Andreas Kerren
email: kerren@acm.org

Visualization of Workaday Data Clarified by Means of Wine Fingerprints

More and more average users of personal computers, standard software, and web browsers come into contact with (information) visualization techniques. Depending on the task they have to perform, such visualizations are used for communication purposes, to provide a better overview of personal data, for instance pictures or emails, or to provide information of everyday commodities. After a brief outline about properties and characteristics of workaday data and their users, we focus in this paper on the visualization of wine attributes. The decision to buy a specific bottle of wine is a complex process that incorporates many different aspects from own experiences and current desires to various aromas and flavors that the wine promises to keep. We have developed two different visual representations for wine related data, which we call wine fingerprints. Both approaches are able to represent the most used wine attributes in literature and practice and can guide the purchase decision process of customers. Pros and cons of our wine fingerprints are discussed and compared with related approaches.

Introduction

Information visualization (InfoVis) for the masses, also called casual information visualization, has become an important new direction within the InfoVis community. In addition to the typical single-analyst, deep-dive analytical component of InfoVis, a growing focus of research is examining how to allow large numbers of people to produce, view and discuss information visualizations as well. The topic came up at the beginning of 2007 and was discussed during the first Dagstuhl Seminar on Information Visualization -Human-centered Issues in Visual Representation, Interaction, and Evaluation [START_REF] Kerren | Workshop Report: Information Visualization -Human-centered Issues in Visual Representation, Interaction, and Evaluation[END_REF][START_REF]Information Visualization, Human-Centered Issues and Perspectives[END_REF] in June 2007. It was a topic of special sessions at IEEE InfoVis Conferences in 2007 and 2008 too. Several researchers work on InfoVis approaches for the masses, often with a focus on collaboration, and have developed several systems, such as the ManyEyes project [START_REF] Wattenberg | ManyEyes: A Site for Visualization at Internet Scale[END_REF] or the Snap-together Visualization [START_REF] North | Snap-together Visualization: A User Interface for Coordinating Visualizations via Relational Schemata[END_REF].

Workaday Data: In this paper, we illuminate the fact that more and more Info-Vis techniques and approaches are slowly dropping in areas where the average customer comes in contact with them. A lot of different commercial and opensource products were developed and are available to those people [START_REF] Shneiderman | Designing the User Interface -Strategies for Effective Human-Computer Interaction[END_REF]. However, either these visualization systems or libraries are too complex and difficult to use or they need at least fundamental programming knowledge to apply them. However, a few approaches are intuitive and can be used and understood by non-experts without large training efforts. This is, perhaps, not the final ultimate breakthrough of the broad usage of InfoVis tools in the population. But, it is one important step in showing the "value" of information visualization [START_REF] Fekete | The Value of Information Visualization[END_REF].

Without doing a serious evaluation of this last statement, one receives the impression that mostly "simple" and single techniques are used, such as treemaps, elementary node-link drawings, tag clouds, or bubble charts [START_REF] Görg | Visual Representations[END_REF]. ManyEyes [START_REF] Wattenberg | ManyEyes: A Site for Visualization at Internet Scale[END_REF] offers many such techniques, which can be used by novice users as well. This impression is not astonishing because of at least two reasons. The first one is obviously located in the simplicity of the visualization methods. They are easy to understand and to explain to casual users. Their interaction possibilities are mostly straightforward, at least from our own perspective as visualization experts. This assumption is not well researched and needs more investigation in the future. The second reason lies, in our opinion, in the visualization aesthetics and metaphor. Often, a visually appealing visualization is more successful (relating to casual users) than a more "efficient" but unappealing visualization that presents the same data. A closer look into such phenomena could improve our vague understanding of this intersection of information visualization and visualization arts. Thus, it could also improve the success of information visualization techniques in practice.

Here, we focus on the visualization of wine attributes, such as vintage, aroma, or producer. Many customers come in contact with such attributes when deciding for a specific bottle of wine. Two different visual representations for wine related data were developed in our research group. Both can be subsumed under the term wine fingerprints and should not provide any interactions in order to give us the possibility to attach them to wine descriptions in actual wine shops. They are able to represent the most used wine attributes in wine literature and daily practice and can guide the purchase decision process of customers. A further aim of our developments is to get an aesthetically pleasing result.

The remainder of this paper is organized as follows. Section 2 gives a general overview about our understanding of workaday data and its potential users. Then, we discuss our sample data set, i.e., wine attributes, and present some related approaches that offer visual representations for subsets of these attributes in Section 3. Our own approaches to visualize wine attributes by using InfoVis techniques are presented in Section 4. First evaluation results are briefly outlined in this section too. We conclude in Section 5 and give an outlook to future work.

A Brief Look to Data and Users

By workaday data, we mainly mean data that is of individual interest to one person or a smaller group/community. In InfoVis, data is typically classified by its type, such as the number of attributes (dimensions) per data item or structural properties (hierarchy, graph, etc.). In the following, we employ a more user-guided categorization of data into personal data and community data as proposed by J. Heer et al. [START_REF] Heer | Creation and Collaboration: Engaging New Audiences for Information Visualization[END_REF]. We restrict our discussion on people with basic computer knowledge (writing a text, sending/receiving email messages, using web browsers, ...), but no programming or visualization experiences. Thus, our target group in this paper usually acts as a customer, and the considered visualizations are used as information and communication medium in most cases.

Personal Data: This kind of data covers various types of information, which is important for the individual, for example, own financial situation, personal collection of pictures, address books, genealogical data, inventory of hobby collectors, and much more. An additional prominent example is managing emails. Data that is related to receiving and sending emails as well as message bodies were in the focus of different research projects. A. Perer and M. Smith visualized hierarchical, correlation and temporal patterns present in individual email repositories [START_REF] Perer | Contrasting Portraits of Email Practices: Visual Approaches to Reflection and Analysis[END_REF]. Users can identify interesting features in their repositories, e.g., how their own communication efforts look like. Another example in this context that focuses more on visualization metaphors is Anymails [START_REF] Horn | Anymails Project Webpage[END_REF]. This tool visualizes received emails by using a microbe metaphor. Here, each email is a microbe of a specific type depending on whether the sender belongs to a specific group.

Other important data for the individual is data about specific goods, such as cars or wines (if someone is interested in to buy such goods), or communal/environmental information like crime statistics in his/her surroundings. As mentioned in Section 1, we will exemplify the application of InfoVis techniques for the visualization of workaday data by means of wine attributes and properties, i.e., our focus is on personal data in this paper.

Community Data: "Examples of community data include the content of political speeches, the number of users online in a World of Warcraft realm, or voting results per county" [START_REF] Heer | Creation and Collaboration: Engaging New Audiences for Information Visualization[END_REF]. Thus, community data might be relevant for a group of persons who share similar interests or attitudes. Its importance is increasing due to the massive increase of social network applications, such as Facebook [START_REF]Facebook: Facebook Webpage[END_REF]. Our current work does not focus on community data.

Wine Attributes and Existing Visualization Approaches

If a consumer is buying a specific bottle of wine at the vineyard, at retailers in actual shops, or even web shops, then he/she may take different information sources into consideration for the final decision. Most people probably decide by watching the wine label on the bottle together with more or less detailed background knowledge of wines in general. Such average wine customers are interested in to know different properties or attributes of shortlisted wines, for instance, name of the wine, producer, complexity, producing country, region, grape type, color, taste, or price. Moreover, a more experienced wine consumer may also consider the storage type (e.g., steel cask or barrique), a rating by wine experts or other customers respectively, or the vintage of the wine. Among these properties, name, vintage, country, producer, etc. are textual attributes. The grape type, also called grape variety, is classified in two basic categories: red grapes and white grapes. Normally, red grapes are used for red wine and white grapes are used for white wine (however, a white wine can also be made from red grapes by only taking the flesh and not using their skin). The final color of a wine can be categorized into three types: red, white, and blush (rosé). In wine literature, one can find a richer color scale of about 12 different colors. All of these attributes are clear facts that can be collected from various data sources. One exception are the two most important attributes: taste and aroma. We will discuss this kind of data in Section 3.1. Note that pre-processing steps are usually needed to extract useful information from text files in idiosyncratic format. This process can be manually performed by human experts or automatically by natural language parsing techniques.

