Prime


Wavelengths of 450, 540, and 610 nm were shown to have a gamut area larger than
daylight by Thornton and he predicted that a source at these wavelengths would provide
superior color discrimination capability [1972b]
A fluorescent source of peak wavelengths of 451, 537, and 613 was shown to be superior
in preference, as calculated and verified with human subjects [Thornton 1974]
Fluorescent lamps are not the only lamp to take into account the discoveries of Thornton.
Incandescent lamps with neodymium glass have energy attenuated in the region of 580 nm, Figure
3-6 [Ohno 2004, Ohno 2005]. The CIE CRI of the source is 77, below what is typically considered
acceptable, yet this type of lamp is preferred by many people. Ohno states the reasons for the increase
in preference are the increase in the chroma of red and green under these sources and the gamut area
is larger in comparison to a reference blackbody radiator, Figure 3-7.
16
1.2
Relative Power
1.0
0.8
0.6
0.4
0.2
0.0
400
450
500 550 600
Wavelength (nm)
650
700
Figure 3-6 The SPD of an Incandescent Lamp with Neodymium Glass [Ohno 2005].
80
CIELAB
60
40
20
a*
0
-80
-60
-40
-20
0
20
40
60
80
-20
-40
Ref.
-60
b*
Test
-80
Figure 3-7 Plots of 14 CIE Test Color Samples Used on CIE L*a*b* Space Illuminated by Test
Neodymium Lamp Compared to Reference Blackbody Radiator [Ohno 2005].
The examination of Thornton’s research by Houser and Hu [2004b] suggests a reduction in energy
consumption can be realized by an ideal SPD compared to a broadband fluorescent source, while
maintaining the same appearance of brightness. Matches completed in a 10 degree bi-partite field
with daylight fluorescent in the bottom field and either a prime source (450, 533, 619 nm) or antiprime source (493, 581, 657 nm) confirm Thornton’s findings. Houser et al. [2004a] additionally
examined the prime wavelength regions by comparing a tuned fluorescent lamp with peaks in the
prime regions to a conventional fluorescent lamp using side-by-side rooms. Expert and naïve
participants completed a forced choice survey when viewing the two rooms illuminated by different
17
lamp types. The survey asked participants to make judgments of clarity, brightness, preference,
colorfulness, and naturalness when comparing the two rooms. The results again demonstrate that the
tuned lamp enhances brightness perception and color preference versus the conventional lamp, and
the authors concluded that perception of brightness, color and visual clarity are more reliant on the
placement of energy in the regions of 450-530-610 nm versus magnitude of energy. The research of
Houser and Thornton clearly demonstrates the potential to optimize sources using spectral tuning.
3.6 RESEARCH METHODS
“The problem facing experimental psychologists interested in the more ethereal aspects of
architectural lighting is resolving the dilemma between a desire to ask meaningful questions about the
environment, and the constraints imposed by rigorous psychophysical procedures.”
Tiller and Rea [1992]
A review of 60 studies that examine the relationship between spectrum and visual perception by
Fotios and Houser [2009b] reveals that much of the work is unreliable due to either experimental or
subjective bias, or insufficient data. This review highlights the importance of examining the
experimental methodologies and results of previous research in order to produce reliable research that
contributes to the body of knowledge on the relationship between spectrum and visual perception.
Fotios, Houser and others focus on the importance of experimental design procedures for the four
different methodologies used to examine the effect of SPD on brightness including: matching [Fotios
et al. 2008b], adjustment [Fotios and Cheal 2010], category rating [Fotios and Houser 2009a], and
discrimination [Fotios and Houser 2013].
Brightness
Three different experimental methods are common in research examining the effect of lamp spectrum
on brightness: Visual matching, brightness ranking and subjective ranking [Fotios 2001]. Visual
matching and brightness ranking are both instances of paired comparisons, and the most common
subjective ranking technique is semantic differential scaling. Therefore, the focus will be on paired
comparisons and semantic differential scaling.
Paired comparisons can be completed with either side-by-side comparisons or rapid sequential
comparisons. The use of one method over the other is commonly determined by limitations of
resources and space. Houser et al. demonstrated that either method can produce reliable data [2009].
Experimental limitations may also require the use of side-by-side booths instead of rooms, however
both methods produce reliable results [Bellchambers and Godby 1972]. Fotios [2001] suggests the
inclusion of balanced design and quantified null-condition testing in side-by-side brightness matches
to counter effect differences independent of lamp spectrum.
In matching paired comparison experiments the participants dim the stimuli to match a reference.
Fotios et al. [2008b] identify three causes of bias in side-by-side brightness matching tasks: response
contraction bias, conservative adjustment bias and positional bias. Fotios et al. suggests participants
dim both stimuli, that stimuli location evenly alternates position or sequence, and the test stimuli are
set below and above the brightness of the reference stimuli to force the participant to dim both up and
down when completing a match. The side-by-side protocol requires counterbalancing to ensure main
effects of the experiment are not disrupted by bias due to position, application of dimming and
direction of dimming. In discrimination paired comparisons subjects simply choose which of two
stimuli is brighter, but the accuracy of the brightness judgments is disputed by some researchers
18
because brightness judgments may magnify differences between spectra [Fotios 2011]. Two
experimental methods are necessary when either the discrimination paired comparisons and semantic
differential scaling methodology is used.
Subjective brightness ratings typically use seven-point semantic differential scaling [Fotios 2001].
Seven point scales are best practice because larger scales exceed the minds capacity for making onedimensional judgments [Miller 1956]. The use of seven point scales can breakdown, as can any odd
number scale, and lead to an unreliable conclusion due to the potential for response contraction bias,
participants favoring the middle value [Fotios and Houser 2008]. The selection of the scale
dimensions is crucial, as many dimensions may not clearly apply to the luminous environment,
leaving subjects with the task of defining what the dimensions means to them before making a
judgment [Tiller and Rea 1992, Houser and Tiller 2003]. Tiller, Rea and Houser note the likelihood of
other environment and psychophysical variables influencing subjective ratings, and researchers often
erroneously assume subjects will evaluate the intended stimulus. The analyses performed by Tiller
and Rea highlights the difficulty in extracting information about human perception and the
importance of carefully designing and conducting experiments that involve semantic-differential
scaling. The researchers conclude by recognizing semantic differential scaling as a crucial step in
developing a hypothesis about higher order phenomenon, but consider the exclusive use of semantic
differential scaling meaningless. Houser and Tiller [2003] found that carefully developed semantic
differential scales do not prevent multiple interpretations of definitions, and stress the importance of
instructing participants on the precise meaning of scales and how to apply it to a luminous
environment.
Color Preference
The experimental methods for determining color preference have not been as thoroughly vetted and
synthesized in comparison to brightness methods. Preference is a multi-dimensional and consequently
difficult to model, but is just as important as brightness perception. Development of sources based
singularly on energy efficiency disregards the most important factor, the human factor. Thornton
[1974] accounted for the human factor in the development of the CPI, and his method for validating
the CPI is similar to the side-by-side comparison method. Thornton set four separate enclosures next
to each other and participants ranked the enclosures based upon what “looked good”. More recent
studies have also examined color preference.
Fotios et al. [1997] used identical adjacent booths, including in the booth a Macbeth ColorChecker
chart, red and blue booklets and a blue pen holder and a white chart with black text posted on the
back of the booth wall. The observers were seated 1.5 meters from the front edge of the booths, and
centered between the two booths. Five minutes was allowed for adaptation, and then observers were
asked to vary the illuminance in the booths to achieve visual equality and focus on the entirety of the
booth. When equality was achieved the observers performed a visual equality task and selected which
booth they would prefer for their workplace. The majority of participants preferred to work in the
environment illuminated by the higher color quality source, although the illuminance of the
environment was lower.
Schanda and Sandor [2006] conducted an experiment examining the visual appearance of the
Macbeth ColorChecker Charts, illuminating the charts with a reference light source and a test light
source in side by side booths. A small sample of the experimental group also examined textile
samples and ink-jet and color laser print outs. An illuminance of 250 lux was used, as this was the
highest they could obtain with the LED test source. All sources had a chromaticity near that of D65.
In addition to the Macbeth ColorChecker charts, two grey samples were placed in order to anchor the
19
color appearance scale. The observers were allowed to only observe one booth at a time, but were
allowed to look at each booth repeatedly before making their final selection. The participant was
instructed to scale the visual color difference between samples of the Macbeth ColorChecker charts.
The researchers concluded that CRI does not describe visual color rendering, and that the concept of
color rendering should be reconsidered.
Rea and Freyssinier-Nova [2008] recognized that subjective evaluations must be clearly defined to
ensure that research participants are judging the same feature of a display in color preference
evaluations. Participants’ attention was directed to specific features of the pictures displayed and used
the objective criteria “naturalness” and “vividness”, noting that while “naturalness” is a vague term it
is a term considered important by the developers of CRI.
Hu and Houser [2006] found that a majority of research participants selected the source with the
lower CCT when asked to choose which source was preferable to work under, given the choice of
3500K or 6500K. Contrasting these finding, Boyce and Cuttle [1990] demonstrated the choice of a
CCT in the range of 2700K to 6300K has little effect on people’s impressions of lighting in a space,
given the lamp has good color rendering. The most important factor in determining the impression of
lighting in the experimental space –which was a small individual office–was illuminance, and as
illuminance increased the pleasantness, uniformity, colourfulness, brightness, friendliness, clarity and
other positive attributes increased. The other factor that had a clearly increased the positive
impression of the office was the presence of natural color in the form of fruit and flowers.
The appearance of skin is considered an important indicator of the quality of a source, particularly
among professionals in the lighting industry. The color rendering of skin under illumination has been
studied by numerous respected researchers [Sanders 1959, Rea et al. 1990, Quellman and Boyce
2002, Veitch et al. 2012]. The skin of the back of the hand is a more sensitive indicator of color
rendering than color chips from the Macbeth Color Checker [Rea et al. 1990]. Sanders [1959] found
that the face was more critically judged than the hand when participants made color rendition
judgments for different illuminants using a five point scale ranging from “good” to “unsatisfactory”.
Boyce and Quellman [2002] found that participants of different skin types generally like different
lamp types based upon participant judgments of both sides of their hands under seven different light
sources. Veitch et al. [2012] had participants make judgments of their non-dominant hands using four
semantic differential scales: natural/unnatural, colorful/colorless, unhealthy/healthy and
pleasing/displeasing. Of the three sources examined, there was a statistically significant effect
between an RGB LED source and a quartz halogen source, with participants viewing their hand as
less desirable under the RGB LED source. There was also a significant difference between these two
lamps for individuals with yellow skin tones versus participants with red skin tones, with yellowskinned participants rating their skin appearance as worse under the quartz halogen source. The third
source examined by Veitch et al. was an LED source with two non-phosphor coated LEDs and two
phosphor coated LEDs, with a SPD more similar to the quartz halogen source than the RGB LED
source.
Additional Considerations
The methodology of research is critical in producing quality data that advances the state of
knowledge. Numerous additional factors separate from the overall methodology are important in the
design of an experiment including the following:
 The presentation of stimuli at a single illuminance ratio inhibits further understanding of
the magnitude of the brightness difference [Fotios 2001].
20




Sensory mechanisms of chromatic adaptation are 90% complete after 60 seconds
[Fairchild and Reniff 1995], although this time varies slightly between individuals
[Fairchild and Reniff 1995].
The amount of information humans are able to receive, process and remember is limited
by the span of absolute judgment and immediate memory [Miller 1956].
Color memory is more accurate in the long/medium excitation dimension of the
MacLeod-Boynton [MacLeod and Boynton 1979] cone-excitation space versus the short
dimension which shifts in saturation and hue when recalled from memory [Jin and
Shevell 1996].
Human memory is fallible, and color shifts occur from the original stimulus [Bartleson
1960, Siple and Springer 1983].
The Maxwell spot was found in experiments conducted by Thornton [1992a] and he noted that the
effect shows most strongly when “one of the spectral constituents falls in the wavelength region near
500 nm, and secondarily when it falls in the region near 580 nm”. These regions are the anti-prime
regions of the spectrum. Wyszecki and Stiles [2000] explain the Maxwell spot as possessing an illdefined boundary distinguished by a difference in color from the uniform large field, with a diameter
of approximately 4°. This spot shifts as the point of regard of the observer shifts. The cause is due to
the yellow macular pigment covering the retinal receptors “in an area covering the fovea and beyond
whose optical density diminishes sharply in moving out from the fovea into the surrounding area.”
Thornton [1992a] noted this area as 2°-3° with a bright bluish-green afterimage in the center of the
retinal field. Maxwell demonstrated the spot in the macular area of the retina where the yellow
macular pigment causes a red patch to be seen when viewing a purple background [Wright 2007].
There are discrepancies in the observed size and color of the Maxwell spot, but what is clear is that a
colored spot occurs with large field viewing areas.
21
Chapter 4
METHODOLOGY
“Colorimetry seems about to enter a new era, one in which the light source will replace the object as
the important variable.”
-Dorthea Nickerson [1960]
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching. Energy was removed from the regions of 500 and 580
nm, with some energy added to other regions of the visible spectrum as necessary to maintain
consistent chromaticity.
Brightness discrimination and brightness matching experimental methodologies were both employed
in order to examine if these methodologies would produce the same results. Observations of the
illuminants in side-by-side booths by the participants were separated into two separate sessions.
During one session the participants completed brightness matches and in another session the
participants made brightness and color preference discrimination forced choices.
