Copyright 1992 by the American Psychological Association, Inc. 0278-7393/92/53.00

Journal of Experimental Psychology: Learning, Memory, and Cognition 1992, Vol. 18, No. 5, 1019-1028

The Picture Superiority Effect in Categorization: Visual or Semantic? Remo Job, Rino Rumiati, and Lorella Lotto

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University of Padova, Padova, Italy Two experiments are reported whose aim was to replicate and generalize the results presented by Snodgrass and McCullough (1986) on the effect of visual similarity in the categorization process. For pictures, Snodgrass and McCullough's results were replicated because Ss took longer to discriminate elements from 2 categories when they were visually similar than when they were visually dissimilar. However, unlike Snodgrass and McCullough, an analogous increase was also observed for word stimuli. The pattern of results obtained here can be explained most parsimoniously with reference to the effect of semantic similarity, or semantic and visual relatedness, rather than to visual similarity alone.

Most studies addressing the issue of categorization have reported that words are categorized more slowly than pictures but can usually be named faster than pictures (Pellegrino, Rosinski, Chiesi, & Siegel, 1977; Potter & Faulconer, 1975; Rosch, 1975). The interpretation of this finding has usually been based on differences in either the mental representation of pictures and words (e.g., Paivio, 1978) or access to mental representations (e.g., Pylyshyn, 1973). Snodgrass and McCullough (1986) reported empirical data that seem to show that the picture superiority effect is due to an early stage of visual analysis in which the degree of structural (or visual) similarity among pictures, but not words, plays a relevant role. Snodgrass and McCullough presented subjects with either a set of pictures or a set of names in one of two conditions in which the visual similarity of the elements of two categories was manipulated. So, fruits was presented once in the context of vegetables, a visually similar category, and once in the context of animals, a visually dissimilar category. The results showed that categorization time for pictures—but not for words—was affected by visual similarity, with longer response times to visually similar categories. Snodgrass and McCullough's interpretation is that visual cues may be used as a first-pass means of categorizing pictures because pictures of elements from the same semantic category are usually visually more similar to each other than to elements from another category. Thus, categorization of both words and pictures is based on access to semantic information; however, pictures may, in addition, involve the use of a parallel visualfeature comparison strategy based on the evaluation of the structural similarity among elements of a category. This strategy, which is usually faster than a strategy requiring access to the semantic system, is responsible for the picture superiority effect in categorization. This research was supported in part by Consiglio Nazionale delle Ricerche Grant CT90.0345.08 and Ministero dell' Universita' e della Ricerca Scientifica e Tecnologica Grant 60%90. We would like to thank Joan Gay Snodgrass, Judith Kroll. and an anonymous reviewer for their insightful comments. Correspondence concerning this article should be addressed to Remo Job, Dipartimento di Psicologia dello Sviluppo e della Socializzazione, Universita' di Padova. via B. Pellegrino 26, 35100 Padova, Italy.

Although the direct manipulation of the degree of visual similarity among elements of different categories has rarely been performed, there are some empirical data that bear on this issue. A first line of evidence comes from studies on intracategory visual similarity. Humphreys, Riddoch, and Quinlan (1988) showed that members of categories (e.g., animals, birds, fruits, insects, and vegetables) with a high degree of structural similarity, as indexed by the number of rated common parts per category and by the average percentage of contour overlap in relation to other elements from the same category, were named more slowly than members of categories with a low degree of structural similarity (e.g., body parts, clothes, and furniture). The same asymmetry was present in the performance of a brain-damaged patient, J.B., who was studied by Humphreys et al. When J.B. was asked to name pictures he could correctly name 75% of the exemplars from the structurally dissimilar categories but only 15.9% of the exemplars from the structurally similar categories. If we assume that structural similarity is indeed a key to category membership for the first type of categories, and we also assume that among those categories there is some degree of visual similarity—either because of parts in common (e.g., head and tail for mammals and birds) or because of contour overlap (e.g., the contour of potatoes and coconuts)—it could then be hypothesized that a visual strategy would be less economical in this case than in the case of structurally distinct categories. If correct, Snodgrass and McCullough's (1986) proposal would be able both to reconcile the often contradictory studies reported in the psychological literature (cf. the papers collected in Snodgrass, 1984) and to explain elegantly some of the data on specific forms of visual agnosia reported in neuropsychology (cf. the papers collected in Job & Sartori, 1988). There are, however, three critical issues that should be considered before accepting the Snodgrass and McCullough proposal. First, the critical contrast in their study is between the categories of fruits and vegetables on the one hand and fruits and animals on the other. However, fruits and vegetables are not only visually similar but are semantically related as well, and it might be difficult to disentangle the two dimensions. Because it has been shown that semantic relatedness has a strong effect on the categorization of words (e.g., Guenther & Klatzky, 1977; Rips, Shoben, & Smith, 1973;

