Interactitrg with Computers uol 3 no 3 (1991) 270-292

Cognitive assessment of alternatives

Robert Spence and Maureen Parr

To support a wide range of cognitive tasks involving the relative assessment of alternative choices, the authors advocate consideration of the simultaneous presentation of those choices, each choice being represented by a multidimensionai icon (a ‘portrayal’) whose features encode the attribute values of a particular choice. An experiment is reported which focused on the relative merits, for a decision-making task, of textual and graphical (iconic) descriptions of alternative choices. Significant effects of representation and choice population on time taken to reach a solution were found.

Keywords: cognition, decision-making, choice represention

The problem

Within the astronomical volume of information processing carried out daily by human beings, a significant proportion involves the relative assessment of alternative choices. The human may be a prospective home-owner searching for accommodation, a financial dealer trading in securities, a seaman assessing sonar targets, an air traffic controller observing plane movements, a company director reviewing the performance of a multinational or a host doing late shopping for a hastily arranged dinner party.

in all these tasks, every item being assessed is characterised by a number of attributes, each having a value. Thus, in the search for accommodation, attributes might include fype, size, cost and stnte of repair, possible values being, respectively, ~~~se~~~~, 3 (bedrooms), ftOOK to EZ50K and poor. The task being performed in each case can be termed decision making, though it can range widely in complexity. At its simplest extreme, the task may be to identify the supermarket offering the least expensive carrots. At its most complex, the task may be to identify, with reasonable confidence, the best accommodation which is close to a good school, affordable, not too far from one’s place of work and, if possible, in a rural area, a task that could be termed multi-attribute oplimisa- tion. Such an imprecisely stated objective, and one which may we11 undergo modification as the search proceeds, is typical and cannot be ignored in the search for a realistic decision support mechanism.

Department of Electrical Engineering, Imperial College, London, UK

270 0953-5438/91/030270-13 0 1991 Butterworth-Heinemann Ltd

This paper

Because real-life problem-solving tends to be complex, with relatively un- defined data, reliant on subject reasoning and a constant redefinition of the task, there is a pressing need for computer-based support for the cognitive (as opposed to the algorithmic) assessment of alternative choices for a range of tasks typified by those discussed above. Ideally, the solution found should be suited to both discretionary and obligatory users. The next section identifies what appears to be an attractive approach - the simultaneous presentation of alternative choices. Following a review in the fourth section of relevant literature, the fifth section proposes that its implementation should exploit the hypothesised - though not yet proven - advantages of graphical as opposed to textual representation. An experiment to test this hypothesis is described in the sixth section. The results and their discussion appear in the final sections. The conclusions drawn point the way to useful directions for further research.

The simultaneous presentation of alternative choices

To support the task of multi-objective optimisation, we advocate the simul- taneous presentation, to the user, of suitable representations of as many choices as possible, each representation encoding the values of all the choice’s attri- butes. An illustrative example can be taken from a person’s search for accommodation: an estate agent (realtor) with 100 choices of accommodation to offer might display (say) a lo-row, lo-column collection of graphical representa- tions such as Figure 1. We shall refer to the image of Figure 1 as a multi- dimensional icon or ‘portrayal icon’. Its shape indicates type (e.g., house or houseboat) and is easily recognised. The squares, like windows in appearance, indicate the number of bedrooms. Clear windows indicate a good state of repair, black ones the reverse. A stylised clock indicates the travelling time to a major railway terminus. The colour of the icon indicates cost, and garden size is indicated by a hatched area. A wavy symbol denotes the presence of central heating, and a simple representation of a garage denotes the presence of this facility.

