The ambiguity of recognition memory tests of schema theories

COGNITIVE

PSYCHOLOGY

16, 421-448 (1984)

The Ambiguity

of Recognition Memory Schema Theories

Tests of

ANNELOCKSLEYANDCHARLESSTANGOR New

York

University

CHRISTINE Pitzer

HEPBURN College

AND

ELLEN GROSOVSKY AND MARIANN HOCHSTRASSER New

York

University

The majority of theories about the effects of schemata (or generic knowledge structures) on information processing share two fundamental assumptions. It is assumed, first, that schema-based inferences about unobserved features of an event are generated, and, second, that such inferences are stored in long-term memory and thereby confused with traces of observed features of that event. To test these assumptions, researchers rely heavily on recognition memory measures of hit and false alarm rates. In the present paper, it is observed that schema theoretic interpretations of recognition memory measures are inconsistent with an interpretation jointly derived from the theory of signal detection and Mandler’s (1980, Psychological Review, 87, 252-271) subjective familiarity theory of recognition memory; from the latter’s perspective, recognition memory measures are considerably more ambiguous than schema researchers realize and do not necessarily support the typical schema theoretic interpretations. Data from four experiments are analyzed from the perspective of the theory of signal detection/ subjective familiarity model and demonstrate the ambiguity of recognition memory measures for testing hypotheses about the function and consequences of schemata for information processing. 0 1984 Academic Press, 1~.

Recently, social and cognitive psychologists have become increasingly interested in the effects of generic knowledge structures, or schemata, The authors are grateful to John Bargh, Murray Glanzer, Tory Higgins, and Alexandra Logue for their helpful criticisms of an earlier draft of this manuscript, and to Scott Cicero, Hal Goldberg, Coline Jean-Baptiste, and Virginia Willsea for their assistance with part of this research. This research was supported in part by NSF Grant BNS-7912940 and in part by a New York University Challenge grant to the senior author. Requests for reprints should be sent to Anne Locksley, Department of Psychology, 6 Washington Place, Room 793, New York, NY 10003. 421 0010-0285184 $7.50 Copyright Q 1984 by Academic Press, Inc. All rights of reoroduction in anv form reserved.

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on the encoding, representation, and retrieval of information in memory. A number of different models have been proposed to describe how schemata influence cognitive processing of information (see Alba & Hasher, 1983; Graesser & Nakamura, in press, for recent summaries). For instance, while some models focus on the effects of schemata on attention allocation in perception and encoding (e.g., Hastle, 1981; Neisser, 1976; Taylor & Cracker, 1981), others focus on their effects on representation in long-term memory (e.g., Abelson, 1981; Bartlett, 1932; Bower, Black, & Turner, 1979; Cantor & Mischel, 1977; Cohen, 1981; Graesser & Nakamura, in press; Minsky, 1975; Neisser, 1976; Taylor & Cracker, 1981; Woll & Graesser, 1982; Wyer, 1980). Despite their differences, these models share important features. They are uniformly concerned with the ways in which schemata distort memory for information, whether the predicted result be more accurate information for schema-related information (e.g., Minsky, 1975; Neisser, 1976; Tsujimoto, 1978) or more accurate information for schema-unrelated information (e.g., Bower et al., 1979; Woll & Graesser, 1982). And they are for the most part based on two important and related assumptions: (1) that schemata provide a basis for generating inferences about unobserved aspects of an event and (2) that internal, schema-based inferences are stored in memory (or, possibly, generated during retrieval and then stored in memory, e.g., Loftus, 1979) along with traces of observed aspects of the event. As a result, recognition memory tests figure prominently in schema research (Alba & Hasher, 1983). Higher hit (H) rates for schema-related information are construed as evidence for the idea that schemata facilitate information processing and storage (e.g., Abelson, 1981; Cohen, 1981; Cohen & Ebbesen, 1979; Neisser, 1976; Taylor & Cracker, 1981). Higher false alarm (FA) rates for schema-related information are construed as evidence for the idea that internally generated, schema-based inferences are confused in memory with externally observed information about an event (e.g., Abelson, 1981; Bower et al., 1979; Graesser & Nakamura, in press; Neisser, 1976; Woll & Graesser, 1982). The present paper is concerned with several ambiguities in recognition memory tests of schema theories, which have not yet been fully appreciated by researchers in the area. These ambiguities become apparent once recognition memory performance in schema research is interpreted from the perspective of the theory of signal detection (TSD). Signal Detection Theory and Recognition Memory The fundamental insight of TSD is that perceptual and memory judgments are determined by two independent psychological processes: (1) the true psychological discriminability of a “signal” along one or more

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criteria1 dimensions and (2) the subject’s response criterion, or the value on relevant criteria1 dimensions which the subject adopts for decisions that a signal was present or absent (Banks, 1970; Coombs, Dawes, & Tversky, 1970; Egan, 1958; Green & Swets, 1966; Lockhart & Murdock, 1970; McNicol, 1972; Swets, 1964; Tanner & Swets, 1954). In TSD terms, schema theories to date focused exclusively on the effects of generic knowledge on the perceptual and memorial discriminability of information. However, schemas may independently and directly influence the selection of a response criterion for memory task decisions (e.g., Bellezza & Bower, 1981). Since H and FA rates can be influenced by response criterion shifts as well as by differential discriminability, they cannot be unambiguously interpreted as evidence of discriminability alone. Further, as will be demonstrated by the research reported below, while TSD measures can be used to disentangle these two sources of memory judgments, their use also leads to the identification of another, less easily solved problem with standard interpretations of recognition memory performance in schema research. Recognition

Memory and Subjective Familiarity

Recognition decisions appear to be determined in part by the subjective familiarity of the test items (Mandler, 1980). Accordingly, schema models may be described in terms of their implications for the subjective familiarity of old and new items on recognition memory tests. For instance, if schema-unrelated information is less likely to be perceived and encoded than schema-related information (as argued by, e.g., Abelson, 1981; Cohen, 1981; Cohen & Ebbesen, 1979; Minsky, 1975; Neisser, 1976; Taylor & Cracker, 1981), then its presentation in a stimulus display is less likely to affect its subjective familiarity value, As a result, the familiarity values of old and new schema-unrelated items will be less discriminable from each other than the familiarity values of old and new schema-related items. This possibility is graphed in row A of Fig. 1, which shows greater overlap in the familiarity distributions for new and old schema-unrelated information than in the familiarity distributions for new and old schema-related information. On the other hand, if unobserved information is inferred from a schema and copied into memory together with traces of observed information (as argued by, e.g., Abelson, 1981; Bower et al., 1979; Cantor & Mischel, 1977; Graesser & Nakamura, in press; Minsky, 1975; Neisser, 1976; Taylor & Cracker, 1981; Woll & Graesser, 1982), the subjective familiarity value of unobserved schema-related information will be increased as well as the familiarity value of the observed schema-related information. This possibility is graphed in row C of Fig. 1, which depicts greater overlap in the subjective familiarity values of old and new schema-related infor-

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A. xc NEW

x, OLD

NEW

OLD

NEW

XC

NEW

x,

c. /?A xc NEW

Schematically

Unrelated

OLD

OLD

XC

NEW

OLD

Information

Schematically

OLD

Related

Information

FIG. 1. Hypothetical distributions of subjective familiarity values of new and old recognition test items as a function of whether the items are schematically related or unrelated. Response criterion values are indicated by the values of x-sub c. False alarm rates are shaded.

mation than in the familiarity values of old and new schema-unrelated information. As a result, old schema-related items are less discriminable from new schema-related items than old schema-unrelated items are from new schema-unrelated items. The null hypothesis is depicted in row B of Fig. 1, where the distributions of subjective familiarity values for old and new items are equally overlapping and their means equidistant whether the items are schema related or schema unrelated. Recognition Memory and the Response Criterion In order to make a recognition judgment, however, subjects not only must assess the familiarity value of an item. They also must adopt a criterion familiarity value, above which items will be judged as old and below which items will be judged as new. Unless the familiarity value distributions of old and new items are completely nonoverlapping (and thus perfectly discriminable), subjects will make some number of judgments which prove to be FAs and some number ofjudgments which prove to be misses (M’s). According to TSD, a subject’s response criterion may be viewed as unbiased if the probabilities of either type of error are identical. This possibility is depicted in row A of Fig. 1. The response criterion is identified by the value x - sub c on the x axis. In row A, the

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425

value of the response criterion is the same in relation to the old and new familiarity value distributions of both schema-related and schema-unrelated information. Only items for which the probability of being old exceeds the probability of being new are judged as OLD, and only items for which the probability of being new exceeds the probability of being old are judged as NEW. Thus, the higher FA and lower H rates depicted there for schema-unrelated information are entirely attributable to the poorer discriminability of schema-unrelated information. The response criterion value which subjects adopt for a recognition decision may shift for different types of items for many different reasons (e.g., Broadbent, 1967; Broadbent & Gregory, 1967; Coombs et al., 1970; Green & Swets, 1966; Tanner & Swets, 1954). The activation of a schema can directly affect the value of the response criterion subjects adopt in order to make recognition decisions. In effect, the activation of a schema (whether induced explicitly by an experimenter or by the subject’s goals and plans, for instance, and/or induced implicitly by the properties of observable stimuli) represents a judgment that the information in the schema is more likely than not going to be observed in a particular context (i.e., that the prior odds for the occurrence of the “signal” of schemarelated information are favorable). If that judgment is not challenged by subsequent exposure to the stimulus information, then there is no reason to suppose that it will not persist. Because this judgment in itself provides some degree of certainty that schema-related information appeared in the stimulus array, subjects may be willing to tolerate more uncertainty when appraising the familiarity value of schema-related items on a recognition memory test than when appraising the familiarity value of schema-unrelated items. Thus, the response criterion for schema-related items may be more biased in the direction of OLD decisions (or, alternatively, less biased in the direction of NEW decisions) than the response criterion for schema-unrelated items. Figure 1 depicts this hypothesis in row B and in row C. There it can be seen that the relationship of the response criterion value to the familiarity distributions of old and new items depends on whether the items are schema related or schema unrelated. In row B, it can be seen that the hypothesized shift will increase H and FA rates for schema-related items relative to schema-unrelated items in the absence of any differential cognitive processing of schema-related information, Row C depicts the possibility that both response criterion shifts and some form of differential cognitive processing occur in consequence of the activation of a schema. Comparison of the H and FA rates for schema-related and -unrelated information depicted in rows B and C indicates that it is difficult, if not impossible, to disentangle the two psychological sources of recognition decisions from the simple assessment of H and FA rates alone. Use of TSD measures to interpret recognition memory data permits

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the investigator to control for response criterion shifts when interpreting effects of schema activation manipulations on H and FA rates. However, from the perspective of TSD, only some aspects of the psychological processes involved in recognition memory can be determined from the analysis of recognition memory data. Specifically, only relative, not absolute positions on subjective familiarity scales can be determined from estimates of H and FA rates, once response criterion shifts have been controlled for. Thus, it is possible to determine whether discrimination is poorer or better because TSD converts estimates of H and FA rates into a measure of the distance between familiarity distributions for old and new items. But it is not possible to determine whether this distance changed because of a shift in the position of the familiarity distribution for old items or because of a shift in the position of the distribution for new items. As can be seen in Fig. 1, when sets of subjective familiarity distributions are similarly distributed, increases in FA rates are fully paralleled by increases in M rates as discrimination worsens. Neither error rate can be considered on its own as an indication of change in the distributions for old or new items. For instance, increases in FA rates do not necessarily reflect a higher subjective familiarity mean for new items, and could equally well be due to lower subjective familiarity values for old items. Typically, schema theorists attribute higher FA rates to confusions between inferences and traces of observed stimulus features because increases in FA rates often are greater in magnitude than corresponding increases in H rates for schema-related information (e.g. Graesser, Gordon, & Sawyer, 1979; Woll & Graesser, 1982). However, FA rates may increase more than H rates because of the simultaneous effect of schemas on the value of a response criterion and on the psychological discriminability of schema related information. Both effects increase FA rates but the former will increase H rates while the latter will decrease H rates if, as presently appears to be the case (Alba & Hasher, 1983), discrimination for schema-related stimuli is inferior to discrimination for schema-unrelated stimuli. Thus, if response criterion shifts are controlled for, FA rates should not increase to any greater extent than M rates, reflecting increasing overlap between subjective familiarity distributions but providing no indication of the direction of change in either old or new item distributions. More generally, from the perspective of TSD, recognition memory data can show only whether subjective familiarity distributions for old and new schematic information are closer (reflecting poorer discrimination) or farther apart (reflecting better discrimination) than the distributions for old and new aschematic information, not how the distributions neared or departed each other. To demonstrate these points, four experiments were conducted which

427

SCHEMATA

operationalized TSD measures of (1) the true discriminability in memory (called “sensitivity”) of old and new items on a recognition test and (2) response criterion bias. Each experiment was designed to permit computation of these measures separately for schema-related and for schemaunrelated information, in order to test the notions, first, that typical schema-activation manipulations affect the value of the response criterion subjects adopt for recognition memory decisions, and, second, that response criterion shifts conceal the comparability of changes in FA and M rates for schema-related information. Experiment 1 investigated effects of schemata about four different personality types on memory for verbal information about the behavior of different individuals, Experiments 2 and 3 investigated effects of a schema about a fifth personality type on memory for visual information about the faces of different individuals, and Experiment 4 investigated effects of this schema on memory for verbal information about the behavior of different individuals. In Experiments 1, 2, and 3 subjects had no control, whereas in Experiment 4 subjects did control the duration of their exposure to the stimulus information. To assess the generalizability of the findings, substantial intraand interexperimental variation was contrived in (1) the subjective familiarity of stimulus and test items, (2) the social desirability of stimulus and test items, (3) the difficulty of the memory task, (4) the marginal distributions of schematic and aschematic stimulus items, and, as already noted, (5) the verbal versus visual nature of the stimulus and test items, (6) the identity of the particular schema involved, and (7) the degree of subjective control over exposure to the stimulus items. EXPERIMENT

1

Overview

Subjects in Experiment 1 read about the behavior of 20 stimulus persons, who were identified either as fashion models, or as university professors, or as salesmen, or as American Nazi Party members. Half of the behavioral instances presented to subjects were either characteristically vain, or intellectual, or friendly, or authoritarian, depending on the social role of the stimulus persons, while the other half were neutral with respect to these trait categories. Afterward subjects’ recognition memory for the stimulus persons’ behavior was assessed. Estimates of sensitivity and response criterion bias were computed and analyzed as a function of the categorical relevance of the behavior. Method Subjects. Subjects were 93 undergraduates enrolled in introductory psychology at New York University. Subjects volunteered for the experiment in exchange for course credit. Procedure. Subjects were randomly assigned to one of four stimulus person groups,

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ET AL.

and informed that they would be presented with descriptions of the behavior of 20 people, identified either as fashion models, university professors, salesmen, or American Nazi Party members. They were then handed one of four sets of 20 cards, according to which type of stimulus person had been assigned to them. On each card appeared the name of a person and descriptions of three acts by that person. For example, the set of 20 university professors included a card with the name “John P” and the itemized behaviors, “(a) . . . is a patron of the Museum of Natural History,” “(b) . . . writes poetry,” and “(c) . checks out a new library book every week.” Subjects had 4 s to read each card, after which they were instructed to continue to the next card, until all 20 cards had been read. Next, subjects worked on a distraction task of amusing logical puzzles for 3 min. They were then presented with a randomized list of 45 behavioral descriptions, 30 of which had appeared in the stimulus display and 1.5of which had not. For each behavior, subjects indicated on an enumerated scale from 0 to 20 how many of the stimulus persons had been described by that behavior. Stimulus mff~@%~fs. The personality types of vain (fashion models) and authoritarian (American Nazi Party members) people, and friendly (salesmen) and intellectual (university professors) people were chosen to permit replication of the effect across socially desirable and socially undesirable person categories. It was determined that subjects believe these personality types occur in the social roles used to identify the stimulus persons by having 360 subjects from the same population as the experimental subjects rate the degree to which the four personality types were characteristic of the social groups, on IO-point scales labeled 1 = “not at all characteristic” and 10 = “highly characteristic.” All four personality types were rated as more characteristic of their corresponding groups, average M = 8.1, than the other groups, average M = 4.8, all t’s > 17.4, all p’s < .OOOl. For each personality type, a set of 23 category-relevant and 22 category-irrelevant behaviors were generated by the experimenters. Two hundred eighty-eight subjects, from the same population as the experimental subjects, judged how characteristic each of the resulting total of 180 behaviors was of any one of the four categories using the same labeled IO-point scale used in the other pretest. The results of several analyses of these data demonstrated that generic knowledge about the characteristic behavior of the four types was highly reliable, highly discriminable, and highly generalizable. First, for all four types, category-characteristic ratings of the 23 category-relevant items, M = 7.4, were higher than ratings of the 22 category-irrelevant items, M = 4.8, all r’s > 3.4, all p’s < .OOl. Second, for all four types, ratings of each behavior were higher for its own category, M = 7.4, than for the other categories, M = 4.7, all r’s > 10.4, all p’s < .oOl. Every schematic behavior was judged as more characteristic of its category than of any of the other three categories, and as more characteristic of its category than neutral behaviors. And, third, across all four categories the average split-half reliability coefftcient for category characteristic ratings of the 45 neutral and relevant behaviors was .85. The pretested behaviors were used to construct descriptions of four sets of 20 stimulus persons each by, first, randomly selecting 15 of the categorically relevant descriptions and 15 of the categorically neutral descriptions of each group. The 30 items were randomly attributed to each stimulus person, with the constraints that each person was described with three behaviors, each behaivor appeared twice in the stimulus array, and no person was described by the same behavior twice.

Results and Discussion of Experiment 1 Preliminary analyses found no effects of serial position or of stimulus person category type on the dependent measures. Accordingly, the data were collapsed across these factors.

SCHEMATA

429

Recognition scores were constructed from the frequency judgments by treating responses of 0 as NEW judgments and responses of 1 or more as OLD judgments (cf. Proctor, 1977). H and FA rates were estimated for each subject and used to compute the nonparametric TSD index, A’, as a measure of each subject’s sensitivity and the nonparametric TSD index, B”, as a measure of each subject’s response criterion bias (Grier, 1971).‘y2A’ increases monotonically as a function of true discriminability; a value of 1 indicates perfect sensitivity, a value of SO indicates perfect insensitivity. B” ranges in value from - 1 to + 1. Negative scores represent bias toward OLD judgments and positive scores represent bias toward NEW judgments. One-way analyses of variance were computed comparing A’ and B” for schematically related and unrelated behaviors. It was found that subjects were more sensitive for schematic behaviors, M = .81, than for neutral behaviors, M = .76, F(1,92) = 13.25, p < .OOl. And, as hypothesized, it was found that subjects’ response criterion was more biased toward OLD judgments when behaviors were schema related, M = - .49, than when the behaviors were schema unrelated, M = - .29, F( 1,92) = 10.05, p -=I .Ol. A post hoc confounding variable problem was discovered in the course of the data analysis. The estimate for schema effects on sensitivity was found to be confounded by differences in the prior subjective familiarity of schema-related and schema-unrelated items. A major determinant of subjective familiarity is frequency, and the schema-unrelated behaviors were found to be more frequent than the schema-related behaviors by analysis of frequency ratings of the items obtained from a sample of 157 subjects from the same population as the experimental subjects. Moreover, a pattern of a higher H and lower FA rates for schema-related than t The formula for computing A’, as taken from Grier (1971), was A, = ,,* + (Y - x)(1 + Y - 4 4YU - 4

where y is the probability of a hit and x is the probability of a false alarm. The formula for computing B”, also taken from Grier (1971), was B,, = Y(l - Y) - 41 - -4 Y(l - Y) + 41 - xl where y and x are as before. * Nonparametric TSD measures were used rather than parametric TSD measures for two reasons. First, they are advisable whenever recognition decisions used to estimate TSD variables are not normally distributed (Grier, 1971). Since well over 1000distributions were generated by the subjects in the four experiments, it was not feasible to check their normalcy. Second, nonparametric measures are advisable whenever each subject provides a single value for a TSD variable, because nonparametric measures represent all possible areas under one point on the hypothetical signal and noise curves (Grier, 1971).

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for schema-unrelated behavior was obtained, in conformity with the typical pattern of recognition performance found for low-frequency words in comparison to high-frequency words (e.g., Glanzer & Bowles, 1976). Post-test frequency ratings were used to partition the test items into highand low-frequency groups. Consistent with the findings of word frequency studies (e.g., Glanzer & Bowles, 1976; Kinsbourne & George, 1974; Shepard, 1967; Underwood & Freund, 1968), A’ was significantly less for frequent behaviors, M = .77, than for infrequent behaviors, M = .81, F(1,80) = 6.97, p < .Ol, while B” remmained unaffected by behavioral frequency, F(1,81) = .lO, p > JO. Thus, the higher sensitivity found for schema-related behavior may have been due to its greater infrequency rather than to its schematicity. An effort was made to control for the frequency effects by deleting appropriate items from the pool of test items until the mean frequency ratings for schema-related and for schema-unrelated items were roughly equal, and recomputing A’ and B” on the remaining set of items. Reanalyses found that subjects were no more sensitive for schema-related behaviors, M = .85, than for schemaunrelated behaviors, it4 = .85, F(1,66) = .03, p > SO. However, subjects were still found to be more biased toward OLD decisions for schemarelated behaviors, M = - .36, than for schema-unrelated behaviors, M = .lO, F(1,64) = 16.48, p < .OOl. Thus, whether B” was estimated across the total set of memory test items or the restricted set, it was found that subjects’ response criteria were more biased toward OLD judgments when items were schema related than when items were schema unrelated. Since effects of item schematicity on A’ were inadvertently confounded by item frequency effects, however, further analysis of the relationship between A’, B”, and H and FA rates was deemed unreliable. EXPERIMENT

2

Overview

Experiment 2 was designed with several considerations in mind. In particular, since the frequency confound in Experiment 1 was discovered post hoc and could only be imperfectly controlled for, it was considered desirable to operationalize orthogonal manipulations of the schematic relevance and the subjective frequency of stimuli in Experiment 2. Further, visual stimuli were used in Experiment 2 to explore the generalizability of the findings of Experiment 1 across memory for different stimulus modalities. Subjects in Experiment 2 were shown 324 slides of faces of people whom they were led to believe were extraverted personality types. Of the total number of faces, 60 were target faces, half of which had been

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431

independently rated as highly extraverted in appearance and half of which had been rated as unextraverted. Moreover, half again of each set of 30 faces had been independently rated as common in appearance (or characterized by often occuring features) and the other half had been rated as uncommon. Subjects’ recognition memory was assessedafter the slide presentation by eliciting old or new judgments for a second slide series of 60 old faces and 60 novel faces characterized by comparable factorial values. Estimates of subjects’ sensitivity and response criterion bias were computed and analyzed as a function of the schematic relevance of the faces and as a function of how common in appearance they were. Method Subjects. Subjects were 110 undergraduates enrolled in introductory psychology at New York University. Subjects volunteered for the experiment in exchange for course credit. Procedure. Subjects were instructed that they would be presented with a slide series of a large number of faces of people who in previous studies “had scored as extraverts on a personality test. For instance, their response to questions on the test indicated that they like meeting and talking to people, enjoy jokes, and tend to do things on the spur of the moment.” Subjects were instructed further to adopt a strong set for accurate memory. They were informed that the faces would be presented quickly and that they would subsequently be asked to remember whether they had seen each face or not. Subjects were then presented with 324 randomly ordered slides of faces. The background illumination in the room was normal. Each slide appeared for 1 s and the interstimulus interval was also 1 s in duration. After the slide presentation was concluded, subjects were presented with another randomly ordered series of slides of 120faces, 60 of which had appeared in the stimulus array and 60 of which had not. To eliminate primacy and recency effects, none of the 60 old faces were sampled from the first 10 or last 10 faces presented in the original stimulus sequence. Each slide was projected for 2 s and subjects were then allowed 3 s to judge whether the face in the slide had appeared in the stimulus display or not by checking appropriately labeled boxes on a sheet of paper. Stimuks materials. Two judges independently sorted a large number of pictures from college yearbooks along the dimension of extraversiomintroversion and independently along the dimension of frequency. Agreement between the judges’ sorting decisions was perfect. Four groups of 30 target faces each were sampled from the judges’ sorting: (1) common extraverted faces, (2) rare extraverted faces, (3) common introverted faces, and (4) rare introverted faces. Forty-nine subjects, from the same population as the experimental subjects, rated a randomly ordered list of the faces on 1l-point scales labeled 1 = “highly introverted” and 11 = “highly extraverted.” A second sample of 51 subjects, from the same population as the experimental subjects, rated the same list on 11= point scales labeled 1 = “extremely unusual” and 11 = “extremely common.” Extraversion/introversion was defined for the first sample of pretest subjects in the same way it had been defined for the experimental subjects. Commonness was defined for the second sample of subjects by instructing them to rate each face according to how many, if any, rarely occurring features it had. Analysis of the pretest ratings corroborated the judges’ sorting decisions. Extravetted faces were rated as significantly more extraverted, M = 7.6, than unextraverted

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ET AL.

faces, M = 4.8, F(1,48) = 301.7, p < .OOOl.And common faces were rated as significantly more common, M = 6.99, than uncommon faces, M = 5.9, F(1,50) = 149.14,p < .OOOl. From each of the four sets of 30 faces, 15were randomly selected to appear in the stimulus display and 15 were randomly selected to appear only on the recognition memory measure. The distributions of the four types of target faces were also equal in the entire stimulus display of 324 faces.

Results and Discussion

of Experiment

2

Table 1 presents the mean H and FA rates as a function of whether faces were schematically related or unrelated and as a function of whether faces were common or rare in appearance. It can be seen in Table 1 that recognition memory performance was poor in general. The proportions of correct decisions made for common schematic faces, SO, rare schematic faces, 54, common unschematic faces, 53, and rare unschematic faces, .58, were all low and close to chance. While the H rate was higher for schematic faces, M = .56, than for nonschematic faces, M = .51, F(1,109) = 15.02, p < .OOl, so too was the FA rate for schematic faces, M = .52, in comparison to nonschematic faces, M = .41, F(1,109) = 67.34, p < .OOl. Facial commonness was also found to affect H and FA rates. The H rate was less for common faces, M = -52, than for rare faces, M = .54, though not significantly so, F(1,109) = 2.29, p = .13. The FA rate was higher for common faces, M = .50, than for rare faces, M = .42, F(1,109) = 38.56, p < .OOl. No interaction between the two factors was obtained, corroborating the post hoc hypothesis that H and FA rates for schemarelated and schema-unrelated items were confounded in Experiment 1 by independent item frequency effects. A’ and B” were computed for each subject and analyzed as a function of the two experimental manipulations (see Table 2). Confirming the theory of frequency effects advanced post hoc in Experiment 1, it can be seen in Table 2 that equivalent frequency effects were obtained in Experiment 2. A’ was less for common faces, M = .49, than for uncommon faces, M = .58, F(1,109) = 19.56, p < .OOl. Also consistent with the TABLE 1 Experiment 2: Hit and False Alarm Rates for Common and Rare Schematic and Nonschematic Faces

Common faces Schematic faces Nonschematic faces Rare faces Schematic faces Nonschematic faces

Hit rate

False alarm rate

.58 .48

.58 .42

.54 .54

.46 .38

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SCHEMATA

TABLE 2 Experiment 2: A’ and B” for Common and Rare Schematic and Nonschematic Faces

Common faces Schematic faces Nonschematic faces Rare faces Schematic faces Nonschematic faces

A’

B”

.46

-.02

s2

.02

.54

- .Ol

.61

.04

frequency effect theory was the failure to find that B” was affected by facial commonness, F(1,109) = 1.42, p > .05. Further confirming the post hoc suspicion that effects of schematic relevance on sensitivity were confounded by independent frequency effects in Experiment 1, it was found in Experiment 2 that A’ was considerably worse for schema-related faces, M = SO, than for schema-unrelated faces, M = .57, F(1,109) = 9.58, p < .OOl, across levels of facial commonness. Consistent with the main argument of this paper, B” was found to be more biased in the direction of OLD judgments when faces were schemarelated, M = - .02, than when faces were schema-unrelated, M = .03, F(1,109) = 8.81, p < .004.3 No interactions between the schematic relevance and the commonness of faces were obtained for the two TSD measures. In order to control for the impact of schema effects on B”, two linear equations were estimated. Across all subjects and experimental conditions, the H rate was regressed on B” and A’, and then, in a separate analysis, the FA rate was regressed on B” and A’. To obtain independent estimates of H and FA rates for schema-related and -unrelated items which are unbiased by B” effects, the resulting linear equations were used to compute the respective values of H and FA rates by setting the value of the term for B” to 0 (i.e., the value of an unbiased response criterion 3 Although the TSD measures of A’ and B” are orthogonal in principle and, in these experiments, generally uncorrelated, the magnitude of both their values are related by virtue of their dependency on the magnitude of H and FA rates. Specifically, as H and FA rates near each other, A’ nears SO, indicating poor sensitivity. At the same time, the possible maximal and minimal values of B” are restricted: B” necessarily takes on values closer to 0. Thus, the smaller deviations of B” from 0 found in Experiment 2, for instance, than in Experiment 1, cannot be interpreted as evidence of “less bias.” Similarly, the smaller deviations of B” from 0 found in the Detail than in the Full Item test conditions of Experiment 3 cannot be interpreted as evidence of “less bias”: the H and FA rates are substantially closer in the former than in the latter conditions and constrain values of B”. In all of the experiments, the effect sizes (as estimated by the proportion of variance) of item schematicity on A’ and B” are approximately the same, averaging 5% for effects of schematicity on A’ and 7% for effects of schematicity on B”.

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for this metric), and by setting the value of the term for A’ first to the mean for schematic items and then to the mean for nonschematic items. In this way, the unbiased H rate for schema-related items was found to be .54 and the unbiased H rate for schema-unrelated items was found to be .57, a difference of - .03. The unbiased FA rate for schema-related items was found to be .46 while the unbiased FA rate for schema-unrelated items was found to be .45, a difference of + .Ol. Thus, it was found that the magnitude of schema-activation effects on the FA rate was if anything less than the magnitude of those effects on the M rate, once response criterion shifts were controlled. The data demonstrate that increases in FA rates due to poorer sensitivity for schema-related stimuli simply reflect increases in the total error rare of judgments about schemarelated items. Since higher M rates cannot possibly be attributed to storage in memory of internally generated inferences, correspondingly higher FA rates cannot be interpreted in this way either. In sum, the results of Experiments 1 and 2 provide strong support for the major points of the argument. First, expectancies for schema congruent stimuli were found to exert a direct effect on the value of the response criterion subjects adopted for memory task decisions. Second, expectancies also affected the true psychological discriminability of schema-related items on the recognition memory task. This result agrees with findings from the few other experiments operationalizing TSD measures of sensitivity (Graesser & Nakamura, in press: Woll & Graesser, 1982), and more broadly with findings from studies which carefully probe memory for schema-related and -unrelated information (Alba & Hasher, 1983). However, current attributions of the accuracy decrement to the representation in memory of schema-based inferences cannot be supported by the data. When response criterion shifts were controlled for, it was found in Experiment 2 that both M and FA rates were higher‘for schema-related items. As noted above, recognition memory data can help to determine whether memory for expected information is better or worse than memory for unexpected information but not why it is so. EXPERIMENT

3

Overview In principle, many different theories can account for an accuracy decrement for expected information. Most schema theorists attribute it to confusions between internally generated inferences and traces of observed stimuli (e.g., Bower et al., 1979; Graesser & Nakamura, in press; Taylor & Cracker, 1981). In the terms of the subjective familiarity theory of recognition memory, it is assumed that the subjective familiarity values of new schema-related items on the recognition memory task were in-

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creased by their internal generation and storage in memory during the original stimulus presentation. Thus, the means of the subjective familiarity distributions for new and old schema related items were nearer (i.e., more difficult to discriminate) than the means for new and old aschematic items, because the familiarity values of new schema-related items were increased by the activation of the schema. It is equally possible, however, that the distribution means are nearer because the familiarity values of old schema-related items were not increased as much by the stimulus presentation as the familiarity values of old schema-unrelated items. At least two different theories would lead to this prediction. One theory, already advanced by Hastie (1980, 1981; Hastie & Kumar, 1979), is that people pay less attention to expected information than to unexpected information. According to this theory, differences in memorial accuracy are due to differences in the amount of time people spend attending to, encoding, and rehearsing expected and unexpected information in short-term memory. Accuracy differences could also arise from differential treatment at other stages than short-term memory. Specifically, people could pay as much attention to expected as to unexpected information, yet devote less mental effort to transferring its details from short-term to long-term memory. This strategy is commonsensical. If the acquisition of generic knowledge allows people to “know what to expect,” then less mental effort need to be spent storing the expected features of a stimulus in longterm memory. Experiment 3 was designed and conducted in an effort to further discriminate among these possibilities. Subjects in this experiment were presented with slides of faces of people whom they were led to believe were extraverted personality types. The one of three different types of recognition memory tests was administered. One type was identical to that employed in Experiment 2. Subjects had to judge whether slides of new and old faces had appeared in the original stimulus display. For the two other tests, subjects were presented with slides of parts of new and old faces. For one test, the parts consisted of the facial features most relevant for the schema of extraversion (e.g., a smiling vs a frowning mouth, crinkled vs narrowed eyes). For the other test, the parts consisted of the facial features least relevant for the schema of extraversion (e.g., chin, nose). This manipulation was derived from the following hypotheses. Hypothesis 1. If subjects pay less attention (in perception and during short-term memory) to expected stimuli than to unexpected stimuli, then memory for all details of expected stimuli should be poorer, whether those details are more or less relevant for the categorization of the stimulus as schematic or not. Hypothesis 2. If subjects use their generic knowledge to generate and

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store in memory inferences as well as observations of stimuli, then those inferences will consist largely of schema-relevant details. For instance, the inference of an extraverted face will consists largely of a grin, bright eyes, while the irrelevant details of the chin or nose will be less articulated. As a result, only the subjective familiarity of schema-relevant details will be increased by the internal generation and storage of schemabased inferences. Therefore, memory for schema-relevant details of schematic stimuli should be poorer than memory for equivalent details from nonschematic stimuli, but memory for the irrelevant details should not differ. Hypothesis 3. If subjects spend less effort transferring from short- to long-term memory the details of a stimulus which they already “know” are there than its other details, they they have more mental capacity remaining for the transfer and storage of those other details. As a result, memory for schema-relevant details of schematic stimuli should be poorer than memory for equivalent details from nonschematic stimuli, but memory for the irrelevant details of schematic stimuli should be better than memory for the equivalent details from nonschematic stimuli. Another experimental factor was operationalized to ensure that any effects obtained by Experiment 3 could be attributable to the experimental activation of schema-based expectancies and also to control for possible stimulus item effects. Thus, the marginal distribution of schematic items in the stimulus presentation was fully crossed with the other experimental factors. Either 25, 50, or 75% of the slides in the stimulus display were independently rated as schematic and the rest as nonschematic. In all conditions, 50% of the recognition test items were schematic and the rest nonschematic. Method Subjects. Subjects were 126 undergraduates enrolled in introductory psychology at New York University who volunteered for the study in exchange for course credit. Subjects were randomly assigned to the experimental conditions.

Procedure. The experimental instructions were identical to those used in Experiment 2. Subjects were presented with one of three sets of 120 randomly ordered slices of faces in a normally illuminated room. In these sets, 25, 50, or 75% of the faces had been independently rated as extraverted in appearance, while the remaining had been independently rated as unextraverted or introverted in appearance (see Experiment 2). Each slide appeared for 4 s and the interstimulus interval was 1 s in duration. After the conclusion of the slide presentation, a recognition memory test was administered. The test consisted of one of three randomly ordered series of 80 slides, 40 of which had appeared in the stimulus series and 40 of which had not. Twenty of the new and twenty of the old items were schema related while the remaining were schema unrelated. Subjects were not informed about which recognition test they would be administered until immediately prior to its presentation. Each slide was projected for 4 s and subjects were allowed 3 s to decide whether the face in the slide had appeared in the stimulus display by checking appropriately labeled boxes on a sheet of paper.

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Stimulus materiah. The same pictures of faces pretested for Experiment 2 were used in Experiment 3. Three judges independently selected categories of facial features which were deemed relevant for the classification of a face as extraverted or not and featural categories deemed irrelevant. Agreement between the judges was perfect: the mouth and eyes were deemed most relevant and the chin and nose deemed least relevant. Accordingly, three types of recognition tests were devised from the same set of 80 faces. In the Full Item test, the complete faces appeared as they had appeared in Experiment 2 and during the stimulus presentation of Experiment 3. In the Schema-Relevant Detail test, the faces were blacked out with the exception of the eyes and the mouth. In the Schema-Irrelevant Detail test, the faces were blacked out with the exception of the chin and the nose. Results and Discussion

of Experiment

3

Preliminary data analysis obtained no effects of the percentage of schematic stimulus items on the dependent measures which had any bearing on findings from the substantive data analysis. Thus, the results cannot be attributed to idiosyncratic characteristics of the stimulus items. Accordingly the data were collapsed across this factor. Each subject’s H and FA rate was computed. Although the H rate for schematic items, M = 58, did not significantly exceed the H rate for nonschematic items, M = 57, F(1,117) = 1.37, p > .05, the FA rate for schematic items, M = .44, did significantly exceed the FA rate for nonschematic items, M = .36, F(1,117) = 32.04, p < .OOl. This is just the sort of pattern which has been interpreted as support for the “inference” theory of schemata. As noted above, though, simultaneous and independent effects of schema activation of the response criterion and on true psychological discriminability both increase FAs but act in opposite directions on H’s. Accordingly, B” and A’ were computed, then, following the procedures used in Experiment 2, subjects’ H and FA rates were regressed on B” and A’, in that order. The regression equations were used to compute H and FA values for schematic and nonschematic items when B” = 0. It was found that the unbiased H rate for schematic items was 58 and the unbiased H rate for nonschematic items was .62, a difference of - .04. The unbiased FA rate for schematic items was .45 and the unbiased FA rate of nonschematic items was .42, a difference of + .03. Thus, replicating Experiment 2, once effects of response criterion shifts were controlled, H’s for schematic items actually decreased. Though the unbiased FA rate for schematic items was found to be higher than the FA rate for nonschematic items, the magnitude of the difference was no greater than the increase in the M rate for schematic items. Table 3 presents the mean values of B” as a function of the type of item judged on the recognition memory test and as a function of the type of memory test administered. The main effect of item type replicated the findings of Experiments 1 and 2; subjects were more biased in the direction of NEW judgments for nonschematic faces, M = .09, than for schematic faces, M = .04, F(1,117) = 4.29, p < .05. A significant main effect

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TABLE 3 Experiment 3: B” for Schematic and Nonschematic Items as a Function of Recognition Memory Test Type Item type Test type

Schematic

Nonschematic

Full Item Schema Relevant Detail Schema Irrelevant Detail

.06 .03 .04

.22 .05 .Ol

of test type revealed that subjects were less biased in general when judging details of faces, whether they were schematically relevant, M = .04 or irrelevant, M = .02, than when judging the full face, M = .14, F(2,117) = 5.08, p < .Ol (see Footnote 3). A reliable interaction between the two, F(2,117) = 7.84, p < ,001, provided an internal manipulation check. As the data presented in Table 3 show, while subjects administered the Full Item test were considerably more biased in the direction of NEW judgments for nonschematic faces than for schematic faces, F(1,115) = 23.09, p < .OOl, and subjects administered the Schema-Relevant Detail test were marginally so, F(1,115) = 2.94, ,lO > p > .05, subjects administered the Schema-Irrelevant Detail test were if anything biased in the opposite direction, F(1,115) = 4.41, p < .05. Table 4 presents the A’ values as a function of the experimental factors. A main effect of item type was obtained, replicating Experiment 2. Overall, sensitivity for schematic items, M = .60, was poorer than sensitivity for nonschematic items, M = .65, F(1,117) = 7.68, p < .Ol. Not surprisingly, a main effect of test type was also obtained; sensitivity for facial details, M = .55, was substantially poorer than sensitivity for the full faces, M = .77, F(2,117) = 58.3,~ < .OOl. A significant interaction between the two factors, F(2,117) = 10.91, p < ,001, provided support for the third hypothesis entertained for the experiment. As can be seen in Table 4, sensitivity for schematic items was poorer than for nonschematic items when subjects judged the full faces, F(1,115) = 5.32, p < .05. This effect was especially pronounced TABLE 4 A’ for Schematic and Nonschematic Items as a Function of Recognition Memory

Test Type Item type Test type

Schematic

Nonschematic

Full Item Schema Relevant Detail Schema Irrelevant Detail

.75 .48 .57

20 .65 .52

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SCHEMATA

when subjects judged the schema relevant details of the faces, F(1 ,115) = 21.39, p < .OOl. However, sensitivity for the schema irrelevant details of the schematic faces was reliably greater than sensitivity for the schema irrelevant details of the nonschematic faces, F(1,115) = 6.46, p < .05.

In sum, the basic findings of Experiment 3 replicated the previous results. In general, the value of subjects’ response criterion shifted in the predicted direction for schematic items. When this shift was controlled for statistically, poorer sensitivity for schematic items was expressed by increases of comparable magnitude in both the M and FA error rates. The character of subjects’ recognition memory performance on the different types of tests was consistent with a “mental effort” theory about the effects of schemas on memory. Although subjects’ memory for the schema relevant details of the schematic faces was considerably poorer than their memory for comparable details of the nonschematic faces, their memory for the schema irrelevant details was reliably better. This finding supports the hypothesis that subjects have more mental capacity to devote to memory storage of irrelevant details of schematic items because they devote less to storage of the relevant details, and, it would seem difficult, if not impossible, to explain from the perspective of the “inference” theory. On the other hand, the hypotheses are not mutually exclusive. Both psychological processes could be occurring simultaneously. EXPERIMENT

4

Overview

The results of Experiment 3 failed to provide support for the theory that people pay less attention in perception and during encoding to expected information. On the other hand, Experiment 3 did not provide a strong test of that theory, primarily because no measures of perceptual attention were operationalized. Further, in Experiments 1, 2, and 3, subjects exercised no control over their exposure to the stimulus items, a factor limiting the generalizability of the results. Accordingly, in Experiment 4 subjects controlled the duration of their exposure to each stimulus items and this time lapse was recorded for each item for each subject. Moreover, the difficulty of the recognition memory task was manipulated (by increasing the number of stimulus items and by increasing the pressure for accurate performance conveyed by the instructions) in order to determine whether effects of schemata on attention allocation may be more pronounced when the task is relatively more difficult. Subjects were presented with with 160 verbal descriptions of behavior (harder condition) or with 80 behavioral descriptions (easier condition) on a CRT screen. Subjects were led to believe that the descriptions were

