Content Differences for Abstract and Concrete Concepts. Katja Wiemer-Hastings & Xu Xu. Northern Illinois University, DeKalb

Transcription

1 Content Differences 1 Content Differences for Abstract and Concrete Concepts Katja Wiemer-Hastings & Xu Xu Northern Illinois University, DeKalb Running head: Abstract and Concrete Concepts Please address correspondence to: Katja Wiemer-Hastings Department of Psychology Northern Illinois University DeKalb, IL Phone: Fax: katja@niu.edu

2 Content Differences 2 Abstract The content of 36 abstract and concrete noun concepts was explored and compared in a feature generation task with 31 participants. Abstract concepts had significantly fewer entity properties, more properties expressing subjective experiences, and overall less specific features. Situation properties generated for abstract and concrete concepts differed in kind, but not in number. Abstract concepts were predominantly related to social aspects of situations, including agents. Abstractness emerges as a function of types and specificity of conceptual components. Systematic relations of conceptual content were revealed with context availability and imageability. The implications of the findings are discussed with respect to cognitive processes involving abstract concepts. Content Differences for Abstract and Concrete Concepts Abstract words, like indecision, difference, or consideration, are abundant in daily conversation. We use such words to describe, interpret, and explain events or circumstances in our social and physical environment. Abstract concepts contain a variety of categories each of which has sparked its own domain of research, including personality traits, behaviors and social cognition, emotions, cognitive processes, and events. As such, there is considerable interest in abstract concepts across disciplines within and beyond psychology. In spite of this, very little is known about their representation. Abstract entities are not experienced directly: they are not spatially constrained entities that we can see, or touch, or interact with. Instead, abstract concepts represent complex entities such as behaviors, scenes, or subjective experiences. Thus, they are more aptly described as constructs of the mind which represent and structure experiences. The instances represented by an abstract concept vary considerably. This is reflected in ratings of contextual variety which tend to be higher for abstract concepts (Galbraith & Underwood, 1973). For example, difference can refer to sensory units, such as the result of adding a bit of salt to a soup or a height difference, or to mental units such as two opinions. At first glance, abstract (e.g., difference) and concrete entities (e.g., bucket) are easily distinguished. However, researchers have struggled to outline exactly in what respect they differ. The obvious difference is that only the concrete things are physical entities with defined by spatial boundaries. This ontological difference has important implications. We experience abstract and concrete things as qualitatively different they seem to belong to different realms. As salient as this distinction may be, however, it mostly reveals what abstract concepts lack (i.e., physical substance, spatial boundaries). This leaves open the interesting question what abstract concepts do represent. The distinction of physical and nonphysical concepts is unsatisfying in a second regard: there are graded differences in concreteness. For example, people perceive scientist to be more abstract than milkbottle, and notion as more abstract than climate. Physicality, being a dichotomous variable, cannot easily explain the more subtle differences. What makes concrete concepts vary in concreteness? These questions motivate the research presented here. We explored the content of abstract versus concrete concepts by systematically analyzing participantgenerated features, to identify differences at a componential level. The analysis focused on the kinds of features that characterize people s knowledge of concrete versus abstract concepts, the quantity of these features, and their relations to perceived concreteness and associated variables, such as imagery. Since a majority of concept processing models are based on the notion of conceptual components, this analysis is an important step in advancing our understanding of abstract concepts. Variables Associated with Concreteness Differences between abstract and concrete concepts have been studied in detail in form of concreteness effects on different kinds of processes. Generally, abstract concept processing is more challenging than concrete concept processing. Concreteness effects have been reported in a variety of tasks, including studies of learning, memory retrieval, comprehension, lexical decision, translation and semantic deficits. Imageability and context availability. The most influential theories that have been put forward to explain concreteness effects are the dual-coding theory and context availability theory. Both make distinct assumptions about storage of and access to abstract and concrete concepts in memory. Put in very simple terms, the dual-code theory assumes that concreteness effects are due to abstract concepts lacking a perceptual representation (Paivio, 1971; 1986), whereas the context-availability theory attributes concreteness effects to more relevant information stored in memory for concrete concepts, which facilitates their processing (Bransford & Johnson, 1972; Kieras, 1978). Ratings of