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R. JOB, R. RUMIATI, AND L. LOTTO

Smith, Baizano, & Walker, 1978), it may be argued that the results reported by Snodgrass and McCullough are due to semantic rather than visual effects; to illustrate, consider some results reported by McCloskey and Glucksberg (1979). They found that it takes longer to reject category statements of the type "All A are y," where x are category members and y is a category label, when x and y are semantically related. Therefore, sentences such as "All watermelons are vegetables" and "All cabbages are fruits" are responded to more slowly than sentences such as "All crystals are vegetables" and "All museums are fruits." From these examples it is easy to realize that what constitutes semantic relatedness for McCloskey and Glucksberg constitutes visual similarity for Snodgrass and McCullough. This first criticism may be dismissed by Snodgrass and McCullough (1986) because they found an effect of relatedness only with pictures and not with words. Such a pattern is difficult to ascribe to the influence of a semantic factor, which would have analogous effects with visual and verbal material, but is easily explained if it is thought of as due to visual similarity, which affects only pictures. The second line of evidence to be considered consists of studies showing that visual similarity of objects has analogous effects when categorizing either pictures of objects or the words referring to those objects. Walls and Siple (1987) reported that subjects were faster at categorizing two successively presented pictures when they referred to physically similar objects (e.g., apple and orange) than to dissimilar objects (e.g., banana and orange); a similar effect of physical similarity was also observed with word stimuli. An analogous pattern for pictures and words was also found for no responses (i.e., when the two elements were not members of the same category). In this case, physical similarity had a decremental effect on performance, with similar items (e.g., ball and orange) requiring longer time than dissimilar items (e.g., pig and orange). These data are difficult to reconcile with the model proposed by Snodgrass and McCullough (1986) in that it would not predict an effect of objects' visual similarity when the words referring to the objects are used. The third critical issue involves two aspects of the data reported by Snodgrass and McCullough (1986) that seem to us to be unaccounted for by their proposal. First, in their Experiment 2. they obtained longer response times to both pictures and words in the visually similar condition in comparison with the visually dissimilar condition. They did not comment on this datum, which contrasts with the prediction derived from the visual-feature comparison strategy. According to this strategy, in fact, processing of words should not be affected by the visual similarity of their referents. This claim may be ill stated, however, because the word results may be due to semantic, rather than visual, similarity (see Introduction of this article), and Snodgrass and McCullough do allow for conceptual similarity effects on word categorization. However, the effects of visual similarity and conceptual relatedness would then be difficult to tell apart. Second, a lack of difference between response time to words and response time to pictures is obtained in some of the visually dissimilar conditions. Of course, words and pictures differ in some of the earlier processing stages involved in their categorization, and

so no firm conclusion can be drawn about absolute response time differences between the two types of stimuli. However, as Snodgrass and McCullough claim, if a categorization decision can be made based only on a picture's gross physical characteristics, thereby bypassing semantic memory, these decisions could be expected to be made more quickly than similar decisions involving words, because in the latter case semantic memory must necessarily be contacted, (p. 148)

In their Experiment 1, in fact, in the visually dissimilar condition, the 54-ms advantage of pictures over words was significant only in the item analysis.1 In order to clarify these issues, and test for the generality of Snodgrass and McCullough's (1986) results, we tried to replicate their work by performing an experiment similar to their Experiment 1 controlling for the degree of category membership of the elements used. The latter manipulation was induced by the findings reported by Rosch, Mervis, Gray, Johnson, and Boyes-Braem (1976; see also Rosch & Mervis, 1975), who showed that central elements of a category share more visual features and present a greater shape overlap than do peripheral elements. It could then be hypothesized that a visual comparison strategy should be more effective for central than for peripheral elements and particularly so in the visually dissimilar condition. Pictures of central elements would thus show a superiority over peripheral elements in the dissimilar condition, because peripheral elements may sometimes require more extensive processing in order to be discriminated from elements of the other category. However, this would be even more necessary in the visually similar condition in which, ceteris paribus, a peripheral element may look more similar to the other category than to a central element. Therefore, the difference in response times between central and peripheral elements should be greater in the similar condition than in the dissimilar condition. If so, we could predict that for pictures, the degree of category membership should interact with the overall degree of similarity of the two categories composing a condition. The Snodgrass and McCullough proposal explicitly rules out such a pattern for word stimuli.

