Rule structure in the psychological representation of physical settings

JOURNAL OF EXPERIMENTAL SOCfAL PSYCHOLOGY 27, 217-238 (1991)

Rule Structure in the Psychological Representation of Physical Settings

W. JEFFREY BURROUGHS

Clemson University

AND

DAVID R. DREWS

Juniata College

Received July 21, 1989

Three experiments were performed to demonstrate how physical settings may be conceptualized in terms of the behavioral rules associated with them. Subjects participated in similarity rating, recognition memory, and likelihood estimation tasks. In the similarity rating task, overlap in the rules listed in free response protocols for 10 different settings predicted global similarity ratings between pairs of settings. In the recognition memory task, after subjects read a description of a stimulus person behaving in ways prescribed by a setting, distractor behaviors highly typical of a given setting were more effective in eliciting false positive recognitions than were distracters less typical of the setting in question. Finally, subjects were asked to predict the future behavior of a stimulus person on the basis of a behavioral description of past behavior characteristic of a particular setting. Subsequently, these subjects rated future behaviors highly typical of that setting as more probable than behaviors less typical of that setting. Taken together, these data argue that behavioral rules should be viewed as a part of the cognitive representation of physical Settin@ 0 1991 Academic Press, IIIC.

How are the physical settings in which behavior occurs represented internally? Russell and Ward (1982) indentified three types of information that contribute to our knowledge of settings: knowledge of objective physical attributes, knowledge of affective responses to settings, and knowledge of the types of behaviors which occur in settings. A consid-

Portions of the present research were presented at the 1981 and 1982 meetings of the Eastern Psychological Association. The authors thank James Klein, Robert Shull, and Chris Johnson for help in conducting the research. Requests for reprints should be sent to W. Jeffrey Burroughs, Department of Psychology, Clemson University, Clemson, SC 29634.

217

0022-1031/91 $3.00 Copyright 0 1991 by Academic Press, Inc.

All rights of reproduction in any form reserved.

218 BURROUGHS AND DREWS

erable body of literature now exists that has investigated both perceptions of physical attributes and affective responses to those attributes (for re- views see Mehrabian & Russell, 1974; Hershberger, 1972; Ward & Russell, 1981; Stokols, 1978). In contrast, the behavioral component of the internal representation of settings has received relatively less empirical attention and is the focus of the present research.

As Genereux, Ward, and Russell (1983) have pointed out, behavior is often the defining feature of settings. For example, we cook in kitchens, dine in dining rooms, and hold classes in classrooms. Knowledge of behavior-setting linkages is clearly important for commerce with the social environment and several authors have provided data that emphasize the importance of behavior in organizing settings. Frederiksen (1972) empir- ically classified settings on the basis of the behaviors they elicited. He suggested that a taxonomy of settings could be created using similarity of behaviors as a criterion for assigning situations to categories. In a conceptually similar study, Price and Bouffard (1974) required subjects to assess the appropriateness of behaviors in a number of settings. When the ratings were collapsed over behaviors, some situations were found to be highly constrained in terms of the number of appropriate behaviors while a much greater tolerance for behavioral variability was found in other situations. Finally, Pervin (1976) analyzed subjects’ free response descriptions of various settings. These individuals seemed to naturally group settings into categories according to the degree of constraint that the setting exerted on behavior. These studies all suggest that individuals conceptualize settings that least partly in terms of the behaviors associated with them and that there is considerable agreement about setting appropriate behaviors. Such shared conceptualizations would not only help predict the behavior of others but would make it easier to make appropriate behavioral choices for oneself and generally smooth the course of social interaction.

A theoretical position that directly links settings with social interaction is symbolic interactionism (Goffman, 1963). Following this perspective we view the behaviors associated with settings in terms of a series of expec- tations or rules that specify actions which are acceptable in different places. This formulation stems from Goffman’s observation that the behavior of individuals in guided by situationally specific behavioral norms. Thus, attached to each setting are rules specifying, for example, the degree of access to different regions that is permitted, the types of activities that are and are not acceptable, and the kind of personal appearance that is appropriate. Goffman argues that these various rules form a composite pattern that describes the way individuals structure their involvement in settings. Such rules are termed situational proprieties (Goffman, 1963) and are generic features connected with all settings.

The research reported below was derived from a symbolic interactionist

RULE STRUCTURE IN PHYSICAL SETTINGS 219

perspective and designed to investigate the existence and operation of rule based representations of settings. Converging measurement opera- tions were employed in an attempt to establish the validity of this approach. An initial experiment attempted to describe global similarities between settings in terms of the similarities between situational proprieties attached to those settings. As noted above, a knowledge of the behavioral rules associated with various settings ought to be highly useful in discrim- inating occasions where a given behavior is appropriate from those where it is not. Moreover, such knowledge should be useful in comparing settings to one another. Settings governed by similar combinations of rules ought to generally be rated as more similar than those where behavior is constrained by different rule structures.

Additional evidence for the importance of behavioral rules in representations of settings was sought in the context of recognition memory and likelihood estimation studies. If settings are internally represented in a fashion similar to object categories (Rosch, 1975) or personality characteristics (Cantor & Mischel, 1979), it should be possible to characterize attributes of settings such as rules in terms of a typicality structure. That is, while several rules may be associated with a given setting, some will be more typical of that setting than others. If such typicality structures exist, they should influence processing of behavioral information when a particular setting is made salient. Accordingly, two studies were designed which allowed measurement of subject responses as a function of the typicality of behavioral rules in various settings. The demonstration that typicality structure is a relevant variable in such processing further broad- ens our understanding of the nature of cognitive representations of settings, particularly of rule-setting linkages.

EXPERIMENT 1

If the behavioral proprieties of physical settings are in fact prominent components of the psychological representation of those settings, it should be possible to demonstrate a close relationship between the behavioral rules attached to different settings and the degree to which those settings are regarded as generally similar. To test this assertion, one group of subjects was asked to perform pairwise similarity scaling of a series of common campus locations. This scaling technique has the advantage of not imposing predetermined verbal labels on the settings as a semantic differential technique would and thus allowed subjects to determine for themselves what aspects of setting similarity they would attend to. A second group of subjects generated situational proprieties associated with the same settings and a second pairwise measure of.similarity was created by assessing the overlap in lists of proprieties. A significant correlation between the two sets of similarity scalings should obtain if subjects do judge settings on the basis of situational proprieties. If behavioral rules


are not salient aspects of settings, the correlation coefficient should be small.

Method Subjects. Two groups of undergraduate students from Juniata College participated in the

experiment as an optional way of fulfilling a course methodology requirement. One group of 40 male and female subjects made global similarity judgments between settings. A second group of 77 male and female subjects produced the situational proprieties.

Procedure. To produce similarity judgments, 40 subjects were presented with all possible pairs of 10 campus settings (see Table 1 for a listing of settings). Subjects made similarity judgments on a 7-point scale anchored with 1 = extremely similar and 7 = not similar at all. A mean judgment of similarity over subjects was then computed for each pair of environments. This resulting matrix served as a measure of global similarity between settings.

In order to produce situational proprieties, a second group of 77 subjects was presented with the name of each of the 10 campus settings and asked to list the five most important rules (either prescriptions or proscriptions) which described how one should behave in each setting. Subjects’ responses were coded by two independent judges into a smaller number of rules by combining alternate wordings of particular rules. As a check on reliability, Pearson correlation coefficients were computed for each of the situations to determine the extent to which the two judges agreed on the number of nominations for each of the rules mentioned by subjects. The average correlation over the 10 situations between the two sets of judgments was .88, indicating acceptable reliability in coding. After disagreements over specific responses were mutually resolved, lists of the 20 most frequently nominated rules for each setting were compiled. Because particular rules were often cited for more than one setting, these lists yielded a total of only 93 different rules. Counts of the number of times a rule was cited for each setting are the cell frequencies in the 10 x 93 (settings x

rules) table presented in Appendix A.

Results and Discussion

Mean global similarity ratings appear in the top half of each cell of Table 1. Although these ratings were made on a 7-point scale, mean ratings range only from 2.40 for the maximally similar snack bar and dining hall to 5.64 for the maximally dissimilar post office/bank area and the auditorium.

Gamma correlation coefficients, which represent an index of similarity between each pair of settings solely in terms of their rules, appear in the lower half of each cell in Table 1. Each coefficient compares the frequency of nomination for all 93 rules in one setting with the same data in a different setting. For example, the .651 coefficient comparing the athletic field grandstands to the auditorium was calculated using columns 1 and 2 of Appendix A. The gamma nonparametric correlation was used because the large number of zeros in Appendix A skews the distribution and violates the linearity assumption underlying parametric tests of association. As with the global similarity ratings, obtained correlations show a relatively small range. The snack bar and the dining hall show the greatest similarity in terms of rule structure (g = .790) while the classrooms and the paths outside show the least (g = - .681).

TABL

E 1

MEA

SURE

S OF

AW

XIAT

ION

BETW

EEN

TEN

SETT

ING

S

Athl

etic

fie

ld

Audi

toriu

m

Snac

kbar

Cla

ssro

om

Dini

ng

hall

Book

stor

e

Libr

ary

Post

offi

ce/b

ank

Hal

lway

s

Path

s ou

tsid

e

Athl

etic

fie

ld

Audi

toriu

m

Snac

kbar

C

lass

room

Di

ning

Bo

okst

ore

Libr

ary

Post

H

all

Path

s ou

tsid

e

- of

fice/

bank

4.80

(.6

51)

(:‘;Z

) 5.

25

(.193

) 4.

48

(.333

) 5.

48

(- ,4

15)

5.56

(-,

112)

(X4)

5.

08

(.255

) 4.

32

(.099

)

5.24

t.4

11

3.56

(.6

52)

4.72

(.4

77)

5.52

(.1

94)

(11;

:) 5.

64

(- .0

21)

5.08

(.4

61)

5.25

( -

,0

24)

4.55

(.2

21)

2.40

(.7

90)

3.48

(.3

58)

3.92

(.1

97)

4.20

(.2

84)

4.48

(.2

51)

4.68

(-

,343

)

5.04

(.2

W

5.28

(-.

137)

3.

44

(.494

) 5.

36

( -

.444

) 3.

92

(.074

) 5.

56

(-.68

1)

4.48

(.0

70)

4.76

5.

20

(.154

) (.3

37)

4.24

2.

88

5.20

(.1

@3

(.429

) ( -

.0

65)

4.52

5.

20

4.64

4.

56

(.268

) (.0

35)

(.lW

(-

,254

) 4.

60

5.16

5.

20

5.08

4.

00

(- ,3

78)

(- ,3

97)

(- 44

6)

(- ,3

44)

(.708

)

Not

e. M

ean

sim

ilarit

y ra

tings

ar

e sh

own

in t

he u

pper

ha

lf of

eac

h ce

ll (s

mal

l nu

mbe

rs

indi

cate

in

crea

sing

sim

ilarit

y).

Gam

ma

corre

latio

n co

effic

ient

s be

twee

n ru

le

lists

are

sho

wn

in

the

lower

ha

lf of

eac

h ce

ll.


