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Cognitive Ability and Computing Experience Influence Interpretation of Computer Metaphors Douglas J. Gillan, Bruce S. Fogas, Suzanne Aberasturi and Shannon Richards Proceedings of the Human Factors and Ergonomics Society Annual Meeting 1995 39: 243 DOI: 10.1177/154193129503900405 The online version of this article can be found at: http://pro.sagepub.com/content/39/4/243

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PROCEEDINGS of the HUMAN FACTORS AND ERGONOMICS SOCIETY 39th ANNUAL MEETING-1995

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COGNITIVE ABILITY AND COMPUTING EXPERIENCE INFLUENCE INTERPRETATION OF COMPUTER METAPHORS Douglas J. Gillan1

Bruce S. Fogas2

Department of Psychology New Mexico State University Las Cruces, NM 88003 (505) 646-1408 [email protected]

Suzanne Aberasturi3

Division of Child Psychiatry Univ. of South Dakota School of Medicine Sioux Falls, SD 57117 (605) 333-7198 [email protected]

Shannon Richards3

Department of Psychology University of Idaho Moscow, ID 83843

Metaphors play a central role in human-computer interaction. Research on general metaphor interpretation has shown that different types of people interpret metaphors differently. The present experiment examined the effects of cognitive ability and computer experience on the interpretation of computer-related metaphors. Subjects completed five cognitive tests, filled out a questionnaire concerning their experience with computers, and interpreted computer metaphor terms. Identification of a term as a metaphor was related to their frequency of computer use and nonverbal cognitive ability. Concreteness of metaphor interpretations decreased with increased knowledge of programming. Abstractness of interpretationsincreased with frequency of computer use. The discussion focuses on metaphors in the design of user interfaces for novices and experienced users. Computers, with their ability to process symbols and to model a wide variety of systems, can be considered to be metaphor machines. One might claim, however, that human-computer interaction (HCI) provides the domain in which metaphors have their greatest power, from metaphors used in training to user dialogue metaphors (see Carroll & Olson, 1988; Carroll, Mack, & Kellogg, 1988; Norman & Chin, 1989). But even in HCI, problems with the use of metaphors occur when the user’s interpretation of the metaphor does not match the system’s operation (e.g., Douglas and Moran, 1983). Thus, if a designer used a metaphor and the user interpreted the metaphor differently from the way that the designer intended, the metaphor could interfere with, rather than facilitate, interacting with the computer. A variety of research indicates that the ways in which people interpret metaphors depends on their personal characteristics. When given metaphors that could be interpreted either concretely or abstractly, children typically provide a concrete interpretation whereas adults typically provide an abstract one (Gentner, 1977; 1988). For example, with the metaphor “a plant stem is like a straw”, children typically focus on the perceptual similarities of the stem and straw (narrow, long, and hollow), whereas adults typically focus on the similarity in underlying operation (drawing up water). In addition, people with learning disabilities and with serious cognitive disabilities due to brain damage also interpret metaphors either concretely or even literally (Ortony, 1979; Winner & Gardner, 1977). These findings suggest that people’s level of cognitive functioning may influence their interpretation of metaphors in general. Applying this suggestion to interaction with computers leads to our first hypothesis: the interpretation of metaphors for computers will be a function of the computer

users’ cognitive abilities. However, the above studies have examined a wide range of cognitive functioning -- from adults to children or from people with deficits to those without. The present study examines whether the finding of an effect of cognitive functioning on interpretation of metaphors about computers would also be observed in a relatively normal range in an adult population. In addition to differences in cognitive functioning, adults and children differ in their experience. Thus, Gentner’s (1978; 1988) findings that children interpret metaphors differently from adults may also have been a function of their general experience, as well as their cognitive functioning. This idea finds support in research with experts who tend to solve problems at a more abstract level than novices (e.g., Chi, Feltovich, & Glaser, 1981). The idea that metaphor interpretation is a function of experience leads to our second hypothesis: the way in which computer users interpret interface metaphors will be a function of the amount and/or types of interactions that they have had. Specifically, frequent use of computers, explicit training about computers, and programming experience should lead to an understanding of the operation of computers and, consequently, to the ability to think abstractly about them. METHOD Subjects. Sixty-six undergraduate students from

psychology classes at the University of Idaho volunteered to participate in the study. Six subjects failed to complete the cognitive test battery correctly; consequently, their data were not analyzed further. Recruitment procedures attempted to gain participation by subjects with a wide range of computer experience. Subjects received extra credit for a course by completing the study. Materials. The materials consisted of a questionnaire