3 imageability and context availability tend to be highly correlated with concreteness (Paivio, 1986; Rubin, 1980; Schwanenflugel, Harnishfeger, & Stowe, 1988). However, the foundations for imagery and the nature of context effects in memory are not well understood. The basis for both is likely linked to conceptual content. The present analysis may thus shed light on why abstract concepts evoke less or no imagery, and why less information may be stored in memory for abstract concepts. A conceptual content analysis may also be able to resolve the interesting recent finding that concreteness and imageability, and concreteness and context availability are not consistently correlated for the entire range of concreteness (Altarriba, Bauer, & Benvenuto, 1999; Wiemer-Hastings, Krug, & Xu, 2001). While high correlations were found for word samples that span the entire concreteness range, the correlation patterns differed when calculated separately for concrete versus abstract words. In particular, imageability ratings were correlated with concreteness ratings only for abstract words (N=155; r=0.57, p<0.05), but not for concrete words (N=100; r=0.02) (Altarriba, Bauer, & Benvenuto, 1999). In contrast, the association between rated context availability and concreteness was much stronger for concrete words (r=0.68, p<0.05) than for abstract ones (r=0.25, p<0.05). Thus, context availability increases strongly with concreteness of physical items, but only little with concreteness of nonmaterial items. In a similar study with 18 abstract and 18 concrete items, we replicated this pattern for context availability (Wiemer-Hastings, Krug, & Xu, 2001). However, in this sample, neither ratings for imageability (r=0.33) nor ratings for context availability (r=0.17) were significantly associated with concreteness of abstract concepts. Both were significantly correlated with concreteness of physical items (imageability: r=0.48, p<0.05; context availability: r=0.58, p<0.05). The findings suggest a qualitative difference between abstract and concrete concepts, which coincides with the dichotomous physicality dimension. This difference may be the main contributor to the strong correlations that are observed for samples spanning both concrete and abstract items. Further, it seems that the main factor underlying ratings of imageability and context availability is predominately varying among concrete concepts, and that imageability ratings may further be influenced by a separate factor that predominately varies for abstract concepts. These hypotheses can be tested through correlations between these measures Content Differences 3 and measures based on the features generated for abstract and concrete concepts. Ease of predication. Jones (1985) suggested in the context of dyslexia that imageability effects may be mediated by a semantic factor which he calls ease of predication, which is the ease with which individuals can access predicates of a concept. He found that rated imageability and ease of predication were highly correlated (r=0.88). However, this account is also unsatisfactory as an explanation for concreteness effects, because it is unclear how people rate ease of predication. It is possible that people base their ratings of ease of predication on the ease with which they can access an image (a related argument is made by de Mornay Davies & Funnell, 2000). Jones instructed his participants to ignore other factors than ease of predication in their ratings, but imagery may be an automatic process that mediates the participant ratings without their knowledge. Thus, ease of predication measures, too, require explanation at a fundamental conceptual level. Overview Knowledge of differences in the conceptual content due to varying concreteness could better explain concreteness effects, as well as provide a better understanding of imageability, context availability, ease of predication, and other variables related to concreteness. Further, a componential analysis of abstract concepts may allow for more sophisticated predictions in research involving such concepts, including encoding and recall, semantic relations, semantic access in priming studies, and categorical organization. What types of information may be involved in the representation of abstract entities like a thought or a goal? While our research is largely exploratory, we are building on a few earlier studies from which we have derived a few hypotheses regarding the content of abstract concepts. Psychological situations and action schemata. A large majority of abstract concepts are agent-centered; that is, they are strongly related to a person or group. This is true of large categories of abstract concepts, including emotions, cognition, actions and interaction, communication, character traits and attitudes, and others. Such abstract concepts would likely be associated with aspects of the internal and external context of the agent, such as behaviors, mental processes, another person, and states of affairs. The extent to which agents and agents contextual properties are part of the content of abstract concepts has not yet been subject of

4 systematic investigation. However, related analyses suggest that they likely are. For example, Hampton (1981) investigated the prototypicality structure of abstract concepts using a property generation task. Many of the generated properties describe a social situation involving an agent and agent-related aspects of the situation. Hampton suggests that abstract concepts would commonly involve behaviors, agent characteristics such as goals, and other aspects of a situation, consistent with our reasoning. Similarly, researchers on personality traits have proposed agents, agent characteristics, and agent experiences as abstract concept components (Chaplin, John, & Goldberg, 1988). Research on psychological situations has revealed quite similar components as the properties of situation prototypes, such as agents, their behaviors, and dispositions, as well as physical attributes such as states, and objects (Cantor, Mischel, & Schwartz, 1982). On the basis of these studies, we predicted that descriptions of abstract concepts would show a strong focus on an agent and agent-related properties, including cognitive and emotional states and behaviors. Subjective experiences. There is evidence that abstract concepts are linked closely to subjective experiences that are only accessible through introspection. Introspective features have recently been proposed as a necessary component of abstract item representation (Barsalou, 1999). For example, a concept like decision cannot be understood without having experienced the mental process of weighing two or more choices against each other in terms of their good and bad sides. A large number of abstract concepts involve such elements of subjective experience, which may be mental, emotional, or even physiological in nature. Recent data also show that such components directly affect the perceived concreteness of an item. The larger the proportion of an item s introspective elements is (i.e., goals, interpretations, evaluations, or emotions), the higher people judge its concreteness (Wiemer-Hastings, Krug, & Xu, 2001). Based on this finding, we predict that people will list significantly more properties for abstract concepts that express subjective experiences of an event or a situation. Such a finding would further