View source for Philosophy:Graphical perception

[[File:Table lookup and pattern perception graphs.png|thumb|right]]
'''Graphical perception''' is the human capacity for [[Visualization (graphics)|visually]] interpreting information on [[Chart|graphs and charts]]. Both quantitative and qualitative information can be said to be encoded into the image, and the human capacity to interpret it is sometimes called decoding.<ref name="Cleveland 1984">{{Cite journal|last1=Cleveland|first1=William|last2=McGill|first2=Robert|date=1984|title=Graphical Perception and Graphical Methods for Analyzing Scientific Data|journal=Journal of the American Statistical Association|volume=79|issue=387|pages=531–544|jstor=2288400|doi=10.1080/01621459.1984.10478080}}</ref> The importance of human graphical perception, what we discern easily versus what our brains have more difficulty decoding, is fundamental to good [[Statistical graphics|statistical graphics]] design, where clarity, transparency, accuracy and precision in data display and interpretation are essential for understanding the translation of data in a graph to clarify and interpret the science.<ref name="Cleveland 1993">{{Cite book|title=Visualizing Data|last=Cleveland|first=William|publisher=Hobart Press|year=1993|isbn=0-9634884-0-6|location=Summit, New Jersey|url-access=registration|url=https://archive.org/details/visualizingdata00will}}</ref><ref name="Cleveland 1994">{{Cite book|title=The elements of graphing data|last=Cleveland|first=William|publisher=Hobart Press|year=1994|isbn=0-9634884-1-4|location=Summit, New Jersey}}</ref><ref name="Tufte 2001">{{Cite book|title=The Visual Display of Quantitative Information|url=https://archive.org/details/visualdisplayofq00tuft|url-access=registration|last=Tufte|first=Edward|publisher=Graphics Press|year=2001|isbn=1930824130|location=Cheshire, Connecticut}}</ref><ref name="Harrell, Jr 2018">{{Cite web|url=http://data.vanderbilt.edu/fh/talks/RCTGraphics/graphscourse.pdf|title=PRINCIPLES OF GRAPH CONSTRUCTION|last=Harrell, Jr|first=Frank|date=April 24, 2017|website=Vanderbilt|access-date=June 9, 2018}}</ref><ref name="Duke 2015">{{Cite journal|last1=Duke|first1=Susan|last2=Bancken|first2=Fabrice|last3=Crowe|first3=Brenda|last4=Soup|first4=Mat|last5=Botsis|first5=Taxiarchis|last6=Forshee|first6=Richard|date=2015|title=Seeing is believing: good graphic design principles for medical research|journal=Statistics in Medicine|volume=34|issue=22|pages=3040–3059|doi=10.1002/sim.6549|pmid=26112209|s2cid=36793148 }}</ref><ref>{{Cite journal|last1=Angra|first1=Aakanksha|last2=Gardner|first2=Stephanie|date=2017|title=Reflecting on Graphs: Attributes of Graph Choice and Construction Practices in Biology|journal=CBE: Life Sciences Education|volume=16|issue=3|pages=ar53|doi=10.1187/cbe.16-08-0245|pmid=28821538|pmc=5589433}}</ref>

Graphical perception is achieved in dimensions or steps of discernment by:

* '''detection''' : recognition of geometry which encodes physical values
* '''assembly''' : grouping of detected symbol elements; discerning overall patterns in data
* '''estimation''' : assessment of relative magnitudes of two physical values.