Independent Variables
1. Reference: Continuous spectrum
(SPDRef)
2. Test 1: 500 nm energy notch
(SPD500)
3. Test 2: 580 nm energy notch
(SPD580)
4. Test 3: 500 and 580 nm energy notch
(SPD500,580)
5. Brightness side-by-side comparisons:
Discrimination
6. Brightness side-by-side comparisons:
Matching
Dependent Variables
1. Brightness
2. Color preference
Study Parameters
Number of Participants: 35
Age of Participants: 20 – 25 years
Participant Screening: Ishihara 24 Plate Color Vision Test, Keystone Visual Skills Test
Chromatic Adaptation: Mixed
Illuminance Range: 264 – 432 lux
CCT: Approximately 4000K
22
Research Questions
1. Do spectra with energy reallocated from the regions of either 500, 580, or 500 and 580 nm
appear brighter than a continuous reference spectrum?
2. Do the experimental methodologies side-by-side brightness matching and side-by-side
brightness discrimination (forced choice) produce comparable results?
3. Does the reallocation of energy from the regions of 500, 580, or 500 and 580 nm change
preference for a person’s own skin tone?
Working Hypotheses

The spectra with energy reallocated from the spectral regions of 500 nm or 580 nm or both
will have a significantly different brightness appearance than the nearly continuous reference
spectrum.

The spectra with energy reallocated from the spectral regions of 500 nm or 580 nm will have
a significantly different brightness appearance than the spectrum with energy reallocated
from both 500 and 580 nm.

The spectrum with energy reallocated from the spectral region of 500 nm will have a
significantly different brightness appearance than the spectrum with energy reallocated from
580 nm.

The spectra with energy reallocated from the spectral regions of 500 nm or 580 nm or both
will have a significantly different preference than the nearly continuous reference spectrum.

The spectra with energy reallocated from the spectral regions of 500 nm or 580 nm will have
a significantly different preference than the spectrum with energy reallocated from both 500
and 580 nm.

The spectrum with energy reallocated from the spectral region of 500 nm will have a
significantly different preference than the spectrum with energy reallocated from 580 nm.

The side-by-side brightness matching experimental methodology will not be significantly
different than side-by-side brightness discrimination comparisons.
4.1 INDEPENDENT VARIABLE
The radiant power of 16 different LED channels of the Telelumen luminaire were adjusted to create
the reference and test SPDs. Figure 4-1 shows the interface of the Light Replicator software. The
software was specifically developed to control the SPD of the Telelumen luminaire, ranging from 380
to 780 nm. Due to differences in the individual LEDs between the two different Telelumen
luminaires, the slider settings for each luminaire were different and were not a factor in the creation
of the reference and test SPDs.
23
Figure 4-1 User Interface of Light Replicator Software Used to Control the Telelumen
Luminaires. The interface allows for the modification of the SPD of the Telelumen by adjusting
the 16 different LED channels.
The SPDs were designed to match as closely as possible, with the similarity of the SPD between the
two different booths given higher priority than the chromaticity coordinates. During the final
calibration phase for both the reference and test spectra, the optical radiation in the left booth was first
adjusted to meet the goal of a relatively uniform SPD, except for the intended notches in the test
spectra, at chromaticity coordinates of (x, y) = (0.376, 0.374). Next, the SPD of the right booth was
adjusted to match the SPD of left booth for the test and reference spectra until the SPDs were
equivalent at each wavelength from 380 to 780 nm. Once the test and reference SPDs were equivalent
for each booth, minor adjustments were made to both SPDs to minimize chromaticity differences
between the left-right pairs of test and reference spectra. The StellarNet was used to take
measurements during this phase of calibration, but not during the study, which is one of the reasons
why the final chromaticity coordinates vary from the initial setting of the left booth. The other reasons
include the minor adjustments to minimize chromaticity difference during the final calibration phase
and general changes in the output of the LEDs from day to day and over time.
Variation in output of the LEDs from day to day and over time is a limitation of the apparatus.
Variations in output during initial calibration of the apparatus made calibration difficult as both
illuminance and chromaticity coordinates varied. The first step of calibration was to understand the
effect of various factors on the output of the Telelumen. Several different methods of calibration were
attempted when creating the test and reference spectra. The first method was to focus on the
chromaticity coordinates and color appearance. Countless hours were spent adjusting the chromaticity
coordinates to equal (x, y) = (0.376, 0.374) while also matching color appearance according to the
24
perception of the experimenter. This was a difficult task with 16 different LED channels. It became
evident that matching chromaticity coordinates and color appearance was a futile exercise because of
the variation in chromaticity coordinates and color appearance from day to day. The lumen output of
the Telelumen also varied significantly and it was clear that settings could not be preset for
illuminance due to hourly and daily variation. The variance of both the lumen output and chromaticity
coordinates of the Telelumen did stabilize over time, with a decrease in the shift of the chromaticity
coordinates and lumen output.
The metrics and graphical representations of the test and reference spectra are calculated based on an
average of five measurements taken in the middle of the month time span of the study before and
after participant sessions. The reference spectra chromaticity coordinates are equivalent to the average
of reference spectra chromaticity coordinates measured before and after each of the 70 sessions. The
spectral power distribution, chromaticity coordinates and luminance measurements taken during the
duration of the experiment were measured with the Photo Research PR-655 SpectraScan®
Spectroradiometer. The measurements were taken at a height of 11 inches from the bottom of the
booth, 3 inches above the height at which the participants rested their chin, at the center of the back
plane of the booth. During the calibration phase of the experiment the StellarNet EPP2000C
spectrometer was used. Circular holes, 7/8 of an inch in diameter, were located in both the left and
right corners of the left and right booths respectively and remote integrating spheres were aligned
with these holes under the bottom plane the booths. The SpectraScan® was not available during the
calibration of the experiment, but was used to take measurements during the experiment because of
the increase in accuracy of the measurements and ease of use.
The notation that will be used to indicate the test and reference spectra follows. Each will be
discussed and illustrated in further detail in the next two sections.
SPDRef
SPD500
SPD580
SPD500,580
nearly continuous reference
energy reallocated near the spectral region of 500 nm
energy reallocated near the spectral region of 580 nm
energy reallocated near the spectral region of 500 and 580 nm
The charts and graphs will only use the subscripts to indicate the reference and test spectra and will
not include “SPD”.
Reference Spectrum
The reference SPD for each booth is shown in Figure 4-2. The SPDs of the reference and test
independent variables are listed in 5nm increments in Appendix A. The goal during calibration was
to adjust the 16 LED channels to create a relatively uniform distribution of energy from 380 to 780
nm. The tuning was limited by the narrow Gaussian distribution of the LED chips, particularly in the
longer wavelengths where significant dips in the spectral power distribution of the reference source
were unavoidable.
25
100
90
Relative Power
80
70
60
50
A-Left
Ref - Left
40
Ref - Right
A-Right
30
20
10
380
396
412
428
444
460
476
492
508
524
540
556
572
588
604
620
636
652
668
684
700
716
732
748
764
780
0
Wavelength (nm)
Figure 4-2 Reference Spectral Power Distribution.
Test Spectra
The test spectra were created by the combination of the Telelumen and custom filters that removed
energy in the spectral regions of interest. The custom filters were produced by Semrock, Inc. The
transmission of the notch filters is shown in Figure 4-3. The Telelumen could not create the notches
necessary for the experiment, but was needed to make minimal adjustments to the spectra of the
illuminants to maintain consistent chromaticity. The three test SPDs for each booth are shown in
Figure 4-4, and are an average of five measurements as previously described.
100
90
Transmission (%)
80
70
60
50
500 (B)
40
580 (C)
30
500,580 (D)
20
10
0
350 375 400 425 450 475 500 525 550 575 600 625 650 675 700
Wavelength (nm)
Figure 4-3 Transmission of Notch Filters.
26
100
90
80
Relative Power
70
60
50
40
30
20
10
380
396
412
428
444
460
476
492
508
524
540
556
572
588
604
620
636
652
668
684
700
716
732
748
764
780
0
Wavelength (nm)
Ref – Left
500 – Left
580 – Left
500,580 - Left
Ref - Right
500 – Right
580 – Right
500, 580 - Right
Figure 4-4 Reference and Test Spectral Power Distributions.
The reason for the difference in transmission in the spectral regions of 500 and 580 nm between
Figure 4-3 and Figure 4-4 is the incident angle of the light rays striking the filter. The filter was
designed assuming the light rays would hit perpendicular to the filter at normal incidence. The lens at
the bottom of the Telelumen diffuses the light exiting the Telelumen, causing the light to exit at a
variety of angles. The filter spectrum becomes highly distorted at larger angles, dependent upon the
design of the filter.
There is also some variation between SPDs of the test and reference spectra. This is mainly due to the
output variation of the LEDs and also some minor adjustments made when taking into account
chromaticity. The slight variation between the left and right booths also causes variations in the
chromaticity coordinates and descriptive metrics of the test and reference spectra. Table 4-1 lists
chromaticity coordinates and descriptive metrics that are commonly used to communicate
characteristics of sources. The Flattery Index and Color Preference Index are also included because
these metrics are the first color rendering metrics to account for human preference. Table 4-2 lists
secondary metrics that are not as commonly used by the lighting industry. Figure 4-5 is a plot of the
chromaticity coordinates of the reference and test SPDs on the 1931 2° xy Chromaticity Diagram. All
SPDs were within a one-step MacAdam ellipse, except for SPDRef of the left booth which was within
a two-step MacAdam ellipse.
27
Table 4-1 Descriptive Metrics of Reference and Test Spectra: Primary
Left Booth SPD
Right Booth SPD
Ref
500
580
500,
580
Ref
500
580
500,
580
CIE 1931 Chromaticity
Coordinate x
0.3789
0.3808
0.3802
0.3794
0.3795
0.3809
0.3802
0.3808
CIE 1931 Chromaticity
Coordinate y
0.3775
0.3769
0.3769
0.3774
0.3799
0.3796
0.3765
0.3774
4048
3992
4007
4032
4050
4007
4005
3994
233
259
237
260
237
262
233
254
S/P Ratio
1.93
1.79
2.02
1.88
1.92
1.78
2.03
1.88
Color Rendering Index
(CRI)
97
88
86
90
94
86
85
91
R9
77
80
69
97
68
70
63
98
Color Quality Scale 9.0
(CQS) - Qa
97
86
90
92
95
85
89
92
Gamut Area Index
(GAI) Equal Energy
78
81
86
86
76
79
86
85
Flattery Index (Judd)
87
84
90
91
86
83
90
91
Color Preference Index
(CPI)
97
98
112
110
94
96
112
110
Correlated Color
Temperature (CCT)
Luminous Efficacy of
Radiation (LER)
28
0.384
0.382
y
0.380
0.378
0.376
0.374
0.374
0.376
0.378
0.380
0.382
0.384
x
Figure 4-5 Plot of Reference and Test SPDs on 1931 2° xy Chromaticity Diagram and a onestep MacAdam Ellipse centered at (x,y) = (0.380, 0.378). The chromaticity coordinates of all
SPDs are within a one-step MacAdam ellipse except for SPDRef of the left booth.
Table 4-2 Descriptive Metrics of Reference and Test Spectra: Secondary.
Left Booth SPD
Right Booth SPD
Ref
500
580
500,
580
Ref
500
580
500,
580
73
76
80
80
72
74
81
80
76
79
84
84
75
77
85
84
CQS v. 7.5 - Qp
96
90
95
97
94
88
95
97
CQS v. 9.0 - Qa
97
86
90
92
95
85
89
92
CQS v. 9.0 - Qf
97
85
86
88
95
84
86
89
CQS v. 9.0 - Qg
98
103
107
108
97
101
108
108
Feeling of Contrast
Index (FCI) - 1994
110
119
126
127
109
116
128
127
FCI - 2002
104
110
113
113
103
108
113
114
FM Gamut (Scaled by
CIE Illuminant C)
Color Discrim. Index
(CDI, Illuminant C)
29
4.2 EXPERIMENTAL APPARATUS
The experimental apparatus was comprised of two viewing booths arranged side-by-side, Figure 4-6.
The booths were each 21 inches wide by 15 inches deep by 15 inches high. The chin of the
participants was 17 inches away from the front of the booth and 7 inches above the bottom plane of
the booth. The bottom plane of the booth was at a height of 36 inches above the floor. The booths
were as identical as feasibly possible and all visible surfaces were painted with Behr Premium Plus
Ultra Paint and Primer in One, Ultra Pure White Interior Flat Enamel. The only visible surfaces not
painted were the following: small brackets above booths, black rotary controls, and illuminance meter
receptor heads connected with gray CAT5 cables. The paint has a relatively even reflectance
distribution across the visible spectrum.
Figure 4-6 Experimental Apparatus: Side-by-Side Viewing Booths.
The Wybron 87250 Eclipse IT Iris 1K Dowser was controlled by the Doug Fleenor Color Wheel, a
black rotary wheel control. The Color Wheel was customized to operate only one channel instead of
multiple channels as originally designed, and it adjusted the iris dowser at 255 steps over a range of
zero to 100% output. This setup resulted in an average change in illuminance of approximately 1.7%
per step between 300 and 400 lux at an initial setting of 75% output at 367 lux. A luminaire, dowser,
rotary wheel control, and a filter holder were integrated into both sides of the apparatus to allow for
brightness and spectral modification separately in each booth. Translucent mylar was also integrated
into each side below the Telelumen and iris dimmer to improve the diffusion of the light from the
luminaires. The mylar was necessary to improve uniformity and color mixing of the LEDs to prevent
color striations, as streaks of red appeared on the back wall of the booth without the mylar.