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and a battery of cognitive tests. Subjects completed individual questionnaires and tests, but the materials were all administered to the subjects in large groups. The questionnaire consisted of questions concerning five areas. (1) A set of questions asked about the subject's frequency of experience with various application software (word processing, spreadsheets, database management, graphics, library, electronic mail, statistical, games, disk management, and programming), types of computer hardware (e.g., mainframe and personal computers), types of input devices (e.g., keyboards, mice, and trackballs); educational experiences related to computers; and a listing of programming languages and rating of knowledge of that language. (2) A list of 41 possible metaphors for computers was provided. Subjects rated the similarity between each item and a computer, then provided a verbal (written) description of those relations that were rated to be somewhat similar, similar, and very similar (i.e., 5, 6, or 7 on the 7point similarity scale). Table 1 lists the metaphor terms in alphabetical order. (Note that the questionnaire listed them in random order.) As Table 1 shows, the metaphor terms were selected to cover a range in the following characteristics -information processing, storage, and presentation, as well as human and machine qualities. In addition to experience questions and metaphor ratings, the questionnaire asked about (3) subjects' emotional responses to specific humancomputer interaction scenarios; (4) subjects' approaches to solving certain HCI problems; and (5) demographic information, including questions about subjects' known or believed cognitive deficits. The cognitive test battery consisted of (1) the Shipley Institute of Living Scale (SILS) (Shipley, 1983), which contains 40 multiple choice vocabulary items and 20 abstract-thinking items and tests general intellectual level -the SILS correlates with the WAIS-R IQ score at .85 (Gregory, 1987); (2) the Logical Memory and Visual Reproduction subtests of the Weschler Memory Scale Revised (WMS-R) (Weschler, 1987) which assess deficits in verbal and visual memory, respectively, and include both immediate tests and delayed tests 30 min after receiving the stimuli; (3) the Passage Comprehension Test from the WoodcockJohnson - Revised (Woodcock & Johnson, 1989) which involves reading a short passage and identifying a key word that is missing and measures verbal skill; (4) the Boston Naming Test (Kaplan, Goodglass, & Weintraub, 1983) which assesses a person's ability to name pictured objects and identifies aphasics; and (5) the Digit Span Test from the WAIS-R (Weschler, 1981) -- a test of attention in which subjects repeat a sequence of from 2 to 7 digits forwards and backwards. Due to poor visibility of the pictures during the administration of the Boston Naming Test, data from that test were not used in the analyses. Procedure. Subjects first received the questionnaire. For computer metaphors, they used a 7-point scale to rate the similarity between a computer and each term shown in Table 1. After making their ratings, subjects provided an interpretation of every metaphor that they had rated to be similar to a computer (i.e., above 4 on the 7-point scale) by describing the specific relation between a computer and that metaphor term. The questionnaire typically took 30 min to complete. Next, subjects received the cognitive test battery

which was administered in a group format with subjects responding in writing. The battery took approximately 60 min to complete. TABLE 1. Computer Metaphor Terms (and Percentage of Subjects who Rated the Term as Similar to a Computer) Alarm Clock (20) Aquarium (3) Automatic Teller Machine (77) Best Friend (7) Blender (7) Bookshelf (7) Brain (72) Calculator (92) Calendar (23) Compact Disk Player.(37) Checkbook (23) Conversational Partner (12) Cookbook (8) Desktop (12) Diary (23) Dictionary (50) Door (3) Encyclopedia (38) Enemy (10) Etch-a-SketchTM(18) Filing Cabinet (52)

Infant (7) Jokester (5) Lamp (0) Magician (0) Microwave (23) Paperweight (2) Pencil and Paper (18) Phonebook (33) Piano ( 10) Record Player (7) ' Safe (12) Tape recorder (25) Telephone (30) Television (33) Toilet (0) Translator (28) Typewriter (82) VCR (35) Video game (90) Yardstick (2)