support the finding that concreteness is in part a function of introspective components, and it would indeed be stronger because the measure for the proportion of introspective content would be based on properties generated by the participants, whereas in the above study, it was based on the experimenters coding schema for abstract concepts. Content Differences 4 Context variability. Abstract words occur in a larger variety of contexts, just as abstract entities can occur in a large variety of situations (Galbraith & Underwood, 1973). Contextual variability may be a factor underlying context availability differences for abstract and concrete concepts. Context may be less accessible in memory because the large number contexts stored with an abstract concept are competing for activation, or because contexts need to be actively generated during concept processing. Since abstract concepts represent instances from a larger variety of contexts, their features may place low constraint on context generation. A likely consequence of contextual variability is that abstract concepts involve components that are relatively unspecified, i.e., features that act like slots in a script or schema (Minsky, 1975; Schank & Abelson, 1977) that allow for a variety of specific attributes. For example, the restaurant script contains a slot for an agent bringing the menu to the table, for the type of food served in the restaurant, etc. Abstract concepts resemble scripts in that they refer to actions, goals, and agents at a general level. Thus, we expected that abstract concepts would be characterized by unspecific features. For example, an intuitive analysis suggests that difference occurs in contexts that contain any two or more items that can be compared on some dimension. At the extreme, perhaps some abstract concepts can be described as content-free schemata (Fiske & Taylor, 1991) in that they only specify abstract components and relations between them. Distinct versus graded differences. Abstract versus concrete items are typically defined dichotomously, as physical and non-physical items. Concreteness ratings are consistent with this view only to some extent. The consistent finding is that concreteness ratings tend to form a clearly bimodal distribution (Nelson & Schreiber, 1992; Wiemer- Hastings, Krug, & Xu, 2001) with each mode centered in one of the two halves of the concreteness scale. This suggests a qualitative difference in representation which may be associated with distinct content elements that form part exclusively of either concrete or abstract concepts. This is in line with the finding that imageability and context availability correlate to different extents with concreteness ratings for abstract versus concrete concepts (Altarriba, Bauer, & Benvenuto, 1999). On the other hand, there are many degrees of concreteness, which suggests that there are additional concept characteristics that vary gradually across sections of the concreteness range. Thus, we expected to find semantic components which were exclusively named

5 for abstract or concrete items, as well as components which varied gradually with concreteness. Participants in our experiments received a property generation task for 36 nouns spanning the entire scale of concreteness. Property generation tasks have proved useful in concept research. Perhaps the best known example of this is the prototype research by Rosch and colleagues (e.g., Rosch & Mervis, 1975). Our focus of interest was on proportions of different knowledge domains in the content of abstract and concrete concepts, and whether these varied in a graded fashion, or whether there were realms of experience exclusively applicable to just either concrete or abstract concepts. While participant-generated features unlikely represent exact conceptual content, they should accurately reflect these types of knowledge and systematic differences in knowledge domains across the concreteness dimension. Features provide insight into only one aspect of representation. We acknowledge that by focusing on features, we are ignoring more complex aspects such as relations among features, or organizations of features according to individuals assumptions about entities, which play an important role for concept processing. However, our hope is that providing this groundwork will facilitate more complex research of this kind. Method Participants Thirty-one undergraduate students at Northern Illinois University participated in this experiment for course credit. All participants were native speakers of English. Materials We constructed a random set of thirty-six nouns, varying in concreteness across the concreteness scale. Words were sampled from an initial sample of 1993 nouns retrieved from the MRC2 database (Coltheart, 1981; Wilson, 1988). The database provided for all of these nouns familiarity ratings and concreteness ratings from a variety of norms. The concreteness scale was divided into six equidistant subsections to include words of all levels of concreteness. From each section, six nouns were sampled randomly but matched in familiarity across the concreteness levels. The complete list is shown in Appendix A. Our sample consists of items representing a variety of categories, including plants (tree), animals (mackerel), nonliving things (beehive), substances (venom), actions (removal), emotions (happiness), social processes Content Differences 5 (emancipation), states (inaction), temporal concepts (day), communication concepts (story), and so on. The word sample was deliberately chosen at random across the concreteness range because our concern was with general trends of feature components as a function of concreteness, rather than types of concepts. A systematic comparison of instances of each of these categories would likely reveal further differences. Design Three sets of 12 items were constructed of the 36 words. Each set contained two words from each concreteness level, matched in familiarity. Each set was presented to a participant with one of three tasks. One task asked participants to generate features for each item. Another task asked participants to generate context elements that always occur with the given item. This second task was included to address the argument that abstract concepts are more strongly anchored in context information, whereas concrete concepts have a component that is independent of context. The two instructions allowed us to compare, between subjects, the