In contrast to the average wine consumer, more experienced people read wine notes (so-called tasting notes) written by wine experts, which are published in books or in the Internet. One popular source is Robert Parker's Wine Advocate [START_REF] Parker | Wine Advocate[END_REF] where tasting notes like the following one can be studied: "The medium ruby-colored 1997 Abadia Retuerta (a blend of 65% Tempranillo, 30% Cabernet Sauvignon, and 5% Merlot) exhibits an attractive spicy, cedary, tobacco, and berry fruit-scented nose. Herbaceousness makes an appearance in the mouth, but the wine is round, soft, and moderately concentrated, with fine cleanliness and accessibility. It should drink well for 5-6 years." Taken from [START_REF] Paradis | This Beauty Should Drink Well for 10-12 Years": A Note on Recommendations as Semantic Middles[END_REF].

Such tasting notes follow strict rules in their structure and diction. Even if-according to G. Morrot et al. [START_REF] Morrot | The Color of Odors[END_REF]-humans have not developed an olfactory terminology to describe odors, there is a need to talk about different aspects of wines like smell or mouthfeel [START_REF] Lehrer | Talking about Wine[END_REF]. How can visualization techniques help to make such textual descriptions more clear or, perhaps, to replace them? To find a general answer is not straightforward as presented in the next subsection.

Related Works

We present three approaches that visually represent wine attributes from various perspectives. Each related work is accompanied by a screenshot to show its aesthetics and to explain the underlying idea of aroma visualization more efficiently.

Wine Aroma Wheels In 1984, a new standard for a common wine aroma terminology was proposed in order to facilitate the verbal communication about wine aromas [START_REF] Noble | Progress Towards a Standardized System of Wine Aroma Terminology[END_REF]. To support this terminology with a graphic representation, the socalled Wine Aroma Wheel was introduced by A. C. Noble et al. [START_REF] Noble | Modification of a Standardized System of Wine Aroma Terminology[END_REF]. Meanwhile, different variants of this aroma wheel came up. Fig. 1 shows a modified version for German red wines. Its hierarchical structure for different aromas is clearly to identify. For example, the more general aroma "vegetal" at the first hierarchy level can be subdivided by "fresh", "cocked" and "dried" at the next level. Then, "cooked" can be further sub-classified into "green bean", "beetroot" and "black olive" at the third level. For the wheel's practical use, the German Wine Institute writes: "The aroma wheels for German white and red wines are subdivided into colored segments that describe seven characteristic aromas and one category for taste impressions. You work your way from a broad classification in the middle to the more detailed descriptions in the outermost circle. Let's begin at "fruity". Within the broad segment "fruity", for example, your more specific impression might be that of "berries". Now, through more intense sniffing, you can determine whether your wine smells more like currants or elderberries, or in some instances, perhaps both. From the individual aromas of a wine you can draw important conclusions about its grape variety and its region of origin. [. . . ] Especially nice is the fact that this method of describing flavors and aromas enables everyone to follow experts' comments, and sooner or later, everyone can fully enjoy the pleasure of wine." [START_REF]AromaWheel: Deutsches Weininstitut[END_REF].

For sure, such static graphics are used as a vehicle for a better communication and originally not for visualization purposes in terms of data analysis. But, the graphical representation is conceptually similar (with minor flaws due to the spatially mixed hierarchy levels) to some standard InfoVis representations for hierarchical data, such as SunBurst [START_REF] Stasko | Focus+Context Display and Navigation Techniques for Enhancing Radial, Space-Filling Hierarchy Visualizations[END_REF]. In this way, wine aroma wheels could serve as inspiring fundamentals for further investigations in wine visualization. Aromicon The German company Aromicon [2] uses Flash animations [5] to visualize different attributes of specific wines as shown in Fig. 2. Each animation consists of three areas. On the top, textural information is displayed, e.g., name of the wine, vintage and origin (area). On the right hand side, some additional attributes (sweetness and tannins) are represented by icons; for example, more sugar bits are shown if the tasted sweetness is high. The rest of the image is the main part for the taste representation and the wine color. The latter one is simply represented by the background color of the image. The taste is more complex, and the different flavors and aromas are shown by using animated "real-life" icons within a wine glass moving up from the bottom of the glass. Thus, our screenshot example conveys the flavor of cherries, coffee, black pepper and cinnamon to us. Even more complicated phenomenons, such as body or sustainability of the wine, are represented by the icons' sizes and transparencies respectively. Aromicon applies such animations to support customers in their decision to buy wines in a web shop. This approach is very intuitive and visually appealing. A cherry aroma is represented by a real cherry image and not by an abstract visual representation or simple text. This is obviously a strong advantage, because wine tasting is a true sensory prescription based on vision, smell, taste, and mouthfeel. Another benefit is the usage of different animation features to represent additional information. For instance, a high velocity of the animated icons is used if the wine is tangy and fresh; a low velocity stands for a more aged wine. Animation frames (or time in general) are also used to preserve the available space in small images. Conceptual drawbacks are restricted possibilities to show many different types of attributes at the same time for comparisons as well as to support more advanced customers with the aroma hierarchy discussed above. Wine Flavor Visualization Fig. 3 shows a screenshot of a course work developed by C. Tashian at Tisch School of the Arts, New York [START_REF] Tashian | Wine Flavor Visualization[END_REF]. This visualization focuses on the relationships between specific wine varieties and flavor components by using a radial hierarchical layout and arcs. The arc width represents the strength of these relationships between a wine variety itself and different attributes classified by four dimensions: sensation, characteristic, aroma, and flavor. Users can interactively choose between 11 different graphics for a small subset of varieties, i.e., no advanced interaction is provided. The underlying data set was gathered from about 5,000 wine tasting notes published over five years by a leading Australian wine magazine. In this figure, Pinot Noir was chosen as a sample wine variety. All arcs have their origin in the node on the right hand side representing this kind of wine. Thicker arcs give a hint of the most relevant properties of it. Thus, the line thickness represents the aggregated occurrences of a specific property over all tasting notes. In our case, the visualization shows that Pinot Noir has, in general, an aroma of oak with a sweet flavor of berry and cherry, a more acid sensation, etc.

This approach uses some well-known InfoVis techniques, especially a hierarchical, radial graph layout together with arcs. It is similar to M. Wattenberg's Arc Diagrams [START_REF] Wattenberg | Arc Diagrams: Visualizing Structure in Strings[END_REF]. Advantages are its intuitive layout and its aesthetics. It could also be used for individual wines; not only for aggregated data. However, no nominal data (e.g., producer) or more general aroma levels can be smoothly included.

Visualization of Wine Attributes by Using InfoVis Techniques

Our idea is to use space-filling and aesthetically appealing InfoVis techniques to represent a variety of attributes that are related to a specific wine [START_REF] Mei | Visualization of Wine Attributes[END_REF]. This data forms a multivariate data set as described in Section 3; a part of it can be hierarchically structured, such as the aroma hierarchy. Other attributes have a nominal, ordinal, or quantitative data type. For test reasons, we built up a MySQL database [START_REF]MySQL: An Open Source Relational Database Management System[END_REF] to store a small test suite of wines together with their attributes. Currently, a wine can be attached with 14 different attributes (see Table 1). Four attributes stand out and need further explanations.

Wine Color: We distinguish between 16 different colors from Greenish Yellow to Garnet Red in ascending order. These colors are well-defined in the wine literature, such as in the Wine Advocate [START_REF] Parker | Wine Advocate[END_REF]. To prevent mistakes, we attached a unique integer ID to each color. Rating: In practice, each professional tasting note is enhanced and complemented by a numerical rating. There are different rating systems depending on the individual wine expert or wine magazine. Parker's Wine Advocate [START_REF] Parker | Wine Advocate[END_REF] uses a 50-100 point quality scale, for example. In order to abstract from such individual systems, we restricted ourselves on an interval of integer values between 0 and 10. This could be easily changed in the future if needed. Grape Variety: This attribute differentiates between hundreds of numbered and textually labeled grape types, whereas a negative value of a grape type number means a white grape, a positive value means a red grape. Note that up to three different grape varieties can be combined in our data set, for example, if the wine is a blend between 170 Shiraz and 35 Cabernet Sauvignon. Aroma: According to the aroma hierarchy, each aroma attribute value consists of a list of three-digit numbers as its related tree has three levels (the root node is at Level 0), cf. Fig. 4. The 100-digit is mapped to the first level of the aroma tree, the 10-digit is mapped to the second level, and so on. For example, looking at number 521, 5 means the fifth node of the first level that is "smoky" in our data model, 2 means the second node of the second level that is "woody", 1 means the first node of the third level that is "toastedOak". It is also possible to show the first and/or the second level only by resetting the appropriate digit to 0. Then, 500 means "smoky", for example. In this way, we can express more general and unspecified aromas, but we are theoretically restricted to nine different aroma values per level. Note that our aroma hierarchy is slightly different from the aroma wheel for German red wines in Fig. 1. The reason for this decision is that we would like to represent red and white wines, and thus, our aroma hierarchy is more generic compared to the presented one. The aforementioned restriction to nine different values is no issue in our current hierarchy.