RESULTS Overview of Analyses. The hypotheses were tested

using multiple regression analyses to determine the relations between metaphor identification and interpretation, on the one hand, and cognitive ability and computer experience, on the other hand. Prior to these analyses, three preliminary analyses were undertaken. First, a factor analysis was performed on the data from the cognitive test battery to determine if the tests might be combined into a limited number of factors. Next, correlations were determined for the data concerning the three measures of computer experience -- frequency of use, education, and programming languages -- to determine whether these measures accounted for a substantial amount of independent variance. Then, the metaphor interpretations were judged for the degree to which they were concrete, functional, or abstract. The results of these three preliminary analyses were used in the multiple regression analyses. Measures of Cognitive Ability. None of the 60 subjects who completed the test battery reported any cognitive deficits. The mean for the SILS (converted to an IQ score) was 107, with a range of 89 to 125, indicating that the sample resembled the United States population as a whole, but had a more restricted range. A factor analysis using an orthogonal rotation was performed on the data from the cognitive test battery. The analysis revealed two factors shown in Table 2. Tests that measured verbal ability had high loadings on one factor, whereas tests that measured nonverbal ability had high loadings on the second factor. Subsequent analyses related to


PROCEEDINGS of the HUMAN FACTORS A N D ERGONOMICS SOCIETY 39th ANNUAL MEETING-1995

cognitive ability used these two factors rather than the individual test scores. TABLE 2. Factors for Cognitive Tests (Loadings 2 SO)

Factor 1 (Verbal Ability)

Test WMS Verbal Immediate WMS Verbal Delayed Passage Comprehension SILS

Loading .93 .94 .57 .52

Measures of Computer Experience. Subjects ranged in frequency of use from nonusers to very frequent (daily use); ranged in education from never having had a computer class, read a manual or textbook, or received tutoring to having had 103 such experiences; and ranged in knowledge of programming languages from novice to expert (with 26 subjects having some knowledge of at least one language). We examined the correlations among these measures and found that the frequency of using computers had a .52 correlation with number of computer-related courses and a .29 correlation with number and rated knowledge of programming languages; the number of courses had a correlation of .34 with the number and rated knowledge of computer languages. The values of these correlations suggest that the three measures provide a substantial amount of independent information. Accordingly, we did not combine the measures for further analysis. However, the correlations indicate that the three measures of experience overlap to a degree, so the multiple regression analysis of their relations to metaphor interpretation was appropriate. of

Metaphor

Rating

nonmetaphorical, received no interpretation scores, and were not analyzed further.) Table 3 lists examples of concrete, functional, and abstract metaphor interpretations given by subjects. Degree of concrete, functional, and abstract interpretation were each rated by the two judges on 3-point scales. Thus, each judge made three ratings for each metaphor interpretation. The interrater reliabilities (Spearman rs’s) were high for all three scales -- .93 for concrete, .84 for functional, and .86 for abstract. Accordingly, the two judges’ scores were averaged for each metaphor interpretation for each subject. Those mean ratings were used in the multiple regression analysis. TABLE 3. Examples of Metaphor Interpretations Rated to be Concrete, Functional, or Abstract

Factor 2 (Non-Verbal Ability) Loadin2 WMS Logical Immediate .so WMS Logical Delayed .75 .69 Digit Span Test SILS SO

Test

Measures Interpretation.

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and

The percentage of subjects who rated each term from the metaphor list to be similar to a computer (i.e., above four on the scale of similarity) is shown next to the term in Table 1. Subjects provided interpretations of the metaphor term if they rated the relation of the term to a computer above four. Two independent judges, blind to the identity and characteristics of the subjects, scored all of the metaphor interpretations on the degree to which they were concrete, functional, and abstract. Concrete interpretations were identified as based on a shared physical attribute (e.g., both have a keyboard); functional interpretations were identified as based on a shared way of behaving or performing (e.g., you can create papers on both); abstract interpretations were identified as based on a shared conceptual capability or feature (e.g., a computer is like an infant -both need to be told what to do). (Interpretations that simply equated the metaphor term and a computer -- for example, “a video game is a computer” -- were considered to be