Three aspects of the simultaneous presentation of available choices make it, in our opinion, appropriate to the task of multiple-objective optimisation. First,

Figure 1. Graphical representation of accommodation

Spence and Parr 271

house f400 000 garage central heating four bedrooms good repair large garden Victoria Railway Station 45 mins

Figure 2. Textual representation of accommodation

it provides the user, especially before evaluation begins, with an overall awareness of what is available, including the ranges of attribute values. In this way a more achievable objective may be formulated, and modified as the search proceeds. Second, the suggested presentation can often provide sensitivity information which can immensely benefit optimisation. Thus, in accommoda- tion search, the question “How do prices change as we go along Route lOl?” may be more readily answered than with other means of information search. Third, and especially if some means is provided whereby promising candidates can be marked, simultaneous presentation facilitates the achievement of a useful measure of confidence that the selected choice is, in fact, the optimum. Essentially, the simultaneous presentation of choices embodies a “Supermarket metaphor” (“This is what’s available - browse around and make a considered choice”) as opposed to the “Storekeeper metaphor” (“Tell me precisely what you want, in my language, and I’ll tell you if it’s in stock”) which characterises the conventional menu system which, for all its advantages, does not normally offer the characteristics delineated above*.

An icon or other graphical representation is not, of course, the only available one. An alternative which is in common use is text: the corresponding description of the house depicted graphically in Figure 1 is shown in Figure 2. In this paper we investigate the hypothesis that the multidimensional icon is more effective than the textual description in the context of multi-objective optimisation.

Literature review

It is well established in the relevant literature (e.g., Paivio, 1972; Richardson, 1980) that image-related encoding of material aids recall. Items with more concrete and/or more imageable properties are able to be better remembered and manipulated in various information processing tasks such as decision- making, than are abstract items. Stimulus imageability is highly correlated with

* Spence & Parr (1990) offer a more extensive discussion of those characteristics of menu systems which render such systems unsuited to the tasks discussed in this paper.

272 Inferacting wifh Computers ~013 no 3 (1991)

performance over a variety of tasks, probably because subjects can process the material either verbally or by using mental imagery. There is, thus, a built-in redundancy in imaged data that supports performance in recall and in cognitive manipulation of the material.

It therefore makes good sense to investigate the possibility of presenting data graphically rather than in tabular or textual form. Early versions of graphical presentation - pie charts, bar charts and histograms - have their uses and are still widely employed, but a major limitation is their inability to describe relationships along more than two dimensions (usually the x- and y-axes, or the percentage of a part to the whole). More recent techniques such as glyphs (Anderson, 1960) and their variant, the polygon, are interesting developments but suffer from similar drawbacks.

Multidimensional displays of data began to appear in the 1970s. Bruntz and colleagues (cited in Wainer and Thissen, 1981) used ‘weather vanes’ to display meteorological information. Each vane was plotted as an (x,y) co-ordinate representing ozone concentration and solar radiation; in addition, the size of the circle on the ‘weather vane’ indicated temperature; the angle and length of the line showed wind direction and wind speed respectively. This is certainly an ingenious way of making possible the extraction of five different pieces of information from within one unified carrier; nevertheless, the graphics are still constrained within the traditional x- and y-axes.

A parallel development used the pictograph as its source and inspiration. These ‘pictorial forms that vary in size or number to represent numerical data’ (De Sanctis, 1984) had been used effectively by researchers in the 1920s and 1930s. This type of data display worked best with very simple comparisons and has continued to be used up to the present, for example on military battlefield displays (see Gittins, 1986).

Over the last few years, graphical and pictorial ideas have begun to be incorporated in computer software packages. Designers have realised that ‘icons’ have the capacity to carry information in a form that is readily accessible to the human tendency to visualise data. The icons in such interfaces are pictorially mapped onto their corresponding objects in the real world. A recent development has been the unification of individual icons through an everyday, easily understood, metaphor, the best known of which is probably the office metaphor. Computer-generated icons representing standard pieces of office equipment allow the computer operator to write, draw, edit, file and throw away on the screen in much the same way as he or she would in the office.

These icons are one-dimensional and static, relying on an unchanging relationship between fixed attributes and functions of real-life objects, and their representation on screen. The next stage for programming systems was to use the dyanamic potential of icons, allowing aspects of the design to change as attribute values of the ‘signified’ (to use a term from linguistics) vary. Multivariate data can be represented in this way.