To map this kind of data onto a visual representation, we developed two different visualizations, called wine fingerprints. They are described in the following subsections.

Rectangular Wine Fingerprints

Our first approach is based on a treemap layout for representing the aroma hierarchy as shown in Fig. 5. The most obvious difference to the second approach (see Section 4.2) is the use of a world map taken from ManyEyes [START_REF] Wattenberg | ManyEyes: A Site for Visualization at Internet Scale[END_REF] to represent the country attribute. It fits very well to the treemap, because they have both a rectangular shape. For space saving reasons, we decided to use only the upper and lower part of the world map according to the fact that wine grapes mostly grow between the 30th and the 50th degree of latitude in both the northern and southern hemisphere [START_REF]Wikipedia: List of Wine-Producing Regions[END_REF]. The space in between is then used for the treemap and other attribute representations.

We defined different saturated border colors and border thicknesses for the three levels of the aroma hierarchy to improve the perception of the tree structure. In the shown rectangular wine fingerprint, three different aromas and one specific taste for that specific wine are marked. The taste is colored in dark blue and corresponds to "sweet" at Level 3 of the hierarchy. The gray blue field represents "fresh" at the second level of the "vegetal" subtree etc. Remember, the deeper items are marked in the aroma hierarchy the more saturated the used colors, see the treemap area labeled with "fruity", for example. Fig. 5. A wine fingerprint using a rectangular drawing approach and a treemap layout [START_REF] Shneiderman | Tree Visualization with Treemaps: A 2-d Space-filling Approach[END_REF] for the aroma hierarchy. The center area is divided into three rectangular areas with the treemap on the left hand side showing the complete aroma hierarchy with highlighted, individual flavors. Then, a small vertical stripe shows the vintage of the wine. On the right hand side, different areas represent nominal attributes (wine name, producer, and grape variety) by using text labels as well as ordinal/quantitative attributes (wine color, price, . . . ) by varying the color scale and/or widths like the two horizontal bar charts in the lower right corner. The background area indicates the origin (country) of the wine by a red dot on a world map.

The two map parts on the top and bottom symbolize wine-producing countries geographically. A red dot together with the country code locate the wine origin. On the right hand side besides the treemap, nine colored rectangles are used to visualize the remainder of the attributes. The vertical blue stripe displays the vintage, which is 2004 in this example. We used a slightly different representation for the vintage, because wine consumers typically have some knowledge about good and bad vintages. Thus, the pure date contains background information, which could be represented in the future too. The rectangles on the top represent from top to down and left to right the wine name, producer, grape variety (here 170 Shiraz ), color & category (white, rose and red together with the color ID; in this example, the color of the wine is 15 Brick Red ), followed by complexity (dark green represents a strong complexity) and barrel type (ocher represents a wood barrel). So far, this part of the fingerprint only shows nominal and ordinal attributes. The next two rectangles are horizontal bar charts to code ordinal and quantitative data of particular importance for the customer, such as price and rating. Consequently, they are double-coded by color and size: the higher the price the longer the bar and the color is more saturated. Fig. 6. Another wine fingerprint using a radial drawing approach (three layers) and a balloon tree layout for the aroma hierarchy. The midpoint of the fingerprint provides the vintage of the wine, the next circular layer represents ordinal/quantitative attributes, then the balloon tree shows the complete aroma hierarchy with marked, individual flavors. The outermost circular layer indicates the origin (country) of the wine in arbitrary order. At the bottom, nominal attributes are displayed, such as wine name, producer, and grape variety.

Circular Wine Fingerprints

Fig. 6 shows a screenshot example of our second approach. It mainly consists of three concentric circles plus a center point, which represents the vintage by using a text label for the year. Then, the innermost circle shows five ordinal and quantitative data items: rating results, price, wine color, complexity, and barrel type. All of these attributes are double-coded in color and size or textual value respectively. The middle circle consists of a balloon tree layout (using the JUNG library [START_REF] Jung | Java Universal Network/Graph Framework[END_REF]) for displaying the complete aroma hierarchy: each subtree represents a specific, color-coded aroma/flavor class as outlined in Fig. 4. Thus, the balloon tree visualizes our aroma hierarchy in a similar way as the treemap in the previous case. Looking at the concrete fingerprint examples, the color-coding is identical for of both the rectangular and the circular approach. Those tree nodes that fit to the current wine are filled with the corresponding color. All other nodes remain unfilled. The saturation of the node coloring is increasing the deeper we are in the aroma hierarchy. Note that the current aroma selection in Fig. 6 is identical to the current aromas in Fig 5 . So, the reader can easily compare both wine fingerprints. The outer ring represents a (probably incomplete) set of countries that grow wine. Each country is represented by a circle and can be identified by its international country code. Similar as before, the current country where the wine was produced is marked in red color. The three circles at the lower part of this wine fingerprint represent nominal data: name of the wine, producer, and grape type. The circle for the grape variety is larger than the ones for name and producer in order to provide more space for up to three different varieties.

User Study and Discussion

We decided to develop both approaches to get a better feeling of which approach is better suited for the underlying wine data set. A first small user study with ten test persons was performed and yielded valuable hints for further improvements. The test design was straightforward. At first, the test persons should learn the different graphical components of the wine fingerprints with the help of an interactive user interface to become familiar with our ideas. For instance, they could select some flavors/aromas in the GUI and verify the resulting fingerprints immediately. After this learning phase, they had to compare various predefined wine fingerprints with a fingerprint based on a specific wine selection in order to identify the best match. Finally, they got a short questionnaire with rating and open questions. Some results were surprising, for instance that our test persons did not understand the rectangular wine fingerprints using the treemap approach very well. Here, they had problems to identify the most appropriate fingerprint in the comparison task (62% correct answers). The results were better for the corresponding task with the circular approach (75% correct answers). Furthermore, seven of ten subjects would prefer our circular approach. Moreover, some test persons had the impression that the various circles within the circular fingerprints are a metaphor for wine grapes, which was originally not intended by us. Of course, the results of this user study are not significant and a more detailed study must be prepared.

If we compare our approaches to those discussed in Section 3.1, then we can identify pros and cons for them. Table 2 gives an overview of differences according to a set of features. As the Aroma Wheel is by definition restricted to represent aromas, tastes and flavors, it is only limited comparable to the other approaches, which can show more information about a wine. It is obvious that Aromicon and the light-weight Wine Flavor Visualization are strong with regard to intuitiveness, natural metaphor and aesthetics. Both cannot exactly show quantitative data and are hard to extend without destroying the overall layout. Our own approaches have their main benefit there: they are able to carry much more different types of data, and at least the Circular Fingerprint is aesthetically appealing and understandable. However, both are not easy to learn and are more difficult to perceive. One reason for this are the missing textual labels within the fingerprints to identify the graphical elements correctly. This was a conscious decision to reduce their visual complexity.

Conclusions

The aforementioned visualizations are only two examples for many applications of InfoVis techniques for workaday data. Other examples can be found in the visualization of community data [START_REF] Heer | Creation and Collaboration: Engaging New Audiences for Information Visualization[END_REF], such as in Facebook [START_REF]Facebook: Facebook Webpage[END_REF] or similar services. To be successful, these visualizations must be easily perceivable, intuitive and understandable for the casual user, easy to produce in case a user plans to build an own visualization as well as follow meaningful metaphors. Our intention for this paper was not to provide a comprehensive survey on solutions for workaday data visualizations. Instead, we focused on one special application domain. We have developed two versions of wine fingerprints for specific wines and their attributes. Outgoing from a first evaluation, the circular wine fingerprint outperformed the rectangular wine fingerprint. Both approaches require some learning efforts of casual users, which is a clear drawback. Advantages are their accuracy to represent the underlying data and extensibility. We plan to perform a more detailed evaluation in the hope that the results can be used to improve the current realization of our ideas.