30
The calibration of the apparatus required an understanding of the changes in chromaticity coordinates
and illuminance due to variations in the openness of the iris and dimming adjustments made with the
light replicator software. The dimmer was initially set at 75% for the brightness matching experiment
to minimize change in chromaticity coordinates. The Telelumen needed to operate for at least 1.5
hours for illuminance and chromaticity coordinates to stabilize, but beyond 3.75 hours the
chromaticity coordinates began to destabilize. The lumen output of Telelumen luminaires was
adjusted using the peak reference level feature of the Light Replicator software. The peak reference
level is equivalent to the highest peak of the Telelumen SPD, which is the LED channel operating at
the highest power. The power measure is not a real-time measure, and can only be considered an
estimate. The Telelumen luminaires used for this study maintained consistent chromaticity
coordinates best when the peak reference level was between 10 and 25. The LEDs in the Telelumen
flickered if the reference level was too high. If the reference was set too low, a significant shift in
chromaticity coordinates occurred. Table 4-3 list the chromaticity coordinates of the test and
reference SPDs at varying dimmer and reference settings.
Table 4-3 Chromaticity Coordinates at Varying Dimmer and Reference Settings. The
measurements were taken over a span of 35 minutes due to time necessary for changing
between SPDs. Measurements were taken in the same order of the SPD list.
Left
Right
Telelumen
Chromaticity Coord.
Chromaticity Coord. Telelumen
SPD
Reference
Reference
x
y
x
y
Setting
Setting
Dimmer Percent Open – 75%
Ref
0.380
0.380
500
0.382
0.379
580
0.381
0.380
500,580
0.380
0.379
Dimmer Percent Open – 85%
15
17
20
21
0.380
0.382
0.381
0.382
0.381
0.381
0.378
0.378
15
17
20
21
Ref
0.382
0.380
500
0.384
0.381
580
0.384
0.380
500,580
0.383
0.381
Dimmer Percent Open – 65%
9
11
13
13
0.381
0.383
0.384
0.385
0.381
0.382
0.379
0.380
10
11
13
14
Ref
0.381
0.380
24
0.381
0.381
24
500
0.379
0.379
30
0.379
0.379
30
580
0.375
0.378
37
0.375
0.377
34
500,580* 0.376
0.378
36
0.377
0.376
31
* The illuminance for this setting was 300 lux because the Telelumen could not increase any higher
when the dimmer was open 65%.
31
During the brightness matching experiment, the fixed booth was always set at 350 lux while the
variable booth was set to either 310 lux or 390 lux. This range of illuminance levels kept the dimmer
generally in a range between 65% and 85% open and minimized the slight shift in chromaticity
coordinates caused by the interactions between the spectral reflectance of the iris fins and the test and
reference spectra. The range of illuminance levels also kept the reference settings of the Telelumen
between 10 and 25 for the majority of trials.
4.3 EXPERIMENTAL TRIALS
The study was approved by The Pennsylvania State University Institutional Review Board, approval
number 39046. The participants were compensated $60 for their participation. Study participation
required participants to attend two experimental sessions, each approximately two hours in length.
The sessions were scheduled three times per day and were conducted over a one-month time span,
starting in late April 2013. The majority of participants were students at Penn State. Students in their
4th or 5th year of study in the field of illumination in the Department of Architectural Engineering
were excluded because they were not considered naive participants.
Thirty-five participants completed the experimental trials. The determination of the number of
participants was based upon variance stable rank sums (VSRS) statistical analysis. The number of
participants required to insure the possibility of significant difference between the 4 levels of the
independent variable at an alpha level of 0.05 requires 22 participants and 0.01 requires 33
participants [Dunn-Rankin and King 1969, Dunn-Rankin et al. 2002]. The experimental variance was
anticipated to be high, in particular for the preference experiment, so the level of 0.01 was chosen as
the target significance level and the number of participants was rounded up from 33 to 35.
Experimental Procedure
Each of the 35 participants completed two basic vision tests. The first test was the Keystone Visual
Skills Test, using Keystone model #1155, and the second test was the Ishihara 24 Plate Color Vision
Test. The Keystone test evaluates vision functions including: binocular and monocular testing of near
and far acuity; binocular testing of intermediate distances at normal and low light levels; depth
perception and ability of the eyes to work together and merge two images into one image. The
Ishihara evaluates red-green color deficiency and color weakness and blindness. Participants with
corrected vision and/or color deficiency were not excluded from participation. Participants with
corrected vision wore their glasses or contacts during the vision tests and during the experiments.
During one session the participants completed the brightness matching experiment and during the
other session participants completed the brightness discrimination and color preference discrimination
experiments. The two sessions were counterbalanced, so half the participants completed the
brightness matches first and half the participants completed the discrimination choices first, with
brightness discrimination always followed by preference discrimination. The participants viewed four
practice trials before beginning the recorded trials of each experiment. Four SPDs were presented,
detailed in Section 4.2, varying in position between the right and left booths. The participants were
never informed of the illuminant type or the amount of light in each booth. The experimenter changed
out the filters between trials, and also adjusted the SPD of the Telelumen. There were a total of six
filters used during the experiment, a set of three notch filters for each booth. The Telelumen had a
specific slider settings pre-set for each booth. The slider settings varied between the left and right
booth due to the differences between the Telelumens and not because of variance in the filters. The
32
illuminance was adjusted between trials using the Light Replicator software. The Wybron dimmer
was set to 75% closed prior to the start of each experiment, however this setting varied during the
brightness matching experiment.
The experimental setup for the brightness matching and brightness discrimination experiments was
very similar. The participant sat with their head positioned within a chin and forehead rest and were
directed to look at the overall booth when making their judgments. The illuminance on the horizontal
workplane of each booth was recorded by a Konica Minolta T-10 Illuminance Meter centered in each
booth. The participants covered their eyes for approximately a minute between trials, which served as
both a washout period and allowed the experimenter to adjust the settings for the next trial. The
participants uncovered their eyes after the washout period and viewed the illuminated booths for 30
seconds, allowing for partial adaptation to the new settings, before making brightness and preference
judgments.
During the brightness matching experiment, participants adjusted the brightness of one booth to
match the adjacent booth by turning the rotary control in front of the variable booth. For each trial the
participant adjusted the dimming level until the two booths appeared equally bright. Each participant
viewed 32 trials. The time necessary per participant was approximately 75 minutes, but varied
between 60 and 90 minutes depending on how long the participant spent adjusting the brightness of
the variable booth. The illuminance varied from 310 to 390 lux.
During the brightness discrimination experiment participants were asked to state if the left or right
booth appeared brighter. 38 trials were viewed by each participant. The time necessary per participant
was approximately 60 minutes. The illuminance level varied between 264 to 432 lux.
During the preference discrimination experiment participants put their left hand in the left booth and
right hand in the right booth and were asked to select the preferred light setting based on how the light
settings rendered their hands. Participants were allowed to rotate their hands, and were not given
specific instructions on where to look on their hands when making their judgments. Participants
viewed their hands in the illuminated booths for 30 seconds prior to being asked which booth light
setting was preferred. An approximately 30 second washout period followed each trial, which also
gave the experimenter time to prepare the next trial. The time needed between trials was reduced for
this experiment because the illuminance for each setting was the same, 350 lux. The illuminance was
set prior to the beginning of the experiment, during a ten minute break between the brightness
discrimination and brightness preference experiments. The variance in illuminance between the
beginning and end of the trial was minimal, with an average increase less than 2 lux based on
measurements taken after the end of the experiment. Each participant completed 20 trials. The time
necessary per participant was approximately 25 minutes.
The design of the preference discrimination experiment originally included fruit as the object on
which participants would base their preference judgment . During the pilot trials it was determined
that the use of fruit was too difficult because obtaining real fruit of the same size and shape
continually over the span of a month was impossible. Other options were considered including the use
of plastic fruit, however too much texture and minor variations in color were missing from the plastic
fruit when compared to the real fruit. The other option considered was the use of commercial
packaged products such as popular brands of candy bars and beverages. The consistency of shape and
color was not a concern, but the ambivalence toward how the various products looked under different
33
light settings was a serious concern. Hands were decided to be the best option for the preference
judgments as participant ambivalence toward his or her hands was expected to be much lower than
for fruit or commercial packaged products. It was assumed that the participants’ left and right hands
would be relatively similar. The participants removed all jewelry from their hands and wrists before
beginning the experiment and were instructed to roll-up their sleeves if their sleeves were long.
Experimental Statistical Design
Null condition trials and counterbalancing were included in the design of each experiment. Four
practice trials were included at the beginning of each experiment to allow the participant time to
adjust to the experiment. Null condition trials were included to understand the bias between the left
and right booth and variance of the brightness matching experiment. The left-right positions of the
spectra were equally alternated between subjects for both the brightness matching and brightness
discrimination experiments to reduce the number of trials necessary as participant fatigue was a
concern. Table 4-4 breaks down the number of trials required for each experiment, accounting for
practice trials, null condition trials and counterbalancing.
Table 4-4 Categorical Break Down of Experimental Trials.
Trial Category
Brightness
Matching
Brightness
Discrimination
Preference
Discrimination
Practice
4
4
4
Null
4
4
4
Spectrum
(All possible pairs)
6
30
6
Spectrum Position
(Left or Right)
Between Subject
Between Subject
6
Dimming Direction
(Up or Down)
6
-----
-----
Dimming Position
(Left or Right)
12
-----
-----
Total
32
38
20
The design of the brightness matching experiment included dimming of the test and reference spectra
in both directions to counterbalance any bias due to dimming direction in the results. The complete
experimental design for each experiment is listed in Appendix B. For half of the participants the right
and left sides were simply interchanged for the brightness matching and discrimination experiments.
The order in which the participants completed the experiments was alternated so that spectrum
position was not confounded with experiment order. Table 4-5 lists the comparison trials for SPD500
34
versus SPDRef. This pattern was repeated for comparisons SPDRef versus SPD580, SPD500,580 versus
SPDRef, SPD500 versus SPD580, SPD500 versus SPD500,580, and SPD500,580 versus SPD580.
Table 4-5 Brightness Matching: Experimental Design for SPD500 versus SPDRef. All comparisons
followed a similar pattern.
Right
Trial
1
2
3
4
Left
Illuminance
(lux)
310
350
350
310
SPD
500 up
Ref
500
Ref up
SPD
Ref
500 down
Ref down
500
Illuminance
(lux)
350
390
390
350
A difference of 40 lux was selected with a center point at 350 lux to provide an apparent difference in
brightness between the two booths, with the same difference in illuminance above the below the 350
lux center point. The fixed booth was always set at 350 lux while the variable booth was set to either
310 lux or 390 lux. This range of illuminance levels minimized shift in chromaticity coordinates
caused by limitations of the apparatus previously discussed.
Each brightness discrimination comparison was designed to determine the linear brightness
relationship between each SPD comparison. Determining the linear relationship between the spectra
allowed for the ratio at which the reference and test SPDs appear equally bright to be estimated.
Table 4-6 illustrates the experimental design for the SPD500 versus SPDRef. The illuminance ratios
differed by 0.04 between five steps, starting at 0.88 and ending at 1.04. The steps were initially 0.05,
but were adjusted to 0.04 after pilot data was collected because steps of 0.05 resulted in too large of
illuminance differences between the SPD comparisons. The center point is where it was predicted that
the two SPDs would appear equally bright. SPD500 and SPD500,580 were found to be approximately
equivalent during the pilot trials, therefore the illuminance ratio ranged from 0.92 to 1.06 with the
center point a ratio of 1.0. SPDRef was perceived as brighter than SPD580 during the pilot trials,
therefore the experimental trials were adjusted from the original design so that the point of predicted
equivalence was SPDRef at 336 lux and SPD580 at 350 lux.
Table 4-6 Brightness Discrimination: Experimental Design for SPD500 versus SPDRef. All
comparisons followed a similar pattern.
Right Booth
Trial
1
2
3
4
5
Left Booth
SPD
Illuminance
(lux)
SPD
Illuminance
(lux)
500
Ref
500
Ref
500
264
325
336
375
416
Ref
500
Ref
500
Ref
300
299
350
375
400
Preset
Illuminance
Ratio
(500/Ref)
0.88
0.92
0.96
1.00
1.04
35
The illuminance in the preference discrimination experiment was a constant 350 lux, as listed in
Table 4-7. The counterbalancing was done within subjects for this experiment. Illuminance
adjustments were not made based on predicted perceived brightness.
Table 4-7 Preference Discrimination: Experimental Design for SPD500 versus SPDRef. All
comparisons followed a similar pattern.
Right Booth
Trial
1
2
SPD
Ref
500
Left Booth
Illuminance
(lux)
350
350
SPD
500
Ref
Illuminance
(lux)
350
350
36
Chapter 5
RESULTS AND ANALYSIS
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching. Thirty-five people between the ages of 20 and 25 were
recruited to complete three different experiments examining brightness and preference. Table 5-1
lists the demographics of the participants. The average age of participants was 22.5. The gender of the
participants was nearly an even split between male (51.4%) and female (48.6%). Caucasians were the
largest race group, 68.6% of participants, followed by Asians (20%), Hispanic/Latino (8.6%) and
other (2.9%). Forty percent of participants wore glasses, 20% contacts, and 40% neither. Two
participants had color deficient vision, and in both cases were initially self-reported and then
confirmed during the Ishihara Color Vision Test. Only two participants were not right-handed.