Experiment 1 Method Subjects and design. Subjects were 16 Italian-speaking undergraduates at the University of Padova, Padova, Italy. They were tested in a 2 x 2 x 2 completely within-subjects design. The independent variables were the form of the visually presented stimuli (pictures vs. words), the degree of visual similarity of the superordinate categories (fruits-vegetables vs. fruits-weapons), and the degree of typicality of elements (central vs. peripheral). We required the subjects to categorize each picture or word as belonging to one of the two categories appearing in each condition. Materials and apparatus. We selected 16 stimuli from each of the categories of fruits, vegetables, and weapons (see Appendix A) yielding a set of 48 pictures and their most common Italian names. 1 A similar datum (52 ms) is also obtained in Experiment 2 in the same condition (i.e., same responses to fruits in the visually dissimilar condition), but no statistical analysis is provided.

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PICTURE CATEGORIZATION Half of the stimuli selected from each category were typical, and the remaining were peripheral exemplars (Job, 1981). Words were matched as closely as possible for length and for frequency (Bortolini, Tagliavini, & Zampolli, 1974). We created four conditions from the picture and word stimuli. Two conditions represented visually similar conditions in that the elements to be categorized were from two structurally similar categories (i.e., fruits-vegetables pictures; fruits-vegetables words). The remaining two conditions represented visually dissimilar conditions in that the elements to be categorized were from two structurally dissimilar categories (i.e., fruits-weapons pictures, and fruits-weapons words). In each condition, we presented slides of either 32 pictures or 32 words, half belonging to the target category and half to the nontarget category. The sequence of 32 slides composing each condition was presented twice. We prepared practice slides consisting of eight pictures with corresponding words. Half of the stimuli were selected from the category of buildings and the other half were selected from the category of body parts. Procedure. We projected slides of the pictures and their names one at a time with a Telema two-channel tachistoscope that was controlled by an Apple HE personal computer. When projected, pictures subtended a visual angle of about 5° horizontally by 4° vertically, and the pictures' names subtended a visual angle of 0.5° per letter horizontally by 1° vertically. The subject was seated at a table in a sound-attenuated room separate from the experimenter. The stimulus duration, interstimulus intervals, intertrial interval, and acoustic-signal duration preceding the appearance of each slide were controlled automatically. Each stimulus was presented for 2 s followed by a 1.5-s lighted blank field that served as the intertrial interval. The onset of the stimulus started a timer. Pressure on either of two response keys by the subject stopped the timer and reaction time (RT) in milliseconds was automatically recorded. The eight practice slides of pictures and the eight practice slides of words were placed at the beginning of the set of picture conditions and of the set of word conditions, respectively. The order of the stimulus slides was determined randomly for each condition with the constraint that no more than three stimuli from the same category could appear sequentially. Furthermore, no more than three central or three peripheral elements could appear next to each other. Once the random order of stimuli for each condition was established, it remained the same for every subject. The order of picture condition and word condition was counterbalanced across subjects with the constraint that the two visually similar conditions always appeared next to each other. We told subjects before each condition what two categories they would receive and the form of presentation (picture or word). Half of the subjects were instructed to press the right response key if the stimulus belonged to the target category (i.e., fruits) and the left response key if the stimulus belonged to the nontarget category. The response key order was reversed for the other half of the subjects. Subjects were encouraged to respond as quickly as possible. Response latency, to the nearest millisecond, and accuracy were recorded for each stimulus. To familiarize subjects with the stimuli, at the beginning of the experiment we showed subjects a block formed by the 16 pictures and the 16 corresponding words that they would encounter in each of the categories, and we asked them to categorize each item.

Results and Discussion We trimmed RTs by discarding responses greater than 3 standard deviations from the mean for that subject and condition. Mean correct RTs and percentage of error rates for

Table 1 Mean Reaction Times (RTs) in Milliseconds and Percent Errors (%) for Experiment 1 Word

Picture List Visually dissimilar Central Peripheral Visually similar Central Peripheral