number and kinds of features generated for these two conditions. To the extent that context is a natural part of a concept s content, the instructions should not have a significant effect on the resulting properties. A third task was a response time measure for a related variable which we will not report here. Each word set was presented as first, second or third word set equally often, and was presented equally often with each of the tasks. Finally, task order was also counterbalanced. Within each set, words were presented in different random orders. The design thus included, as independent variables, word type (with six concreteness levels) and instruction (item versus context feature), both varied within participants. Analyses averaged effects across the word sets. The smaller sets of twelve items each were formed so that the task was doable in a reasonable amount of time. Ten participants generated features for each item in each condition. Procedure Data were collected in individual sessions. The experimenter read the instructions aloud. Instructions included examples ranging in concreteness that were not used in the study. Participants were given a practice trial with three words: one abstract, one concrete, and one intermediate word. After clarifying questions, participants started on the 12 words for this block. A standard list of near-synonyms for unknown target

6 words was provided to participants to not bias their answers by describing the items. All descriptions were tape-recorded with the participants consent. The experiment took between 20 and 40 minutes to complete. Participants worked on the two tasks in succession, with twelve of the items presented in each. One task asked participants to list properties for each item. Part of the instructions was repeated before every new item to remind participants of the main task. The intent was to reduce carry-over of processing strategies such as exhaustive scene descriptions to subsequent items. The instructions for the item feature generation task read as follows: Please describe as exhaustively as you can what aspects characterize this object: (item). The instructions for contextual features were: Please describe as exhaustively as you can aspects of a situation that MUST be true for this object to occur in it: (item). Results Analysis of Transcripts The recorded data were transcribed manually and then coded for types of knowledge. Only twenty-six of the transcripts were used in the analyses. The data from five participants were unusable for a variety of reasons, including misunderstanding of the instructions, experimenter error in the presented items, and most commonly, too many item omissions to include a transcript without biasing the results. Omissions were not necessarily due to unfamiliarity with words; a few participants struggled with descriptions of the more abstract items (e.g., aspect and exception). Coding of transcripts. The transcripts were parsed into feature units, which consisted for the most part of individual words. For a systematic evaluation and comparison of conceptual content, all features were coded using a coding schema that was developed based on feature occurrences in previous data analyses (Wu & Barsalou, 2004). The Wu and Barsalou coding schema was developed for coding features of concrete objects. This coding schema was chosen for two reasons. First, it allows coding of features for both concept types because it covers several domains of concept knowledge: item properties, situation properties, and introspective properties, which are aspects of a person s subjective experience related to an item. As such, the schema covers all knowledge domains which were hypothesized to be of relevance for abstract concepts. Second, this coding schema has been successfully Content Differences 6 applied in previous studies involving feature representations and has been shown to cover relevant aspects of conceptual knowledge (e.g., McRae & Cree, 2002; Cree & McRae, 2003). Entity properties describe the item s characteristics such as parts, appearance, material, and characteristic behaviors. These properties cover the internal structure of a concept, as opposed to knowledge of associated items, actions and the like. For example, a tree s item properties include roots, branches and leaves, green and brown colors, wood, growing, producing oxygen, etc. Situation properties cover knowledge of the context in which an item is used. This includes animate beings, actions, physical and social states, functions, locations, and other aspects. Situation properties generated for tree typically include animals such as birds and squirrels; water and soil; actions such as climbing or felling a tree; and functions such as producing furniture, making fire, or offering shade. Finally, introspective properties are produced when participants access personal experiences with an object. This can include emotional or evaluative responses, negation, representational states and more complex features such as contingencies and causal relations. Some of these properties are procedural in that they organize the feature descriptions (e.g., if then, or not ). Others are more informative by themselves, especially emotions and representations. For tree, example experiential features may include evaluations such as valuable or beautiful. Strictly speaking, only entity properties are actual features of a concrete item. The other domains express knowledge surrounding the use and typical occurrence of an item. For example, when an agent is mentioned in describing a basketball (e.g., kids throw them through a basket), then kids are not a feature of the basketball. However, features such as situational and introspective ones indicate that a participant accesses knowledge about how the item is typically used, rather than just information confined to the item itself. This coding schema also accommodates taxonomic properties, such as exemplars, category membership, coordinates, synonyms, and the like. By listing features falling into this domain, participants describe an item s characteristics by positioning it in relation to other items. Such statements reflect predicative knowledge about items, but they do not specifically reflect an item s content, but rather how it is organized in semantic memory. For example, listing joy, a synonym, for happiness does not reveal any of its content, but only shows that the participant correctly understands the target.