Describing sensory perceptions, such as vision, smell, taste, and mouthfeel, is a challenging task. Language researchers are interested in how humans talk about such sensory perceptions and what types of metaphorizations or lexical resources exist. Here, we are currently working together with Carita Paradis from the Center for Languages and Literature at Lund University. We plan to develop a visualization tool that can help to discover possible correlations or patterns in wine tasting databases in order to give linguists a better understanding of those (textual) descriptions. We are convinced that this research will also have a positive influence on the further development of our fingerprints.

Fig. 1 .

 1 Fig. 1. Wine aroma wheel for German red wines [1]. Image courtesy of Deutsches Weininstitut GmbH.

Fig. 2 .

 2 Fig. 2. Animated taste visualization of a red wine offered by the German company Aromicon [2]. Image courtesy of Aromicon GmbH.

Fig. 3 .

 3 Fig. 3. Radial wine flavor visualization with regard to four different dimensions. Pinot Noir was chosen previously. Taken from the web page [26].

Fig. 4 .

 4 Fig.4. A cutout of our own aroma tree structure realized in our database[START_REF] Mei | Visualization of Wine Attributes[END_REF]. The complete subtree of the aroma "smoky" is unfolded. The colored bars at the right hand side of the subtrees stand for the used color coding in our wine fingerprints.

Table 1 .

 1 [START_REF] Mei | Visualization of Wine Attributes[END_REF] wine attributes in our database together with their data type, range and visual mapping for both fingerprints (FPs). The fingerprints and their visual structures are explained in Section 4.1 and 4.2.

	Attribute Data Type Range	Circular FP	Rectangular FP
	Name	nominal	string	circle+label	rect.+label
	Producer	nominal	string	circle+label	rect.+label
	Country	nominal	13 IDs	circle+label	dot+label in a map
	Region	nominal	any string	not used	not used
	G. Variety nominal	ID+string	circle+label	rect.+label
	Complexity nominal	3 values	circle+color	rect.+color
	Barrel	nominal	2 values	circle+color	rect.+color
	Category	nominal	3 values	together with Color together with Color
	Wine Color ordinal	16 IDs+string circle+color+label rect.+color+label
	Vintage	ordinal	1900-2008	circle+number	rect.+number
	Viscosity	ordinal	any string	not used	not used
	Rating	ordinal	0-10	circle+color+size	rect.+color+size+label
	Price	quantitative 0-1000 SEK circle+color+size	rect.+color+size+label
	Aroma	hierarchical three-digit	color+balloon tree color+treemap

Table 2 .

 2 Comparison of related together with our own approaches.

	Feature	Aroma Wheel Aromicon Flavor Vis. Circular FP Rectangular FP
	Accuracy	-	•	-	++	++
	Extensibility --	•	+	++	++
	Simplicity	+	++	++	+	•
	Metaphor	•	++	•	+	•
	Aesthetics	+	+	++	+	•
	Perception •	+	++	•	•
	Intuitiveness -	++	+	+	+
	Training	-	+	+	-	-
	Display	++	++	+	+	+

Acknowledgments I would like to thank Yuanxun Mei for implementing the wine fingerprint visualization as part of his Master's Thesis. Furthermore, I am thankful to Ilir Jusufi for his constructive comments that helped to develop and to improve our approach. Finally, I wish to thank Carita Paradis from the Center for Languages and Literature at Lund University, Sweden, to show us the need for visualizations of wine related data a well as to provide us with background information about this interesting field.


Tera Marie Green
email: terag@sfu.ca

Brian Fisher

The Personal Equation of Complex Individual Cognition during Visual Interface Interaction

This chapter considers the need for a better understanding of complex human cognition in the design of interactive visual interfaces by surveying the availability of pertinent cognitive models and applicable research in the behavioral sciences, and finds that there are no operational models or useful precedent to effectively guide the design of visually enabled interfaces. Further, this chapter explores the impact of individual differences, and in particular, inherent differences such as personality factors, on complex cognition. Lastly, it outlines how knowledge of human individuality, coupled with what is known about complex cognition, is being used to develop predictive measures for interface interaction design and evaluation, a research program known as the Personal Equation of Interaction. . . .

Introduction

Generally speaking, interactive visualizations are considered to have a number of advantages over more conventional visual interfaces for learning, analysis, and knowledge creation. Much of the support for these claims comes from a variety of sources, such as user evaluations, comparative studies of error rates, time to completion, etc., as well as and designer/developer intuition. One common claim concerns the development of insight. From early on, visualization has been proposed as a preferable interface approach for generating insight (e.g. [START_REF] Card | Readings in Information Visualiza-tionUsing Visualization to Think[END_REF], [START_REF] Saraiya | Evaluation of microarray visualization tools for biological insight[END_REF]. [START_REF] Spence | InformationVisualization[END_REF]). As a concept, however, insight is a construct that is often either loosely defined as some variety of meaningful knowledge or is left undefined (e.g. [START_REF] Saraiya | An Insight-Based Methodology for Evaluating Bioinformatics Visualizations[END_REF], [START_REF] Springmeyer | A Characterization of the Scientific Data Analysis Process[END_REF]). More recently there have been efforts to define insight, although not in ways that might enable it to be quantified. For example, North described insight as a broad construct, which is complex, deep, qualitative, unexpected, and/or relevant [START_REF] North | Toward measuring visualization insight[END_REF], without characterizing the cognitive processes that give rise to it, or the outcomes that are generated by it. Chang et al. defined insight as comprising two categories: knowledge-building insight, which is a form of learning and/or knowledge generation, and spontaneous insight, which is method of problem-solving for previously intractable problems, commonly described as an a-ha! moment. This dual definition has advantages over a unitary definition in that it supports focused analysis of the component aspects of the overall construct. Spontaneous insight, however, has been an elusive notion for researchers in several disciplines; neuroscientists and psychologists have studied the phenomenon, but as yet do not know how insight is triggered [START_REF] Chang | Defining insight for visual analytics[END_REF].

By any definition, there is little empirical evidence that supports claims of visualization superiority in insight generation, though user evaluations are often conducted to demonstrate visualization efficacy over other types of interface. Plaisant et al. [START_REF] Plaisant | The challenge of information visualization evaluation[END_REF] identified four current themes in the evaluative literature: controlled experiments comparing design elements, usability evaluation, controlled experiments comparing two or more tools, and in situ case studies. In all four groups, evaluations and comparative studies have largely focused on perception, motor learning, focal attention, target recognition and/or target acquisition. For example, musing behaviors were used as a predictor of user focus in a geospatial visualization [START_REF] Aoidh | Understanding geospatial interests by visualizing map interaction behavior Information Visualization[END_REF]. Jeong et al. compared two visualization tools to determine in which interface users were more efficient in finding outliers and identifying highly correlated items in a matrix [START_REF] Jeong | Comparative Evaluation on Two Interface Tools in Performing Visual Analytics Tasks Proceedings of BELIV Workshop, CHI 2010[END_REF]. Nodes were the subject of an evaluation of target identification in large tree tools [START_REF] Plaisant | SpaceTree: Supporting exploration in large node-link tree: design evolution and empirical evaluation[END_REF]. And Wang et al. evaluated whether users could focus on the count of visualized objects (in this case, paper proposals) over a period of time [START_REF] Beth | Mathematical Epistemology and Psychology[END_REF]. In these evaluations as well as in cognition as a whole, perceptual, cognitive, and motor processes are important to the overall interaction. However, each of these identified cognitive systems are feeder processes. That is to say, they support and inform the more complex processes, such as reasoning, problem-solving, and knowledge generation, which form the backbone of systematic analysis or task solution. These complex processes are impacted demonstrably, as we will see, by the individuality of the interface environment, the knowledge domain, and the inherent differences within the user, over which visualization design has no control. To date, visualization evaluation has insufficiently considered the complexity of human cognition. This, in turn, has hampered the design of intuitive interfaces capable of mixed-initiative collaboration.

In this chapter, we will explore a variety of challenges to the consideration of cognitive complexity in visual analytics design, from the current lack of operational models and applicable research to a consideration of individual differences. We will then explore how an consideration of how these complex processes impact the understanding of common visual analytics tasks, and discuss a continuing exploration of how human individuality can be measured and charted, leading to a differentiating set of predictive measures that can not only predict interface performance, but guide visualization design. We call this the Personal Equation of Interaction.