Concrete Interpretations ATM - “has a keyboard” Blender - “both have buttons” Telephone - “hook up with cords” Television - “both have screens” Typewriter - “keyboard” Functional Interpretations ATM - “data management” Calculator - “adds numbers“ Checkbook - “can keep track of money“ Diary - “can keep entries“ Typewriter- “can word process“ Abstract Interpretations Best friend - “helps out“ Checkbook - “difficult“ Conversational Partner - “feedback from both” Desktop - “tons of garbage in it“ Diary - “can keep things locked away“ Relations of Metaphor Rating and Interpretation With Cognitive Ability and User Experience. The multiple regression analyses examined

two criterion variables: (1) the total number of metaphor terms rated as similar to computers (i.e., rated above 4), and (2) the mean ratings of the metaphor interpretations by the judges on the concrete, functional, and abstract scales. The predictor variables in these analyses were (1) frequency of computer use, (2) computer-related education, (3) knowledge of programming languages, (4) verbal cognitive ability and ( 5 ) nonverbal cognitive ability. In the first analysis, the five predictor variables accounted for 27% of the variance in the number of metaphor terms rated above 4 on the similarity scale -- a significant proportion of variance, F(5, 54) = 3.96, p = .0039. Among the individual predictors, ( 1 ) people who reported using computers most often also tended to identify a greater number of terms as metaphors for computers (i.e., with ratings above 4 ) [F(1,54) = 3.52, p = .071, standardized R = .25] and (2) people with higher nonverbal cognitive capabilities tended to identify a greater number of terms as metaphors for computers [ F ( 1 , 54) = 4.68, p = .035, standardized B = .32]. In the analyses of metaphor interpretation, the five


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predictor variables accounted for 15% of the variance in the judges’ mean rating of concreteness of interpretations, 5% of the variance in the judges’ mean rating of degree of functional interpretation, and 21% of the variance in the judges’ mean rating of abstractness of interpretations, Fs(5, 54) = 1.93, ~ 1 . 0and , 2.87, respectively. (Only the F value for ratings of abstractness had a p < .05.) Examining the individual predictor variables showed that (1) people with programming knowledge were less likely to interpret metaphors concretely [F(I, 54) = 7.50, p = .008, standardized R = -.38], and (2) people who reported using computers most frequently gave the most abstract interpretations of metaphors [F(1, 54) = 4.90,p= .031, standardized R = .34]. GENERAL DISCUSSION

The results support the hypothesis that people’s computer experience influences both their identification and interpretation of metaphors about computers. Frequent computer users identified more items as metaphors for computers and provided more abstract interpretations for the metaphors than did less frequent users. In addition, people with knowledge of programming languages provided less concrete interpretations for items that they identified as metaphors for computers than did people with no programming knowledge. The results also provide some support for the hypothesis that people’s cognitive abilities influence their likelihood of identifying metaphors about computers. The measures of nonverbal cognitive ability related to the frequency of rating a metaphor item as similar to a computer. The measures of nonverbal ability centered around attention and spatial memory suggesting that people with strong spatial thinking skills have a higher likelihood of identifying computer metaphors than people without strengths in those spatial cognitive skills. Future research will be needed to determine whether spatial cognition is related to metaphorical thinking in general or is specific to metaphorical thinking about computers. Implications for interface design. The present research has two primary implications for user interface design. First, the present results indicate that users of a system will interpret a metaphor about the system differently based on their computer experience. For example, given the metaphor of an ATM, a user without programming experience would be more likely to focus on its shared physical features with a computer -- a keyboard and a screen -- than would a user who knew programming languages. Similarly, given the metaphor of a diary, a frequent computer user might concentrate on the abstract relation with computer -- that both keep things locked away -whereas a novice user might only recognize that both can display words. With users of different experiences and spatial cognitive ability interpreting metaphors in different ways, the likelihood that some users will “misinterpret” the metaphor increases. (Note that we use the term “misinterpreting” a metaphor to mean interpreting the metaphor differently from the designers’ intended interpretation.) As Douglas and Moran (1983) have shown, misinterpretation of metaphors can be detrimental to performance. Misinterpretation of currently-used interface metaphors (e.g., the desktop) and