An early use of such a ‘multidimensional icon’ was Chernoff’s Faces (1973). Statistician Herman Chernoff devised a system in which points in k-

dimensional space (where k 5 18) could be represented graphically. In this way, geological fossil specimens were classified in a meaningful and immediately

Spence and Parr 273

comprehensible way by means of cartoon faces where each of a variety of facial features corresponded to a single fossil component.

The ‘picturing of data’ (Tukey, 1974) caught on in a big way after Chernoff had blazed the trail and the literature is full of Faces and their variants. The Flury-Riedwyl modifications (see De Soete, 1986) added realism, making the Faces schematic representations rather than cartoon objects. The assignment of variables was also re-ordered so that perceptual salience of facial feature corresponded more cIoseIy with the relative degree of importance of the variable concerned.

Chemoff’s Faces have been used in a number of experimental situations designed to test the effectiveness of Faces against other, more traditional, graphical and textual displays. Accounting tasks given to subjects by Moriarity (1971) and by Stock and Watson (1984) showed that subjects made more accurate evaluations using Faces than with conventional tables. MacGregor and Slovik (1986) also found ‘a rather dramatic superiority of the face display over the other display formats’ (bar chart, deviation display, and spoke display). The suggestion they give for this result is that the face provides ‘a convenient organisational framework not so readily available in the other display formats. This conclusion is at least partially supported by other workers such as Goldsmith and Schvaneveldt (1985) who, in experiments to test speed and accuracy of decision-making, find that ‘integral’ (enclosed configuration) dis- plays, of which the Face is an example, are superior to ‘separable’ bar graphs for both complex and simple stimuli.

It should not be assumed, however, that Faces are necessnrily better than other techniques in displaying information and in aiding decision-making. Such sweeping generalisations of efficacy fail to discriminate type of task and type of user. As part of work already cited, Goldsmith and Schvaneveldt ran a pilot test which found Faces inferior to a bar graph display for representing multiple information cues. Benbasat, et al, (1986) claim that superiority is task depen- dent, with graphics better than text for evaluating information, and the opposite for determining exact data values. As MacGregor and Slavic (1986) report, ‘graphical formats appear to facilitate judgemental performance in some contexts, but not in others.’

It makes sense that familiar stimuli like faces are easily processed. We are used to making judgements, sorting information, categorising and responding to facial features in various, often very subtle and complex, ways. Moreover, this facility develops very early in life (Fan& 1962; Bower, 1966) and has therefore been reinforced in adults by years of practice. But the facility with which faces are recognised and responded to has inbuilt disadvantages, one of which is simply that the face is so familiar that it is overlain with emotive and affective significance. It has also been noted (e.g., by Goldsmith and Schvaneveldt, 1985) that certain facial features are more highly salient than others. When we process information from a face we tend to pay more attention to eyes and mouth than to eyebrows or shape of chin. It may be useful, therefore, to look for multidimensional icons that retain the advantages of familiarity, integrality and simplicity which make an effective multivariate display Uacob in Kleiner & Hartigan, 1981) while avoiding the issue-douding

274 interacting with Computers vat 3 no 3 0991)

connotations of faces. Surprisingly, there are very few instances of such icons in the literature.

‘Trees’ and ‘Castles’ (Kleiner and Hartigan, 1981) can accommodate a large number of variables - their originators suggest at least 30 for Trees and potentially many more for Castles. These icons are built up by clustering variables to obtain a ‘matching of relationships between features of the reprsenting symbol’ (op cit.). This is only feasible where variables all take comparable values as is the case for numerical data. For example, figures corresponding to successive time periods are shown as successive branches of the Trees. If the information on display is unrelated, it cannot satisfactorily be represented in this way.