RT

%

RT

%

467 493

0.78 1.56

560 572

1.56 0.39

577 634

3.12 6.64

572 648

2.34 5.06

the four conditions are presented in Table 1. Error rates were consistent with the pattern of RTs, ruling out a speed-accuracy trade-off. The correct RT data were analyzed using an analysis of variance (ANOVA) with both subjects and materials as random factors (Clark, 1973). The factors considered were form of the stimulus (picture vs. word), degree of structural similarity (structural similarity vs. structural dissimilarity), and degree of typicality of elements (central vs. peripheral). The ANOVA by subjects was a 2 x 2 x 2 completely withinsubjects design, and the ANOVA by items was a 2(2 x 2) design with the degree of typicality as a between-subjects factor. In all analyses, the criterion for statistical significance wasp < .05. There was an effect of structural similarity, F'mm (1, 26) = 31.07, MS, = 5,091.222, with structurally dissimilar items being responded to faster than structurally similar items (523 ms vs. 608 ms); an effect of stimulus form, F'min (1, 29) = 11.22, MS, = 3,335.36, due to the fact that pictures elicited faster responses than words (543 ms vs. 588 ms); and an effect of the degree of typicality, F' min (1, 18) = 10.98, MS, = 628.9, with central members being faster than peripheral members (544 ms vs. 587 ms). Also, two first-order interactions reached significance. The interaction of structural similarity with stimulus form, F'min (1, 28) = 21.63, MS, = 48.7, was due to the fact that the difference between RTs to pictures and words was significant in the dissimilar condition (480 ms vs. 566 ms) but not in the similar condition (606 ms vs. 610 ms). The interaction between structural similarity and degree of typicality, F'mm (1, 24) = 5.05, MS, = 987.2, was due to the fact that the degree of typicality played a role mainly in the structurally similar condition, with central members being responded to 67 ms faster in this condition and only 19 ms in the structurally dissimilar condition. The triple interaction was significant in both the analysis by subjects, F(\, 15) = 4.87, MSC = 415.05, and the analysis by items F(l, 14) = 5.33, MS, = 423.23, but not in the combined analysis. A post hoc analysis showed that the difference in RTs between the similar and dissimilar condition was significant (p < .01) for both pictures and words, although the 126-ms difference for the former type of stimuli is substantially higher than the 44ms difference for the latter.

2

The mean square error given refers to the analysis by subjects.

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The results of Experiment 1 show that it takes longer to categorize instances of fruits when they are to be discriminated from vegetables than when they are to be discriminated from weapons. These results replicate Snodgrass and McCullough's (1986) finding that a visually similar context delays picturecategorization time with respect to a visually dissimilar context. However, our data show that word and picture stimuli present an analogous asymmetry between the visually similar and the visually dissimilar context, subjects' responses being slower in the former context. So, the manipulated variable (i.e., visual similarity) affects not only picture categorization but also word categorization, and we are faced with the fact that the visual similarity of objects apparently influences speed of responses to their names. This constitutes strong evidence against Snodgrass and McCullough's model because their model posits the locus of the effect at an early perceptual stage of processing, and at this level the only effect of visual similarity among words that might play a role would be orthographic similarity. Some comments are needed before discussing our results more fully. First, although these data are consistent with those reported by Snodgrass and McCullough (1986) in their Experiment 2, showing a 159-ms slow-down in responses to words in the visually dissimilar condition in comparison with the visually similar condition, they are in marked contrast to the 1-ms difference found in their Experiment 1. Second, because subjects were tested with both words and pictures, the effect for words may be due to a carry-over effect from pictures. The data from Snodgrass and McCullough's study— whose design was replicated here as closely as possible—seems to rule out such an explanation. However, the possibility remains that our subjects were influenced by the presentation of both pictures and words. For these reasons, and to test for the generality of the visual similarity effect for words, we conducted Experiment 2 using only word stimuli. Another aim in conducting Experiment 2 was to detect possible relatedness effects with verbal material in a condition in which subjects had to make a positive response to accept an element (i.e., when subjects had to decide whether an item was a member of a given category). This was considered important because the studies, cited earlier, that have shown a semantic relatedness effect in categorization have manipulated such a factor only for no responses (i.e., when subjects had to decide that an item was not a member of some category). In Experiment 2 the same relevant variables of Experiment 1 (i.e., degree of structural similarity and degree of category membership) were manipulated, but only word stimuli were used. In addition to the three categories used in Experiment 1, three new categories were selected: birds, mammals, and vehicles. For these categories, the visually similar condition consisted of the categories of birds and mammals, and the visually dissimilar condition comprised the categories of birds and vehicles.

Experiment 2 Method Subjects and design. Subjects were 24 undergraduates at the University of Padova, Padova, Italy. They were tested in a 2 x 2

completely within-subjects design, the independent variables being the degree of visual similarity of the superordinate categories (fruitsvegetables/birds-mammals vs. fruits-weapons/birds-vehicles) and the degree of typicality of elements (central vs. peripheral). Materials, apparatus, and procedure. The stimuli were 192 words selected from six categories. Half of the stimuli selected from each category were central exemplars, and the remaining half were peripheral exemplars (Job, 1981). The items are listed in Appendix B. To avoid presenting subjects twice with the items of the target category, we created two lists of equal length randomly assigning the selected elements to each list with the constraint of an equal number of central and peripheral elements in each list. Half of the subjects received List 1 of the target and nontarget categories in the visually similar condition and List 2 of the target and nontarget categories in the visually dissimilar condition. For the other half of the subjects, the reverse was true. Practice-word stimuli were presented as in Experiment 1. Subjects were seated at a table in a sound-attenuated room separate from the experimenter. We projected the stimuli sequentially on the screen of an Apple personal computer and subtended a visual angle of 0.46° per letter horizontally by 0.57° vertically. The appearance of each stimulus was preceded by an acoustic signal. The stimulus remained in view until the subject responded or for a maximum of 2 s. The onset of the stimulus started a timer that was stopped by pressing one of two response keys. We recorded the RTs in milliseconds automatically by the computer.

Results and Discussion We trimmed RTs by discarding responses greater than 3 standard deviations from the mean for that subject and condition. Mean correct RTs and percentage of error rates for the four conditions are reported in Table 2. Error rates were consistent with the RT data, ruling out a speed-accuracy trade-off. The correct RT data were analyzed with two 2 x 2 withinsubjects ANOVAs, one for fruits and one for birds. In each ANOVA the factors considered were degree of structural similarity (structural similarity vs. structural dissimilarity) and degree of typicality of elements (central vs. peripheral). Because we presented subjects with List 1 in two of the four experimental conditions and List 2 in the remaining conditions, supersubjects and superitems were created. Each supersubject consisted of two subjects, and each superitem consisted of two items. For the fruits category, there was an effect of both structural similarity, F'min (1, 23) = 36.46, MSC = 2,803.07, and degree of typicality, F'mm (1, 19) = 8.50, MSe = 929.10. Moreover, the interaction between the two factors reached significance, Table 2 Mean Reaction Times (RTs) in Milliseconds and Percent Errors (%) for Experiment 2 Fruits List Visually dissimilar Central Peripheral Visually similar Central Peripheral

Birds

RT

%

RT

%

546 581

2.60 6.25

593 615

4.68 6.77

638 729

5.20 15.62

654 671

9.89 10.41

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PICTURE CATEGORIZATION

F'min (1, 22) = 5.78 MSe = 387.75. The pattern of results reflects the fact that responses were faster in the visually dissimilar condition than in the visually similar condition (563 ms vs. 684 ms) and were faster to central elements than to peripheral elements (592 ms vs. 655 ms). This latter effect was more pronounced in the similar condition (91 ms) than in the dissimilar condition (35 ms). For the birds category, only the factor of structural similarity proved to be significant, F'min (1, 14) = 4.87, MSt = 7,430.02. Responses were faster in the visually dissimilar condition than in the visually similar condition (604 ms vs. 663 ms). No other effect was observed in either the subject analysis or the item analysis. The main result of Experiment 2 is the obtained asymmetry between RTs in the visually similar and dissimilar conditions. As in Experiment 1, an increase of categorization time is obtained for word stimuli in the visually dissimilar condition when compared with the visually similar condition. The size of the effect is comparable to that obtained for words in Experiment 1 but again smaller than that found for pictures. For fruits, but not for birds, there is an interaction between degree of visual similarity and degree of category membership. So, the data replicate quite closely the general pattern found in Experiment 1 and suggest that words also are affected by the variable of structural similarity. Conclusion The pattern of results obtained in Experiment 1 and in Experiment 2 is only partially congruent with the predictions derived from Snodgrass and McCullough (1986). On the one hand, there is a tendency for pictures presented in a visually dissimilar condition to be categorized faster than the words presented in the corresponding condition; also, pictures are categorized more slowly when presented in a visually similar condition than when presented in a visually dissimilar condition. This is exactly what should be expected on the basis of the Snodgrass and McCullough model, and it can be interpreted as being due to a fast picture categorization process based on perceptual cues in the case of dissimilar categories and to a slower process based on access to semantic information in the case of similar categories. On the other hand, there are two aspects of our data that are less predictable from the Snodgrass and McCullough (1986) model. First, the categorization of words is slower in the visually similar than in the visually dissimilar condition. Snodgrass and McCullough's model is clearly inadequate to explain this pattern of results. This may be due to a confounding between visual similarity and semantic relatedness present in their study and in our study. That is to say, although it is true that fruits and vegetables (and birds and mammals) are visually similar categories, it is also true that they are semantically related, both at the level of possible taxonomies of living things and at the level of pragmatic and encyclopedic knowledge. So, what makes fruits and vegetables similar to each other and dissimilar from weapons (and, ceteris paribus, the relationship holding among birds, mammals, and vehicles) might be more their functions in the world than their appearance.3 Thus, the reported effect would be an occurrence of

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semantic relatedness effects. As stated previously, these effects have been reported repeatedly in the literature and have been shown to affect both picture and word categorization. In this perspective, the observed increase in response times in the so called visually similar condition may reflect an increasing difficulty in discriminating between exemplars of two semantically related categories. As Flores d'Arcais and Schreuder (1987) have shown, many common semantic properties— mainly perceptual attributes called P elements—are activated on presentation of exemplars of related categories, and few, if any, shared semantic elements are activated for exemplars of unrelated categories. Given the absence of overlap between activated P elements in the latter categories, their categorization should be less difficult than that of the former. Alternatively, the increase in response time in the so-called visually similar condition may be interpreted as arising from an inhibitory mechanism operating to block access to related categories (cf. Hinton & Anderson, 1981). A similar explanation has been put forward by Kroll and Sholl (in press) to account for the within-category inhibition with word stimuli reported by Kroll and Stewart (1991). In Kroll and Stewart's study fluent bilingual subjects were asked to translate, either from List 1 to List 2 or from List 2 to List 1, lists of words that were semantically categorized or randomly mixed. The results showed that for categorized lists there was an increase in the time to translate from List 1 to List 2, which is assumed to be conceptually mediated, but not in the time to translate from List 2 to List 1, which is thought to be lexically mediated. Such an increase was interpreted as due to the fact that multiple access to conceptual memory may inhibit selection of a single lexical entry. As for our pattern of results, it might be assumed that the activation of the representation corresponding to an exemplar of a related category (e.g., vegetables) would temporarily inhibit access to the exemplars of the category fruits, but the activation of the representation corresponding to an exemplar of an unrelated category (e.g., weapons) would have no effects on access to the category fruits. As a consequence, an increase in response time should be observed in the former but not in the latter case. The second piece of evidence incongruent with the Snodgrass and McCullough (1986) model concerns the effects of the degree of category membership. As can be recalled, on the assumption that central elements of a category share more visual features than do peripheral elements (Rosch & Mervis, 1975), it was hypothesized that a perceptual strategy would be particularly apt with the former elements. Therefore, it was expected that the increase in response time to pictures in the visually similar condition would have been more marked for central elements than for peripheral elements. In fact, central elements seem to be only those that benefit less from the manipulation of structural similarity, because it is peripheral elements that show a larger (by about 30 ms) increase in the visually similar condition. The fact that the same trend is

3

Of course, perceptual attributes of objects might, and sometimes certainly are, part of their semantic representation. The issue here is whether perceptual information is sufficient for categorization to occur (i.e., without access to other semantic information).

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also apparent with word stimuli definitely rules out that a visual strategy is being used by our subjects. Afinalissue we would like to touch on concerns the increase in response time in the similarity condition, which is systematically larger for pictures than for words. It might then be the case that in our experiments both semantic factors and visual factors affect response time and that the two factors are additive. This proposal needs empirical support before it is accepted; however, some evidence for the conjoint effects of semantic and perceptual factors may be found in the results reported by Guenther and Klatzky (1977), Vitkovitch and Humphreys (1991), and Walls and Siple (1987). In addition, the proposal seems to allow for effects of visual factors not at an early visual processing stage, as claimed by Snodgrass and McCullough (1986), but at a level of representation directly linked to the semantic representation (i.e., at the level of the structural description; e.g., Jackendoff, 1987; Marr, 1982: Riddoch, Humphreys, Coltheart. & Funnell, 1988). In summary, we have presented data that show longer categorization time to pictures and words in a condition in which the elements to be categorized are members of two visually and semantically related categories in comparison with a condition in which they are members of two visually and semantically nonrelated categories. The results for pictures were predicted on the basis of Snodgrass and McCullough's (1986) model of categorization, but the results for words cannot be directly accounted for by such a model. An alternative account based either on semantic relatedness alone or on the combined effects of visual similarity and semantic relatedness has been proposed.