The analysis of primary interest in this study concerns the degree to which global estimates of similarity are predictable from the commonality of rules associated with settings. In order to represent the relationships between the settings, the matrix of global similarity ratings and the matrix of correlations based on rule profiles were subjected to multidimensional scaling (MDS). Stress values for the MDS solutions based on the global similarity ratings were .322, .107, and .048 (Kruskal’s stress formula 1) for solutions in 1, 2, and 3 dimensions, respectively. Stress values for the solutions based on rule profiles were .339, .107, and .034, again for solutions in 1, 2, and 3 dimensions. Based on Spence and Ogilvie’s (1973) criterion measures for goodness of configuration fit to the original data, two-dimensional configurations were selected as the optimal representation for both sets of data. In order to compare the two scalings, a Pearson correlation coefficient was computed over the interpoint distances in the two configurations. This coefficient was .68, p < .OOl, indicating substantial agreement between the two configurations.

The agreement between global similarity ratings and those based on situational proprieties demonstrates the importance of behavioral rules in the psychological representation of environmental settings. When the rules (and the relative frequency of their nomination) that control behavior in one setting are similar to those in another setting, the settings are seen as being similar in general. If the rules are dissimilar, so are the settings. It is, of course, likely that physical properties of settings also play a significant role in their cognitive representation. Further, because physical properties of settings often constrain behaviors, the relative contributions of behavioral rules and physical attributes to the conceptualization of settings are confounded to some degree. Rather than arguing that physical attributes are unimportant, our data demonstrate that it is possible to account for a substantial amount of the variance in the relationships between physical settings solely in terms of behavioral rules. While subjects may use a variety of attributes to make global similarity judgments, situational proprieties clearly play a significant role.

The present data are consistent with those reported by Genereux, Ward, and Russell (1983) who also determined the degree to which global dis- similarities could be predicted by various indices of behaviorial similarity. Most similar to our situational rules perspective was their index of behavioral similarity based on subjects’ ratings of the likelihood of 11 possible behaviors. Using a sample of 20 settings they obtained a correlation of .42 between the two similarity matrices. It seems likely that our some- what higher correlation was due to the greater variety of behaviors considered in predicting behavioral similarity.

EXPERIMENT 2

If situational proprieties are an important component of normal cognitive representations of different settings, knowledge of those proprieties


should influence the cognitive processing of information about behavior in settings. In order to predict such effects, further assumptions about the structure of this knowledge must be made. In particular, we assume that the rules associated with any given setting form a category with properties similar to other natural language categories.

Research on the use and representation of categories in natural language has suggested that category instances are structured around best examples or prototypes. Rather than trying to establish critical features which an exemplar must possess in order to be considered a category member, investigators have often emphasized the continuous nature of natural categories and have focused on the idea that some exemplars of a category are better or more typical than others and that category boundaries may be fuzzy (Lakoff, 1972; Zadeh, 1965). Empirical support for these qualities of category structure has been found for categories of concrete objects (Rosch, 1975) and for personality characteristics (Cantor & Mischel, 1979).

It is reasonable to treat sets of rules associated with physical settings as categories with attributes like those of object or person categories. Such categories are hypothesized to contain some rules that will be better representatives of the behaviors expected in that setting than others. Some rules will therefore be more typical exemplars of the category of rules associated with that setting than others. The category as a whole should have a typicality structure much like the structure present in object or person categories. It should be noted that such rule categories are probably not well-established in memory like the categories of concrete nouns studied by Rosch (1975). Rather, rule categories are ad hoc in nature (Barsalou, 1983) and are organized “on-line,” from available examples (Kahneman & Miller, 1986) as needed. Nevertheless, Barsalou (1983) has provided evidence that such categories also exhibit typicality effects.

We therefore hypothesize that when any setting is brought to mind, an individual will not only bring into memory the rules associated with that setting but that the typicality structure of those rules will make some of them more salient than others. If such a typicality structure can be shown to affect processing, further support for a rule-based conceptualization of settings will be demonstrated.

To test these assumptions, a recognition memory experiment was performed using a “false alarm paradigm” (Rogers, Rogers, & Kuiper, 1979). The rules gathered in Experiment 1 were converted to short descriptions of the behavior of a hypothetical stimulus person. Subjects were presented with a group of 10 target behaviors highly typical of behavior in a particular physical setting. Subsequently, the 10 target behaviors and an additional 10 distractor behaviors were presented and subjects were required to distinguish between them. Five of the distractor behaviors were generated from rules that were highly typical of the given setting and five were generated from rules of low typicality. It was predicted that highly typical


distractor behaviors would be more confusable with target items and would therefore cause more false alarms than behaviors which were of low typicality in the given setting. The experiment was replicated four additional times with different settings in order to provide a more thorough test of the predicted effects.

Method Subjects. Subjects were 20 male and female members of an undergraduate learning and

memory class at Juniata College who participated for extra course credit. Stimulus materials. To create the behavior descriptions that served as stimuli in the

recognition memory experiment, the typicality of each of the rules gathered in Experiment 1 was determined in each setting. In order for a rule to be useful in characterizing a setting it should be both highly associated with that setting and should have a low degree of association with other settings (see Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976 for a related discussion on the cue validity of features in object categories). Therefore, to create an index of rule typicality, the mean frequency with which a rule was nominated in the nine other settings considered in Experiment 1 was subtracted from the frequency of nomination of the rule in the setting in question. This index tends to be large if a rule is frequently nominated in one setting and rarely occurs in other settings. It tends to be small if a rule is relatively infrequently nominated in one setting and occurs more frequently in other settings. Using this index, the 15 most typical rules in each setting were identified and preserved for further study. It should be noted that this typicality structure is based on subject’s nominations of the rules most salient to them. The typicality structure reflects this saliency; many rules that could apply in a situation were not included because they were not seen as important.

Based on the derived typicality ratings, the five settings with the highest mean typicality indices for the 15 most typical rules were selected for further study (athletic field, bookstore, snack bar, campus post office/bank, and academic building hallways). Ten of the 15 rules in each setting were randomly selected and transformed into target behaviors for the recognition memory test. For example, from the rule “don’t make noise” from the library setting, the behavior description “Beth was very quiet” was generated. The remaining five rules in each setting underwent similar transformations and served as five high typicality distractor items. These behaviors were combined with five additional behaviors that were of low typicality for that setting to form the distractor set in the experiment. The recognition test list thus consisted of 10 high typicality target items, 5 high typicality distractor items, and 5 low typicality distractor items for each of the five settings considered in the experiment. As an example, the 10 target items and the 10 distractor items generated for the library setting are presented in Table 2.

Procedure. Each subject was presented with the descriptions of five hypothetical stimulus persons. Each description was a list of 10 sentences in the form noted above. Subjects were given 90 s to read each person’s description and asked to form an image of the person engaging in the behaviors described. They were also told to form an impression of each stimulus person and to try to keep each impression distinct from the other four. The behaviors associated with each stimulus person were presented in one of two different random orders and the order of presentation of the five stimulus persons was counterbalanced over subjects. The instructions to subjects focused on descriptions of persons rather than settings because

of our intent to tap settings via the behavioral component of their representation. A recognition memory test was run 72 h after the original presentation. For this test,

each subject was faced with five sets of 20 phrases each. For each set, 10 sentences were target bahaviors and 10 were distracters (five of high typicality and five of low typicality). An example recognition set is shown in Table 2. Within each set, the ordering of targets


TABLE 2 EXAMPLE TARGET AND DISTRACTOR BEHAVIORS FOR SNACKBAR SETTING

1. Jenny played pool. Target behaviors

2. Jenny did not shout. 3. Jenny stood in line for her food. 4. Jenny politely asked how much her food cost. 5. Jenny ate her food. 6. Jenny tried to use manners while eating. 7. Jenny did not enter barefooted. 8. Jenny sat down at a table. 9. Jenny didn’t throw her food.

10. Jenny put her trash in the garbage can.

High typicality distractor behaviors 1. Jenny waited in line patiently. 2. Jenny did not sit on a table. 3. Jenny paid for her food. 4. Jenny took her tray back. 5. Jenny ordered a hamburger.

Low typicality distractor behaviors 1. Jenny answered the professor’s question. 2. Jenny didn’t block the walkway. 3. Jenny talked quietly to her study partner. 4. Jenny let people walking faster pass her. 5. Jenny came with her homework completed.

and distracters was randomized. The order of presentation of the five sets was counterbalanced across subjects. Subjects rated each behavior on the recognition list on a 6-point scale anchored with 1 = sure sentence is old (seen before) to 6 = sure sentence is new (not seen before).


Prior to analysis, mean recognition confidence ratings were computed for each subject by collapsing across the five individual distractor items in each high and low typicality condition. This was done for each setting to yield 10 values for each subject. Presented in Table 3 are the group mean recognition confidence ratings by replication for distracters of high and low typicality. These recognition confidence ratings were subjected to a 2 (high distractor typicality vs low distractor typicality) x 5 (replications) repeated measures analysis of variance. In keeping with the sug- gestions of Santa, Miller, and Shaw (1979) and Clark (1973), both subjects and the five situations in the replications were treated as random effects in the analysis. Consequently, Quasi-F statistics were used to test the main effects. In spite of the fact that there was relatively little forgetting, high typicality distracters were recognized with greater confidence than low typicality distracters, F’(l) 11) = 60.13, p < .OOl. This finding provides


TABLE 3 MEAN RECXGNITION CQNFIDENCE RATINGS FOR HIGH AND Low TYPICALITY DISTRACTORS

Mean recognition confidence

Environment High typicality distracters

Grandstands 3.76 Snackbar 3.23 Bookstore 3.77 Post office/bank 3.78 Hallways 4.06

Low typicality distracters

.5.s2* 5.54* 5.46* 5.31* 5.40*

Note. On recognition scale, 1 = sure old, 6 = sure new. Asterisks indicating significance levels of simple effect of typicality for each replication are placed next to largest of means included in the comparison.

* p < .Ol.

convergent validity for the conclusion of Experiment 1 that situational proprieties are important determinants of the way people represent and compare settings. Manipulations that would increase the overall rate of forgetting, such as increasing the retention interval, would be expected to further enhance subjects’ reliance on this schematic information (cf. Homa, Cross, Cornell, Goldman, & Schwartz, 1973; Homa, 1978).

In order to examine memory discrimination for high vs low typicality distracters, two d’ scores were computed for each subject. In determining hit and false alarm percentages each subject’s responses were summed over the five replications and d’ scores were computed from the percentage of hits and high typicality false alarms and the percentage of hits and low typicality false alarms. The mean d’ scores collapsed over subjects were 1.21 and 2.88 for high and low typicality distracters, respectively. This difference was significant by a paired t test, 41.8) = 5.06, p < .OOl, and indicates that high typicality distracters were significantly less discrimin- able from target behaviors than were low typicality distracters.

The present results may be accounted for in terms of selective information availability or a probabilistic response bias. In the first case, subjects are assumed to be guided in the storage and/or retrieval of behavioral information by a summary representation of the typical behavior in a setting (Rothbart, Evans, & Fulero, 1979; Graesser, Woll, Kowalski, & Smith, 1980). Such a high typicality summary representation would lead to the false recognition of high typicality distractor behaviors. In contrast, subjects may be led to retrospectively reconstruct how a stimulus person probably behaved independent of the information actually recalled from memory (Bartlett, 1932; Lingle, Geva, Ostrom, Leippe, & Baumgardner, 1979). Such a response bias again reflects subjects’ conceptions of which behaviors are most likely in a given situation. Regardless of the underlying

RULE STRUCTURE IN PHYSICAL SElTINGS 227

mechanism, the present data argue that the proprieties attached to settings serve an organizing function in processing behavioral information. Whether these effects derive from information availability or probabilistic reconstruction cannot be deduced from this study.

The analysis of variance also showed the replications main effect to be nonsignificant, F’(7, 6) < 1, n.s.

The distractor typicality x replications interaction was significant, F(4, 76) = 4.64, p < .Ol. To analyze this interaction, the simple main effects of typicality were computed for each of the replications. All of the comparisons were significant at p < .Ol and in the predicted direction. The Fvalues obtained were 106.10 (grandstands), 86.06 (snackbar), 42.56 (post office/bank), 54.92 (bookstore), and 51.31 (hallways). The effect of typicality thus is stronger in some settings than in others, although it is substantial in all. The reason why the manipulation was more successful in some settings than in others is not clear and we do not offer an explanation at this time.

A possible alternative explanation for the present data is that subjects are forming impressions of the people described in the stimulus materials and are primarily using these person categories to organize target behaviors rather than the setting based categories that the stimulus materials were designed to make salient. Subjects may falsely recognize high typicality distracters because those behaviors are characteristic of the people they have imagined rather than because the recognition decisions are based on the typicality structure of rules in an inferred setting. Our effects might therefore be due to person categories rather than setting categories.

To what extent might these two organizing principles be confounded as subjects form impressions? If subjects are using person categories to organize the stimuli, personality ratings of the stimulus persons ought to show enough stability to allow discrimination between stimulus persons on the basis of person characteristics. If, on the other hand, subjects are not forming coherent person impressions, variability in the personality rating data would prevent such discriminations.

In order to test this possibility, 20 undergraduate subjects were presented with the stimulus materials in Experiment 2 using procedures identical to those in the original experiment. Subjects were then required to rate the stimulus person from memory on 22 trait scales selected to tap dimensions of evaluation, activity, and potency. Using 1 x 5 analyses of variance for repeated measures, we found significant differences in the impression ratings on only 2 of the 22 scales (F( 1, 18) = 4.41 for p < .05). The person impressions formed were not coherent enough to lower variance in the ratings and result in significant differences. Our failure to find coherence in virtually any of the ratings suggests that person organizing categories are not strongly salient in the stimuli we have used.


EXPERIMENT 3

In addition to affecting memory, the behavioral rules associated with settings should also have an impact on how we predict the future behaviors of others. To demonstrate this effect, subjects were given a description of the behaviors engaged in by a hypothetical stimulus person and asked to form an impression of that person. All of the behaviors in the description were highly typical of a particular setting. Subjects were then shown another list of behaviors and asked to make memory-based judgments of how likely it would be for the stimulus person to engage in each of those behaviors. Four additional replications were run using different settings.

Consistent with the rationale for Experiment 2, it was assumed that the presentation of a set of high typicality behaviors for any given setting should make that setting and its associated rule structure salient. Faced with the job of predicting other behaviors, subjects would be expected to rely on that rule structure for their inferences. Thus other behaviors highly typical of the setting made salient by the rule-based behavior descriptions ought to be judged as more likely than behaviors less typical of that setting.

Method

Subjects. Subjects were 20 male and female introductory psychology students at Juniata College who participated for extra course credit.

Stimulus materials. Each subject was asked to read separate descriptions of five hypothetical stimulus persons. Each description was compsed of a list of 10 phrases randomly selected from one of the pools of 15 highly typical rules associated with each setting used in Experiment 2. These lists of phrases were identical to the five sets of target items in Experiment 2 (see Table 2 for an example). As in Experiment 2, there were five such descriptions.

The remaining five sentences from each pool were combined and scrambled to form a randomly ordered 25-item prediction list. It should be noted that, for analysis, one could extract 5 five-sentence blocks from this list. Because of the way the list was constructed, each block would include behaviors highly typical of one and only one setting. These same behaviors would be of low to moderate typicality for each of the other settings. Booklets were constructed with a description presented on one page and followed on the next page by the prediction list. The order of the behavior descriptions in the booklets was counterbalanced over subjects. Likelihood judgments were made on a scale anchored with 1 = extremely likely and 6 = extremely unlikely.

Procedure. Subjects were run in groups of five. Each subject was given a booklet and instructed to read the first list of behaviors, trying to form an overall impression of the person engaging in the behaviors listed. After 90 s, they were told to turn the page and to rate, using the 6-point scale, the likelihood that the stimulus person would engage in each of the 25 behaviors listed. When all subjects had completed the first set of ratings, they turned the page to present a second set of behavior descriptions. Subjects were told not to turn back to previous pages or to move ahead until they were instructed to do so. This procedure was repeated for all five sets of behavior descriptions.


TABLE 4 MEAN LIKELIHOOD ESTIMATES FOR BLOCKS OF PREDICTED BEHAVIORS AS A FUNCTION OF

SITUATIONS PRESENTED TO SUBJECTS

Situations presented to subjects

Behavior blocks

1 2 3 4 5

Grandstands Snackbar Bookstore

1.71 2.45 2.68 1.89 1.54 1.97 1.79 1.91 2.09 1.65 1.67 1.45 1.79 1.94 1.93

Post office/bank

2.49 1.83 1.63 1.20 1.66

Hallways

2.68 1.93 1.95 1.63 1.66

Note. On likelihood scale, 1 = highly likely, 6 = highly unlikely.


For each setting, predictions of individual behaviors were combined to form mean likelihood estimates for each of the five blocks of behaviors in the prediction list. These means are presented in Table 4 for each of the five settings studied. This table is arranged so that the first block of behaviors (row 1) is highly typical of the setting of column 1, the second block is highly typical of the setting of column 2, and so on. If reading a behavioral description elicits a representation of the setting the actor is likely to be in and the attendant rules on that setting, then for each block of behaviors the highest mean likelihood should be found in the setting for which it is most typical. In general, this should produce a pattern where the highest likelihoods are on the diagonal.

To test the general proposition outlined above, a planned comparison was conducted which contrasted the likelihood estimates for the entries on the diagonal in Table 4 with the other entries. In order to generalize across different blocks of highly typical behaviors as well as to subjects, a Quasi-F test was used. This indicated that when behaviors were highly typical of a setting, they were predicted as more likely to be performed than when those same behaviors were of lower typicality F’(1, 26) = 6.90, p ==I .05.

In order to determine if the effects of the comparison were consistent over blocks of highly typical behaviors, the comparison by blocks of behavior interaction was tested and yielded a significant result F(4, 304) = 21.07, p < .02. To further explore this interaction, the simple effects of the comparison were computed for each block of rules. Four of the blocks produced significant results in the predicted direction with F values of 118.30 (grandstands), 90.32 (snackbar), 79.01 (post office/bank), and 12.84 (hallways) being obtained, df(1, 95), p < .OOl for all comparisons. The bookstore situation produced a significant reversal, with the stimulus


person who was initially described as engaging in bookstore related behaviors being seen as less likely to engage in behaviors highly typical of the bookstore than behaviors less typical of that setting, F(1,95) = 33.13, p < .ool.

In order to understand the reversal effect in the bookstore setting more completely, each of the five behaviors in that block were considered separately. The reversal effect was caused principally by one of the behaviors which was seen as highly unlikely in the bookstore setting relative to the other four behaviors which constituted the block. Evidently when the rule “browse” (see Appendix A, Rule 52) was made into a concrete behavior “browse through the clothes,” subjects saw this specific activity as much less typical of behavior in the bookstore. In addition, this rule had the lowest index of rule typicality of the 5 behaviors which comprised the bookstore block. When the analyses were repeated excluding this rule the mean likelihood of the behaviors in the bookstore block change to 1.61, 1.67, 1.30, 1.24, and 1.77, respectively, for the 5 settings presented to subjects. When the simple effect of the comparison at the bookstore setting was reassessed the effect was significant in the predicted direction, F(1, 95) = 12.39, p < .OOl.

GENERAL DISCUSSION

When individuals think about settings they have experienced in the past or consider what future settings will be like, what salient characteristics come to mind? The data presented in this report suggest that a significant component of the internal representation of any setting is a set of situational proprieties that define appropriate behavioral options for that setting. Three results support such a conception. First, when rules were obtained from subjects in free response protocols, the pattern of similarity between settings which was based on the rules was highly predictive of global similarity ratings between the settings. Thus, it was possible to predict the structure of relationships between a series of settings by know- ing the rules or proprieties attached to those settings. Second, when frequency of rule nomination was used as an index of the typicality of a rule in a setting, that index was highly predictive of memory for behaviors of a person in that setting. Finally, when subjects read a description of a stimulus person that contained behaviors highly typical of a particular setting, they rated future behaviors which were highly typical of that same setting as highly likely for that person.

The present results are consistent with other investigations that point to the importance of behavioral rules in the representation of social situations. For example, Cantor, Mischel, and Schwartz (1982) identified a number of social prescriptions for behavior in their content analyses of subjects’ descriptions of the characteristics of situations. Argyle, Graham, Campbell, and White (1979), after interviewing subjects for possible rules


and getting their ratings of the applicability of these rules to various situations, found significant intersubject agreement for which rules applied to which situations. The data presented in this report conceptually rep- licate these descriptive studies in a different domain. In addition, Ex- periments 2 and 3 extend the argument by showing how such rule-based representations may influence processing of information about people behaving in settings.

Of special relevance to the current investigation is Cantor et al.% (1982) data which suggest that social information generally and behavioral prescriptions in particular make up a large portion of the content of overall summary representations of situations. The present data do not allow comment on the relative importance of behavioral prescriptions versus other types of decriptive information which might constitute situational prototypes. However, the present results do suggest that situational proprieties are an important component of the way individuals represent situations. One particular difference between the Cantor et al. research and the present studies further highlights the importance of situational proprieties. Throughout their work, the situations used by Cantor et al. were generic in the sense that they had no specific physical referents. It seems likely that, in the absence of specific physical settings, subjects would tend to rely on information such as that found in behavioral prescriptions. In the present research all of the settings used had specific physical referents which were well-known to subjects. However, even with concrete locations available to subjects, behavioral rules were important determinants of the way subjects processed setting related information.

Although our data support a typicality interpretation, it is useful to consider the extent to which the principle of similarity may also be re- sponsible for our results. It is possible that typicality of behaviors within settings may covary with the similarity of target and distractor behaviors (in Experiment 2) or with the similarity of stimulus and target behaviors (in Experiment 3). If such covariation is systematic, then our results may be due to similarity between stimuli rather than to any typicality organization. When subjects make recognition or likelihood judgments, they presumably do so by referring to a memory representation of the original stimulus items. The nature of the memory representation will determine whether subjects are responding on the basis of typicality or similarity. To the extent that this representation is a summary or prototypical representation of the presented behaviors, subjects will respond on the basis of typicality (similarity judgments to prototypical memory representations will be typicality judgments). Several conditions of the experiments op- erated to encourage the creation of summary representations. First, subjects were given impression formation instructions, not instructions to memorize behaviors. Second, the memory load conditions in the exper-


iment clearly did not favor representation of individual behaviors-subjects were given five sets of 10 behaviors in rapid succession. Finally, unlike everyday experience where it might be useful for a variety of reasons to remember specific behaviors, our subjects never expected to contact these stimulus materials again. All of these conditions would influence subjects to create a summary representation in memory that would serve as a basis for future processing and support the principle of typicality as an explanation for our results.

The social rules approach described above also has implications for the concept of person-environment (PE) correlation in personality research (Starr & McCartney, 1983; Buss, 1984). In particular, active PE correlation entails seeking environments that allow expression of and reinforce existing dispositions. For example, individuals high on the trait of soci- ability might seek social stimulation by attending parties or offering as- sistance to others. Through this process, individuals select and create environments consistent with their personalities and at the same time reinforce the initial propensities. How are individuals guided in their selection of environments? The social rules approach suggests that individuals have complex knowledge of the most appropriate and likely activities in various settings. Such information would be highly valuable as individuals select and mold their environments in ways that facilitate the expression of their personalities.

Three qualifications on these results should be mentioned. First, it should be noted that the range of settings sampled in the studies are very limited. Given this constraint, we have not emphasized descriptions of behavioral rules but have tried to document ways that situational proprieties may be used in the processing of information about settings. Further, we have used conservative statistical procedures (Quasi-F tests) in order to generalize beyond the specific stimuli in our experiments.

A second, related, qualification is that the behavioral rules that emerged may be specific to either the particular subject population or the specific settings used in the study. It is assumed that individuals learn the relationships between situational proprieties and settings as a part of their socialization. To the extent that the experience of our subjects differs from that of other segments of the population, the rules seen as relevant for particular settings may differ from other groups. It is also possible that the typicality structure of our subjects might differ from these other groups of subjects so that different rules may be seen as more or less representative of given settings.

Finally, it is clear that situational proprieties are not the only attributes coded in the internal representations of settings (cf. Magnusson, 1981; Furnham & Argyle, 1981). However, the salience of rule structures and their implications for processing of information about others suggests that situational proprieties are of considerable importance in the representation of settings.

APPE

NDIX

A

Freq

uenc

y of

Nom

inatio

n of

Beh

avio

ral

Rule

s in

10

Situ

atio

ns:

(1)

Athl

etic

fie

ld g

rand

stan

ds

(6)

Book

stor

e (2

) Au

dito

rium

(7

) Li

brar

y (3

) Sn

ackb

ar

(8)

Post

al

and

bank

co

unte

r (4

) C

lass

room

(9

) H

alls

in

cla

ssro

om

build

ings

(5

) Di

ning

ha

ll (1

0)

Path

s on

cam

pus

Situ

atio

n

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

1. C

heer

fo

r te

am

48

0 0

0 0

0 0

0 0

0 2.

Don

’t th

row

thin

gs

26

23

27

7 50

0

0 0

0 0

3. N

o al

coho

lic

beve

rage

s 20

7

0 0

0 0

0 0

4 5

4. W

atch

la

ngua

ge

(no

prof

anity

) 23

5

0 0

0 0

0 0

6 0

5. D

on’t

litte

r or

mak

e m

ess

with

fo

od

19

17

46

0 27

0

9 19

16

35

6.

Don

’t bl

ock

som

eone

’s vi

ew

15

6 0

0 0

0 0

0 0

0 7.

Rem

ain

seat

ed

15

0 0

0 0

0 0

0 0

0 8.

Sta

y of

f th

e fie

ld

9 0

0 0

0 0

0 0

0 0

9. N

o fig

hts

9 0

0 0

0 0

0 0

0 0

10.

Don

’t be

des

truct

ive

8 14

0

0 0

24

0 14

11

14

11

. So

cializ

e 6

0 12

0

4 0

0 0

15

16

12.

Eat

and

drin

k 6

0 16

0

20

0 0

0 0

0 13

. Ke

ep

feet

off

sea

ts

6 12

0

0 0

0 0

0 0

0 14

. Cl

ap

6 16

0

0 0

0 0

0 0

0 15

. Be

qui

et

0 44

0

45

0 13

57

8

0 0

16.

No

smok

ing

0 32

0

17

7 0

23

0 17

0

17.

Sit

down

10

26

16

29

17

0

29

0 0

0 18

. N

o sh

outin

g 0

18

17

11

8 0

0 0

15

0 19

. Be

disc

iplin

ed

(not

ro

wdy

) 0

13

16

0 10

13

0

0 11

0

APPE

NDIX

A-

Con

tinue

d P

Situ

atio

n

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

20.

Talk

quie

tly

21.

No

eatin

g 22

. N

o dr

inkin

g 23

. M

ove

in i

f a

seat

is n

eede

d 24

. St

and

in l

ine

25.

Don

’t ha

ssle

the

empl

oyee

s 26

. St

udy

27.

Play

poo

l 28

. Ta

ke

back

tra

y an

d di

shes

29

. Pr

oper

dr

ess

code

(sh

oes

and

shirt

) 30

. Be

pat

ient

in

lin

e 31

. Pa

y at

tent

ion

to t

he l

ectu

res

(and

ev

ents

) 32

. Ta

ke

note

s 33

. N

o sle

epin

g 34

. R

espe

ct t

each

ers

and

othe

rs

35.

Rai

se h

and

to t

alk

36.

Don

’t wa

lk in

lat

e 37

. As

k qu

estio

ns

if ne

eded

38

. Re

spon

d wh

en

catte

d up

on

39.

No

chea

ting

40.

Don

’t m

ove

arou

nd

41.

Parti

cipat

e 42

. D

on’t

wast

e fo

od

43.

Be e

nthu

sias

tic

when

yo

u wi

n 44

. Ke

ep

food

in

the

dini

ng

hall

45.

Put

silve

rwar

e in

bu

cket

46

. H

ave

man

ners

whil

e ea

ting

0 10

13

6

5 8

23

0 41

0

9 0

6 0

6 8

0 0

0 7

0 0

0 0

0 0

0 0

4 0

0 0

0 0

0 0

0 0

17

0 35

13

0

42

0 0

0 14

0

0 10

0

22

0 0

0 9

6 0

0 46

0

0 0

0 8

0 0

0 0

0 0

0 0

7 0

53

0 0

0 0

0 0

7 0

22

0 0

0 0

0 0

6 0

0 0

0 11

0

11

29

0 50

0

0 0

0 0

0 0

0 35

0

0 0

0 0

0 0

0 32

0

0 0

0 0

10

0 0

27

0 0

0 0

5 0

0 0

20

0 0

0 0

0 0

0 0

10

0 0

0 0

0 0

0 0

10

0 0

0 0

0 0

0 0

8 0

0 0

0 0

0 0

0 7

0 0

0 0

0 0

8 0

6 0

0 11

0

0 0

0 0

6 0

0 0

0 0

0 0

0 0

18

0 0

0 0

6 0

0 0

0 0

0 0

0 0

0 0

0 16

0

0 0

0 0

0 4

0 8

0 0

0 0

0 0

7 0

6 0

0 0

0

0 0 0 0 0 0 0 2

0 c

0 s

0 0 2 cd

0 0 z

0 6 E

0 0 0 0 0 0 0 0 0 0 0 0

47.

Sit

with

fri

ends

48

. Le

ave

book

s ou

tsid

e 49

. D

on’t

ruin

bo

oks

50.

Don

’t to

uch

unle

ss b

uyin

g 51

. Bu

y ne

eded

bo

oks

52.

Brow

se

53.

Buy

(ask

for

ass

ista

nce)

54

. In

the

ent

ranc

e an

d ou

t th

e ex

it 55

. D

on’t

loite

r 56

. Re

ad

57.

Exam

ine

book

s ca

refu

lly

58.

Ret

urn

item

s to

pla

ce f

ound

59

. D

on’t

talk

60

. R

etur

n bo

oks

on t

ime

61.

No

runn

ing

62.

Wal

k qu

ietly

63

. D

on’t

leav

e bo

oks

(etc

.) to

sav

e sp

ace

64.

Leav

e bo

xmat

es

mai

l al

one

65.

Get

yo

ur m

ail

66.

Mov

e on

whe

n fin

ishe

d (o

rder

ly)

67.

Show

id

entif

icat

ion

68.

Be r

eady

in

bank

an

d po

stal

lin

es

69.

Fully

pos

tage

an

d ad

dres

s m

ail

70.

Hav

e ke

y fo

r m

ail

box

71.

Wai

t un

til ot

hers

m

ove

befo

re

getti

ng

mai

l 72

. H

ave

a di

me

to c

ash

chec

ks

73.

Pay

for

good

s (s

tam

ps,

book

s,

food

) 74

. Be

car

eful

of

oth

ers

(be

cour

teou

s)

75.

Don

’t to

uch

othe

rs’

mai

l 76

. Pu

t m

ail

in t

he c

orre

ct

slot

77

. D

on’t

stea

l 78

. St

ay o

n th

e rig

ht

6 0

0 0

4 0

0 0

0 0

0 0

0 0

0 31

0

0 0

0 0

0 0

0 0

15

18

0 0

0 0

0 0

0 0

13

0 0

0 0

0 0

0 0

0 10

0

0 0

0 0

0 0

0 0

10

0 0

0 0

0 0

15

0 0

9 0

0 0

0 0

0 0

0 0

9 0

0 0

0 ‘W

0 0

0 0

0 8

0 0

20

18

0 0

0 0

0 0

26

0 0

0 E

0 0

0 0

0 0

23

0 0

0 ?

0 0

0 0

0 15

21

0

0 0

E

0 0

0 0

0 0

19

0 0

0 0

0 0

0 0

0 8

0 0

0 9

0 0

0 0

0 0

7 0

33

4 R

0

0 0

0 0

0 7

0 0

0 2

0 0

0 0

0 0

10

0 0

0 0

0 0

0 0

0 0

32

0 0

ii!

0 0

0 0

0 0

0 17

0

0 Es

0 0

0 0

5 0

0 17

0

0 B

0 0

0 0

13

16

0 15

0

0 r

0 0

0 0

0 0

0 15

0

0 i+

0

0 0

0 0

0 0

11

0 0

3 0

0 0

0 0

0 0

11

0 0

52

0 0

0 0

0 0

0 12

0

0 Q

vl

0

0 0

0 0

0 0

10

0 0

0 0

48

0 0

26

0 9

0 0

26

15

12

14

7 7

25

8 11

0

0 0

0 0

0 0

0 8

0 0

0 0

0 0

0 0

0 9

0 0

0 0

0 0

0 46

7

7 0

0

0 0

0 0

0 0

0 0

25

16

2

APPE

NDIX

A-

Con

tinue

d

Situ

atio

n

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

W-9

79.

Don

’t bl

ock

hall

0 0

0 0

0 0

0 0

23

14

cm

80.

Don

’t di

stur

b cl

asse

s 0

0 0

0 0

0 0

0 16

0

E

81.

No

push

ing

0 0

0 0

0 0

0 0

15

27

82.

Wal

k 0

0 0

0

s

0 0

0 0

11

12

83.

Wai

t fo

r cl

ass

to b

egin

0

0 0

0 0

0 0

0 5

0 8

84.

Wat

ch

wher

e ar

e go

ing

you

0 0

0 0

0 0

0 0

5 0

;:

85.

Say

“hel

lo”

0 0

0 0

0 0

0 0

15

22

86.

Stay

on

the

path

s 0

0 0

0 0

0 0

3 0

0 18

87

. Fo

r pe

dest

rians

on

ly

0 0

0 0

0 0

0 0

0 13

:

88.

No

spitt

ing

0 0

0 0

0 0

0 0

0 9

89.

Don

’t tri

p pe

ople

0

0 0

0 0

0 0

0 0

9 90

. Ke

ep

pers

onal

di

stan

ce

0 0

0 0

0 0

0 0

0 8

91.

Let

peop

le

pass

0

0 0

0 0

0 0

0 0

6 92

. Ru

n 0

0 0

0 0

0 0

0 0

5 93

. D

on’t

lay

on w

alkw

ays

0 0

0 0

0 0

0 0

0 5


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Rule structure in the psychological representation of physical settings

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