Table 5-1 Participant Demographics (n = 35).
Characteristic
Gender
Male
Female
Race
Caucasian
Asian
Hispanic/Latino
Other
Vision Aid
Glasses
Contacts
None
Normal Color Vision
Yes
No
Dominant Hand
Right
Left
Average Age
Count
Percentage
17
18
48.6%
51.4%
24
7
3
1
68.6%
20.0%
8.6%
2.9%
14
7
14
40.0%
20.0%
40.0%
33
2
94.3%
5.7%
33
2
94.3%
5.7%
22.5
The chromaticity coordinates and luminance of the center back plane of the booth were measured
with the SpectraScan® spectroradiometer approximately 15 minutes before and after each
experiment. The Telelumen luminaires were turned on 1.75 hours prior to the start of the experiment.
During the duration of the experiments the chromaticity coordinates increased slightly, with the
average increase in x greater than the average increase in y. Only the chromaticity coordinates of the
SPDRef were measured before and after each session, as the goal of the measurement was to
37
understand the variation of the output of the Telelumen luminaire throughout the study. The averages
of the chromaticity coordinate measurements are listed in Table 5-2. A further breakdown of the
chromaticity coordinates based upon the type of experimental session is detailed in Table 5-3. The
average of the chromaticity coordinates prior to the discrimination and matching experiments is the
same, however there is a difference in chromaticity coordinates after the experiments. The
chromaticity coordinates were always higher after the discrimination experiment versus the matching
experiment. The discrimination experiment tended to take longer than the matching experiment, so
this result is not surprising given the average of the chromaticity coordinates increased slightly over
the span of the experiment.
Table 5-2 Chromaticity Coordinates of Reference Spectra Before and After Experimental
Sessions.
Left Booth
Right Booth
Difference
x
y
x
y
x
y
Overall Average
0.3790
0.3781
0.3796
0.3804
0.0006
0.0023
Before Average
After Average
Difference
0.3778
0.3802
0.0024
0.3777
0.3785
0.0008
0.3778
0.3815
0.0037
0.3798
0.3810
0.0013
0.0000
0.0012
0.0012
0.0020
0.0025
0.0005
Before Std. Dev.
After Std. Dev.
Difference
0.0011
0.0010
0.0001
0.0015
0.0008
0.0006
0.0008
0.0008
0.0000
0.0008
0.0011
0.0003
0.0003
0.0002
0.0001
0.0007
0.0003
0.0004
Table 5-3 Chromaticity Coordinates of Reference Spectra Before and After Experimental
Sessions: Matching and Discrimination.
Left Booth
Right Booth
Difference
x
y
x
y
x
y
Overall Average
0.3790
0.3781
0.3796
0.3804
0.0006
0.0023
Before Discrimination
Before Matching
Difference
After Discrimination
After Matching
Difference
0.3778
0.3777
0.0001
0.3807
0.3797
0.0010
0.3777
0.3777
0.0000
0.3788
0.3782
0.0007
0.3778
0.3778
0.0000
0.3816
0.3814
0.0002
0.3798
0.3798
0.0000
0.3814
0.3807
0.0007
0.0000
0.0000
0.0000
0.0008
0.0016
0.0008
0.0020
0.0020
0.0000
0.0025
0.0025
0.0000
Aft. Disc. Std. Dev.
Aft. Mat. Std. Dev.
Difference
0.0005
0.0011
0.0007
0.0010
0.0005
0.0005
0.0008
0.0008
0.0000
0.0014
0.0006
0.0008
0.0003
0.0003
0.0000
0.0004
0.0001
0.0003
38
5.1 BRIGHTNESS MATCHING
The brightness matching experiment was designed to answer the following question:
Do spectra with energy reallocated from the regions of either 500, 580, or 500 and 580 nm
appear brighter than a nearly continuous reference spectrum?
Two of the three test spectra appeared brighter than the reference spectrum at the same illuminance.
The brightness matching experiment was carefully designed to allow participants to adjust one of the
booths to match the brightness of the other booth. Despite careful design of the apparatus, the
variance of the brightness matching was expected to be higher than the brightness discrimination
experiment due to the greater range of participant response possible during the brightness matching
experiment.
There are several reasons the variance was expected to be high including: participant care, dimming
direction and difficulty of the task. The majority of participants spent time adjusting the brightness of
the booth, waiting after they were finished adjusting before stating that they were done. Some
participants quickly turned the rotary control in one direction and then stated that they were done. An
approach to making the brightness matches was not suggested, however the participants were
instructed that they may take as much time as necessary and could keep adjusting the rotary control
until the two booths were equally bright. Overall, the pattern of adjusting the brightness varied greatly
among the participants.
The task of matching brightness requires more effort by the participant versus simply stating which
booth appears brighter as required in the brightness discrimination experiment. During the matching
experiment the participant is continually making the judgment as to which booth appears brighter
when matching the brightness of the booths. Participant fatigue likely affected the results of the
brightness matching experiment more than the brightness discrimination experiment, but in both
experiments the order of the presentation of trials was randomized to balance the effect trial order.
Another source of variance in this experiment was left bias, however experimental design was
counterbalanced so that positional bias would not significantly affect the final results. The variance of
the dimming direction was also counterbalanced, although there was no statistically significant bias
due to dimming direction. This was not expected based on previously published studies [Fotios et al.
2008b]. The use of a rotary dimmer without lower or upper stops in this experiment may have
mitigated bias due to dimming direction.
Results
The participants viewed each SPD comparison four times. The illuminance ratios from the brightness
matching experiment are listed in Table 5-4. Subjects were split into two groups because spectrum
position was counterbalanced between subjects.
39
Table 5-4 Brightness Matching: Average Ratio of Comparisons. The “u” and “d” next to the
spectra indicates the variable spectra and if the spectra were expected to be adjusted up or
down respectively. The ratios listed are ratios of the illuminance in the booths when the
participant set the booths to equal brightness appearance. The ratio is calculated as indicated in
the Final Ratio column. The final ratio for each comparison is the result of 140 brightness
matches. The asterisk (*) indicates the trial has a statistically significant positional bias.
Positional bias was calculated using a two-sample t-test. One asterisk denotes significance at an
alpha level of 0.05 and two asterisks denote an alpha level of 0.01. The following notation is
used: A = SPDRef; B = SPD500; C = SPD580; D= SPD500,580.
SPD Position: Group 1
SPD Position: Group 2
SPD Position Average
Trial
1
2*
3
4
5**
6*
7**
8**
9
10
11*
12
13
14
15
16
17**
18
19**
20**
21
22
23
24
Right Left
Ratio
Right Left
Ratio
Ratio
Bu
A
B
Au
Cd
A
C
Ad
Du
A
D
Au
Cd
B
Bd
C
B
Du
D
Bd
C
Du
D
Cd
100.8%
96.1%
99.7%
96.8%
94.9%
102.5%
96.2%
101.9%
94.6%
92.2%
98.6%
95.2%
96.7%
102.8%
100.3%
98.7%
103.4%
99.7%
94.6%
103.1%
96.5%
96.8%
100.2%
95.8%
A
Bd
Ad
B
A
Cu
Au
C
A
Dd
Ad
D
B
Cu
C
Bu
Dd
B
Bu
D
Dd
C
Cu
D
96.9%
100.9%
94.7%
98.9%
102.5%
97.1%
102.7%
95.5%
95.7%
97.6%
92.8%
98.4%
103.5%
97.6%
96.4%
101.3%
97.6%
100.9%
100.6%
96.1%
98.4%
96.1%
99.2%
102.3%
98.9%
98.4%
97.3%
97.9%
98.4%
99.8%
99.2%
98.7%
95.1%
94.8%
95.8%
96.8%
100.0%
100.3%
98.4%
99.9%
100.6%
100.3%
97.5%
99.7%
97.4%
96.4%
99.7%
98.9%
A
Bd
Ad
B
A
Cu
Au
C
A
Dd
Ad
D
B
Cu
C
Bu
Dd
B
Bu
D
Dd
C
Cu
D
Bu
A
B
Au
Cd
A
C
Ad
Du
A
D
Au
Cd
B
Bd
C
B
Du
D
Bd
C
Du
D
Cd
Standard
Deviation
7.0%
6.8%
7.4%
6.1%
6.1%
7.2%
5.9%
5.9%
5.5%
8.0%
7.9%
6.7%
10.2%
8.1%
6.1%
6.4%
5.6%
5.8%
6.0%
5.6%
7.5%
6.2%
6.1%
9.5%
Final
Ratio
B/A
98.12%
A/C
99.04%
D/A
95.64%
B/C
99.65%
B/D
99.51%
D/C
98.12%
Analysis
The SPD500,580 versus SPDRef ratio has the largest difference from unity, with a ratio of 95.64%. The
second largest difference from unity was 98.12%, the ratio of both SPD500 versus SPDRef and
40
SPD500,580 versus SPD580. These three SPD comparisons were significantly different from unity. Table
5-5 lists the p-values and confidence intervals for each SPD comparison. Positional bias was present
in the brightness matching experiment and had a statistically significant effect on approximately onethird of the experimental trials. The null condition ratios, Table 5-6, were calculated by dividing the
illuminance of the left booth by the illuminance of the right booth. The ratios for all four null
condition trials are less than unity, indicating that the left booth appeared brighter than the right booth
at equal illuminance. The ratio of the final illuminance settings of the left booth compared to the right
booth for all matching trials, excluding null conditions, was 97.7%. This ratio is significantly
different than unity (p<0.001). The comparisons with the highest prevalence of positional bias are
SPDRef versus SPD580 (trials 5 to 8) and the SPD500 versus SPD500,580 (trials 17 to 20). These
comparisons are not significantly different from unity, however SPD500 versus SPD580 is also not
significantly different than unity and there is no indication of positional bias for this comparison.
Table 5-5 Brightness Matching: Statistical Significance of Brightness Matching Ratios.
SPD Comparison
Final Ratio
500 vs. Ref
Ref vs. 580
500,580 vs. Ref
500 vs. 580
500 vs. 500,580
500,580 vs. 580
98.12%
99.04%
95.64%
99.65%
99.51%
98.12%
One-sample
t-test
P-value
0.016
0.106
<0.001
0.660
0.305
0.029
Significant
Difference
from Unity
Yes
No
Yes
No
No
Yes
95% Confidence
Interval
(0.96609, 0.99622)
(0.99782, 1.02152)
(0.94375, 0.96902)
(0.98051, 1.01251)
(0.98549, 1.00468)
(0.96449, 0.99799)
Table 5-6 Brightness Matching: Null Condition Trials. The “u” and “d” next to the spectra
indicates the variable spectra and if the spectra were expected to be adjusted up or down
respectively. The ratios were calculated by dividing the illuminance of the left booth by the
illuminance of the right booth. The following notation is used: A = SPDRef; B = SPD500; C =
SPD580; D= SPD500,580.
SPD Position: Group 1
SPD Position: Group 2
Right Left
Ratio
Right Left
Ratio
Final
Ratio
C
Bu
D
Ad
96.01%
95.73%
99.43%
95.38%
Cd
B
Du
A
99.70%
95.19%
98.97%
94.01%
97.86%
95.46%
99.20%
94.69%
Trial
25
26
27
28
Cd
B
Du
A
C
Bu
D
Ad
One-sample t-test
Pvalue
0.046
<0.001
0.380
<0.001
95%Confidence
Interval
(0.96226, 0.99966)
(0.94074, 0.97167)
(0.97515, 1.00971)
(0.93205, 0.96479)
5.2 BRIGHTNESS DISCRIMINATION
The brightness discrimination experiment was designed to answer the same question as the brightness
matching experiment:
41
Do spectra with energy reallocated from the regions of either 500, 580, or 500 and 580 nm
appear brighter than a nearly continuous reference spectrum?
Two of the three test spectra appeared brighter than the reference spectra at the same illuminance,
reaching a statistical significance at an alpha level of 0.01. The brightness discrimination experiment
was designed to have participants make a forced choice brightness judgment between the test and
reference spectra at the same illuminance and to find the ratio at which the two sources appear equally
bright by varying the ratio of the illuminance of multiple comparisons of the same spectra. The linear
models created from the multiple comparison results have R2 values ranging from 0.9579 to 0.9959.
These linear models allow for direct comparison with the brightness matching experiment, answering
the question:
Do the two experimental research methodologies, side-by-side brightness matching and sideby-side brightness discrimination (forced choice), produce comparable results?
The side-by-side brightness matching experimental methodology and the side-by-side brightness
discrimination experiment methodology produced comparable results. The rank order of the spectra is
similar for both experimental methodologies. The brightness matching ratios were closer to unity,
with unity indicating no difference, for every spectrum comparison versus the brightness
discrimination ratios.
Results
The results of the brightness discrimination experiment are summarized in Table 5-7. Each
participant viewed each trial once. The percentage of trials that a reference or test SPD was selected
as appearing brighter by the participants is listed.
42
Table 5-7 Brightness Discrimination: Percentage of Trials Spectra Selected as Brighter. Each
participant viewed each trial listed. The percentage is calculated based on the total number of
trials that the SPD was selected as appearing brighter divided by the total judgments for each
trial, 35. The trial number at which the two spectra were compared at the same illuminance is
highlighted in gray.