The Challenge of Complex Cognition

Very little research examines the use of what is commonly known as higher cognition during interaction, which includes processes such as reasoning, problemsolving, and decision-making. Frequently, when a visualization design or evaluation argues that a specific technique or tool improves insight (which is learning and/or problem-solving) or analysis (which involves every major cognitive process), the evidence is actually task completion times for single-step tasks, improved target identification, or other simple outcomes. One reason for this, perhaps, is the practice of inferring the success of complex behaviors from measurements of simpler ones. A common example is the generalization of findings from simple, semantically-unrelated target acquisition tasks to human problemsolving as a whole, without a discussion of which of the many problem-solving theories or heuristics the finding might speak to (e.g. [START_REF] Dou | Recovering reasoning processes from user interactions[END_REF] , [START_REF] Robinson | Collaborative synthesis of visual analytic results[END_REF]). This practice over-simplifies the complexity of cognition, but is understandable, given that our best complex cognitive models are black box or descriptive. We will now consider the best known of these descriptive models, the sensemaking model.

The Sensemaking Loop

The most familiar approach to descriptively outline task-oriented processes is Pirolli and Cards sensemaking loop ( [START_REF] Pirolli | The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis[END_REF], [START_REF] Russel | The Cost of Sensemaking[END_REF]). See Figure 1. Russell et al. defined sensemaking as the process of searching for a representation and encoding data in that representation to answer task-specific questions [START_REF] Russel | The Cost of Sensemaking[END_REF]. In the rest of this section, we will summarily explore the complexity of analytical cognition through a brief discussion of the sensemaking loop in the broader context of human reasoning. This seems necessary, for, as valuable as the sensemaking loop is to describing certain analytical tasks, its use tends to be overgeneralized in the visualization literature. Indeed, sensemaking is often the term given to most or all of the cognitive processes analysts employ during visual analytics tasks (e.g. [START_REF] Stasko | Jigsaw: Supporting Investigative Analysis through Interactive Visualization[END_REF], [START_REF] Pirolli | The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis[END_REF], [START_REF] Heer | Design considerations for collaborative visual analytics[END_REF]).

Sensemaking, as defined in the previous paragraph, creates a mental or physical representation (i.e. a "mental model" or "story"). This singular representation may well be necessary for problem solving, but may not be in itself sufficient for generating valid implications. Analyses may create multiple alternative mental representations of a situation in order to compare them in a variety of ways, using a variety of evaluative heuristics in order to draw their conclusions. At this larger scale of problem solving strategy, analytical cognition exhibits a great deal of variability, and is informed by both human and task individuality. For example, the sensemaking loop makes an effort to delineate top-down and bottom-up task descriptions. However, as some of Pirolli and Cards participants indicated, the cognition involved in the early tasks of the loop can rarely be so cleanly categorized. Further, though seemingly simplistic, even the first steps of the sensemaking loop (the "lower-effort" tasks of searching and filter) require complex cognition in the form of various reasoning heuristics to categorize, evaluate, and assemble pertinent information. These heuristics could be elimination heuristics like elimination by aspects [START_REF] Wang | Aligning temporal data by sentinel events: discovering patterns in electronic health records[END_REF], or satisificing [START_REF] Kozielecki | Elements of a psychological decision theory[END_REF] or they could be more complicated, such as the comparing possible shoebox members (concepts the analyst has gathered during sensemaking and think may be related to each other) to an ideal before addition. According to the Loop, pertinent shoebox members become part of and Evidence File which is used as part of the formal structure of the Schema, which is a sturctured narrative of how the evidence collected thus far fits together [START_REF] Pirolli | The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis[END_REF]. Thus, while sensemaking effectively describes Fig. 1. The Sensemaking Loop. From [START_REF] Russel | The Cost of Sensemaking[END_REF].

one key component of the complex cognitive processes involved in the generation of insight, it does not claim to capture higher levels of abstraction, i.e. generation of problem-solving strategy, nor does it attempt to capture lower-level pattern recognition processes that support its operation. The latter set of processes are arguably the most critical for visualization to support, since they are precisely those aspects that are most closely tied to the visual systems own information processing capabilities (i.e. Irving Rocks logic of perception [START_REF] Rock | The logic of perception[END_REF]).

Similarly, Cherubinis "models to rules mechanization" [START_REF] Cherubini | From models to rules: Mechanization of reasoning as a way to cope with cognitive overloading in combinatorial problems[END_REF] suggests that the formation of schemata and hypothesis generation are not necessarily higher effort tasks. According to Cherubini, after human reasoning uses the generated model (which does require some cognitive effort to create in novel instantiations), the human infers a rule from the knowledge structure in the mental model. Or in his own words: After grasping the common structure of the problems, most people should be able to devise a simple rule to solve all problems with the same structure (i.e., a domain-specific rule), disregarding the number of possibilities underlying them. [START_REF] Cherubini | From models to rules: Mechanization of reasoning as a way to cope with cognitive overloading in combinatorial problems[END_REF] This rule may be created after only one or two uses of a newly created model. Thus, depending on the information under consideration, hypothesis generation could actually require less cognitive bandwidth than the initial information search.

At a higher level, the sensemaking loop articulately describes one subset of reasoning, that of reasoning types which generate inferred hypotheses or generalized rules, or abduction and induction. Abduction (or abductive reasoning) is process of approaching seemingly unrelated information with the assumption that the data points or concepts are indeed interconnected; abduction creates and infers relations between two previously unrelated data points; the end product of abductions series of inferences is the creation of a hypothesis which explains these relational inferences [START_REF] Sebeok | You Know My Method[END_REF], and usually about a specific item or concept. This fits well with the structure of the sensemaking loop, which situates the search and compartmentalization of small, unassociated details in the early stages of the loop and builds from there to an identifiable hypothesis or story.

The sensemaking loop also illustrates induction. Inductive reasoning, as is described in the behavioural literature, is generally referred to the process of making acceptable generalizations from the similarities of facts or properties (see for example [START_REF] Rips | Inductive judgments about natural categories[END_REF], [START_REF] Tversky | Elimination by aspects: A theory of choice[END_REF]). The validity or strength of the generalization depends in large degree upon the strength of these similarities. One of the characteristics of induction which makes induction different from abduction is that relationships are not as important to the successful generalization. Once a fact(s) or similarity has been accepted as valid, it or they become the basis of generalization; additional information may or may not be considered. Induction is a powerful form of reasoning that allows us to quickly categorize and infer rules; it is often referred to as a form of bottom-up reasoning, but can utilize top-down cognition as is needed. To some degree, the sensemaking loop describes induction, as this type of reasoning tends to build from smaller facts to a broader concept. However, unlike with hypothesis generation and analysis, induced generalizations do not necessarily require vetting, e.g. careful consideration of available evidence for the generalization. Induced hypotheses can jump past multiple steps, such as the shoebox or schema creation, straight to a generality.

While abduction or induction may accurately describe behavior during discovery or during exploration of novel problems for which the analyst does not already have a script or mental model to guide her as to what to do next, most problem-solving or decision-making tasks are driven by an articulated goal, theory or hypothesis from the start. These reasoning heuristics are neither abductive, which ends with a hypothesis, nor inductive, ending in an inferred rule, but rather deductive.

One of the more actively studied reasoning types, deductive reasoning falls outside of the sensemaking loop. By deduction (or, for that matter, induction), we are referring to reasoning as the subject of decades of empirically conducted research, which is usually evaluated through use of normative tasks like those that we will briefly itemize in the next section. A discussion of the philosophy of deduction, such as the traditional deduction of Aristotle, propositional (or first order) deduction, or natural deduction ( [START_REF] Jaskowski | On the Rules of Suppositions in Formal Logic[END_REF], [START_REF] Gentzen | Investigations into Logical Deduction[END_REF]), and the similarities or differences between these definitions, is neither intended nor implied.

There are several theories of deduction in the behavioral literature, but we will focus on two of the broader categories of deduction, which assume that human deductive reasoning is either rule based or model based. This dichotomy of rules vs. models raises interesting issues for visual analytics interfaces. Rulebased theories assume that humans use a script or a formal sequential logic while working through a deductive task (e.g. [START_REF] Johnson-Laird | Deductive Reasoning[END_REF]). From this perspective, the content or information manipulated during the task doesnt demonstrably influence the reasoning, because the inferences drawn during induction are part of this formal process [START_REF] Johnson-Laird | Deduction[END_REF]. From the rules perspective, for the development of more intuitive deductive visual analytics tools, it would only be important to uncover the pertinent rules the analyst would use; theoretically, these deductive rules would generalize to all similar visual analytics tasks.