7 Another indication for taxonomic properties not strictly expressing conceptual content is that many of the properties that could be classified as taxonomic could be double-classified in one of the other domains. For example, when generating fork for the target knife, fork is both a coordinate in the same category, and it is also an object that is found in the same context (thus, a situation property). Because of this, we coded features as item, situation or experience properties wherever possible. The items that were coded as taxonomic were analyzed separately. Slightly more taxonomic properties were mentioned for abstract items than for concrete items, but this difference was only marginally significant in a subject analysis (F 1 (1, 25)=4.21, MSE=0.19, p=0.051). Individual analyses of the taxonomic properties reveal that this is mostly due to more coordinate terms and synonyms being listed for abstract items. The feature lists for all words were coded by one of the authors. Only one person used the coding schema so that the codes would be used consistently. Beforehand, a subset of features were coded by both authors to discuss the use of the schema categories and to establish clear coding guidelines. Many features were coded by assigning a code to individual words; in other cases, a code was applied to a proposition. Items belonging to a proposition were marked with a \, and only the last word belonging to that feature was coded using the feature code. Examples for one coded concrete and one coded abstract item are shown in Appendices B and C, respectively. Feature summary scores. We evaluated differences between abstract and concrete conceptual content based on type and token measures for the broader knowledge domains (i.e., object vs. situation vs. experience features) and for the subcategories within the situation domain. Types refer to the different feature categories mentioned for a word; tokens refer to the number of instances that a particular feature category is mentioned for a word. Thus, the type scores show how many kinds of features were used for a word, but not how often. This score is useful in evaluating the complexity with which participants flesh out the features related to an item in each of the domains. For example, a situation type score of 1 would indicate that people on average only refer to one type of situation property (e.g., an action), whereas scores of 3 and higher suggest that a scenario is described (e.g., a person, an action, and an object). The token score reflects more accurately the proportion of features generated for a word that fall Content Differences 7 into the different categories. Variation in these scores can be quite informative. For example, people might list fewer parts for a less concrete object, or a situation for pity is likely to involve two persons, so that the person code would justly apply twice. Tokens did not count an item more than once when it was evident that it referred to an identical element; e.g., an object, it, it may all refer to the same object and were not counted as three occurrences of a situation object, but as one. The type scores depict more schematic, ontological characteristics of the items. Category scores summed up the number of its subcategories used to code the feature list for a word. Category type scores counted just the number of different subcategories mentioned, whereas category token scores also counted individual instances for each. Likewise, the subcategory token score counted how often each subcategory was used for a given word and participant, whereas the subcategory type only counted whether or not a subcategory was used, resulting in scores of either 1 or 0. Category Type Analysis The mean number of situation, entity and introspective features are listed in Table 1. These scores are absolute scores, meaning that they do not present the proportions of features mentioned overall that fall into each category. Proportion scores do not make much sense here because the tokens were not counted, and thus the type measures do not accurately reflect the actual proportion of features generated for each domain. A weighed type score was calculated additionally which divided all scores by the number of subcategories in their respective knowledge domain. As can be seen from Table 1, the scores in the situation domain are much larger (if not weighed) than those in the other two domains. In fact, participants used altogether 16 types of situation properties, 11 types of entity properties, and only 6 types of experience-related properties. The weighed scores presented in Table 1 indicate the proportion of the kinds of features in a domain that were, on average, presented by the features generated by a participant in the different instruction conditions. Analyses were performed on the original type scores. How did abstract and concrete concepts differ with respect to the kinds of features generated? Domain differences were confined to entity and experience-related properties. There were no concreteness effects on situation properties. Instructions to generate relevant context features (as

8 opposed to item features) increased the number of different kinds of situation properties only marginally (subject analysis only: F 1 (1,25)=3.51, MSE=1.66, p=0.07). Thus, only few participants used additional situation properties to the ones used to describe item features when their direction was focused on the context. This suggests that participants access roughly the same number of different types of situation knowledge for abstract and concrete concepts. Whether these types are the same for concrete and abstract concepts is not evident from this analysis; we will address this question below. Further, the data suggest that shifting participants focus from items and their features to features of the context does not appear to increase the number of different kinds of situation knowledge. Again, this does not show whether participants use the same types of situation properties in each case, or whether they switch them. It does show, though, that the complexity of situation properties that are generated does not increase. As expected, significant differences were observed between abstract and concrete concepts both in the number of types of entity and experiential features. First, participants generated significantly more kinds of entity properties for concrete than for abstract concepts (F 1 (1,25)=27.67, MSE=0.36, p<0.001; F 2 (1,34)=21.31, MSE=1.16, p<0.001). Instructions to generate