Chernoff’s Faces and Kleiner and Hartigan’s Trees and Castles have no intrinsic semantic link with the data they are portraying. A rare example of an attempt to hold together various pieces of information in an icon closely representing the signified object can be found in the work of Lansdale (1988) who used icons in a job-hunting task, simulating the work of an employment agency. Subjects were asked to retrieve certain pieces of information as though they were clerical workers retrieving office files. Despite the caveat that ‘iconic methods do not automatically result in dramatically high levels of performance’, Lansdale concluded that the ‘enrichment of cues provided’ did aid information retrieval. Nevertheless, although these icons did have inherent associations with the type of employment they were depicting (industry being represented by a factory icon, education by a mortar board and so on), each icon was still manipulated only along three parameters - shape, colour and location (on a page or screen). Higher-dimensional icons which have a necessary association with the information being displayed are notably absent from the existing literature.

Originality

The work presented in this paper attempts to fill the gap that has just been identified, firstly by employing iconic representation as a portrayal, that is, by using the parameters of the object of inquiry (in this case accommodation alternatives) as the parameters of the visual metaphor. In addition, the icon presented here is multidimensional, being able to support a number of attributes (8 in this instance) in one carrier. It is used as decision support for a problem- solving task, in contrast to much previous use of icons solely for information display (in computer interfaces, for example) and it is also facilitates concurrent examination of a large number of targets.

We now report an experiment which focuses on the relative merits of textual and graphical representation, but which also identifies other issues of interest.

The experiment

An experiment was conducted in the context of accommodation search. A pair of displays, each measuring 21 in by 32 in (53.3 X 81.2 cm’) was set up, one containing 28 (4 rows of 7) textual representations (see Figure 2) and the other

Spence and Parr 275

Table 1. Attributes, codes and sample values used in the experiment

Attribute Code Number of values Example values

Type cost Size Repair Garden Garage Central Heating Time to Victoria

Railway Station

Shape Colour Windows Window shade Shape Symbol Symbol

Clock 10

House, Houseboat El00 000, f200 000 1, 2, 3, 4, 5 Good, poor None, small, medium Present, absent Present, absent

15, 20, 30 minutes

containing 28 (4 rows of 7) graphical representations (Figure 1). Each rep- resentation depicted the eight attributes set out in Table 1 above. The attributes themselves were located at various points along a range of values that took the dimensions shown in the Table. In a second stage of the experiment subjects were presented with displays either of 56 texts or of 56 icons (7 rows of 8), the size of display remaining constant at 21 in by 32 in. The use of two displays containing populations of different size was prompted by the findings reported by Arend et al. (1985) that search time for a word command set was linearly dependent on population size, whereas that for an iconic set increased much less rapidly.

Certain guideiines were foIlowed when devising the two tasks that would be presented to the subjects. In any single task only a subset (numbering 4) of the set of 8 attributes was employed. Furthermore, both equality constraints (‘there must be a garage’) and inequality constraints (‘any price up to f200 000’) were included in each task. Finally, it was arranged that each task had a unique solution so that, in this investigation at least, there was no requirement for a subjective judgment. The two tasks were:

Task I: You can spend up to E200 000 on accommodation. Locate the best you can with regard to the number of bedrooms and the size of the garden, but it must have central heating.

Task 2: The accommodation you select must be in good repair and as close to Victoria as possible, but there must be a garage and at least three bedrooms; price is no problem.

It was anticipated that the search strategies employed by subjects could well require the use of ‘tags’ to identify candidate selections. To satisfy any such requirement subjects were provided with strips of Post-itTM stickers of conve- nient size for the 28 item display, or a Dry-Writem marker for the 56 item display.

276 Interacting with Computer5 ~013 no 3 (19911

Subjects Twenty-eight subjects from a University engineering department, and 17 subjects from a College of Art participated in the 28 item display, and a further 31 engineering students took part in the 56 item display. Each was paid f2 for their participation. Subjects were asked to imagine they were selecting some- where to live from a choice of accommodation. Their task was to select the best possible accommodation to suit certain criteria (as set out in Task 1 and Task 2). Both tasks were presented (order was controlled) to each subject; similarly both icons and textual descriptions were used by each subject (order again being controlled). For example, a subject who solved Task 1 using the icons then went on to address Task 2 using text.

Before the iconic presentation, a sample of a typical icon was shown and all the attributes fully explained. Subjects were given time to familiarise them- selves with all the attributes. Similarly, a sample of textual description was shown before each text presentation. Subjects were told that there was no time limit and that an accurate decision was more important than speed. Neverthe- less, it was stressed that they were being timed and should therefore work as fast as possible within the constraints of accuracy.

Subjects were given the chance to ask about anything that was not clear before the specification was shown. When they were ready to begin, the experimenter turned over the Task for them to read and simultaneousty read it aloud. Timing began as soon as the experimenter had finished reading the Task and was terminated when the subject indicated his/her choice of accommoda- tion. While subjects were engaged with the Tasks, the experimenter noted method of search and other observable qualitative features. Subjects were subsequently asked to explain in their own words how they had solved the problems.

Results

Time to soIution A 2 X 2 analysis of variance was conducted which looked at the effects of display size (28 or 56 items) and representation (icon or text) with repeated measures on this second factor. The ANOVA found a very significant effect of representation (F&69) = 53.3, p < 0.001) with icons being associated with faster solutions: see Table 2 for means. Display size also had a very significant effect (F&69) = 29.54, p < O.OOl), with the 56 choice solution taking significantIy longer. The interaction between display size and representation was not significant (F < 1). The average time to solution was 136.4 seconds (SD = 60.8).

A paired t-test did not find significant differences between tasks (t with 70 df = 0.57, p < 0.06). No significant effects were observed across orders of presentation of representation format (text first, or icon first), or across orders of task format (task 1 first or task 2 first).

Errors If a subject’s final choice was not identical with the known unique solution an

Spence and Parr 277

Table 2. Means and standard deviations (in seconds) of the time to solution for the different conditions of display size, representation and task

Mean(s) Standard deviation(s)

28 items 114.9 50.88 56 items 167.5 60.9

Text 161.5 61.2 Icons 111.2 49.2

Task 1 138.9 56.3 Task 2 133.8 65.3

error was deemed to have occurred. Table 3 summarises the effect of represen- tation, task and display. Chi* tests were conducted on the number of errors made for each of the variables in Table 3, and a significant difference in errors was found between the tasks (Chi2, df = 1, = 3.94, p < 0.05). No other significant differences were found for this data.

Tagging Subjects responded in a number of ways to the opportunity to use tags and markers on the items among which they were making a choice (Table 4). Some used ‘positive’ marking to highlight items which appeared to meet criteria and were possible optimal choices. Others ‘negatively’ crossed off properties which violated the specifications in some way, progressively homing in on a correct solution. One or two used mixed techniques. Some subjects did not mark properties at all and made their choice without aides-memoires. An interesting strategy was one we have named ‘best-to-date’ - the marking of one property to be used as a standard against which succeeding accommodations were compared. When a better alternative presents, the ‘standard’ mark is moved to this later accommodation and checking continues against this new standard.

Table 3. Total number of correct solutions, observations and percentage of correct solutions for the different conditions of display size, representation

and task

Variable

28 items 56 items

Text Icons

Correct To taf.