Model-based theories, however, are quite different. Models can be either concrete or abstract, complete or incomplete, pictorial or conceptual (e.g. [START_REF] Johnson-Laird | Deductive Reasoning[END_REF], [START_REF] Johnson-Laird | Deduction[END_REF]). Models are also flexible; they can change as pertinent information changes, and can quantify degree (such as few or often), as well as causal conditions. Models depend heavily on semantic relationships, and so can be heavily influenced by the content of the information at hand. This, too, influences visualization design, for what data is presented when, and in what context, can influence the development of the model, as well as its completeness and validity. From the model-based perspective, discovering quickly inferred rules is not nearly as helpful as assuring that the human has all pertinent information readily at hand. With pertinent information, the human can generate a mental model, which can be manipulated as needed to reasoning through the task to a valid conclusion. Shneidermans Mantra [START_REF] Shneiderman | The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations[END_REF]: Overview first, zoom and filter, details-on-demand assumes deductive reasoning. The big picture, or hypothesis, drives the interactive behavior. It is not surprising, then, as powerful as models would seem to be in the successful use of visual analytics interfaces, that they have a place in the visual analytics literature, even if the theory and implications of models are rarely discussed.

This has been only a brief, general discussion of the sensemaking loop and how it fits into the broader context of common reasoning types. Human reasoning is about reaching a usable or verifiable conclusion, but the ways in which we reach these conclusions, as we have seen, can vary widely. For this reason, it is easy to see why analytical reasoning processes have yet to be operationalized in a manner that meaningfully informs research and design. For while descriptive models like the sensemaking loop do much to frame the big picture, intuitive interfaces will require a more detailed working-order understanding of what lies inside the frame.

The Absence of Precedent

As we saw in the last section, there is, as yet, no unifying theory of reasoning (if such a thing is even possible). What does exist is a complication of decades of research into specific laboratory tasks, usually characterized by small-scale problems, which are intended to uncover reasoning heuristics and biases. These are of limited use for real-world applications, and in particular map poorly onto visually enabled human reasoning (e.g. interactive visualization for cognitive tasks). Further, the theories that motivate these studies are often bound to a particular task and environment. Thus the field of behavioural research as a whole is characterized by contradictory, often esoteric theories that fail to explain the narrative of reasoning from beginning of task to end.

For example, deductive reasoning is almost entirely studied in a laboratory trials. Both rule based and model-based deduction has traditionally studied by presenting participants with syllogisms and evaluating the conclusions that are drawn. Phillip Johnson-Laird often uses syllogisms to study aspects of reasoning, which can take forms such as this inference about object properties:

Only one of the following statements is true: At least some of the plastic beads are not red, or None of the plastic beads is red. Is it possible that none of the red beads is plastic? ( [START_REF] Johnson-Laird | How We Reason[END_REF], pg. 150).

Other common uses of syllogisms involve mental reasoning and inferences about spatial relationships, such as:

The cup is on the right of the plate. The spoon is on the left of the plate. The knife is in front of the spoon. The saucer is in front of the cup. What is the relation between the knife and the saucer? ( [START_REF] Johnson-Laird | How We Reason[END_REF], pg. 130) Cherubini and Johnson-Laird [START_REF] Cherubini | Does everyone love everyone? The psychology of iterative reasoning[END_REF] studied qualified inferences in iterative reasoning through word problems like the following: Everybody loves anyone who loves someone. Anne loves Beth. Does it follow that everyone loves Anne? .. . . Does it follow that Carol loves Diane? Cherubini and Mazzocco also evaluated the mental models to rules mechanization through use of a computer program loaded with a series of virtual card problems [START_REF] Cherubini | From models to rules: Mechanization of reasoning as a way to cope with cognitive overloading in combinatorial problems[END_REF] as illustrated in Figure 2. The participant was asked whether, based on the presented cards, a proposed sentence was certainly true.

Gigerenzer, in his evaluation of "fast and frugal" reasoning heuristics, used what he considered to be common knowledge about cities in questions about which he asked participants to make quick reasoning decisions. The questions were simple, such as Is this [city name] the capital of the country? [START_REF] Gigerenzer | Reasoning the fast and frugal way: Models of bounded rationality[END_REF]. Gigerenzer postulated that humans could make quick decisions based on very simple elimination heuristics which depended on accumulated general knowledge. These decisions were found to be more accurate than more sophisticated human and computer reasoning simulations. The behavioral literature contains decades of research similar to the research we have discussed, with each study having its own novel, usually non-real world, problem formulation. Study problems are often designed to study some small subcategory of reasoning (iterative inferred, probabilistic, etc.) and very few or no studies are published which are designed to explain how humans solve a complex problem from start to finish.

Perhaps it is not surprising then that, with all of this research, there is still a lack of precedent on how to conduct research into visually enabled reasoning. It is not at all clear how one might evaluate interfaces with respect to their ability to scaffold higher-order cognitive tasks. Further, unlike many of the simpler cognitive tasks, higher cognition is almost never binary, sequential, or singly threaded. It is, in practice, dynamic, combinatorial, and capable (at least to some degree) of parallel processing. Which heuristics are used during complex cognition and when will depend on the task, the environmental framing, and, as we will now discuss, differences in how an individual assimilates and manipulates new information.

Individual Differences

Complex cognition, for all of its variety, is also influenced by human individuality. There is no standardized unit of human cognition. It is influenced, sometimes profoundly, by the users distinctive abilities and bottlenecks, beliefs about the world, preferred methods of categorizing and prioritizing information, and other individual differences. This is one reason that the modeling of reasoning has traditionally been difficult. Human behavioral research has demonstrated the impact of individual differences on learning and analysis in traditional environments.

There is also a plethora of examples in the behavioral literature of how individual differences impact cognition; for the sake of brevity, we will focus on the impact of personality factors, which also have a broad literature of their own. For example, personality factors predicted preferences and visual perception of landscapes [START_REF] Macia | Visual perception of landscapes: Sex and personality differences. Our national landscape: A conference on applied techniques for analysis and management of the visual resource[END_REF]. Visual impairment in children is heavily influenced by individual personality differences [START_REF] Corn | Visual function: A theoretical model for individuals with Low Vision[END_REF]. Individual differences also affect how humans categorize, including the categorizing of stereotyping and prejudice [START_REF] Heaven | Personality factors predict racial prejudice[END_REF]. Palmer found that interactive behaviors in information search can be categorized by personality factors [START_REF] Palmer | Scientists and information: II. Personal factors in information behaviour[END_REF]. Another study found that problem-solving behaviors could be predicted by responses to the Thematic Apperception Test [START_REF] Ronan | Personal problem solving scoring of TAT responses: Known groups validation[END_REF]. In reasoning behaviors, individual differences impact rationality and reasoning as well ( [START_REF] Stanovich | Individual differences in reasoning: Implications for the rationality debate?[END_REF], [START_REF] Stanovich | Who is rational? Studies of individual differences in reasoning[END_REF]). These are just a handful of studies in a deep literature of individuality and the impact of these differences on every major cognitive process, as well as behavioural outcomes, such as academic or organizational performance. The question is not whether individual differences impact cognition, but how we can use individual differences to improve our understanding of visually enabled analysis. In addition, users in a particular domain can share personality characteristics and learning preferences, both inherent and institutional, which implies that some common traits can be aggregated into specific user profiles which can inform superior design requirements and aid in evaluation protocols. These differences will be discussed as part of the Personal Equation of Interaction in a following self-titled section.

The Human Cognition Model

In earlier work ( [START_REF] Green | Visual analytics for complex concepts using a human cognition model[END_REF], [START_REF] Green | Using a human cognition model in the creation of collaborative knowledge visualizations[END_REF], [START_REF] Green | Building and applying a human cognition model for visual analytics[END_REF]) we outlined an operational framework, the Human Cognition Model (HCM), whose objective was to inform customization of human-computer cognitive collaboration in mixed-initiative interactive systems. Todays information visualization applications tend to be passive; primary interface processes sit and wait for user initiation. This is not a problem if the user knows exactly where to go and what to do. But for the large semantically-rich datasets which visualizations are increasingly called upon to capture, and the complex analytical reasoning the visualization must scaffold and support, a truly intuitive interface must be capable of initiating a variety of processes on its own. The HCM identifies many of these tasks and the varieties of cognition the tasks.

The central process identified by the HCM is Knowledge Discovery. (See Figure 3.) This was envisioned as a human and computer paired process: the interface presents information and the human user indicates interest in a specific area or point, which the computer in turn presents in a related context. If Knowledge Discovery is goal oriented, the human will, among other processes, use temporally moderated perception, semantic categorization, and elimination reasoning heuristics to search and filter through the information space. If the discovery is not goal-oriented, the user may browse, stopping the explore data items that stand out or that are associated to items of interest.