Percentage of
Trials SPD
Trial
Illuminance Illuminance
Selected as
Number Ratio
Value (lux)
Appearing
Brighter
500
Ref
500
Ref
1
0.88
264
300
22.9%
77.1%
2
0.92
299
325
31.4%
68.6%
3
0.96
336
350
51.4%
48.6%
4
1.00
375
375
68.6%
31.4%
5
1.04
416
400
85.7%
14.3%
Ref
580
Ref
580
6
0.88
264
300
2.9%
97.1%
7
0.92
299
325
11.4%
88.6%
8
0.96
336
350
37.1%
62.9%
9
1.00
375
375
57.1%
42.9%
10
1.04
416
400
85.7%
14.3%
500,580 Ref
500,580 Ref
11
0.88
264
300
8.6%
91.4%
12
0.92
299
325
22.9%
77.1%
13
0.96
336
350
68.6%
31.4%
14
1.00
375
375
94.3%
5.7%
15
1.04
416
400
97.1%
2.9%
500
580
500
580
16
0.88
264
300
8.6%
91.4%
17
0.92
299
325
14.3%
85.7%
18
0.96
336
350
42.9%
57.1%
19
1.00
375
375
62.9%
37.1%
20
1.04
416
400
85.7%
14.3%
500
500,580 500
500,580
21
0.92
276
300
14.3%
85.7%
22
0.96
312
325
28.6%
71.4%
23
1.00
350
350
54.3%
45.7%
24
1.04
390
375
85.7%
14.3%
25
1.08
432
400
97.1%
2.9%
500,580 580
500,580 580
26
0.88
264
300
11.4%
88.6%
27
0.92
299
325
25.7%
74.3%
28
0.96
336
350
40.0%
60.0%
29
1.00
375
375
80.0%
20.0%
30
1.04
416
400
91.4%
8.6%
43
Analysis
The percentages at an illuminance ratio of 1.0 are of particular importance as they directly compare
the brightness of the test and reference spectra at the same illuminance. A VSRS analysis was used to
analyze all SPD comparisons at the ratio of 1.0 and is shown in Table 5-8. VSRS is a distribution free
subject by treatment analysis, and is an adaptation of a two-way analysis of variance [Dunn-Rankin
and King 1969, Dunn-Rankin et al. 2002]. VSRS linearly transforms the frequency matrix of pairedcomparisons to scale scores and all scale scores have equal variance, independent of the nature of the
items being scaled. Therefore, VSRS can be used to scale a wide variety of stimuli including
psychophysical objects. VSRS has been used by other lighting researchers to analyze pairedcomparison experimental data [Quellman and Boyce 2002, Fotios et al. 2009].
Table 5-8 Brightness Discrimination: Dunn-Rankin VSRS Matrix of Rank Differences and
Critical Ranges. Ri is the scaled rank score, and the difference in the scaled rank scores is the
critical range.
Ref
500
580
500,580
Ri
68
100
70
112
Ref
68
32
2
44
500
100
580
70
500,580
112
30
12
42
-
α = 0.01 if critical range > 33.6
α = 0.05 if critical range > 27.7
There is a significant difference in brightness appearance between SPD500,580 versus SPDRef and
SPD500 versus SPDRef at an illuminance ratio of 1.0 according to the VSRS analysis. SPD500,580 was
selected as appearing brighter than SPDRef at equal illuminance for 94.3% of the trial comparisons.
SPD500 was also selected as appearing brighter than SPDRef at equal illuminance for 68.6% of the
trials. The linear relationships between these SPDs were used to estimate the illuminance at which the
SPDs appear equally bright. The percentage of trials a SPD was selected as appearing brighter versus
another SPD at the varying illuminance ratios was plotted. A linear regression line was fit to the data.
The linear relationship between SPD500,580 versus SPDRef and SPD500 versus SPDRef is illustrated in
Figure 5-1. Fifty percent is the percentage assumed to represent equal brightness, as half the
participants are expected to choose one side and half the other side when the booths appear equally
bright. The illuminance ratio at equal perceived brightness, 50%, for the SPD500 versus SPDRef is
95.51% and 94.19% for SPD500,580 versus SPDRef. Stated otherwise, to achieve an equal perception of
brightness SPD500 should be set to an illuminance 4.49% less than SPDRef and SPD500,580 should be set
to an illuminance 5.81% less than SPDRef.
Only four of the five trial percentages were plotted for SPD500,580 versus SPDRef because of the skew
caused by the small difference between trial 14 and trial 15, 94.3% and 97.1% respectively. One
participant’s selection is equivalent to 2.9%, so the entire difference is due to the selection of one
participant. Four trial percentages were also used for the determination of the relationship between
SPDRef versus SPD580, Figure 5-2, as the percentage of trial six was only 2.9%. Figure 5-3 and
Figure 5-4 illustrate the linear relationship of the remaining SPD comparisons.
44
Percentage of Trials SPD500 or SPD500,580 Selected as
Percentage of Trials 500 or 500,580 SPD Selected
Brighter when Compared to SPD
as Brighter when Compared to RefRefSPD
100%
500,580 / Ref
y = 7.5714x - 6.6314
R² = 0.9653
90%
80%
70%
60%
50%
500/ Ref
vs. Ref
500
40%
500,580/ Ref
vs. Ref
500,580
500 / Ref
y = 4.0714x - 3.3886
R² = 0.9881
30%
20%
10%
0.9419
0%
0.84
500 and 500,580
Ref
0.88
264
300
0.92
299
325
0.9551
0.96
336
350
1.00
375
375
1.04
416
400
1.08 Illuminance Ratio
Illuminance (lux)
Percentage of Trials SPDRef Selected as Brighter
Selected as Brighter
Percentage of Trials Ref SPD
when Compared to SPD580
when Compared to 580 SPD
Figure 5-1 Brightness Discrimination: Linear Relationship of SPD500 versus SPDRef and
SPD500,580 versus SPDRef.
100%
90%
80%
y = 6.0714x - 5.4714
R² = 0.9959
70%
60%
50%
Ref/ vs.
Ref
580580
40%
30%
20%
10%
0%
0.84
Ref
580
0.9835
0.88
264
300
0.92
299
325
0.96
336
350
1.00
375
375
1.04
416
400
1.08 Illuminance Ratio
Illuminance (lux)
Figure 5-2 Brightness Discrimination: Linear Relationship of SPDRef versus SPD580.
45
Percentage of Trials SPD500 or SPD500,580 Selected as
Percentage of Trials 500 or 500,580 SPD Selected
Brighter when Compared to SPD
SPD
as Brighter when Compared to 580580
100%
500,580 / 580
y = 5.3571x - 4.6457
R² = 0.9579
90%
80%
70%
60%
50%
500/ vs.
500
580580
500,580/ vs.
500,580
580580
40%
500 / 580
y = 5.0714x - 4.44
R² = 0.9732
30%
20%
10%
0.9605
0%
0.84
500 and 500,580
580
0.88
264
300
0.92
299
325
0.9741
0.96
336
350
1.00
375
375
1.04
416
400
1.08 Illuminance Ratio
Illuminance (lux)
Percentage of Trials SPD500 Selected as Brighter
Selected as Brighter
Percentage of Trials 500 SPD
when Compared to SPD500,580
SPD
when Compared to 500,580
Figure 5-3 Brightness Discrimination: Linear Relationship of SPD500 versus SPD580 and
SPD500,580 versus SPD580.
100%
90%
80%
y = 5.5714x - 5.0114
R² = 0.9794
70%
60%
50%
500/ vs.
500, 580
500
500,580
40%
30%
20%
10%
0%
0.84
0.9892
0.88
0.92
500 276
500,580
300
0.96
312
325
1.00
350
350
1.04
390
375
1.08 Illuminance Ratio
432
Illuminance (lux)
400
Figure 5-4 Brightness Discrimination: Linear Relationship of SPD500 versus SPD500,580.
46
The VSRS analysis shows a statistically significant difference between comparisons SPD500 versus
SPD580 and SPD500,580 versus SPD580 at equivalent illuminance. The significant difference is expected
given no significant difference was found for SPDRef versus SPD580 and a significant difference was
found for SPD500 versus SPDRef and SPD500,580 versus SPDRef. No significant difference was found for
SPD500 versus SPD500,580. This result was expected based on the pilot trials and is the reason the
SPD500 versus SPD500,580 illuminance ratio was set to 1.0 for the center step instead of 0.96. The
SPD500 versus SPD500,580 ratio is the SPD comparison closest to unity, 98.92%. The slope and
intercept values of all of the linear relationships of the SPD comparisons are listed in Table 5-9.
Table 5-9 Brightness Discrimination: Linear Regression Model of Spectrum Comparisons
y = mx + b
SPD
Comparison
m
b
y
x
R2
500 vs. Ref
Ref vs. 580
500,580 vs. Ref
500 vs. 580
500 vs. 500,580
500,580 vs. 580
4.0714
6.0714
7.5714
5.0714
5.5714
5.3571
3.3886
5.4714
6.6314
4.44
5.0114
4.6457
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
95.51%
98.35%
94.19%
97.41%
98.92%
96.05%
0.9881
0.9959
0.9653
0.9579
0.9794
0.9732
A binary logistic regression analysis was completed for each comparison, modeling how the SPD
response depends on the explanatory variables ratio and booth selected. The probability of the
response taking a particular value is modeled with logistic regression in contrast to linear regression
which is based upon a combination of predictor values. The SPD selected and booth selected were
converted to binary numbers (0, 1) in order to model the data. The coefficients of the binary logistic
regression models and ratios at 50% are listed in Table 5-10. The selection of left or right booth was
initially modeled as a factor, however it was not a significant factor and was removed for the
determination of the final model coefficients. The ratios resulting from the binary logistic regression
analysis are the same as the simple linear regression model.
Table 5-10 Brightness Discrimination: Binary Logistic Regression Model of Spectrum
Comparisons
SPD
Comparison
β0
β1
y
x
500 vs. Ref
Ref vs. 580
500,580 vs. Ref
500 vs. 580
500 vs. 500,580
500,580 vs. 580
-17.9378
30.3554
-40.1431
26.2584
31.2072
-27.3493
18.7912
-30.8336
42.571
-26.8948
-31.6190
28.4699
50.0%
50.0%
50.0%
50.0%
50.0%
50.0%
95.46%
98.45%
94.30%
97.63%
98.70%
96.06%
Difference
from
Linear Fit
x value
0.05%
0.10%
0.11%
0.22%
0.22%
0.01%
47
The results of the brightness discrimination and brightness matching experiment are comparable.
Table 5-11 compares the findings of significant difference and ratios for each SPD comparison for
both experiments. A one-sample t-test was used to compare the brightness discrimination and
brightness matching ratios. Only two of the six comparison ratios were found to not be significantly
different from each other, however inspection of the confidence intervals in Table 5-12 reveals how
comparable the ratios are even if statistically different. The SPD500,580 versus SPDRef confidence
interval is (0.94375, 0.96902), so if the resulting ratio of the brightness discrimination experiment
was 0.185% higher, the two ratios would no longer be significantly different. The rank order is
similar for both experimental methodologies. The significant difference from unity of the comparison
ratios resulting from the two experiments are the same except for SPD500 versus SPD580. The
granularity of the experiments was different, and likely contributed to the differences found between
the two experiments. The participants provided continuous responses for the brightness matching
experiment, varying the brightness of the booth in small increments, and a binary response, choosing
the left booth or right booth, for the brightness discrimination experiment. The differences in
participant effort and time required between the two experiments are also likely reasons for the small
differences between the two methods.
Table 5-11 Comparison of Brightness Matching and Brightness Discrimination Results.
SPD
Comparison
500 vs. Ref
Ref vs. 580
500,580 vs. Ref
500 vs. 580
500 vs. 500,580
500,580 vs. 580
Bright.
Discr.
Ratio
95.51%
98.35%
94.19%
97.41%
98.92%
96.05%
Bright.
Match.
Ratio
98.12%
99.04%
95.64%
99.65%
99.51%
98.12%
Ratio
Diff.
Pvalue
2.61%
0.69%
1.45%
2.24%
0.59%
2.07%
<0.001
0.236
0.026
0.007
0.221
0.017
Ratio
Sig.
Diff.
Yes
No
Yes
Yes
No
Yes
Discr.
Ratio
Rank
2
5
1
4
6
3
Match.
Ratio
Rank
2
4
1
6
5
2
Discr.
Sig.
Diff.
Yes
No
Yes
Yes
No
Yes
Match.
Sig.
Diff.
Yes
No
Yes
No
No
Yes
Table 5-12 Comparison of Brightness Matching and Brightness Discrimination Results : OneSample t-Test 95% Confidence Intervals.
SPD
Comparison
500 vs. Ref
Ref vs. 580
500,580 vs. Ref
500 vs. 580
500 vs. 500,580
500,580 vs. 580
95 % Confidence Interval
(0.96609, 0.99622)
(0.99782, 1.02152)
(0.94375, 0.96902)
(0.98051, 1.01251)
(0.98549, 1.00468)
(0.96449, 0.99799)
The frequency of selection for each brightness discrimination trial is listed in Table 5-13 and is
divided into two groups based upon spectrum position which was counterbalanced between subjects.
The number of participants that viewed the spectrum in the positions listed under Group 1 was 18,
while 17 participants viewed the spectrum in the positions listed under Group 2. The number of
counts should be very similar between Group 1 Left and Group 2 Right in the absence of positional
bias.
48
Table 5-13 Brightness Discrimination: Selection Count Comparison between Left and Right
Booths. The trials highlighted in gray are the trials in which the count difference between
Group 1 Left and Group 2 Right is greater than 5.
Trial
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Spectrum Position: Group 1
Right
Left
SPD
Count SPD
500
5
Ref
Ref
10
500
500
5
Ref
Ref
3
500
500
14
Ref
580
17
Ref
Ref
1
580
580
10
Ref
Ref
7
580
580
2
Ref
Ref
17
500,580
500,580
2
Ref
Ref
4
500,580
500,580
16
Ref
Ref
1
500,580
500
2
580
580
13
500
500
9
580
580
6
500
500
16
580
500,580
14
500
500
2
500,580
500,580
5
500
500
13
500,580
500,580
1
500
500,580
2
580
580
13
500,580
500,580
6
580
580
5
500,580
500,580
16
580
Ref
0
Ref
500
3
500
580
4
580
500,580
3
500,580
Count
13
8
13
15
4
1
17
8
11
16
1
16
14
2
17
16
5
9
12
2
4
16
13
5
17
16
5
12
13
2
18
15
14
15
Spectrum Position: Group 2
Right
Left
SPD
Count SPD
Ref
14
500
500
3
Ref
Ref
4
500
500
9
Ref
Ref
1
500
Ref
0
580
580
14
Ref
Ref
5
580
580
4
Ref
Ref
14
580
500,580
2
Ref
Ref
11
500,580
500,580
10
Ref
Ref
0
500,580
500,580
17
Ref
580
16
500
500
0
580
580
11
500
500
10
580
580
3
500
500
1
500,580
500,580
9
500
500
6
500,580
500,580
0
500
500
17
500,580
580
15
500,580
500,580
4
580
580
9
500,580
500,580
15
580
580
1
500,580
Ref
3
Ref
500
3
500
580
3
580
500,580
3
500,580
Count
3
14
13
8
16
17
3
12
13
3
15
6
7
17
0
1
17
6
7
14
16
8
11
17
0
2
13
8
2
16
14
14
14
14
Left bias is evident in the experimental trials, although the bias in the experimental trials is not as
severe as the null condition trials, 31 to 34. Statistically significant left bias occurred in the brightness
matching experiment for each null condition except SPD500,580. Statistical analysis of the frequency
counts of the 1.0 ratio was completed using a general linear model (GLM). Position was a statistically
significant predictor for SPDRef (p=0.027), SPD500 (p = 0.010) and SPD500,580 (p = 0.025).
49
The left bias was not evident in the pilot trials and was not expected based upon high dynamic range
(HDR) images taken prior to the start of the experimental trials. The HDR images were taken of the
booths under each null condition and converted into falsecolor images with Radiance. An HDR image
of the booths both illuminated by SPDRef is shown in the top of Figure 5-5 and both booths
illuminated by SPD500,580 is shown in the bottom. There were no clear visual differences in
distribution between the left and right booths in any of the HDR images taken of the null condition
trials.
Additional characteristics of the apparatus and measurement equipment were checked. The
illuminance meters located at the center of each booth were switched between the booths to check for
a difference in the illuminance meters. No difference in illuminance was found. This was expected as
the illuminance meters had been calibrated by the manufacturer approximately two months before the
start of the experiment.
An illuminance meter was also placed at the approximate position of the participant’s eyes when the
participant’s head was located within the chin and forehead rest. The Telelumen of the left booth was
turned off while the right booth Telelumen was adjusted to an illuminance of 350 lux at the center of
the bottom plane of the booth, in the same manner as during the experimental trials. The procedure
was again repeated, but with the left booth illuminated and the Telelumen of the right booth turned
off. The illuminance at the plane of the eye when only the right booth was turned on was 0.5 lux
greater than when only the left booth was turned on. This difference is within the error tolerance of
the measurement equipment and setup.
The reference and test SPD measurements were used to calculate luminous flux, Table 5-14. The
greatest difference in luminous flux for the null conditions was for SPDRef, with the left booth having
a 0.12% greater luminous flux than the right booth, however this difference is too small to be the
cause of the left bias. Further evidence that luminous flux is not the cause of the bias is found when
examining the SPD500 null condition. The luminous flux of SPD500 in the right booth is 0.08% greater
than the left booth, yet the left booth was still chosen by participants 29 out of 35 times as appearing
brighter for the SPD500 null condition trial.
Table 5-14 Comparison of the Luminous Flux of the Left and Right Booths.
SPD
Ref
500
580
500,580
Left Booth
Flux (lumens)
88.89
88.43
88.46
88.42
Right Booth
Flux (lumens)
88.78
88.51
88.37
88.40
Percent
Difference
0.12%
0.08%
0.11%
0.03%
The reason for the left bias is unknown. Small, unavoidable differences likely led to the selection of
the left side over the right side when the two booths appeared equally bright. The booths appeared
more similar in color appearance for the null conditions trials than when the SPD varied between
booths. This is one of the unavoidable differences due to limitations of the experimental apparatus,
and is likely a cause of the difference in left bias between the null conditions and the other
experimental trials.
50
Figure 5-5 Null Condition HDR Radiance Falsecolor Luminance Images (Top- Both booths
illuminated by SPDRef ; Middle- Both booths illuminated by SPD500,580; Bottom- Luminance
difference between the two booths). The Falsecolor HDR images of the luminance distributions
for the test and reference SPDs are comparable.
5.3 PREFERENCE DISCRIMINATION
The preference discrimination experiment was designed to answer the following question:
Does the reallocation of energy from the regions of 500, 580, or 500 and 580 nm change
preference for a person’s own skin tone?
51
Two of the test spectra were preferred over the reference spectrum. The preference discrimination
experiment was designed to have participants make a forced choice preference judgment between the
test and reference spectra at the same illuminance based upon their preference for the rendering of
their hands. Participants seemed to enjoy the preference discrimination experiment more than any
other experiment. There are likely several reasons including shorter transition time between trials,
shorter duration of the experiment, and looking at one’s hands was a welcome change after looking at
two blank white booths for over an hour during the brightness discrimination experiment.
Results
The results of the preference discrimination experiment are summarized in Table 5-15. The
participants viewed each comparison twice. After completing the entire experiment the participants
were asked if they looked at a particular part of their hands. The majority of participants said their
judgment was based on their overall hand. Four participants said the judgment was based on the back
of their hand. One participant said their judgment was based upon imagining his/her entire body being
in the space. Other responses included: veins, fingers, knuckles, wrists, scars, between index finger
and thumb, edge of palm, and top of hand. Participants also commented that their decision was based
upon color and splotchiness of skin.
Table 5-15 Preference Discrimination: Percentage of Trials Spectra Selected as Preferred. Each
participant viewed each trial listed below once, with the final calculated percent of the non-null
condition trials calculated from 70 judgments.
Trial
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Right Booth
SPD
Ref
500
Ref
580
Ref
500,580
500
580
500
500,580
580
500,580
Ref
500
580
500,580
Left Booth
Count
14
18
10
26
11
25
11
26
12
22
19
17
17
22
14
20
SPD
500
Ref
580
Ref
500,580
Ref
580
500
500,580
500
500,580
580
Ref
500
580
500,580
Percent
Count
21
17
25
9
24
10
24
9
23
13
16
18
18
13
21
15
60.00%
51.43%
71.43%
74.29%
68.57%
71.43%
68.57%
74.29%
65.71%
62.86%
54.29%
51.43%
51.43%
37.14%
60.00%
42.86%
Final Participant SPD Preference
55.71%
Prefer 500 vs. Ref
72.86%
Prefer 580 vs. Ref
70.00%
Prefer 500,580 vs. Ref
71.43%
Prefer 580 vs. 500
64.29%
Prefer 500,580 vs. 500
52.86%
Prefer 580 vs. 500,580
47.86%
Prefer Left vs. Right
Analysis
SPD580 and SPD500,580 were both preferred by participants, with 70% or more of the preference
selections versus SPDRef. SPD580 and SPD500,580 were not significantly different, and both were
52
statistically different from SPDRef at an alpha level of 0.01. Table 5-16 summarizes the VSRS critical
ranges for the preference discrimination experiment. SPD580 was preferred by participants versus
SPDRef and SPD500, and was statistically significant in both cases at an alpha level of 0.01. SPD500,580
was preferred over SPD500 at an alpha level of 0.05. The rank order resulting from the preference
experiment matches the rank order of the following metrics: GAI, CQS 9.0 Qg, FM Gamut, CPI, CDI
and FCI. SPD580 had the highest preference rank, however it had the lowest CRI and R9 rank.
In this experiment there was no indication of left bias. The total count on the left was 209 and on the
right was 211. There was greater variation between the left and right counts for the null conditions,
which may be due to the slight color differences between the left and right settings. The slight color
difference is difficult to characterize, as the color difference will appear different to different people
and the chromaticity coordinates cannot correctly characterize the minor differences.
Table 5-16 Preference Discrimination: Dunn-Rankin VSRS Matrix of Rank Differences and
Critical Ranges. Ri is the scaled rank score, and the difference in the scaled rank scores is the
critical range. The trials comparing the same SPDs, only varying in position, were combined
because the observations of the pairs are not independent.
Ref
500
580
500,580
Ri
106
119
173
162
Ref
106
13
67
56
500
119
580
173
500,580
162
54
43
11
-
α = 0.01 if critical range > 47.6
α = 0.05 if critical range > 39.2
A GLM analysis of the data revealed that race was a statistically significant factor for SPD500. Asian
preference was statistically different that Caucasian and Hispanic preference, with Asians having a
stronger preference for SPD500. Race was only a significant factor for SPD500, at an alpha level of 0.01
(p = 0.007). Due to the small sample size of the experiment, with only seven Asians participating, the
generalization that Asians prefer SPD500 cannot be stated.
5.4 DISCUSSION
The results of the research can be used with the findings of other researchers to improve source
efficiency, source preference, and further understanding of the strengths and limitations of current
metrics and models. This work builds directly upon the spectral tuning research in the prime and antiprime regions of Houser, Hu and Tiller [2004a, 2004b]. Houser et al. [2004b] examined the prime
wavelength regions by comparing a tuned fluorescent lamp with peaks in the prime regions to a
conventional fluorescent lamp using side-by-side rooms. Expert and naïve participants completed a
forced choice survey when viewing the two rooms illuminated by different lamp types. The survey
asked participants to make judgments of clarity, brightness, preference, colorfulness, and naturalness
when comparing the two rooms. The results demonstrated that the tuned lamp enhances brightness
perception and color preference versus the conventional lamp, and the authors concluded that
perception of brightness, color and visual clarity are more reliant on the placement of energy in the
regions of 450-530-610 nm versus magnitude of energy. Houser and Hu [2004a] found that when
53
participants visually matched a reference daylight fluorescent lamp using two sets of spectral
primaries, prime 453-533-619 nm and anti-prime 493-581-657 nm. The prime set required 5% less
power to match the fluorescent while the anti-prime set required 117% more power. The brightness
experiments in this study showed an increase in efficacy when energy in the anti-prime regions was
reallocated.
SPD500,580 appeared as bright as SPDRef using approximately 5.1% less lumens. The LER of SPD500,580
was 8.9% greater than SPDRef. The combination of these values is a 14% improvement in efficacy. A
similar improvement in efficacy is possible with SPD500. The LER of SPD500 was 10.3% greater than
SPDRef, and SPD500 required approximately 3.2% less lumens than SPDRef, resulting in a 13.5%
improvement in efficacy. The 13.5% efficacy improvement of SPD500 versus SPDRef should be stated
with caution due to the variation in results of the brightness matching and discrimination experiments.
The 3.2% approximation is an average of the results of the brightness matching experiment and the
brightness discrimination experiment, 1.9% and 4.5%, respectively. The 5.1% approximation is also
an average of the brightness matching and discrimination experiments, 5.8% and 4.3% respectively.
Table 5-17 lists the LER values and percent differences for all of the SPD comparisons.
Table 5-17 Luminous Efficacy of Radiation Comparison of Reference and Test SPDs.
Ref
500
580
500,580
LER
235
260.5
235
257
Ref
235
--10.3%
0.00%
8.9%
500
260.5
580
235
500,580
257
--10.3%
1.4%
--8.9%
---
The following metrics have been suggested as correlates for brightness perception: CRI, CDI, S/P
Ratio, and GAI. Table 5-18 compares the brightness rank of the reference and test SPDs to these
metrics. All of the metrics fail to predict the rank of brightness found in this research. S/P has been
suggested as a correlate for brightness [Berman et al. 1990], however S/P also fails to predict
brightness [Houser et al. 2009]. CCT has also been suggested as a correlate to brightness, however
CCT was the same for the reference and test SPDs and therefore CCT also fails to predict brightness
rank.
54
Table 5-18 Experimental Brightness Rank Compared to Brightness Appearance Prediction
Metrics. The brightness rank resulting from the matching and discrimination metrics is
compared to the brightness rank according to CRI, CDI, S/P Ratio and GAI. The computed
value of the metrics is listed, with the color indicating the rank. The rank values in the first row,
Brightness Rank, indicate the color assigned to a given rank.
Left Booth SPD
Ref
500
580
Right Booth SPD
500,580 Ref
500
580
500,580
Brightness Rank
(Discrimination 4
and Matching)
2
3
1
4
2
3
1
CRI
97
88
86
90
94
86
85
91
CDI (Illum. C)
76
79
84
84
75
77
85
84
S/P Ratio
1.93
1.79
2.02
1.88
1.92
1.78
2.03
1.88
GAI (Equal
Energy)
78
81
86
86
76
79
86
85
The reallocation of energy from the spectral regions of 500 and 580 nm was based upon the work of
Thornton [Thornton 1971, 1972b, 1992a-c] and Houser and his colleagues [Houser and Hu 2004a,
Houser et al. 2004b] and these specific wavelengths were chosen because the relative sensitivity of
the opponent signals cross zero at 480, 500 and 580 nm according to opponent sensitivities derived by
Hurvich [1981]. Brightness models accounting for opponent theory have been proposed by numerous
researchers including Thornton, Guth and Howett [Houser 2001], however the models all fail to
predict brightness because the inputs of the models are tristimulus values which were equivalent for
all of the SPDs examined in this study. The reference and test SPDs scaled by Hurvich’s derived
sensitivity of the opponent signals are plotted in Figure 5-6 and reveal the most likely reason for the
brightness rank found in this experiment.
The SPDs with the largest fraction of optical radiation in the prime regions of 530 nm and the
smallest fraction in the anti-prime region of 500 nm are SPD500,580 and SPD500. These SPDs are also
the SPDs that appeared significantly brighter than SPDRef. The SPD500,580 also had a slightly larger
fraction than the other reference and test SPDs in the prime region of 450 nm. The fraction of optical
radiation of SPD500,580 and SPD580 at 580 nm is half of SPDRef and SPD500, however is well above zero
and may be a reason why SPD580 did not appear brighter than SPDRef. SPD500,580 and SPD500 also have
slightly larger fractions of optical radiation than SPDRef and SPD500 in the prime region of 610 nm.
55
0.002
0.0015
Amplitude
0.001
0.0005
0
-0.0005
-0.001
-0.0015
400
450
Black-White Channel
500
550
Wavelength (nm)
600
650
Red-Green Channel
Yellow-Blue Channel
Ref-L
Ref-L
Ref-L
Ref-R
Ref-R
Ref-R
500-L
500-L
500-L
500-R
500-R
500-R
580-L
580-L
580-L
580-R
580-R
580-R
500,580-L
500,580-L
500,580-L
500,580-R
500,580-R
500,580-R
Figure 5-6 Plot of the Reference and Test SPDs Weighted By Hurvich Opponent Signal
Sensitivity.
56
The values plotted in Figure 5-6 are summed and listed in Table 5-19. SPD500,580 ranks lowest for the
black/white channel, however is ranked second for both the red/green and yellow/blue channels. The
values for the black/white channel are very similar, which is expected because the black/white
channel is V(λ) and the SPD measurements were taken at the same photopic quantity. The computed
signal values for both the red/green and yellow/blue channels have greater variance than the
black/white channel. Although the SPD500,580 average signal ranks second for both of these channels,
it ties for the highest average rank with SPD500. When SPD500 was compared to SPD500,580 during the
brightness matching and discrimination experiments, no significant difference was found between the
SPDs in either experiment. These SPDs are also the SPDs that appeared significantly brighter than
SPDRef. The computed values support the results of the brightness experiments, however do not
predict the number one rank for SPD500,580 that resulted from the brightness experiments.
Table 5-19 Computed Values for Reference and Test SPDs Weighted by Hurvich Opponent
Signal Sensitivity. The values shown are a summation of the absolute values of the signals of the
weighted SPDs.
Black/White Channel
Ref
500
580
500,580
Red/Green Channel
Ref
500
580
500,580
Yellow/Blue Channel
Ref
500
580
500,580
Left Booth
Signal
Right Booth
Signal
Average
Signal
Average
Signal Rank
0.026029
0.025896
0.025904
0.025893
0.025997
0.025917
0.025876
0.025885
0.026013
0.025906
0.025890
0.025889
1
2
3
4
0.019016
0.019055
0.020732
0.020228
0.018807
0.018888
0.020774
0.020211
0.018912
0.018972
0.020753
0.020219
4
3
1
2
0.015388
0.015866
0.015212
0.015784
0.015295
0.015787
0.015186
0.015713
0.015342
0.015826
0.015199
0.015748
3
1
4
2
The principle of univariance states that the effect of the absorption of a photon by a visual pigment
molecule does not depend of wavelength [Goldstein 2007]. Two or more receptors are required in
order to see color, otherwise energy could be modified to elicit the same magnitude of response for all
wavelengths. The principle of univariance explains why photometry often fails to characterize
brightness perception. Photometry only accounts for one receptor, however our visual system uses
multiple receptors. This is the most likely explanation for why illuminance did not predict the
perception of brightness in this experiment. SPD500,580 had more energy in the 530 to 555 nm region
than the other reference and test SPDs and in this region the sensitivity of all three channels peaks.
All brightness metrics examined failed to predict that SPD500,580 would appear the brightest, however
preference metrics did not fail to predict that SPD500,580 would be preferred.
The preference rank of the reference and test SPDs are compared to ten metrics commonly used to
predict preference in Table 5-20. CRI was not developed to predict preference, however it is
commonly used as a correlate to preference. SPD580 had the lowest CRI and R9 values, yet the highest
57
preference rank. CQS was developed to improve upon CRI by accounting for human preference,
however it also fails to predict the preference rank. The preference metrics based on gamut area all
correctly predicted the preference rank.
Table 5-20 Experimental Preference Rank Compared to Preference Prediction Metrics. The
computed value of the metrics is listed, with the color indicating the rank. The rank values in
the first row, Preference Rank, indicate the color assigned to a given rank.
Left Booth SPD
Ref
500
580
Right Booth SPD
500,580 Ref
500
580
500,580
Preference Rank
4
(Discrimination)
3
1
2
4
3
1
2
CRI
97
88
86
90
94
86
85
91
R9
77
80
69
97
68
70
63
98
CQS
97
86
90
92
95
85
89
92
GAI (Equal
Energy)
78
81
86
86
76
79
86
85
CPI
97
98
112
110
94
96
112
110
FM Gamut
(Illum. C)
73
76
80
80
72
74
81
80
CQS v. 9.0 - Qg
98
103
107
108
97
101
108
108
CDI (Illum. C)
76
79
84
84
75
77
85
84
FCI (2002)
104
110
113
113
103
108
113
114
Figure 5-7 illustrates the gamut area of the 15 CQS reflective samples [Davis, Ohno 2010]
illuminated by the test and reference SPDs in comparison to the gamut area of the samples
58
illuminated by a Planckian radiator. SPD500,580 and SPD500 had larger gamut areas than the Planckian
reference, increased chroma along the a*-axis, and were preferred by participants. SPD580 is similar to
the SPD of neodymium incandescent lamps, which have energy attenuated in the region of 580 nm,
and these lamps are preferred by many people [Ohno 2004, Ohno 2005]. Ohno states the reasons for
the increase in preference are the increase in the chroma of red and green under these sources and the
gamut area is larger in comparison to a reference blackbody radiator. The gamut area of SPDRef is
very similar to the gamut area of the Planckian radiator. SPD500 had increased chroma along the b*axis, yellow and blue. No significant difference in preference was found between SPD500 and SPDRef.
The increase in chroma does not improve preference based upon the results of the experiment,
however SPD500 was preferred by Asian participants.
Figure 5-7 Color of the 14 CQS Samples in CIELAB under Planckian Illumination (blue) and
the Reference and Test SPDs (red). The points plotted on the two dimensional a*b* CIELAB
plot represent the hue and saturation of the CQS reflective samples illuminated by different
spectra [Davis, Ohno 2010]. The origin represent neutral gray and the distance from the origin
represent chroma and the angle represents hue.
Quellman and Boyce [2002] examined the color preference of people with different skin tones, and
found a difference in preference based on skin tone. The judgment of preference was based upon the
back of the hand of the 32 participants, who were equally divided into four different skin types: White
59
of European descent, Asian of Chinese, Japanese or Thai nationality, Indian or Sri Lanka nationality
and African American. The seven different lamps examine dined in the experiment ranged from 2,850
CCT to 5,000 CCT and all had a color rendering index above 80. White participants preferred lamp
types with warmer tones that made their skin look natural and relatively tan. Asian participants
preferred lamp types that were neutral and close to daylight and wanted to appear white and not too
yellow or too green. The authors do not provide the spectral power distribution of the lamps or other
detailed information in order to make a more direct comparison between their results and the results
of this dissertation research.
5.5 LIMITATIONS AND FUTURE RESEARCH
Energy efficiency and quality are typically antonyms in the lighting industry. Quality is often
sacrificed to improve efficiency. The results of this research are exciting because the choice between
quality and energy efficiency is not required. This research can be used in conjunction with previous
spectral tuning research to improve source efficiency and quality, however a clear understanding of
the limitations of this research is needed to apply and build upon this research.
This research was limited to 35 participants, ranging from 20 to 25 years old, and a majority of the
participants were Caucasian. The small sample of participants of varying races prevented the results
from providing an understanding of the effect of race on preference.
The granularity of the brightness matching and brightness discrimination experiments was different,
and is likely one of the reasons for the differences found between the two experiments.
Limitations of the apparatus include: the same LED channels of the Telelumen luminaires differing in
color appearance, matching chromaticity coordinates and color appearance, color shift due to
dimming. The individual LED channels of the Telelumen luminaires did not all visually match, with
some channels having larger color difference than others between the luminaires. This characteristic,
as well as the change in output of the LEDs over time and day to day, made it difficult to match
chromaticity coordinates and color appearance. The shift in chromaticity was minimized by
understanding how dimming using the mechanical iris and the Telelumen caused the chromaticity to
change. The illuminance range in this study was dictated by the settings of both the Telelumen and
iris dimmer that resulted in the smallest change in chromaticity coordinates.
Another limitation of the apparatus was the inability to create a larger dip in the spectral region of 580
nm. The deviation of light from a normal incident angle when striking the custom filter resulted in a
higher transmission than was desired in the spectral regions of 500 and 580 nm.
The aforementioned limitations are also opportunities for future research. For example, increasing the
number of participants could improve upon several limitations of this study including: a broader age
range, a larger sample of different races, and an increase in granularity of the brightness
discrimination experiment. Further research should also seek to determine if brightness increases
when energy in these spectral regions is zero. Addressing the limitations of this research is important
to improving the results and furthering the broader application of the results of this research, and
more generally the results of spectral tuning research.
60
Chapter 6
CONCLUSION
This dissertation examined brightness and preference differences between a reference spectrum and
three test spectra that had less optical radiation in the spectral regions near 500, 580, or 500 and 580
nm. Two experimental methodologies were employed, forced choice discrimination at fixed
illuminance levels, and brightness matching.
The research aimed to answer the following questions:
1. Do spectra with energy reallocated from the regions of either 500, 580, or 500 and 580 nm
appear brighter than a nearly continuous reference spectrum?
2. Do the experimental methodologies side-by-side brightness matching and side-by-side
brightness discrimination (forced choice) produce comparable results?
3. Does the reallocation of energy from the regions of 500, 580, or 500 and 580 nm change
preference for a person’s own skin tone?
The test spectrum with energy reallocated near the spectral region of 500 and 580 nm (SPD500,580)
appeared brighter than the reference spectrum (SPDRef) at equal illuminance, reaching statistical
significance for both the brightness matching and brightness discrimination experiments.
The side-by-side brightness matching experimental methodology and the side-by-side brightness
discrimination experimental methodology produced comparable results. The rank order of the
reference and test spectra was similar for both experimental methodologies. The illuminance ratio of
the SPD500,580 versus SPDRef at equal perceived brightness was 94.2% for the brightness
discrimination experiment and 95.6% for the brightness matching experiment.
The test spectrum with energy reallocated near the spectral region of 580 nm (SPD580) was preferred
versus SPDRef for 72.9% of the comparisons between the two spectra. Similarly, SPD500,580 was
preferred versus the reference spectrum for 70.0% of the comparisons between the two spectra.
SPD500,580 and SPD580 both were significantly different than SPDRef at an alpha level of 0.01, and there
was no statistical difference in preference between SPD500,580 and SPD580.
SPD500,580 is the superior spectrum given the results of all three experiments: brightness matching,
brightness discrimination and preference discrimination. SPD500,580 appeared brighter than the other
spectra when compared to the SPDRef in both the brightness discrimination and matching
experiments. SPD500,580 was also preferred versus SPD500 and SPDRef, and was not significantly
different than the SPD580, which had the highest preference rank. The findings of this research clearly
demonstrate, with comparable results from brightness matching and discrimination methodologies,
that spectral energy reallocated in the regions of 500 and 580 nm simultaneously was perceived as
brighter and is preferred for rendering one’s own hand.
The LER of SPD500,580 was 8.9% greater than SPDRef and SPD500,580 required an average of 5.1% less
lumens than SPDRef at equal brightness appearance. When the difference in LER is combined with the
results of the brightness experiments, SPD500,580 provides a 14% improvement in efficacy versus
SPDRef at equal perceived brightness.
61
This research builds upon the past work of other researchers to further our understanding of human
psychophysical response to spectra and the role of spectral tuning in source optimization. The results
of this research can be used with the findings of other researchers to improve source efficiency,
source preference, and further the understanding of the strengths and limitations of current metrics
and models. This research demonstrates that a spectrally tuned source can simultaneously improve
source efficiency and preference, evidence that spectrally tuned sources can lead to lighted
environments that are more efficient and visually pleasing.
62
Appendix A
Reference and Test SPDs
Table A.1 Reference and Test Average Relative Spectral Power Distributions
380
385
390
395
400
405
410
415
420
425
430
435
440
445
450
455
460
465
470
475
480
485
490
495
500
505
510
515
520
525
530
535
540
545
550
555
560
565
A-Left
0.215448
0.445882
0.793431
1.226489
2.052729
5.10816
11.66512
19.78668
30.69748
39.38637
42.52996
45.21752
43.85514
37.89035
36.55833
43.70992
52.7381
53.62679
49.90614
49.17172
47.98097
44.76934
44.07486
49.02098
55.49275
58.62881
58.8359
59.06713
60.94722
63.4194
65.39201
66.75849
67.6072
68.21059
68.37044
68.09589
67.37214
66.71476
A-Right
0.203991
0.384224
0.814568
1.334079
2.240254
5.20135
11.91095
19.52219
28.92355
38.65667
43.92977
47.25988
45.75665
39.2258
37.37603
44.26461
53.93473
55.2741
50.97692
49.75829
48.72912
45.84561
45.16831
49.48295
54.92011
57.55588
58.14913
58.93194
61.96821
65.37434
68.16437
70.23014
71.63543
72.60663
72.9287
72.62104
71.69374
70.65234
B-Left
0.199329
0.494998
1.174353
1.98395
3.258232
7.788598
16.93466
27.15774
39.9272
49.46222
51.84892
53.19009
50.92367
45.25069
45.68362
56.03599
65.08617
62.16487
53.39223
46.19304
36.66431
24.55394
15.72488
15.036
18.65525
26.68951
38.88193
53.23485
66.90093
78.15001
86.02562
90.37379
91.4983
90.50158
87.64064
83.36755
78.58652
74.10513
B-Right
0.282561
0.486089
1.197895
2.144483
3.642649
8.358274
18.04393
27.63506
38.40348
49.07779
53.583
55.23968
52.78259
46.66831
46.82191
57.25859
66.84214
63.45452
52.81865
44.38142
34.97831
23.77218
15.83884
15.26019
18.74522
26.40004
38.19791
52.53326
66.79141
79.22581
88.6279
94.45296
96.50326
95.65289
92.55486
88.06963
83.0486
78.22045
C-Left
0.162469
0.365975
0.878666
1.310101
2.007757
4.223248
8.982916
14.18722
21.12684
28.51374
34.95127
41.8654
42.29066
38.2646
38.77054
48.03735
56.68257
54.7728
47.51961
43.88681
41.40339
39.17903
39.99725
46.69329
54.82215
60.90938
65.39689
70.96251
77.53758
82.62706
84.35527
83.14944
78.14649
71.22796
62.82024
53.4219
43.63025
34.68442
C-Right
0.107358
0.294044
0.750071
1.190069
1.72704
3.245262
6.618759
10.23751
14.65428
21.98061
32.15863
42.55761
43.70197
38.82225
38.18646
46.02212
54.6037
53.77289
46.6975
43.12577
41.14077
39.78833
41.20061
47.55164
54.26573
58.82079
62.55357
68.19995
75.09329
80.48842
82.41304
81.18965
75.92766
68.76306
60.42025
51.47781
42.36803
34.12483
D-Left
0.206235
0.335354
0.986418
1.588732
2.496883
5.538055
11.78068
18.87469
28.33408
37.72581
44.58513
51.30564
51.21618
46.53661
47.45804
57.97247
65.56321
59.43343
47.12213
37.93479
29.08424
19.62632
13.29549
13.90026
19.75954
31.07911
47.59277
65.77744
81.49532
93.04736
99.44148
100
95.56947
87.6668
77.61146
66.21777
54.24699
42.45979
D-Right
0.107452
0.336596
1.3877
2.526207
3.461048
5.84898
11.32874
17.66568
25.8117
35.65392
43.72369
50.25325
49.93351
45.20911
45.87381
56.24843
65.11447
61.07652
49.95661
41.21183
32.26258
22.20443
15.14861
15.41729
20.89579
31.52761
47.27594
65.10518
80.84862
92.72528
99.46542
100
95.05711
86.63348
76.41046
65.29405
53.92587
42.69396
63
570
575
580
585
590
595
600
605
610
615
620
625
630
635
640
645
650
655
660
665
670
675
680
685
690
695
700
705
710
715
720
725
730
735
740
745
750
755
760
765
770
775
780
67.03337
68.99991
73.77468
82.04624
84.49742
76.90094
69.16306
69.43988
78.18656
85.18192
84.38588
85.21092
89.11831
79.76203
60.80171
52.52758
55.44447
66.70788
74.98355
62.49444
43.69077
36.03941
38.16225
46.90352
59.9073
74.52686
81.75547
77.60649
69.51243
68.95917
78.10647
92.45477
100
94.133
73.88661
48.01748
26.27981
14.63344
8.364712
5.05333
3.761682
3.211379
2.568514
70.68055
72.39827
77.09293
85.87676
89.1354
81.72198
72.38176
71.64151
79.71443
86.49256
85.83891
86.89635
90.39602
80.27241
60.84406
52.19546
54.51921
65.51017
75.30875
66.41796
46.84339
36.96104
37.85262
45.60374
58.15963
73.09322
82.36384
80.61054
73.21348
72.01212
79.6934
92.40745
100
95.19995
77.0113
52.26457
29.7849
16.69675
9.535454
5.566232
4.201157
3.06077
2.769795
71.83479
71.88369
75.28807
82.94327
85.24913
77.05341
68.91188
70.39678
81.64483
91.28907
91.43699
93.75173
100
91.08522
70.38331
62.64756
69.56619
86.46552
98.95353
81.49735
50.9254
34.25223
30.47573
33.76893
40.83254
48.59526
50.78389
46.71862
42.57596
44.9547
55.51698
68.106
74.0907
70.03409
55.17487
35.56948
19.44384
10.97181
6.108756
3.531392
2.369476
2.264735
1.633874
75.5842
75.35416
78.38195
86.07731
89.03697
81.38254
72.28233
72.44407
82.81023
91.94497
92.179
94.77243
100
90.64621
69.65734
61.46821
67.18596
82.83
96.92173
85.20728
55.24612
36.21273
30.71746
33.0628
39.55041
47.34248
50.59256
47.78824
44.45123
46.91001
57.07388
69.10216
75.53636
72.5984
59.18401
39.64842
22.51147
12.7247
7.048415
3.894852
2.426874
1.942514
1.698128
33.29526
39.35444
51.39488
71.41196
84.45517
79.99756
70.6439
72.62821
87.24133
100
99.08848
96.42248
99.38816
89.51745
67.86033
58.95285
64.61223
80.26274
92.27092
77.10881
49.17672
34.24453
32.20566
37.34848
46.22163
56.19274
60.40124
56.32822
50.34449
51.07643
60.65133
73.26206
80.95099
77.70133
62.40193
41.4729
23.27407
12.96163
7.073089
4.475424
3.157347
2.327901
2.02037
32.77466
38.28962
49.06095
66.89162
80.00244
77.11303
68.66125
69.92216
83.24462
95.97677
96.63904
95.9368
100
90.91792
68.52394
58.05605
61.83817
76.15344
89.89015
81.27807
54.18507
36.49525
32.20562
36.08133
44.22487
54.47821
60.76114
59.07042
53.46978
53.31456
61.16248
72.28656
80.08598
78.72877
66.17926
47.03085
28.31886
16.31558
9.016475
5.173036
3.44989
2.444391
1.889161
39.26265
44.72133
57.30568
77.7813
92.39686
88.55436
77.82583
76.83937
86.99188
95.24589
92.53865
89.01394
90.63045
82.11597
66.08576
61.15616
68.88412
84.83428
96.84682
80.76127
52.0578
36.48941
33.54105
37.41
43.94583
50.3136
49.67058
42.12183
35.57715
36.60154
43.79706
52.28093
59.21227
57.52271
46.51434
31.77027
17.94698
10.20112
5.4103
3.203416
1.837399
1.451172
1.030014
39.45348
44.50147
56.58006
76.5525
93.29512
91.28234
80.23137
78.03084
87.5959
96.47253
94.52138
90.92704
92.13098
83.46095
66.64013
60.42102
66.81303
81.88423
95.96552
87.27123
58.93089
40.15657
35.11942
38.05184
44.63848
51.99954
54.44296
49.16405
42.51956
42.35609
48.034
55.05258
62.16656
62.01705
52.85208
38.80452
23.59421
13.829
7.799528
4.527763
3.146047
2.857888
1.259594
64
Appendix B
Experimental Designs
Table B.1 Brightness Matching Experimental Design: The “u” and “d” next to the spectra
indicates the variable spectra and if the spectra were expected to be adjusted up or down
respectively.
Brightness Matching
Trial
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Group 1
Right Booth
Bu
310
A
350
B
350
Au
310
Cd
390
A
350
C
350
Ad
390
Du
310
A
350
D
350
Au
310
Cd
390
B
350
Bd
390
C
350
B
350
Du
310
D
350
Bd
390
C
350
Du
310
D
350
Cd
390
C
350
Bu
310
D
350
Ad
390
Group 2
Left Booth
A
350
Bd
390
Ad
390
B
350
A
350
Cu
310
Au
310
C
350
A
350
Dd
390
Ad
390
D
350
B
350
Cu
310
C
350
Bu
310
Dd
390
B
350
Bu
310
D
350
Dd
390
C
350
Cu
310
D
350
Cd
390
B
350
Du
310
A
350
Right Booth
A
350
Bd
390
Ad
390
B
350
A
350
Cu
310
Au
310
C
350
A
350
Dd
390
Ad
390
D
350
B
350
Cu
310
C
350
Bu
310
Dd
390
B
350
Bu
310
D
350
Dd
390
C
350
Cu
310
D
350
Cd
390
B
350
Du
310
A
350
Left Booth
Bu
310
A
350
B
350
Au
310
Cd
390
A
350
C
350
Ad
390
Du
310
A
350
D
350
Au
310
Cd
390
B
350
Bd
390
C
350
B
350
Du
310
D
350
Bd
390
C
350
Du
310
D
350
Cd
390
C
350
Bu
310
D
350
Ad
390
65
Table B.2 Brightness Discrimination Experimental Design.
Brightness Discrimination
Trial
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Group 1
Right
Booth
B 264
A 325
B 336
A 375
B 416
C 300
A 299
C 350
A 375
C 400
A 300
D 299
A 350
D 375
A 400
B 264
C 325
B 336
C 375
B 416
D 300
B 312
D 350
B 390
D 400
D 264
C 325
D 336
C 375
D 416
A 300
B 400
C 276
D 350
Left Booth
A
B
A
B
A
A
C
A
C
A
D
A
D
A
D
C
B
C
B
C
B
D
B
D
B
C
D
C
D
C
A
B
C
D
300
299
350
375
400
264
325
336
375
416
264
325
336
375
416
300
299
350
375
400
276
325
350
375
432
300
299
350
375
400
300
400
276
350
Group 2
Right
Booth
A 300
B 299
A 350
B 375
A 400
A 264
C 325
A 336
C 375
A 416
D 264
A 325
D 336
A 375
D 416
C 300
B 299
C 350
B 375
C 400
B 276
D 325
B 350
D 375
B 432
C 300
D 299
C 350
D 375
C 400
A 300
B 400
C 276
D 350
Left Booth
B
A
B
A
B
C
A
C
A
C
A
D
A
D
A
B
C
B
C
B
D
B
D
B
D
D
C
D
C
D
A
B
C
D
264
325
336
375
416
300
299
350
375
400
300
299
350
375
400
264
325
336
375
416
300
312
350
390
400
264
325
336
375
416
300
400
276
350
66
Table B.3 Preference Discrimination Experimental Design.
Preference Discrimination
Trial Right Booth
Left Booth
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
B
A
C
A
D
A
C
B
D
B
D
C
A
B
C
D
A
B
A
C
A
D
B
C
B
D
C
D
A
B
C
D
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
67
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73
VITA | ANDREA M. WILKERSON
Andrea grew up in Kansas and moved to Nebraska after high school to attend the University of
Nebraska-Lincoln (UNL), receiving her Bachelor of Science in May 2008 and her Master of
Architectural Engineering in May 2009. While at UNL, Andrea served as a graduate teaching
assistant for the senior-level Lighting Design course and as a graduate research assistant working with
professors, students, and the local utility company to evaluate the effectiveness of residential realtime energy monitoring. She also received an Undergraduate Creative Activities and Research
Experiences (UCARE) award and worked with an Emeritus Professor in the College of Architecture
researching Nebraska buildings. Prior to completing her Master’s degree, Andrea held internships
with SmithGroupJJR, RTKL and Smith & Boucher Engineers. She was awarded the R.J. Besal
Scholarship in 2007 and received the Thomas M. Lemons Scholarship in 2009.
Andrea moved to Pennsylvania in 2009 to obtain a Ph.D. in Architectural Engineering at The
Pennsylvania State University. Andrea’s research activities at Penn State include study of
psychophysical response to spectra, optical radiation’s non-visual effects on the elderly and energy
modeling. She assisted with Project CANDLE, an alliance between Penn State, the International
Association of Lighting Designers (IALD) and lighting industry partners to enhance lighting
education. Andrea also taught Architectural Illumination Systems and Design, a course focused on
the basic fundamentals of lighting design.
Architectural lighting is the perfect field of study for Andrea, as it blends her interest in the arts,
psychology, math and science. Andrea enjoys teaching and mentoring university students. She also
enjoys traveling, and developed a passion for the international community during trips to Rwanda and
Honduras. She hopes that her future research will incorporate examining the lighting needs of the
international community, particularly third world countries, and providing for these needs in a
culturally and regionally sensitive and sustainable manner.