item features increased the number of kinds of entity properties, but only significantly for concrete items: a significant interaction was found for item concreteness and instruction, F 1 (1,25)=22.90, MSE=0.33, p<0.001; F 2 (1,34)=8.97, MSE=0.51, p< Thus, in general, when participants focus on an object s context instead of on the object itself, they appropriately list fewer item properties. At the same time, they still make significantly more mention of entity properties when asked to describe the context, suggesting that the concrete target object may dominate in the process of activating situation knowledge. This difference is critical for our understanding of abstract items, since entity properties are the only internal features of items. The data show that for abstract items, predominantly situational features are mentioned, suggesting that these concepts are firmly entrenched and not separable from events and states in a situation and subjective experiences associated with these. This is of course consistent with the typical definition of abstract items as nonmaterial entities. In fact, it is surprising that any entity properties are listed for abstract concepts at all. A closer look at the kinds of Content Differences 8 entity properties shows that such properties were only listed for items of the somewhat abstract group. For many of these, it may be argued that the features coded as entity properties may be coded as situation properties more appropriately. These include parts listed for story and saga such as beginning, climax and ending, plot, etc., all of which could alternatively be considered social artifacts. Clearly, these are as little material as the targets themselves. Possession, too, yielded some entity properties such as my initials on the bottom, where possession was treated as a specific object. A reverse pattern of differences was obtained for experience-related features. Participants listed significantly more different introspective features for abstract than for concrete items (F 1 (1,25)=76.99, MSE=0.24, p<0.001; F 2 (1,34)=20.09, MSE=0.64, p<0.001). This difference, too, was robust across instructions. Interestingly, the proportion of experience-related features was increased significantly when participants were instructed to generate context features (F 1 (1,25)=4.72, MSE=0.48, p<0.05; F 2 (1,34)=10.58, MSE=0.17, p<0.005). Thus, as participants focus on an item s context, they appear to access more differentiated information about mental processes and states, such as goals, emotions and so on. An average of one experience-related property was generated for each abstract target. This suggests that subjective experience is a regular aspect of such concepts, rather than knowledge that is occasionally activated when processing the concept. We also looked through the codes to examine more closely what kinds of experience-related properties were generated by participants for concrete items. Such properties were listed predominantly for a few specific targets, most notably, for labyrinth and prize. A mental state like confusion was listed quite regularly for labyrinth; likewise, emotions were frequently mentioned for prize. About half of the features coded as introspective were more procedural in nature, indicating feature contingencies and negated features. Entity and experiential properties across the concreteness scale. Do different knowledge domains apply dichotomously either to abstract or to concrete concepts, as suggested by simplified definitions along the physical / not physical dimension, or are there gradual differences that may account for the large variation in perceived concreteness? In previous work, it was found that particularly the proportion of introspective (or experiential) properties is significantly correlated with rated concreteness of abstract items, suggesting that abstract items vary

9 gradually on this dimension. Similarly, an object with many internal features may be perceived as more concrete. Thus, we may expect to see an increase in the proportion of experience features within the abstract target sample as items get more abstract, and an increase in the proportion of entity features for concrete items as these get more concrete. We observed before that there are items that seem to be somewhat concrete and somewhat abstract. It may be that there is a grey range in the middle of the scale where items would be described by a mix of features that are typically part of abstract or concrete items. We plotted the proportions of entity and introspective features across the six concreteness levels for easier comparison. They are displayed in Figure 1. The Figure does not suggest a dichotomous break in the types of features predominantly activated for abstract versus concrete concepts. Rather, it seems that only concepts at the two most concrete and the two most abstract levels have clearly distinctive conceptual content. Very concrete concepts contain a large amount of item properties and evoke few if any experiential properties. In contrast, experiential features are regularly part of very abstract concepts, for which few if any concrete item properties are produced. The relation between the number of properties (tokens) expressing knowledge about entities (r=0.74) and experience (r=-0.69) to concreteness ratings was significant in a correlation analysis (p<0.01 for both). That is, the more concrete a concept is, the more entity properties and the fewer experience properties are generated. The concepts rated as somewhat concrete and somewhat abstract have a lower proportion of both entity and experiential features. By exclusion, this means that their conceptual content may be largely dominated by situation properties. Further, the amount of experiential and entity features is almost balanced for these concepts: they contain a few concrete properties, but also knowledge that is experiential in nature. Subcategory type analysis for situation properties. The analyses at the category level did not reveal a difference in the number of different situation properties generated for abstract versus concrete concepts. This could indicate that situation properties do not distinguish between abstract and concrete concepts. On the other hand, differences may be hidden at the finer level of subcategories. To test for this possibility, we examined whether there were specific subcategories within the situation knowledge domain that would be mentioned Content Differences 9 significantly more often for one of the