della lingua italiana [Category membership ratings for 547 concepts of the Italian language]. Report n. 50 dell'Istituto di Psicologia dellVniversita di Padova. Padova, Italy: CLEUP. Job, R., & Sartori, G. (Eds.). (1988). The cognitive neuropsychology of visual and semantic processing [Special Issue]. Cognitive Neuropsychology, 1(5). Kroll, J. F., & Sholl, A. (in press). Lexical and conceptual memory in fluent and nonfluent bilinguals. In R. Harris (Ed.), Cognitive processes in bilinguals. Amsterdam: Elsevier. Kroll, J. F., & Stewart, E. (1991). Category inference in translation and picture naming: Evidence for asymmetric connections between bilingual language representations. Unpublished manuscript. Marr, D. (1982). Vision. New York: Freeman. McCloskey, M, & Glucksberg, S. (1979). Decision processes in verifying category membership statements implications for models of semantic memory. Cognitive Psychology, 11, 1-37. Paivio, A. (1978). The relationship between verbal and perceptual codes. In E. Carterette & M. Friedman (Eds.), Handbook ofperception (Vol. 8, pp. 375-397). San Diego, CA: Academic Press. Pellegrino, J. W., Rosinski, R. R., Chiesi, H. L., & Siegel, A. (1977). Picture-word differences in decision latency: An analysis of single and dual memory models. Memory & Cognition, 5, 383-396. Potter, M. C, & Faulconer, B. A. (1975). Time to understand pictures and words. Nature, 253, 437-438. Pylyshyn, Z. W. (1973). What the mind's eye tells the mind's brain: A critique of mental imagery. Psychological Bulletin, 80, 1-24. Riddoch, M. J., Humphreys, G. W., Coltheart, M., & Funnell, E. (1988). Semantic systems or system? Neuropsychological evidence reexamined. Cognitive Neuropsychology, 5, 3-25. Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior, 12, 1-20. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104, 192-233. Rosch, E., & Mervis, C. B. (1975). Family resemblances. Studies in References the internal structure of categories. Cognitive Psychology, 3, 573605. Bortolini, U.. Tagliavini, C, & Zampolli. A. (1974). Lessico di Rosch, E., Mervis, C. B., Gray, W., Johnson, D., & Boyes-Braem, P. Frequenza della Lingua Italiana [Frequency norms for Italian]. (1976). Basic objects in natural categories. Cognitive Psychology, Milan: Garzanti. 8, 382-439. Clark. H. H. (1973). The language as afixed-effectfallacy. A critique Smith, E. E., Balzano, G. J., & Walker, J. (1978). Nominal, percepof language statistics in psychological research. Journal of Verbal tual, and semantic codes in picture categorization. In J. W. Cotton Learning and Verbal Behavior, 12, 335-359. & R. L. Klatzky (Eds.), Semantic factors in cognition (pp. 137Flores d'Arcais, G. B., & Schreuder. R. (1987). Semantic activation 168). Hillsdale, NJ: Erlbaum. during object naming. Psychological Research, 49, 153-159. Snodgrass, J. G. (Ed.). (1984). Concept and their surface representaGuenther, R. K., & Klatzky, R. L. (1977). Semantic classification of tion. Journal of Verbal Learning and Verbal Behavior, 23, 1-113. pictures and words. Journal of Experimental Psychology: Human Snodgrass, J. G., & McCullough, B. (1986). The role of visual simiLearning and Memory, 3, 498-514. larity in picture categorization. Journal of Experimental PsycholHinton. G. E., & Anderson, J. A. (1981 )• Parallel models ofassociative ogy: Learning, Memory, and Cognition, 12, 147-154. memory. Hillsdale. NJ: Erlbaum. Vitkovitch, M., & Humphreys, G. W. (1991). Perseverant responding Humphreys, G. H., Riddoch, M. J.. & Quinlan, P. T. (1988). Cascade in speeded naming in pictures: It's in the links. Journal of Experiprocesses in picture identification. Cognitive Neuropsychologv, 5, mental Psychology: Learning, Memory, and Cognition, 17, 66467-103. 680. Jackendoff, R. (1987). On beyond zebra: The relation of linguistic Walls, W. F., & Siple, P. (1987, November). Similarity effects on and visual information. Cognition, 26, 89-114. semantic activation by pictures and words. Paper presented at the Job, R. (1981). Giudizi di appartenenza categoriale per 547 concetti annual meeting of the Psychonomic Society, Seattle, WA.

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Appendix A Italian Names and English Translations of Stimuli of Experiment 1

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Fruits ananas arancia banana ciliegia fragola mela pera pesca

Central pineapple orange banana cherry strawberry apple pear peach

Peripheral peanut arachide chestnut castagna raspberry lampone lemon limone almond mandorla melon melone walnut noce currant ribes Vegetables

Central asparagus asparago carciofo artichoke carota carrot onion cipolla lattuga lettuce peperone pepper pomodoro tomato sedan o celery

aglio broccoli cetriolo fungo mais porro rapa zucca

Peripheral garlic broccoli cucumber mushroom corn leek turnip pumpkin

Weapons

Peripheral axe ascia bastone stick chain catena sling fionda forbici scissor whip frusta club mazza mortar mortaio

Central bomb bomba cannone gun fucile rifle lance lancia machine gun mitraglia pistola revolver pugnale dagger spada sword

Appendix B

Italian Names and English Translations of Stimuli of Experiment 2 Fruits

Peripheral

Central List 1 ananas anguria arancia banana fico mandarino mela susina

pineapple watermelon orange banana fig tangerine apple plum

dattero limone mandorla mango melograno melone mirtillo ribes (Appendix B continues on next page)

date lemon almond mango pomegranate melon bilberry currant

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Appendix B {continued) Fruits {continued)