Cleveland and McGill's experiments<ref name="Cleveland 1984" /> to elucidate the graphical elements humans ''detect'' most accurately is a fundamental component of good [[Statistical graphics|statistical graphics]] design principles.<ref name="Cleveland 1993" /><ref name="Cleveland 1994" /><ref name="Harrell, Jr 2018" /><ref name="Duke 2015" /><ref>{{Cite journal|last1=Cleveland|first1=William|last2=McGill|first2=Robert|date=1985|title=Graphical Perception and Graphical Methods for Analyzing Scientific Data|journal=Science|volume=229|issue=4716|pages=828–833|doi=10.1126/science.229.4716.828|pmid=17777913|bibcode=1985Sci...229..828C|s2cid=16342041 }}</ref><ref>{{Cite book|title=Creating More Effective Graphs|last=Robbins|first=Naomi|publisher=John Wiley & Sons|year=2005|isbn=0985911123|location=Hoboken, NJ|pages=47–62}}</ref><ref>{{Cite journal|last=Carswell|first=C. Melody|date=1992|title=Choosing Specifiers: An Evaluation of the Basic Tasks Model of Graphical Perception|journal=Human Factors: The Journal of the Human Factors and Ergonomics Society|volume=34|issue=5|pages=535–554|doi=10.1177/001872089203400503|pmid=1459565|s2cid=19818462 }}</ref><ref>{{Cite journal|last1=Hollands|first1=J. G.|last2=Spence|first2=Ian|date=1992|title=Judgments of Change and Proportion in Graphical Perception|journal=Human Factors: The Journal of the Human Factors and Ergonomics Society|volume=34|issue=3|pages=313–334|doi=10.1177/001872089203400306|pmid=1634243|s2cid=28097022 }}</ref><ref>{{Cite web|url=http://flowingdata.com/2010/08/26/rule-2-explain-your-encodings/|title=Graph Design Rule #2: Explain your encodings|date=2010-08-26|website=Flowing Data|access-date=June 9, 2018}}</ref> In practical terms, graphs displaying relative position on a common scale most accurately are most effective.  A graph type that utilizes this element is the [[Dot plot (statistics)|dot plot]]. Conversely, angles are perceived with less accuracy; an example is the [[Pie chart|pie chart]].  Humans do not naturally order color hues. Only a limited number of hues can be discriminated in one graphic.  

Graphic designs that utilize visual [[Philosophy:Pre-attentive processing|pre-attentive processing]] in the graph design's ''assembly'' is why a picture can be worth a thousand words by using the brain's ability to perceive patterns.  Not all graphs are designed to consider pre-attentive processing.  For example in the attached figure, a graphic design feature, table look-up, requires the brain to work harder and take longer to decode than if the graph utilizes our ability to discern patterns.<ref name="Cleveland 1994" />

Graphic design that readily answers the scientific questions of interest will include appropriate ''estimation''.  Details for choosing the appropriate graph type for continuous and [[Categorical variable|categorical data]] and for grouping have been described.<ref name="Duke 2015" /><ref>{{Cite web|url=https://www.ctspedia.org/do/view/CTSpedia/SelectRightGraph|title=Select the Right Graph|last=Bancken|first=Fabrice|date=September 6, 2012|website=CTSpedia Safety Graphics Home|access-date=June 10, 2018}}</ref>  Graphics principles for accuracy, clarity and transparency have been detailed<ref name="Cleveland 1993" /><ref name="Cleveland 1994" /><ref name="Tufte 2001" /><ref>{{Cite web|url=http://biostat.mc.vanderbilt.edu/wiki/Main/RCTGraphics|title=Graphics for Clinical Trials|last=Harrell, Jr|first=Frank|date=April 24, 2017|website=Vanderbilt Dept of Biostatistics|access-date=June 10, 2018}}</ref> and key elements summarized.<ref>{{Cite web|url=https://www.ctspedia.org/do/view/CTSpedia/BestPractices|title=Best Practices Recommendations|last1=Lane|first1=Peter|last2=Duke|first2=Susan|date=Aug 12, 2012|website=CTSpedia Safety Graphics Home|access-date=June 10, 2018}}</ref>

==See also==
*[[Physics:Spatial visualization ability|Spatial visualization ability]]

== References ==
{{reflist}}

== External links ==
* A [https://rpubs.com/greddi/GR-DV-Perception-essay brief description and picture of Cleveland and McGill's nine graphical elements]
* [https://priceonomics.com/how-william-cleveland-turned-data-visualization/ "How William Cleveland Turned Data Visualization Into a Science"] (2016) from Priceonomics.com
* John Rauser's 2016 presentation, "[https://www.youtube.com/watch?v=fSgEeI2Xpdc How Humans See Data]" at Velocity Amsterdam. Describes how good visualizations optimize for the human visual system
* [[Biography:Michael Friendly|Michael Friendly]]'s [http://www.datavis.ca/gallery/ Gallery of Data Visualization: The Best and Worst of Statistical Graphics]

[[Category:Cognition]]

{{Sourceattribution|Graphical perception}}