Other processes in the HCM include information search by pattern and ex-Fig. 3. The Human Cognition Model [START_REF] Green | Using a human cognition model in the creation of collaborative knowledge visualizations[END_REF].

ample. Search is an interesting cognitive task, as it is deceptively simple. Some types of search are simply target identification, utilizing perceptual logic, manipulating the interface and information space through a procedural script, and using the simplest of elimination heuristics (a binary filter that asks a question: Is this the specific item Im looking for?). Other types of search can be much more complex. When the task is to find items similar to an exemplar, for example, all the cognitive processes from the simpler search tasks serve to feed more complex processes, such as inferential and deductive reasoning, which utilize more complicated models or rules for comparison and contrast. Thus, even in the more routine of interface tasks, complex cognition cannot be ignored.

The HCM also outlines the creation and analysis of hypotheses. As discussed in previous sections, hypotheses can be created in a variety of ways, from the loose associations in abduction to the use of more demanding heuristics. Sometimes hypothesis are brought to the task by the user, and drive the interaction from the start. Wherever hypotheses are generated, they serve an important function. They drive discovery and search behaviors, they determine how information is viewed and filtered, and they promote some data-derived conclusions over others. For these reasons, interfaces which promote the generation of valid hypotheses, either through framing, adaptive search, or more intuitive interaction, might considered more valuable than others.

Other pertinent processes discussed in the HCM literature (e.g. [START_REF] Green | Using a human cognition model in the creation of collaborative knowledge visualizations[END_REF]) include an interface being aware of and predicting user intent in order to keep pertinent information visible, supporting human working memory, caching data subsets of interest to introduce them in a sequence and timing that will support the flow of reasoning and ongoing discovery, and conducting analyses and providing their findings in a contextual framework, which supports a variety of hypotheses generation. In short, the HCM sketches out an interface that collaborates on cognitive processes per se, informed by a growing understanding of human preferences, abilities and limitations.

The Personal Equation of Interaction (PEI)

Humans are cognitive individuals. As we have seen, humans individuality influences cognitive performance. These differences, as discussed in Section 3, shape the way we approach and perform cognitive tasks. We have discussed personality and self-beliefs in this chapter for sake of brevity, but we are also aware that humans also exhibit differences in psychophysical characteristics, such as perceptual categorization, focused attention, and haptic preferences. These individual variations interact with each other and the task to which they are applied in manners not yet understood.

Further, as we have seen, there is great variety and complexity to analytical tasks, and so it makes sense that not all cognitive tasks would be equally impacted by the same inherent differences. For example, in our research, we have found that persons who tend to believe in that good things that happen to them are due to "luck" (an external locus of control) are predictably slower in target identification. [START_REF] Green | Using personality factors to predict interface learning performance[END_REF]. But the same cannot be said for more cognitively complex problems, such as comparing and contrasting multi-dimensional glyphs [START_REF] Green | Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction[END_REF]; for these tasks, believing in luck seems to give users a decided advantage. (See the section on Research in Personal Equation of Interaction.) It makes sense, then, that not all cognitive tasks would be equally impacted by the same inherent differences; some reasoning tasks may be better predicted by one collection of inherent traits over others.

The Personal Equation of Interaction Defined

Our goal of parameterizing a general model in order to predict performance of a particular individual builds upon foundational work in human perception conducted in the early 19th century by Friedrich Bessel [START_REF] Brooks | The Improbable Progenitor[END_REF]. Bessel recognized that the variability of reports of astronomical observations could be separated into the differences between the average ratings of observations made by each individual observer (in statistical terms, the between-subject variance around the global mean) and variation within observations made by a given observer (the withinsubject variance around that individual's mean for a given task and situation). While within-subject variability could not be easily factored, the deviation from the overall mean judgment for a given individual was relatively consistent over a range of similar situations. The error for a given individual could be measured to generate a "personal equation" for that individual. This could be used to factor out their characteristic error to bring their data into agreement, more or less, with the global mean. This meant that data from fewer observers were needed in order to achieve the same level of precision. In addition, one could predict any given observer's raw measurement to a reasonable degree of accuracy given the global mean and their personal equation.

Much of the research [START_REF] Po | Pointing and Visual Feedback for Spatial Interaction in Large-Screen Display Environments[END_REF], [START_REF] Fisher | Science and Smart Graphics[END_REF], [START_REF] Green | Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction[END_REF] in our laboratory has been devoted to defining a modern version of the personal equation, the "personal equation of interaction". This is defined as the quantifiable interaction of human perceptual, motor, and cognitive limitations with tasks and perceptual stimuli that are generated by modern visual information systems. In Po et. al., we demonstrated that system variables such as cursor visibility and display lag interact with individual differences to produce characteristic patterns of behavior for subpopulations of individuals [START_REF] Po | Pointing and Visual Feedback for Spatial Interaction in Large-Screen Display Environments[END_REF]. These effects are unlikely to be observed by perceptual testing that does not focus on the particular characteristics of active visual information displays and how they differ from those we experience in the physical world that have informed both human evolution and visual experience of individuals.

There are three goals in this effort: first, to to predict how a given individual will perform on a given task and information display; second, to build a framework for interface design that to support customization of a visual information system along dimensions that are psychologically valid (i.e. that would track aspects of individual differences in such a way that their accurate fit to an individual's capabilities would measurably improve their performance with the system); and lastly, to build increasingly accurate and comprehensive estimates of personal equations and methods for assessing them. This includes both persistent differences between individuals (e.g. color blindness) and short-term shifts in capabilities (e.g. their performance under stress). The latter could potentially be generated "on the fly" by an attentive system and applied as conditions changed to maintain optimal performance over changes in the capabilities of the human operator.

Our approach builds on existing psychometrics methods and materials. However the goal of this particular line of inquiry is to build towards a natural science of human-information interaction. By focusing on the specific kinds of changes in perceptual and interactive experience that are generated by modern visual information systems, we can address how changes in the properties and statistical regularities of information displays interact with the human visual system in general, and that of an individual observer in particular. For example, many studies in perception (e.g. [25]) show how our ability to parse complex visual scenes given limited perceptual information (the so-called "poverty of the stimulus") is supported by our internal expectations, which in turn are built through a lifetime of sampling the statistical regularities of our physical environment. Phenomena such as change blindness [START_REF] Grimes | On the failure to detect changes in scenes across saccades in[END_REF], [START_REF] Rensink | To see or not to see: The need for attention to perceive changes in scenes[END_REF] demonstrate the adaptation of human vision to an environment where abrupt changes are rare. Our visual system is not optimized to detect those changes, but pays a small price for this in the physical world. Active updating of visual displays increase the frequency of abrupt display events, and the probability increases that one will coincide with the observer's saccadic eye movement and so escape detection. Thus we find that the study of human performance with information systems cannot simply rely on applying perceptual and cognitive science research taken from the literature. It is necessary to actively seek answers to questions that arise from the use of information systems in cognitive task performance, and to do so using methods from the natural sciences.

It is an open question to what extent aspects of the PEI are inherent and what aspects are acquired through experience with complex visualizations. Some factors such as low vision, color and stereo blindness etc. clearly fall into the former category. Estimation of these factors might require psychophysical testing of a given individual, but may also be estimated by covariates of the given conditions, whether or not we can establish a causal link between them. To the extent that these covariates are predictive, they can be used to support the design and customization of information systems now, as well as contributing to ongoing research about human differences.

The first clustering factor for individual differences is through cognitive experience, e.g. in a given institution or profession. Members of a professional or skilled cohort tend to share jargon and conceptual understanding. Additionally, they are often practiced in methodologies and task heuristics that are specific to the group. These methodologies become a part of the way in which the user searches for and uses information, and can impact the conclusions drawn. They can also introduce group-specific biases that might not be found in the general user population. For these reasons among others, understanding an institutional user profile is important to the design of an expert system interface. A second factor might be perceptual and perceptuomotor experience through interaction with some specific environment.

We define the Personal Equation of Interaction as a compilation of predictive measures based upon inherent individual differences, including but not limited to personality; each measure will be validated to predict performance for one type of cognitive task integral to analysis. The PEI has three current and future end goals: the prediction of analytical performance based on inherent differences [START_REF] Green | Using personality factors to predict interface learning performance[END_REF], [START_REF] Green | Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction[END_REF], the ability to inform real-time interface individuation, and the creation of fuller-bodied user profiles, broken down by the reasoning tasks performed [START_REF] Green | The impact of personality factors on interface interaction and more robust user profiles: Next steps in the Personal Equation of Interaction[END_REF].