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Central List 2 albicocca cachi ciliegia fragola pera pesca prugna uva

Peripheral

apricot khaki cherry strawberry pear peach prune grapes

avocado castagna cedro lampone mora nespola noce tamarindo

avocado chestnut citron raspberry blackberry medlar walnut tamarind

Vegetables Central List 1 carciofo cardo cavolo cicoria cipolla peperone rapanello spinaci List 2 asparago carota fagiolini lattuga patata piselli pomodoro sedano

Peripheral

artichoke thistle cabbage chicory onion pepper radish spinach

broccoli capperi fagioli fave funghi orzo porro salvia

broccoli capers beans broad beans mushrooms barley leek sage

asparagus carrot runner bean lettuce potato peas tomato celery

aglio barbabietola ceci cetriolo crauti grano rape zucca

garlic beet chick-peas cucumber sauerkraut wheat turnips pumpkin

Weapons Central List 1 baionetta bayonet bomba bomb fucile rifle manganello cudgel mitraglia machine gun pistola pistol pugnale dagger spada sword List2 cannone cannon carabina carbine coltello knife lancia lance missile missile rivoltella revolver sciabola sword stiletto dagger

Peripheral accetta bastone catena fionda freccia frustino mazza picozza

hatchet club chain sling arrow whip stick ice ax

ascia forbid frombola frusta giavellotto martello mortaio rasoio

ax scissor sling whip javelin hammer mortar razor

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Appendix B (continued) Birds

Peripheral

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Central List 1 canarino cardellino cuculo gabbiano gufo merlo passero picchio List 2 aquila colomba condor corvo fringuello rondine tordo usignolo

canary goldfinch cuckoo sea gull owl

blackbird sparrow woodpecker eagle pigeon condor crow chaffinch swallow thrush nightingale

airone anatra cicogna fagiano nibbio pellicano pernice tucano

heron duck stork pheasant kite pelican partridge toucan

avvoltoio cigno faraona

vulture swan guinea fowl goose peacock penguin ostrich turkey

oca

pavone pinguino struzzo tacchino Mammals

Peripheral

Central List 1 capra cavallo gatto giraffa leone lepre pecora topo List 2 agnello asino cane elefante leopardo mucca scimmia tigre

goat horse cat

giraffe lion hare sheep mouse lamb donkey dog

elephant leopard cow

monkey tiger

canguro cammello gattopardo iena marmotta stambecco toro volpe

kangaroo camel serval hyena marmot ibex bull

capriolo cervo giaguaro ippopotamo pantera scoiattolo talpa zebra

roe

fox

stag jaguar hippopotamus panther squirrel mole zebra

Vehicles

Peripheral

Central List 1 aeroplano bicicletta camion elicottero furgone macchina motocicletta taxi

airplane bicycle truck helicopter van car

motorcycle taxi

barca canoa dirigibile panfilo pattini trattore vagone zattera (Appendix B continues on next page)

boat canoe dirigible yacht skates tractor wagon raft

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Appendix B (continued) Vehicles (continued)

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

Central List 2 ambulanza autobus automobile jeep motorino nave tram treno

Peripheral

ambulance bus car jeep moped ship tram train

canotto carriola funivia motoscafo razzo slitta sottomarino triciclo

canoe wheelbarrow cableway boat rocket sledge submarine tricycle Received December 5, 1991 Revision received March 9, 1992 Accepted March 10, 1992 i

Search Opens for Editor of New APA Journal The Publications and Communications Board has opened nominations for the editorship of a new journal, Journal of Experimental Psychology: Applied, for the years 1995-2000. Candidates must be members of APA and should be prepared to start receiving manuscripts early in 1994 to prepare for issues published in 1995. Please note that the P&C Board encourages more participation by members of underrepresented groups in the publication process and would particularly welcome such nominees. To nominate candidates, prepare a statement of one page or less in support of each candidate. Submit nominations to

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The Journal of Experimental Psychology: Applied will publish original empirical investigations in experimental psychology that bridge practically oriented problems and psychological theory. The journal also will publish research aimed at developing and testing of models of cognitive processing or behavior in applied situations, including laboratory and field settings. Review articles will be considered for publication if they contribute significantly to important topics within applied experimental psychology. Areas of interest include applications of perception, attention, decision making, reasoning, information processing, learning, and performance. Settings may be industrial (such as human-computer interface design), academic (such as intelligent computer-aided instruction), or consumer oriented (such as applications of text comprehension theory to the development or evaluation of product instructions). First review of nominations will begin December 15, 1992.

The picture superiority effect in categorization: visual or semantic?

Two experiments are reported whose aim was to replicate and generalize the results presented by Snodgrass and McCullough (1986) on the effect of visua...
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