68 84 41 58

55 71 54 71

% Correct

81 71

77 76

Task 1 49 71 69 Task 2 60 71 84

278 ~n~erucfi~g with Computers 43 no 3 f1992)

Table 4. Percentage of tasks on which various forms of marking were used

Subjects (display size) Type of marking

Positive Negative Mixed Best-to-date None

Engineering (28) 37 23 2 27 11 Art (28) 38 6 6 23.5 26.5 Engineering (56) 24 45 5 15 11

Discussion

Overall, a principal outcome of the experiment is a confirmation of the hypothesis that iconic representation leads to faster multi-objective optimisa- tion than does textual representation. I-Iowever, as one of our referees has pointed out, there is a need to re-examine the nature of the tasks employed. Although the two tasks were chosen with care, re-examination shows that there may be good reason, as can be seen from Figures 3 and 4, to expect that the two tasks placed different cognitive loads on the subjects: Task 1 requires one binary decision, two ‘best’ selections and one limit, whereas Task 2 requires two binary decisions, one ‘best‘ selection and choice within a limit (3) on a scale of five. Investigation to gain further insight into the nature of the cognitive load is indicated.

With regard to error it must be noted that an error was deemed to have occurred if any other than the unique optimum choice was selected. Especially with the farger display, distracters may not merely function as ‘noise’ around the target but actively approach the target attributes. Therefore, it might be we11 worth considering in future a more meaningful, though much more complex,

2oc

28 choice display

+-

I Icon

I Text

C

I-

)-

)-

56 choice display

I Icon

I Text

Figure 3. Time to solution as a function of task for iconic and textual representation

Spence and Parr 279

50 28 choice display

50

0

56 choice

I I Icon Text

Figure 4. Percentage error as a function of task for iconic and textual representation

measure of error involving ‘distance’ between the selected and optimum choices. For example, on the 56 item display, some of the choices presented offered extremely viable alternatives to the correct solution, so that discrimina- tion became correspondingly more difficult. An additional unexpected source of error that came to light in post-experiment interviews was the inclination of some subjects to make subjective weightings and to reason that an extra 5 minutes journey time was well worth an extra bedroom or a much cheaper house!

Post-experiment interviews appeared to identify combinations of attribute specifications and representations to which performance is particularly sensi- tive. For example (Task 2), satisfaction of the specification ‘as close to Victoria as possible’ was apparently eased within the iconic representation in that subjects tended to search for the clock face with the least black whereas, in the textual representation, the time was, psychologically, ‘up for grabs’ alongside all the other numbers. Wheatley (1977) cites work that shows that number can be identified more accurately than colour or shape: the apparently high salience of number in the text condition of our study supports this finding. A second example concerns the unspecified state of repair in Task 1: where poor repair became visually obvious in a row of black (‘rotten, decayed’) windows, subjects were affected by the assumed implications and chose to look for a ‘better’ alternative. This is an important finding: although ‘Houses’ do not possess inbuilt salience variability as Faces do, it is hard to design an iconic code in which all signs possess equal psychological salience. We agree with MacGregor and Slavic (1986) that ‘performance is markedly enhanced or degraded by the degree to which the display format . . . facilitates a matching between the relative importance of information and the psychological salience of the display’s graphic features’. The nature of the task in terms of perceived

280 Interacting with Computers vol.3 no 3 (1991)

complexity, wording, inferences made, etc. obviously affects the error rate on both icons and text.

The results and post-experiment interviews have strongly suggested other

issues worthy of future investigation. There is, for example, a need to explore the effect, on optimisation performance, of the number of attributes encoded in each representation. In the reported experiment there were 8 attributes, but other results (Chernoff, 1973) suggest that a much greater number (perhaps around 20) might be handled by a subject without a proportionate increase in time to solution.

As we have already noted, we are well aware that subject performance can be influenced, perhaps strongly, by the design of the icon used in the iconic display. A fundamental issue concerns the question as to whether the icon is an integral image’ (Jacob, 1981) and perceived as a whole, or simply a collection of

pictures which are scanned serially as one scans text. We are grateful to one of the referees for pointing out that certain aspects of the icon of Figure 1 are integral whereas others, such as the clock, are not. Thus, if the icons were displayed on a map, with distance from Victoria linearly related to journey time, then this attribute would be encoded integrally. There is considerable scope here for further research. It is hard to design an iconic code in which all the signs possess equal psychological significance.