The first goal -that of performance prediction -is being undertaken through a series of human research studies. Participants are asked to complete a series tasks similar to common interface tasks, such as interface learnability, target identification, categorization, etc. The measured outcomes vary by task, and in-clude completion times, errors, and free response feedback. In addition to the performance and qualitative feedback, participants are asked to complete hundreds of items from a battery of psychometric measures we have chosen for their inter-relatedness and their relationships to learning outcomes in the behavioral literature. Post-study analysis includes evaluating the trending of the psychometric items with measured outcomes. In addition, followup testing such as factor analysis is done to isolate highly predictive psychometric items or groups of items, such as in [START_REF] Green | Using personality factors to predict interface learning performance[END_REF] for a particular type of reasoning task. This allows us to delineate our findings by cognitive process and compare multiple interfaces or problem-solving environments in hopefully a more even fashion. Currently, we can predict performance on simple outcomes like completion times in a variety or common interface tasks; these findings are being replicated and expanded. Not surprisingly, this research is currently quite fluid, and continues to inform the second goal of what matrices will be needed to support real-time interface adaptation. In addition, having hundred of participants complete these studies has allowed us to sketch out initial user profiles, or describe inherent characteristics of a user based how the user performs on an analytical task [START_REF] Green | The impact of personality factors on interface interaction and more robust user profiles: Next steps in the Personal Equation of Interaction[END_REF].

The goal of the PEI is not, at least in the short term, to tell designers and developers specifically which visualization techniques to use and which to avoid generically, but rather to give interface creators a robust understanding of what the individual analyst needs in order to optimally support the analytical tasks that must be performed. The Personal Equation of Interaction does not replace interface design; it augments design by making designers aware of user strengths and weaknesses. It cannot replace user studies for a particular interface, but it provides new metrics with which to evaluate study outcomes. And as a research program, it amplifies visual analytics as the study of analytical reasoning supported by interactive visual interfaces by adding to the body of understanding on analytical reasoning and analytical reasoners.

These are aggregated to build an ever more comprehensive and accurate personal equation of interaction that could be used by an application to parametrically modify its display of information in such a way as to optimize the cognitive performance of an individual decision-maker. As research progresses we hope to find a way to integrate system models with models of human interaction to better predict the course of fluent human-information interaction.

Research in the Personal Equation of Interaction

Our research has demonstrated that inherent differences can and do influence learning and analytical performance during interface interaction. In combination with the environmental and institutional variations, there is evidence that the impact of inherent differences could theoretically be used to derive a personal equation of interaction.

Our recent research has demonstrated that personality factors can predict efficiency during varying task types [START_REF] Green | Using personality factors to predict interface learning performance[END_REF]. We designed a series of tasks we asked participants to complete in two visual interfaces using the same dataset: menudriven web application, and an information visualization using direct interaction on hierarchical graphs. These tasks were designed to test two very different types of learning: procedural and inferential. Procedural learning, as defined for this study, was the ability to use the interface to find target information. This type of learning tends to be inductive: a rule is inferred which generalizes to other similar target identification tasks in the interface. Other the other hand, the inferential learning tasks were highly deductive. Participants were asked to evaluate a multi-dimensional exemplar and find another conceptual object in the hierarchy that was similar to (or different from) the exemplar for the specified dimensions. This type of reasoning involves the creation of a mental model, which is then used to evaluate complex concepts to reach a valid conclusion. For each task, we tracked errors, completions, as well as qualitative feedback.

In addition, we administered several longstanding and well-documented psychometric measures to participants [START_REF] Green | Using personality factors to predict interface learning performance[END_REF]. These measures were created to measure personality traits such as neuroticism (a tendency toward emotional instability), extraversion (a tendency toward sociability or seeking the company of others), and trait anxiety, which is a tendency to be more anxious generally, regardless of the environment. Trait anxiety differs from state anxiety, which is the tendency to be anxious when in a situation that triggers anxiety.

Another personality trait that proved to have a demonstrable impact was locus of control, which is a measure of how in control a person feels he or she is over the events in life. Persons with an external locus tend to believe strongly that they are not in control, and attribute events to factors outside themselves, such as luck, other people, or circumstances outside of their control. On the other hand, persons with an internal locus tend to believe that they are responsible for both positive and negative events in their lives. They are more likely to attribute events to some behavior or attitude of their own than to outside influences, and tend to give very little credence to luck.

Other measures designed to test other personality factors, such as a discomfort with problem-solving situations where important factors are unknown (an intolerance of ambiguity) or self-regulation, which is the ability to hold it together emotionally when the problem or situation becomes difficult, were also evaluated but were not found to be particularly predictive in performance of the tasks under study.

Results of Study 1.

Results demonstrated [15] that whole score locus of control predicted inferential task efficiency in the data visualization; participants with a more external locus were able to complete the deductive inferential tasks more quickly.

For the inductive procedural tasks, no singular full measure could predict behavior, which was not unexpected, given that none of these psychometrics were designed to evaluate these traits in this interface environments. But factor analysis uncovered 9 items, largely from the trait anxiety measure, that predicted target identification efficiency across both interfaces. Participants who were more trait anxious found target items more quickly, even when the target was buried several layers down in the hierarchy.

Results indicated that no single personality factor measure could predict errors in either interface. Results of Study 2. Results of a similar study using procedural tasks [START_REF] Green | Towards the personal equation of interaction: The impact of personality factors on visual analytics interface interaction[END_REF], currently under submission) expanded these findings somewhat. This study used the same interfaces and similar procedural tasks. The scores and outcomes of participants in both studies were combined for greater statistical power (N = 105). Results demonstrated that both neuroticism and extraversion predicted efficiency; the more neurotic/extraverted participants found items more quickly. Additionally, analysis of the combined set found that locus of control predicted procedural performance, in directly the opposite way to that of the inferential tasks. Participants with an internal locus (a belief that they were in control of life events) found targets more quickly than those with an external locus. This evidence alone demonstrates that not only that personality factors affect interface interaction performance, but that different tasks are impacted differently by inherent individual differences. See Figure 5.

Discussion of Results

The existence of significant trending between personality factors and interface interaction outcomes is interesting for a variety of reasons. First, it demonstrates that even complex cognition can, at least to some degree, be predicted. Secondly, it demonstrates that inherent individual differences, over which we as designers have no control, could inform design if we knew the psychometric makeup of our target user group. This holds potential for experts systems, which are designed for users whose differences are likely to trend in similar ways. Thirdly, these studies open a promising doorway; if these few personality factors can predict performance, what else about complex cognition might we be able to predict if we knew more about our users, as well as about the expert cohorts for whom we design visually enabled interfaces?

Conclusion

The reasoning used during task analysis is complex. In this chapter, we have discussed this complexity by highlighting a handful of reasoning heuristics. We have underscored this complexity with a discussion of Pirolli and Cards sensemaking loop. And we have explored how this complexity complicates the current state of design and evaluation thanks to the absence of applicable reasoning research and pertinent precedent in the behavioural literature.

We have also broadly discussed the impact of human individuality on every primary cognitive process, and surveyed our current research in pursuit the generation of new system development models that optimize the cognitive performance of human decision-makers. Optimization in this context must include complex criteria such as insight, innovation, creativity and awareness in uncommon, unique and novel problems and situations. Research has shown that inherent individual differences between users impacts the task and learning performance in visually embedded interfaces. Our previous work in the development of the Human Cognition Model continues to inform our research direction. Our ongoing research in the Personal Equation has highlighted the need to study not only inherent differences in personality factors, but also other user differences, including those in which affect other inherent individualities as well as differences in institutional cohort and environment. These individual differences in human capabilities are great enough that any unitary system will be at best a compromise between the needs of the various sub-populations of users. Our ongoing research seeks to take advantage of human individuality, rather than to ignore it. While still in the early stages of this research, we have already highlighted several inherent differences which predict performance, depending on reasoning task. We intend to explore further, with the expectation of isolating and understanding influential individual differences and how they impact interface interaction, which could benefit visual analytics interface designers by informing design requirements and opening up new areas for innovation.

Fig. 2 .

 2 Fig. 2. The reasoning task from Cherubini [6].

Fig. 4 .

 4 Fig. 4. Items in the 9 item short measure. Adapted from [15].

Fig. 5 .

 5 Fig. 5. Summary of findings from Study 2. From [16].