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sampled from observations of extraverted people. Afterward, subjects were administrated a recognition memory test composed of 40 new and 40 old items. The schematic relevance and subjective frequency of the items were orthogonally manipulated as in Experiment 2. Estimates of sensitivity and response criterion bias were computed for each subject and analyzed as a function of the schematic relevance of the test items the subjective frequency of the test items, and the difficulty of the memory task, controlling for duration of time spent looking at these categories of items in the initial stimulus presentation. Method Subjects. Subjects were 107 undergraduates enrolled in introductory psychology at New York University who volunteered for the experiment in exchange for course credit. Subjects were randomly assigned to experimental conditions. Procedure. Each subject sat in a small room facing a Zenith Data Systems Model ZVMl21 CRT screen and response panel connected to an Apple II microcomputer located in a separate control room. The CRT display was under computer control. The computer recorded the latency of responses to the stimulus presentation items and the decisions on the recognition memory test. Subjects were instructed that the experiment concerned how well they remembered particular behavioral episodes. They were informed that a series of behavioral episodes would appear on the screen, told to read each description for as long as they felt necessary, and to push the button on the response panel marked “Continue” to let the computer know when to go on to the next description. Subjects were then allowed to practice this procedure on 12 behavioral descriptions which were unrelated in content to the experimental stimuli. Subjects were further instructed that all of the behavioral descriptions they would read were sampled from an earlier, observational study of people who scored as Extraverts on a personality test. The construct was defined for these subjects as it had been defined for subjects in Experiments 2 and 3. Subjects in the Easier condition were then told, You will see a total of 80 behavioral descriptions, one at a time. Take your time and read each description carefully before continuing on to the next one. The test of your memory for these descriptions will be easy, so the main thing is to take your time and study each behavior thoroughly. In contrast, subjects in the Harder condition were told, You will see a total of 160 behavioral descriptions. Obviously that is a lot of descriptions to read and there isn’t much time. Try to read each description as quickly as you can in order to continue on to the next one as fast as possible. The test of your memory for these descriptions will be difficult, but the only thing you can do is try to read each description as fast as possible. Further, subjects in the Easier condition were told that their memory for the behaviors would be “measured” while subjects in the Harder condition were told that their memory for the behaviors would be “evaluated.” At the conclusion of the stimulus presentation, the experimenter familiarized subjects with the procedures for the recognition memory test. During the test, subjects had 5 s in which to decide whether each of the 80 test items had or had not appeared in the original stimulus display by pressing either a “NEW” button or and “OLD” button on the response panel.

SCHEMATA

441

Stimulus materials. A pool of 230 behavioral descriptions was generated by the experimenters, 155 of which were characteristic of extraverts and 75 of which were neutral with respect to extraversion. An example of an extraverted behavioral description is, “invited friends he met at a party to come over for dinner,” while an example of a neutral description is, “went to the dentist for a cleaning and a checkup.” Eighty descriptions were sampled from the pool to be used on the recognition memory task: forty to appear only on the recognition task and forty to appear both in the stimulus display and on the recognition task. Of each group, half again were extraverted descriptions while half were neutral. Furthermore, subjective frequency effects were controlled by selecting equal proportions of frequently and infrequently occurring behaviors within the extraverted and neutral stimulus and test item sets. Of the 135 extraverted items remaining for stimulus presentation, 107 were randomly selected for presentation in the Harder condition and 53 were randomly selected for the Easier condition, with the constraint that the 20 schema-related test items appeared in both conditions. Of the 55 neutral items remaining for stimulus presentation, 53 were selected for the stimulus presentation in the Harder condition and 27 were selected for the Easier condition, with the constraint that the 20 schema-unrelated test items appeared in both conditions. The mean frequency of words per description, the mean frequency of letters per word, and the mean frequency of syllables per word were rendered roughly equivalent in each experimental cell. A group of 230 subjects, from the same population as the experimental subjects, rated the behavioral descriptions on 11-point scales labeled 0 = “not characteristically extraverted” and 10 = “very extraverted.” A range of 35-42 subjects rated randomly selected sets of about 40 items each. Analysis of these ratings found that (1) schema-related descriptions presented in the Easier conditions were rated as more extraverted, M = 6.7, than schema-unrelated descriptions, M= 2.3, r(78) = 16.1, p < .OOOl;(2) schema-related descriptions presented in the Harder condition were rated as more extraverted, M = 6.5, than schema-unrelated descriptions, M = 2.3, ~(158) = 20.7, p < .OOOl;and (3) schema-related items presented on the recognition memory test were rated as more extraverted, M = 5.9, than schema-unrelated items, M = 2.3, r(78) = 11.7, p < .OOOl.These findings confirm the manipulations of item schematicity during the stimulus presentation and during the recognition test. In order to determine whether subjective frequency effects were sufficiently well controlled on the recognition test, a second sample of 230 subjects from the same population as the experimental subjects rated the behavioral descriptions on 1l-point scales labeled 0 = “rare” and 10 = “frequent.” A range of 35-42 subjects rated randomly selected sets of about 40 items each. Analysis of these ratings found that (1) schema-related descriptions presented in the Easier condition were rated as less frequent, M = 4.3, than schemaunrelated descriptions, M = 5.1, t(78) = -2.2, p < .05; (2) schema-related descriptions presented in the Harder condition were rated as less frequent, M = 4.4, than schemaunrelated descriptions, M = 5.9, t(158) = -4.6, p < .OOl; but that (3) schema-related items on the recognition test did not differ in frequency, M = 5.5, from schema-unrelated items, M = 5.7, r(78) = -0.5, p = .60. These results show that the confounding variable problem of subjective frequency (see Experiments 1 and 2) was successfully controlled by counterbalanced item selection for the recognition memory task. Results and Discussion of Experiment 4 Analysis of the amount of time spent looking at each behavioral description revealed that subjects in the Harder condition spent less time looking at each behavior, M = 3.23 s, than subjects in the Easier con-

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dition, M = 5.49 s, F(1,105) = 21.2, p < .OOl, confirming the task difficulty manipulation. However, no evidence of any effect of item schematically was found on time spent looking, either overall, F(1,105) = 1.57, p = .21, or within levels of the task difficulty factor, F( 1,105) = .25, p < .50. In this experiment, then, subjects did not pay any more or less attention to schema-related items than to schema-unrelated items. H and FA rates were computed for each subject. The proportions of correct decisions in the Easier condition were high for both schemarelated items, .92, and schema-unrelated items, 194. The proportions of correct decisions in the Harder condition were lower than in the Easier condition, z = 2.00, p < .05, confirming the task difficulty manipulation, but were similarly high for both schema-related items, .86, and for schema-unrelated items, .88. Analysis of the H and FA rates found a main effect of item schematicity across levels of task difficulty, such that both rates for schema-related items, M = .89 and M = . 11, respectively, were higher than the rates for schema-unrelated items, M = .87 and M = .05, respectively, F(1,105) = 39.9, p < .OOOl,though a reliable interaction showed that the magnitude of the difference was greater for the FA rate than for the H rate, F(1,105) = 6.35, p < .Ol. Thus, Experiment 4, like the previous experiments, obtained the typical pattern of recognition decisions cited as evidence for the role of internal inferences on memory for information. To unbias the H and FA rates, each was regressed on B” and A’ and the regression equations were used to estimate their values when B” = 0. Replicating Experiments 2 and 3, the unbiased H rate for schematic items was found to be .86 and the unbiased H rate for nonschematic items was .90, a difference of - .04. The unbiased FA rate for schematic items was .14 and the unbiased FA rate for nonschematic items was .ll, a difference of + .03. Again, then, once response criterion shifts are controlled for, FA rates are not found to increase to any greater extent than M rates. Table 5 presents the mean value of A’ and B” as a function of task difficulty and as a function of item schematicity. Confirming the task TABLE 5 A’ and B” for Schematic and Nonschematic Behavior as a Function of T&k Difficulty

A‘ Easier task Schematic behavior Nonschematic behavior Harder task Schematic behavior Nonschematic behavior

B”

.93 .96

.03 ..53

.88 .91

-.07 .38

SCHEMATA

443

difficulty manipulation, sensitivity was worse when the task was harder than when the task was easier, M = .90 versus M = .95, F(1,105) = 24.9, p < .OOl. However, a main effect of item schematically on A’ was also found, such that, regardless of whether the task was easier or harder, sensitivity was worse for schema-related items than for schema-unrelated items, M = .91 versus M = .94, F(1,105) = 28.5,~ < .OOl. No interaction between item schematicity and task difficulty was obtained on A’ values. Consistent with the previous findings and across levels of the task difficulty factor, subjects’ response criterion shifted in the direction of greater bias toward OLD judgments when items were schema related, M = - .02, in comparison to when items were schema unrelated, M = .46, F(1,105) = 54.56, p < .OOOl.The effect of task difftculty on B” did not reach criterion, F(l,lOS) = 2.69, p > .05, nor did the interaction of task difficulty and item schematicity, F(1,105) = .OS,p > .50. The possibility that time spent looking at behavioral descriptions during the stimulus presentation might mediate effects of item schematicity on A’ and B” was investigated by computing two repeated-measures multiple regressions. In the first, A’ was regressed on mean time spent looking, item schematicity, and task difficulty. Despite controlling for mean time spent looking, the increment in variance explained by the item schematicity variable was highly reliable, F(1,209) = 12.62, p < .OOl. In the second analysis, B” was regressed on mean time spent looking, item schematicity, and task difficulty. Again, despite controlling for mean time spent looking, the increment in variance explained by the item schematicity variable was highly reliable, F(1,209) = 46.85, p < .OOOl.Thus, effects of item schematicity were not found to be mediated at all by the measure of perceptual attention used in the experiment. Finally, the possibility that subjective frequency effects could be confounding analysis of A’ or B” was checked. Items were sorted into highand low-frequency groups on the basis of pretest ratings. Analysis obtained a reliable Item Frequency x Task Difficulty interaction, F( 1,105) = 6.24, p < .05. Probably due to a performance ceiling effect, A’ for high-frequency items did not differ from A’ for low-frequency items in the Easier condition, M = .94 versus M = .93, respectively, t(53) = 1.35, p < .05. However, in the Harder condition A’ for high-frequency items was worse than A’ for low-frequency items, M = .89 versus M = .91, t(50) = -2.09, p < .05, consistent with the findings by Experiments 1 and 2 of poorer sensitivity for increasingly subjectively frequent social stimuli. Fortunately, the effort to control for item frequency effects was found to be successful. Item frequency did not interact with item schematicity in analyses of A’ values, p values for all F’s > .05. Also consistent with the findings of Experiment 1 and 2, item frequency was not found to affect B” values, p values for all F’s > .50.

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In sum, the results of Experiment 4 show that effects of item schematicity on the value of the response criterion subjects adopt to make recognition decisions occur independently of the amount of perceptual attention paid to stimuli, as do the effects of item schematicity on the true psychological discriminability of memory traces of the stimuli. As in the previous experiments, once response criterion shifts were statistically controlled, FA rates were found to have increased no more than M rates for judgments of schema-related items. GENERAL

DISCUSSSION

Experiments l-4 provided strong support for the points raised in the introduction. Typical schema-activation manipulations affected the value of the response criterion subjects adopted to make recognition decisions across a wide range of experimental conditions, including intra- and interexperimental variation in (1) the subjective familiarity of stimulus and test items, (2) the social desirability of stimulus and test items, (3) the verbal or pictorial nature of stimulus and test items, (4) the difficulty of the memory task, (5) accuracy of performance on the memory task, (6) the identity of the particular schema involved, (7) the degree of subjective control over exposure to the stimulus items, and (8) the amount of perceptual attention paid to the stimulus items. Although it was initially supposed that schema-activation manipulations would induce bias toward OLD judgments for schema-related items, the results suggest that they induce as great if not greater bias toward NEW judgments for schemaunrelated items. Thus, while B” values for schema-related items were often negative (indicating bias toward OLD judgments), positive B” values (indicating bias toward NEW judgments) for schema-unrelated items were considerably further from 0. Once response criterion effects were detected and statistically controlled, increases in FA rates due to poorer sensitivity alone were shown to be fully paralleled by increases in M rates, in other words to be a function of a general increase in the total error rate for judgments about schema-related items. The data were consistent, then, with the perspective of TSD. According to TSD, changes in any one parameter of a decision matrix cannot be interpreted without reference to its other parameters. Changes in FA rates can be decomposed into (1) a factor distributed across FA and H rates (attributed to response criterion effects) and (2) a factor distributed across FA and M rates (attributed to sensitivity effects), and can only be interpreted in that light. Since for the most part current schema theories treat decision matrix parameters as independent of each other, they are fundamentally inconsistent with TSD, and, in fact, with the data presented here. These data demonstrate that accuracy decrements may be detected by recognition

SCHEMATA

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performance measures but that the nature of accuracy decrements cannot. Accuracy decrements are equally well attributable to poorer memory for observed stimuli (stemming, for instance, from less effort invested in storage process) as to “false” memory for inferred stimuli. Experiment 3 obtained evidence in support of the idea that people spend less effort storing expected features of stimuli in long-term memory, but it was noted then that both processes could occur and could contribute to recognition memory performance. What is no longer clear is how the “internal inference” theory of schematic information processing could be proven or disproven. Other Measures of Schema-Based Inferences

Other measures besides recognition memory performance have been used to test schema theories, primarily response latency measures (e.g., Sentis & Burnstein, 1979), recall measures (e.g., Hastie & Kumar, 1979), and time delayed memory measures (e.g., Bower et al., 1979). Although TSD has not been well elaborated yet for these measures, there is no reason to suppose that the processes detectable by recognition memory performance do not operate over time, do not involve different response latencies, or are not involved in recall memory. Response criterion effects may persist or even strengthen over time, may correspond to different response latencies for judgments about schema-related vs -unrelated information, and may influence recall memory as well as recognition memory. Thus, similar patterns of findings with other memory and memory judgment response measures do not necessarily weaken the implications of the present argument. Schemata and the Metaphor

of the Computer Program

The concept of a schema, as a description of generic knowledge, has been shaped largely by the concept of a computer program, because cognitive psychology was reinvigorated by advances in computer science and the study of artificial intelligence. Like computer programs, schemata have been viewed, implicitly or not, as inert frames for the integration of input and organization of output. The analogy to a computer program gave rise to the (somewhat contradictory) expectations that (1) schematic information would be better remembered, because of the greater likelihood of its having been perceived and processed, and (2) memory for observed schematic information would be easily confused with memory for inferred information, because of the generic nature of its cognitive representation. After over a decade of intensive research by social and cognitive psychologists, serious doubts may be expressed about the utility of the computer program analogy, and, in turn, the utility of related conceptions of

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schemata (cf. Alba & Hasher, 1983). While there is no questions about the existence of generic knowledge, the nature of its structure and functions in information processing and memory remains unclear. Memory for nonschematic information is far more accurate than would seem to be warranted from schematic theories of information processing (Alba & Hasher, 1983). And as the present research shows, evidence for the generation of schema-based inferences is far more ambiguous than has been recognized to date. REFERENCES Abelson, R. (1981). The psychological status of the script concept. American Psychologist, 36, 715-729. Alba, J., & Hasher, L. (1983). Is memory schematic? Psychological Bulletin, 93, 203-231. Banks, W. (1970). Signal detection theory and human memory. Psychological Bulletin, 74, 81-99. Bartlett, F. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge Univ. Press. Bellezza, E, & Bower, Cl. (1981). Person stereotypes and memory for people. Journal of Personality and Social Psychology, 41, 856-865. Bower, G., Black, J., & ‘Ibmer, T. (1979). Scripts in memory for text. Cognitive Psychology, 11, 177-220. Broadbent, D. (1967). Word-frequency effect and response bias. Psychological Review, 74, l-15. Broadbent, D., & Gregory, M. (1967). Perception of emotionally toned words. Nature (London), 215, 581-584. Cantor, N., & Mischel, W. (1977). ‘Baits as prototypes: Effects on recognition memory. Journal

of Personality

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