metaphors used in training (e.g., the wordprocessor is like a typewriter) would likely be more detrimental for novices than for experienced computer users. Gentner (1988) has shown that people who tend to interpret metaphors abstractly, as experienced computer users would do with computer metaphors, still have the ability to interpret metaphors concretely when appropriate. In addition, even if experienced users were to have difficulty interpreting a metaphor, they would have prior experience with similar systems to fall back on. In contrast, people who tend toward concrete interpretations do not appear to be able to switch to an abstract mode, even when appropriate. In addition, the novice users would not be able to transfer their knowledge of other systems. Thus, the focus of interface and training design on concrete and functional metaphors -- appears to be well placed. The present data may help explain why novice users have difficulty with certain currently-used metaphors that designers ask them to interpret abstractly. One such metaphor is the trash can in the Macintosh0 interface. The trash can has two uses -- (1) deleting files and (2) ejecting a disk and erasing the disk icon (see Erickson, 1990, for a discussion of the development of the trash icon). Informal data collection from novice Macintosh users indicates that they have serious reservations about using the trash can for the second purpose, fearing that they will erase all of the information on the disk. The relation between a trash can and deleting (i.e., throwing away a file) is relatively concrete and apparently easy for novices to grasp. In contrast, they have difficulty with the more abstract relation between a trash can and ejecting a disk. Although novice users may need concrete and functional metaphors, Ortony, Reynolds, and Arter (1978) and Black (1962) have proposed that the power of metaphors comes from the more abstract interpretations. Thus, for experienced users who are able to make the conceptual leap to abstract interpretations, many interface designs may appear to be metaphorically barren. Nelson (1990), who has criticized the over-reliance on concrete interface metaphors, and has proposed to substitute “virtuality“, makes a similar point. For systems and applications to be used by experienced users, interface designers should move away from the metaphors based on the concrete relations with everyday objects and begin to make use of relevant abstract relations. The second design implication from the present research concerns differences between types of interface designers. The types of computer experience that many HCI designers have -- frequent use of computers and knowledge of programming languages -- match the conditions that lead to low concrete interpretation and high abstract interpretation in this study. In contrast human factors or graphic design experts on a design team may use computers less frequently and have little programming experience. Accordingly, they might interpret metaphors about computers more concretely and less abstractly. Differences in metaphorical thinking may have contributed to previous findings that software development experts differ from human factors experts in the organization of semantic knowledge concerning computers (Gillan, Breedin, & Cooke, 1990) and in the usability of the designs that they produce (Bailey, 1993). Future research will be needed to examine whether different types of interface


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designers interpret metaphors differently and how differences in metaphorical thinking affects their designs. Theoretical implications. According to the Dual Actor/Multifunction theory of figurative interfaces (Gillan & Bias, 1994), the effect of a figurative interface depends on the user's interpretation of the metaphor or analogy contained in the interface. Thus, despite any intentions of the designer, the user takes control of the figurative interaction. The theory also proposes that giving a metaphor to a relatively inexperienced user can produce changes in the structure of semantic memory and facilitate the mapping of existing procedural knowledge to the domain of the computer system Given the above finding that computer experience influences metaphor interpretation, the theory predicts that certain figurative interfaces would produce different cognitive changes in experienced computer users versus novices. For example, an inexperienced user given the metaphor "Help on a computer is like a coach" might focus on the concrete feature that Help consists primarily of words and that coaches also use words. This would increase the strength of the link between the concept of 'Help' and the attribute of 'words'. In contrast, an experienced computer user given the same metaphor might focus on the abstract relation that both Help and a coach can provide guidance. This would increase the strength of the link between the concept of 'Help' and the attribute of 'guide'. The amount of a user's previous experience relates to a second important point in the theory of figurative interfaces. Highly experienced users will be likely to have relatively fixed semantic networks and sets of production rules. Thus, metaphors and analogies, even with abstract interpretations would be more likely to have only a transient effect on their cognitive structures, unless they provided information not already represented in memory. In other words, a figurative interface would be expected to produce few long-term changes in people who have extensive hands-on knowledge about computers. In contrast, a novice user or one who has had limited experience with computers would be likely to have less fixed, more fluid semantic networks and few production rules. Accordingly, interpreting a metaphor, even with a concrete interpretation, would likely produce longlasting changes in inexperienced users' cognitive structures. In other words, hearing that a mouse is like an orchestra conductor's baton would be unlikely to produce a long-term change in an experienced mouse user, but might produce a change in a novice. In addition, Aberasturi (1993) found that computer users who had moderate memory impairments

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were more likely to show changes in their declarative memories due to metaphors than were users who had no cognitive impairments; this finding suggests, reasonably enough, that, despite having substantial experience, users with memory impairments may have the same fluidity in their memory structures as less experienced users. References

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