two concept types. For abstract concepts, we expected to see frequent mention of a person, actions and events, and social categories. In contrast, we expected that more concrete situation elements such as objects, buildings, locations, physical states and the like would be more central to concrete concepts. Table 2 shows the type scores obtained for concrete and abstract concepts for the situation subcategories. All these scores vary between 0 and 1. They indicate whether, on average, a feature of each given subcategory was generated. Seven out of the 14 categories listed (two categories were not included in this analysis: quantity, because it is not readily interpretable, and manner because of no occurrences) revealed significant differences in the extent to which the feature was mentioned for abstract versus concrete concepts, across items and participants. For the most part, the patterns are consistent with our expectations. In particular, abstract concepts are clearly centered around a person most of the time, whereas concrete concepts only mention an agent a third of the time (F 1 (1,25)=37.03, MSE=0.11, p<0.001; F 2 (1,34)=14.13, MSE=0.29, p<0.01). In terms of the scores, actions and a person are the most frequently mentioned situation properties for abstract concepts (M=0.84, averaged across instructions). This means that on average, participants mentioned an agent and an action for 8 out of 10 abstract items. Actions were also mentioned considerably more often for abstract than for concrete concepts, but this difference only approached significance in the subject analysis (p=0.11 or p=0.07 for item and subject analysis, respectively). Less frequently used properties mentioned more often for abstract concepts were social states (M=0.23) and social artifacts (M=0.16). Social states are mentioned close to never for concrete concepts (M=0.02; F 1 (1,25)=46.23, MSE=0.03, p<0.001; F 2 (1,34)=11.63, MSE=0.07, p<0.005), neither were social artifacts (M=0.02; F 1 (1,25)=25.24, MSE=0.02, p<0.001; F 2 (1,34)=6.52, MSE=0.05, p<0.05). Thus, social concepts can be considered quite distinctly associated with the contents of abstract concepts. All these situation elements are consistent with suggestions made in previous research (e.g., Hampton, 1981) on the content of abstract concepts, such as agents, behaviors, and social aspects. For concrete concepts, the most frequently mentioned situation properties that were mentioned significantly more often than for abstract concepts were objects (F 1 (1,25)=29.56, MSE=0.09, p<0.001; F 2 (1,34)=5.09, MSE=0.33, p<0.05), location (F 1 (1,25)=16.19, MSE=0.05, p<0.001; F 2 (1,34)=4.92,

10 MSE=0.14, p<0.05), living things (F 1 (1,25)=56.81, MSE=0.08, p<0.001; F 2 (1,34)=10.27, MSE=0.24, p<0.005), and a function (F 1 (1,25)=12.10, MSE=0.02, p<0.005; F 2 (1,34)=4.05, MSE=0.05, p=0.05). Thus, out of the 14 situation properties, seven are mentioned significantly more often for either abstract or concrete items. Three of the remaining items, actions, social organizations, and spatial relations approached significance. Buildings were mentioned so rarely that no differences emerged. The analysis at the level of individual properties shows that while both abstract and concrete concepts are described in terms of situations, they seem to activate aspects of context that are to some extent distinct. If we were to summarize these, the best description for abstract item specific situation properties would be socially relevant aspects (agents, behavior, social states, social artifacts), whereas those for concrete items would be item predicates (e.g., function and location) and co-occurring concrete items (living things, objects). These separate aspects reflect the ontologically different domains of things material and nonmaterial. The data suggest that abstract and concrete conceptual content does not converge on the level of situation knowledge, but that each are associated with (or contain) different aspects of situations. Category Token Analysis The patterns for types were repeated for tokens. As a reminder, tokens measure the overall number of features generated for an item in a domain, regardless of the number of different kinds of properties. Different features for a type of property are counted separately in this analysis; so, for example, if a participant lists eight different parts for an object, then the token for parts will be 8. The average number of tokens generated within each knowledge domain, entity, situation, and experience, are summarized in Table 3. Consistent with the results for types, there was no effect of the concreteness level on the number of situation properties. Instructions to describe the context increased this count. This difference was significant only in the subject analysis (F 1 (1.25)=4.32, MSE=2.30, p<0.05). As before, there were main effects for concreteness on the number of entity and experiential features generated. Participants listed significantly more entity properties for concrete items regardless of instruction (F 1 (1,25)=72.13, MSE=0.64, p<0.001; F 2 (1,34)=24.08, MSE=1.28, p<0.001). A main effect Content Differences 10 was also observed for instruction, but as the data clearly show, this was due to a large change only for the concrete items. The interaction was significant, with more entity properties listed for concrete than abstract items, and more for concrete items that were listed under feature instructions (F 1 (1,25)=20.90, MSE+0.34, p<0.001; F 2 (1,34)=7.82, MSE=0.56, p<0.01). So, when asked to list features that are true of an abstract concept, participants list on average one entity property for every fifth item, whereas they list an average of two per concrete item. For experiential features, we once again observed the reverse pattern. Abstract concepts evoked significantly more knowledge involving subjective experiences than concrete concepts across both conditions (F 1 (1.25)=69.72, MSE=0.34, p<0.001; F 2 (1,34)=22.15, MSE=0.77, p<0.001). Furthermore, instructions to focus on context increased the number of experiential properties for both abstract and concrete concepts. This effect was significant only in the item analysis (F 2 (1,34)=9.09, MSE=0.19, p<0.01), the subject analysis revealed a marginal effect, p=0.08. Subcategory token analysis for situation properties. The token scores showed stronger differentiation for situation properties of abstract versus concrete concepts, particularly in the subject analyses. Items were analyzed in a mixed ANOVA with item type (abstract vs. concrete) as betweenitem and instruction (features vs. context) as withinitem factor. The subject analyses were a withinsubject analysis with item type (abstract vs. concrete) and instruction (features vs. context) as repeated measures. Table 4 shows the mean number of tokens obtained for each situation property, separate by item type and instruction. The first five situation properties listed in the table were all mentioned significantly more frequently for abstract than for concrete targets, including person (F 1 1,23)=29.12, MSE=0.37, p<0.001; F 2 (1,34)=14.82, MSE=0.64, p<0.001), action (F 1 (1,23)=5.36, MSE=0.22, p<0.05; F 2 marginal effect, p=0.08), social state (F 1 (1,23)=38.56, MSE=0.03, p<0.001; F 2 (1,34)=9.79, MSE=0.09, p<0.01), and social artifacts (F 1 (1,23)=19.95, MSE=0.02, p<0.001; F 2 (1,34)=6.39, MSE=0.06, p<0.05). These results are consistent with the type analysis except for actions. These were mentioned more repeatedly for abstract concepts, whereas its type score had not differed as a function of concreteness. Concreteness effects were observed for six further situation properties, which were mentioned significantly more often for concrete concepts. These were object (F 1 (1,23)=12.54, MSE=0.12, p<0.005; F 2

11 only marginal, p=0.11), location (F 1 (1,23)=15.34, MSE=0.07, p<0.005; F 2 (1,34)=4.42, MSE=0.16, p<0.05), living things (F 1 (1,23)=35.86, MSE=0.10, p<0.001; F 2 (1,34)=9.88, MSE=0.29, p<0.01), time (F 1 (1,23)=26.09, MSE=0.02, p<0.001; F 2 not significant), function (F 1 (1,23)=10.08, MSE=0.03, p<0.01; F 2 marginal, p<0.06, and physical state (F 1 (1,23)=6.24, MSE=0.04, p<0.05; F 2 not significant). There was also a marginal effect for spatial relations in the subject analysis (p=0.06). Time and physical state scores were only significantly higher in the token analysis. Given that abstract concepts are more temporal in nature, since they include processes and events, it is somewhat surprising that time was mentioned more often for concrete concepts. We explain this effect by the effects of random sampling of our word materials, which put two time concepts (day, daybreak) into the low-concrete sample. Especially day triggered a lot of time features such as week, 24 hours, etc. Meanwhile, temporal aspects could also be expressed differently, such as in the mention of actions, events, or emotions. Overall, the differences in the use of situation properties in the description of abstract versus concrete concepts were more pronounced in the token analysis, involving more kinds of situation properties. Again, the data show that situation knowledge features strongly in both types of concepts, but that there are aspects of situations that are predominantly relevant for either abstract or concrete concepts. In fact, the situation properties for which no significant differences were observed had token scores close to zero (with the exception of events). It is quite possible that in a larger sample, some of these would also be used differently. A small number of instructional effects were observed. When participants were instructed to list features of the relevant context rather than of the target itself, they mentioned a person significantly more often (F 1 (1,23)=29.12, MSE=0.37, p<0.001; F 2 (1,34)=4.80, MSE=0.19, p<0.05), and social organizations less often (F 1 (1,23)=9.78, MSE=0.05, p<0.01; F 2 (1,34)=6.03, MSE=0.01, p<0.05). For social organizations, the interaction approached significance in an item analysis (p=0.10); tokens were mostly high for abstract concepts when asked to generate item features. Living things, events, objects and physical state were marginally affected by instructions (with p values between 0.06 and 0.10); all of them tended to be generated more often when describing context. Closer analysis shows that for all but events, the increase in token scores happens predominantly for concrete concepts. Content Differences 11 The analyses so far have shown that abstract and concrete concepts both contain knowledge about the situations in which they occur, and that entity properties are predominantly part of concrete concepts, whereas experiential properties are mostly found in abstract concepts. Situational and experiential features are rarely considered parts of a concept s internal structure. Our analysis suggests that abstract concepts (at least those rated as highly abstract) have a weak internal structure (cf. Gentner, 1981). That is, in place of the internal structure that is common to concrete concepts, abstract concepts are combinations of specific situation properties combined with subjective experiences. As such, they are more akin to abstract schemata for scenes and events than to concrete concepts. Features versus Associative Knowledge One possible confound in our data is that participants may have generated features that are not really part of the target s conceptual representation. In particular, participants may first list some features, and then start to give examples, which could result in the high number of situation properties. The first features mentioned may be used as cues by participants to mention related knowledge, which may be more related to the first features rather than to the target itself (comparable issues have been discussed for associative sets, cf. Nelson & Schreiber, 1992). Since our participants could end their feature lists anytime, we were not strongly concerned about this, however, quite a few participants generated very lengthy lists for some targets. To estimate whether such processes affected our data, we therefore repeated the token analysis just for the first five coded properties listed for each target. We expected that if situational knowledge is mostly activated as an afterthought through associations, then their counts may be lowered in this analysis, and entity (and perhaps experiential) features may emerge as more central sources of conceptual knowledge. The analysis was performed only on features generated under feature instructions since these are supposed to yield the actual conceptual knowledge. Table 5 shows the resulting average numbers of features. Overall, the numbers resemble the results from the full analysis. However, the differences between abstract and concrete items appear somewhat stronger in these first features. There are close to no entity properties listed for abstract concepts, and close to no experiential features listed for concrete concepts. Both of these were highly significant in both item and subject analyses (Entity: