ON THE DEVELOPMENT OF PROTOTYPES AND PREFERENCES ...

EMPIRICAL STUDIES OF THE ARTS, Vol. 13(2) 161-170, 1995

ON THE DEVELOPMENT OF PROTOTYPES AND PREFERENCES*

LINDA BRANT PHILIP H. MARSHALL Texas Tech University, Lubbock BRETROARK Oklahoma Baptist University, Shawnee

ABSTRACT Using a methodology previously established to investigate prototype development, the present study evaluated the hypothesis that prototypicality is the basis for aesthetic preference. Over the course of several sorting trials, subjects classified (with feedback) computer-generated random asterisk patterns (exemplars) into two categories, each of which represented a different predetermined prototype pattern. Subjects did not see the prototype patterns during this learning phase, but were exposed to them in a subsequent sorting test phase during which sorting speed and accuracy measures were taken for old exemplars, new exemplars and prototypes. Following this test phase, preference ratings for old exemplars, new exemplars and prototype patterns were obtained. Various indices of prototype development, reflecting sorting speed and accuracy of classification of test patterns, were derived for individual subjects. The results indicated that although overall "classic" prototype effects emerged for both latency and accuracy measures, there was no evidence that prototype development was involved with preference judgments. Alternative theoretical and methodological considerations are offered.

*Portions of this research were originally presented at the 13th Congress of the International Association for Empirical Aesthetics, Montreal, Canada, 1994. 161

© 1995, Baywood Publishing Co., Inc.

doi: 10.2190/L03R-KH4E-DC6E-HWUD http://baywood.com

162 / BRANT, MARSHALL AND ROARK

INTRODUCTION

Observations that ease of stimulus categorization increases as a function of prototypicality of category members [1, 2] led to the hypothesis that aesthetic judgments may also be mediated by cognitive processing of stimuli, and that prototypical stimuli may be preferred aesthetically. This idea, originally investigated by Whitfield and Slatter [3], was later refined by Martindale [4-6] who advanced a cognitive model of aesthetic preference, asserting that degree of prototypicality is an important determinant of aesthetic preference. Several studies by Martindale [4, 5] and others [7-10] have been offered to support that hypothesis. Despite this empirical base, the preference-for-prototypes hypothesis has been pointedly criticized by Boselie [11] who contends that prototypicality is not a necessary precondition for preference. In one representative study, Boselie showed that while subjects rated "iron" and "steel" as the "most typical" metals, "gold" and "silver" were "most preferred" [11]. This and other such studies demonstrate the importance of considering the context within which particular preference judgments are embedded. We have noted that many studies supporting the preference-for-prototypes hypothesis have defined prototypes as "best examples" of a given category, and have used normative data (e.g., "goodness of example" ratings) to determine this designation. One problem with this approach is that the prototype is always conceptualized as a fixed, tangible entity. There are however, alternatives to this view. In some conceptualizations the prototype is considered to be an evolving, abstract ideal, not a fixed entity. This view has been adopted by some researchers in the domain of category learning. In this regard, Posner and Keele's research on the development of abstract ideas serves as a model for the present study [1]. These researchers conceptualized the prototype of a category as an average or central tendency of various experienced exemplars, which themselves are distortions of a prototype, and "information which is common to the individual instances is abstracted and stored in some form ... " [1, p. 354]. The subject presumably develops and stores a mental representation of the prototype based on exposure to a series of exemplars (varying instances of the prototype). This hypothesis is based on experiments which demonstrate prototype effects--evidence of the development of a mental prototype. In Posner and Keele's experiments, subjects learned to classify random dot patterns as members of different categories. Each category was composed of a set of systematic distortions (exemplars) of a different prototype pattern. After learning to classify the patterns to a specified criterion (2 complete trials with no errors), subjects engaged in a test phase, during which they classified old exemplars (already seen during the learning phase), new exemplars (never before seen) and the prototype patterns from which all of the exemplars were derived. These prototype patterns were also "new," in that they were themselves never seen by subjects during the previous learning phase. At test,

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speedand accuracy of classification of the prototype patterns were superior to that of the new exemplars. Presumably, subjects abstracted the common elements of the old exemplars, forming mental representations of the prototype patterns. Therefore, subjects showed an advantage in speed and accuracy of classification of the prototype patterns at test, relative to the new (control) exemplars. We propose that the methodology used by Posner and Keele [1, 12], and others [13], could provide a productive alternative to the study of prototypes and preferences for several reasons. The methods are less subject to the confounding effects of context and differential value judgments. Subjects learn to classify novel stimuli (e.g., random dot patterns), and, theoretically, they build up mental representations of novel prototypes. This method of utilizing prototype development would be especially advantageous if, after forming mental representations of the prototypes, subjects were asked to produce preference ratings for various patterns, including the prototypes. Subjects would have little basis, other than appearance, for assigning preference ratings to random dot patterns. Also, this method provides an independent, a priori designation of the prototypes. This is preferable to having subjects provide ratings for both prototypicality (e.g., goodness of example) and preference, because it eliminates the possibility of prototypicality ratings being assigned on the basis of preference [11] thereby confounding the results. Finally, this method could also provide a more stringent test of the preference-for-prototypes hypothesis, since the prototype is actually being developed at the time of the experiment. If the preference-for-prototypes hypothesis is valid, people should not only prefer prototypes that they are already familiar with (e.g., colors, fruit, furniture, faces, etc.) but also newly-formed prototypes. Using this rationale, we sought to assess how the development of a prototype relates to the development of an aesthetic preference. METHOD Overview

We computer-generated four random asterisk patterns, designated a priori as the prototypes. Exemplars (systematic distortions of the 4 prototype patterns) were produced according to a specific rule. Subjects were instructed to sort exemplars into groups, representing the prototypes from which the exemplars were derived. During this learning phase subjects were given feedback on their accuracy, but were never exposed to the actual prototype patterns. Following the learning phase, subjects engaged in a "no feedback" test phase, during which they classified some old exemplars (those sorted during the learning phase), some new exemplars (never before seen) and, for the first time, the two prototype patterns. Measures of sorting latency and accuracy were collected during the test phase. Following the test, subjects assigned preference ratings to the same collection of old, new, and prototype patterns that were presented during the test phase.

164 I BRANT, MARSHALL AND ROARK

We hypothesized that prototype effects would emerge. Overall, classification of prototype patterns was expected to be both faster and more accurate than classification of new exemplars. Support for the preference-for-prototypes hypothesis would be found if subjects later indicated an absolute preference for prototype patterns relative to old and new patterns, and/or, if indices of prototype development were positively related to prototype preference.

Subjects Initially, 108 volunteer subjects were recruited from the pool of undergraduates enrolled in an introductory Psychology course. Ninety-one of these subjects met the criteria for inclusion in the analyses (see below). These individually tested subjects (70% of whom were women) ranged in age from eighteen to thirty-six, with a mean age of nineteen.

Materials A specially written computer program was used to generate random asterisk patterns (15 asterisks each), four of which served as the predetermined prototypes. Twenty exemplars (systematic distortions of the prototypes) were produced from each of the four prototypes. This resulted in two different learning sets (arbitrarily designated A and B), each consisting of two prototypes and their respective exemplars. The exemplars for each of the four prototypes were developed according to specific probability rules which determined whether anyone asterisk would move, and if so, how far. These rules specified the probability of any given asterisk in a prototype pattern moving as p = .5. Moving asterisks were equally likely (randomly decided) to move "north," "south," "east" or "west," and, if selected, asterisks moved one cursor space from their original positions. Figure 1 illustrates two of the prototypes used in the study, along with some of their exemplars.

Procedure The experiment had three phases: learning, test, and preference. In the learning phase, subjects sorted a series of exemplars (either set A or set B, depending on the order of presentation)! into two categories, each of which represented the prototype from which the exemplars were derived. Subjects engaged in seven learning trials, with each trial requiring twenty sorts (10 exemplars of each prototype were sorted on each trial). The same set of twenty patterns was randomly presented on each of the seven learning trials, resulting in a total of 140 sorts. When a pattern appeared on the computer screen, the subject made a sorting I The presentation of sets A and B was counterbalanced. For half of the subjects, set A was the first set of patterns learned, and set B the second; the remaining half learned set B first and A second.

PROTOTYPES AND PREFERENCES / 165

PROTOTYPE A

PROTOTYPE B

.

. . . ..· .. . · . . . ..

1st EXEMPLAR

. · . . .... . . .· . ..

2nd EXEMPLAR

. . .. . ·· ..· · . . . . . . ··. · . .· . . .

nth EXEMPLAR

Figure 1. A sample of prototype patterns and their exemplars.


response by pressing one of two designated keys on the computer keyboard. Subjects were given computer-generated feedback on their accuracy during learning. A high-pitched tone was emitted following correct classifications, and a low-pitched tone was emitted following incorrect classifications. Prior to beginning the learning phase, subjects were informed that the feedback would be discontinued, but not told why, after they had sorted a number of patterns. They were asked to continue sorting the patterns after the feedback was terminated. In fact, and unknown to the subjects, the termination of the feedback marked the beginning of the test phase of the experiment. In the test phase, five no-feedback practices or buffer sorts were presented in order to ease the transition between the previous feedback (learning), and the current no-feedback (test) conditions. Following' the five practice sorts, the actual test patterns were randomly presented to subjects. These patterns included five old (already seen) exemplars from each prototype, five new (never before seen) exemplars from each prototype, and the two prototype patterns. Subjects sorted these patterns (a total of 22) without feedback, and measurements of accuracy and latency of response were obtained. Following the test phase, subjects engaged in the preference phase, during which they assigned preference ratings to the same set of twenty-two test patterns presented in a new random order. Subjects rated how much they "liked each pattern," using a scale which ranged from 1 ("like a lot") to 7 ("dislike a lot"). After completing the first preference phase, subjects repeated the entire task (learning phase, test phase, and preference phase) with the alternate set of stimulus patterns (either set A or set B). Thus, in all, four prototypes and their respective old and new exemplars were tested for sorting speed, accuracy, and preference. RESULTS Inclusion Criteria

Subjects (n = 91) were included in the final analyses if their mean performance on the seventh and final sorting trial of both learning phases exceeded the chance value of 50 percent.v 3 Did Prototype Effects Emerge?

Although the present study focuses on individual differences in prototype development, and potentially related differences in prototype preferences, we also 2 We reasoned that at or below chance performance after seven sorting trials indicated an inability to perform the task.

3 For the record, there was no significant difference between the performance of men and women following the seventh learning trial and gender differences were not analyzed further.

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wanted to determine the extent to which we obtained overall prototype main effects. Pooling over both sets of stimuli (A and B), we calculated, and compared, subjects' mean accuracy and latency scores on the sorting of prototypes and new patterns. Overall prototype effects emerged for both accuracy and latency measures. At test, subjects were significantly more accurate in sorting the neverbefore-seen prototypes than they were in sorting the new patterns, t(1,90) = 2.33, P < .02, and these prototype patterns were also sorted significantly faster than the new patterns, t(I,90) = -5.04, p < .0001 (see Table 1). These results confirm that our procedures were conducive to prototype development as considered and defined by previous researchers. Were Prototypes Preferred over Old and New Patterns?

In order to assess preference main effects, mean preference ratings, pooled across both sets of stimuli (A and B), were calculated and compared for old, new, and prototype patterns. Analyses by r-tests revealed no significant differences (p's> .05) in mean preferences for old, new, and prototype patterns (the respective means being 3.65, 3.72, and 3.62). Thus, overall, subjects' preference ratings of old, new, and prototype patterns did not differ. Was Degree of Prototype Development Related to Preference for Prototypes?

While overall preference main effects were of some interest and import in our study, our primary objective was to investigate the relationship between degree of prototype development and preference for prototypes. In accordance with the preference-for-prototypes hypothesis, we reasoned that as subjects' degree of prototype development increased, so too should their sorting speed and accuracy at test, and that these variables could be taken as indices of prototype development. We derived these indices of prototype development for each set of stimuli (A and B), by calculating weighted means for accuracy and latency of response to old, new, and prototype patterns, at test. This resulted in four indices of prototype

Table 1. Mean Accuracy and Latency Scores (Standard Deviations in Parentheses) for Each Type of Stimulus Pattern" Pattern Type Prototype Old New

Accuracy

Latency

80.50 (26.56) 77.97 (19.56) 76.26 (18.91)

.93 (.379) 1.02 (.353) 1.05 (.387)

"Accuracy measures reflect percent correct; latency measures are given in seconds.

168 I BRANT, MARSHALL AND ROARK

development: a weighted mean accuracy index for set A, a weighted mean accuracy index for set B, a weighted mean latency index for set A, and a weighted mean latency index for set B. These four indices were correlated with subjects' respective mean prototype preference ratings in sets A and B (see Table 2). There was no evidence that degree of prototype development was related to preference judgments. In terms of latency, degree of prototype development was unrelated to preference for prototypes in both sets A and B. When the accuracy data were considered, degree of prototype development was unrelated to preferences for prototypes in set A, but significantly correlated with preferences for prototypes in set B (r =.21, p < .05). Given the orientation of our preference scale, however, the latter correlation was, in fact, opposite to preference-for-prototype predictions. DISCUSSION

The present study tested the preference-for-prototypes hypothesis by employing the methodology reported in Posner and Keele's study on the development of abstract ideas [1]. Using that methodology, we found no support for the notion that preferences for prototypes increase as a function of degree of prototype development. Our results suggest that when stimuli such as ours are presented to subjects in the absence of meaningful contextual cues, the preference-for-prototypes hypothesis may not be valid. The effect may only emerge when some critical level of stimulus context or "meaningfulness" is present. In terms of aesthetic preferences, it is possible that the preference-for-prototypes hypothesis may be more valid with respect to representational works than to abstract works, of which our stimuli may be taken to be an example, and that notion has acquired some initial support in recent years [14]. But if the preference-for-prototypes hypothesis is valid for context-free stimuli, why then, did we not find more evidence to support it? There are at least two possible explanations for these results. First, the preference-for-prototype

Table 2. Products of Correlations (by Set) between Mean Prototype Preferences and Accuracy and Latency Measures of Prototype Development Stimulus Set

A

8

Accuracy

Latency

Accuracy

Latency

-.15

-.07

.21*

-.07

"p « .05

PROTOTYPES AND PREFERENCES I 169

hypothesis may be in need of some modification. The modification might include the idea that preferences for prototypes emerge only after a hypothetical threshold of prototype development is exceeded. Although our results yielded evidence of significant overall prototype development (e.g., prototype effects), the degree of prototype development achieved by subjects was small, and may have been sub-threshold with respect to influencing preference scores; that is, the degree of prototype development achieved may not have been sufficient to result in a preference for prototypes. It is possible that with greater levels of prototype development, a preference for prototypes would emerge. On the other hand, although there was a sufficient overall range of preference scores for the prototypes, subjects may have been unable to distinguish, in the assignment of ratings, between their preferences for old, new and prototype patterns at test. If subjects could not distinguish between their preferences for prototypes, old and new patterns, they may have despaired at the task of making the ratings. Preferences for old, new and prototype patterns were highly correlated, indicating that subjects tended to assign similar ratings to each of these three classes of events. An actual preference for prototypes may have been obscured if (out of despair) subjects used only a limited range of the preference scale. What this may mean is that our preference scale, and/or the task of making these ratings, may have lacked the sensitivity to detect preference differences between old, new and prototype patterns, if such differences did in fact exist. Recall that our preference scale required subjects to indicate how much they liked each individually presented pattern, on a scale of 1-7. An alternative method, in other research contexts, for assessing overall preferences for prototypes relative to other events, might involve the presentation of successive pairs of stimuli to subjects with the requirement that they indicate which member of each pair they prefer. This method would force the subject to make a choice, but would not burden the subject by requiring that he/she assign a specific value. The preference-forprototypes hypothesis would predict that over the succession of pairs, subjects should more often choose the prototype. This procedure, which requires relative choices, may be more sensitive to subtle differences in preference. ACKNOWLEDGMENT We wish to thank Miss Mary Murphy of Winter Park, Florida, for her financial support of this research project. REFERENCES 1. M. I. Posner and S. W. Keele, On the Genesis of Abstract Ideas, Journal of Experimental Psychology, 77, (3, Pt. 1), pp. 353-363, 1968. 2. S. K. Reed, Pattern Recognition and Categorization, Cognitive Psychology, 3, pp. 382-407, 1972.


3. T. W. A. Whitfield and P. E. Slatter, The Effects of Categorization and Prototypicality on Aesthetic Choice in a Furniture Selection Task, British Journal ofPsychology, 70, pp. 65-75, 1979. 4. C. Martindale and K. Moore, Priming, Prototypicality and Preference, Journal of Experimental Psychology; Human Perception and Performance, 14, pp. 661-670, 1988. 5. C. Martindale, K. Moore, and A. West, Relationship of Preference Judgments to Typicality, Novelty and Mere Exposure, Empirical Studies of the Arts, 6, pp. 79-96, 1988. 6. C. Martindale, K. Moore, and J. Borkum, Anomalous Findings for Berlyne's Psychobiological Theory, American Journal ofPsychology, 103, pp. 53-80,1990. 7. A. T. Purcell, The Aesthetic Experience and Mundane Reality, in Cognitive Processes in the Perception of Art, W. P. Crozier and A. J. Chapman (eds.), North-Holland, Amsterdam, pp. 189-210, 1984. 8. D. M. Pedersen, Perception of Interior Designs, Perceptual and Motor Skills, 63, pp. 671-676,1986. 9. J. H. Langlois and L. A. Roggman, Attractive Faces are Only Average, Psychological Science, 1, pp. 115-121, 1990. 10. J. H. Langlois, L. A. Roggman, and L. Musselman, What's Average and What's Not Average about Attractive Faces? Psychological Science, 5, pp. 214-220, 1994. 11. F. Boselie, Against Proto typicality as a Central Concept in Aesthetics, Empirical Studies ofthe Arts, 9, pp. 65-73, 1991. 12. M. I. Posner and S. W. Keele, Retention of Abstract Ideas, Journal of Experimental Psychology, 83, pp. 304-308, 1970. 13. S. H. Evans and E. M. Edmonds, Schema Discrimination as a Function of Training, Psychonomic Science, 5, pp. 303-304, 1966. 14. P. Hekkert and P. C. W. van Wieringen, Complexity and Prototypicality as Determinants of the Appraisal of Cubist Paintings, British Journal of Psychology, 81, pp. 483-495, 1990.

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