Experimental results provided no evidence to support our expectation, following the conclusions of Arend ef nl. (1987) that the percentage increase in time to solution for a doubling of display size would be less for the iconic representation.

Despite the valuable experience gained from the experiment, and the consequent opportunity to identify issues requiring further investigation, it would be premature to suggest that design guidelines have emerged: these must await the results of subsequent experiments.

Acknowledgements

We wish to acknowledge useful discussion with Danen van Laar, Mark Apperley, William Edmondson, Paul Booth and Simon Halkiel, as well as the helpful comments of two referees. Darren van Laar’s assistance with the analysis is also gratefully acknowledged.

References

Anderson, E. (1960) ‘A semi-graphical method for the analysis of complex problems’ Technometrics 2, 387-392

Arend, U., Muthig, K.-P. and Wandmacher, L. (1987) ‘Evidence for global feature superiority in menu selection by icons’ Behnu. info Technol. 6, 4, 393402

Benbasat, I., Dexter, A.S. and Todd, P. (1986) ‘The influence of color and graphical information presentation in a managerial decision simulation’ ~~~~~-Co~z~~fer ~?~ferffcf~o~ 2, 65-92

Bower, T.G.R. (1966) ‘The visual world of infants’ Scientific American 215, SO-92

Chernoff, H. (1973) ‘The use of faces to represent points in k-dimensional space

Spence and Parr 281

graphically‘ 1. American Statistical Association 68, 342, 361-368

DeSanctis, G. (1984) ‘Computer graphics on decision aids: directions for research’ Decision Sciences 15, 46-87

De Soete, G. (1986) ‘A perceptual study of the Flury-Riedwyl faces for graphically displaying multi-variate data’ Int. I. Man-Mach. Stud. 25, 549-555

Far&, R.L., Ordy, J.M. and Udelf, M.S. (1962) ‘Maturation of pattern vision in infants during the first six months’ 1. Comparative & Physiological Psych. 55, 907-917

Gittins, D. (1986) ‘Icon-based human-computer interaction’ Int. 1. Man-Mach. Stud. 24, 519-543

Goldsmith, T.E. and Schvaneveldt, R.W. (1985) ‘Facilitating multiple-cue judgements with integral information displays’ in J. Thomas and M. Schneider (eds), Human Factors in Computing Systems 243-270

Jacob, R.J.K. (1981) Comment on Kleiner and Hartigan

Kleiner, B. and Hartigan, J.A. (1981) ‘Representing points in many dimensions by Trees & Castles’ I. American Statistical Association 76, 374, 260-276

Lansdale, M.W. (1988) ‘On the memorability of icons in an information retrieval task’, Behav. If. Technol. 7, 2, 131-151

MacGregor, D. and Slavic, P. (1986) ‘Graphic representation of judgemental informa- tion’ Human-Computer interaction 2, 179-200

Moriarity, S. (1979) ‘Communicating financial information through multidimensional graphics’ 1. Accounting Research 17, 1, 205-224

Paivio, A. (1972) ‘A theoretical analysis of the role of imagery in learning and memory’ in P.W. Sheehan (ed) The Function and Nature of Imagery Academic Press

Richardson, J.T.E. (1980) ‘Mental imagery and human memory’ Macmillan

Spence, R. and Parr, M. (1990) ‘The cognitive assessment of alternatives’ Information Engineering Report 90/7, Department of Electrical Engineering, Imperial College, London

Stock, D. and Watson, C.J. (1984) ‘Human judgement accuracy, multidimensional graphics and human versus models’ 1. Accounting Research 22, 1, 192-206

Tukey, J.W. (1974) ‘Mathematics and the picturing of data’, Proc. Int. Congress in Mathematics

Wainer, H. and Thissen, D. (1981) ‘Graphical data analysis’ Annual Review of Psychology 32, 191-241

Wheatley, E. (1977) ‘An experiment on coding preferences for display symbols’, Ergonomics 20, 5, 543-552

282 Interacting with Computers vol3 no 3 (1991)