Draft Only: Comments Welcome
Address correspondence to:
Dr. Robert Goldstone Lawrence W. Barsalou
Psychology Department Psychology Department
Indiana University University of Chicago
Bloomington, IN. 47405 5848 S. University Ave.
firstname.lastname@example.org Chicago, IL. 60637
Work in philosophy and psychology has argued for a dissociation
between perceptually-based similarity and higher-level rules in
conceptual thought. Although such a dissociation may be justified
at times, our goal is to illustrate ways in which conceptual processing
is grounded in perception, both for perceptual similarity and
abstract rules. We discuss the advantages, power, and influences
of perceptually-based representations. First, many of the properties
associated with amodal symbol systems (e.g. productivity and generativity)
can be achieved with perceptually-based systems as well. Second,
relatively raw perceptual representations are powerful because
they can implicitly represent properties in an analog fashion.
Third, perception naturally provides impressions of overall similarity,
exactly the type of similarity useful for establishing many common
categories. Fourth, perceptual similarity is not static but becomes
tuned over time to conceptual demands. Fifth, the original motivation
or basis for sophisticated cognition is often less sophisticated
perceptual similarity. Sixth, perceptual simulation occurs even
in conceptual tasks that have no explicit perceptual demands.
Parallels between perceptual and conceptual processes suggest
that many mechanisms typically associated with abstract thought
are also present in perception, and that perceptual processes
provide useful mechanisms that may be coopted by abstract thought.
Reflecting on the sophistication of human thought, we can be impressed with how far we've come, or how we got here. The first perspective emphasizes the distance between starting and final states, whereas the second emphasizes continuity. In adopting the second perspective, we will be arguing that conceptual thought is grounded in perceptual similarity. The organization of concepts is derived from perceptual similarities between objects. Certainly, many concepts are partially organized around perceptual similarities (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976), but we will argue further that perceptual processes guide the construction of abstract rules even when this direct link may not be so obvious. Completely modality-free concepts are rarely, if ever, used even when representing abstract contents. In short, concepts usually stem from perception, and active vestiges of these perceptual origins exist for the vast majority of concepts.
Although we certainly do not wish to deny the use of abstract rules in concept use, our position is that abstract conceptual knowledge depends on perceptual similarities, both in its development and active use. Our use of "similarity" will be tied explicitly to information available from perception. If one were worried about the limited information available perceptually, one might try to "salvage" similarity by freeing it from its tether of perception, and by allowing similarity to reflect conceptual, abstract commonalities. Similarity, thus freed, would be able to account for abstract rules. Ditches in a road and lapses of logic in an argument would both be members of the concept holes because of their similarity on the abstract feature "lack of substance surrounded by substance" (Casati & Varzi, 1993). However, similarity thus freed would have dramatically reduced explanatory value as a solid ground for our concepts because of its complexity and pliability (Goodman, 1972; Rips, 1989).
Instead, we will keep similarity tethered to perception but will argue that this restraint does not doom similarity to irrelevance for concepts. Although similarity judgments certainly depend on highly abstract and strategic factors (Gentner & Markman, 1995; Markman & Gentner, 1993; Medin, Goldstone, & Markman, 1995), we assume that perceptual representations are still fundamentally involved and that they have more force in determining conceptual structures, including rules, than might be thought. Perception's usefulness in grounding concepts comes from several sources. First, perception provides a wealth of information to guide conceptualization. Second, perceptual processes themselves can change as a result of concept development and use. Third, many of the constraints manifested by our perceptual systems are also found in our conceptual systems.
The Allure of the Perception/Conception Distinction
An impressive lineage of theorists has drawn a distinction between perceptual and conceptual systems (for a historical review, see Arnheim, 1969). The Pythagoreans saw a fundamental separation of the "heaven" of conceptual abstractions and the "earth" of perceptual experience. Plato believed in "generic forms" that could not be found by induction across perceptual aspects of specific instances. Parmenides was perhaps the first philosopher to distinguish reasoning from perceiving, on the basis of perceptual illusions (e.g. a stick dipped in water appearing bent) that must be overcome by the powers of rationality (Kirk & Raven, 1962).
This Greek tradition has continued into the present day, with the notion that perceptual similarities must be, and at least sometimes are, cast aside when creating categories. Quine (1977) considers it a sign of an advanced science if its theoretical concepts are not based on perceptual qualities. Evidence suggests that scientific category construction incorporates increasingly deep, abstract properties, as opposed to perceptual properties, with increasing expertise (Chi, Feltovich, & Glaser, 1981). Similarly, part of the notion of the recent "theory" theory of concepts is that concepts are not organized around clusters of perceptual properties, but rather around organized systems of knowledge (Medin, 1989; Murphy & Medin, 1985; Murphy & Spalding, 1995). Developmental support for this hypothesis indicates that even young children have inchoate theories about concepts that allow them to disregard perceptual similarities. Children can group animals by their names (Gelman, 1988; Gelman & Markman, 1986), hidden internal structure (Carey, 1985), or genetic heritage (Keil, 1989) in manners that conflict with perceptual similarity. In adults, categorization judgments can be dissociated from similarity judgments such that X is judged more similar to Y than to Z, but is still placed in Z''s category rather than Y''s (Kroska & Goldstone, in press; Rips, 1989, 1993).
Finally, there have been several recent efforts to separate similarity-based from rule-based systems. Whereas similarity-based systems establish concepts on the basis of perceptual similarities between a concept's instances, rule-based systems form concepts on the basis of explicit symbolic expressions. Shanks and St. John (1994) and Sloman (in press) review evidence for such a distinction. Evidence for the use of rules comes from occasions when categorization drastically and suddenly changes when a simple instruction is provided, or when superficial similarities can be completely ignored when they are inconsistent with an explicit instruction. According to Sloman, evidence for the existence of both rule- and similarity-based reasoning exists in the form of simultaneous, conflicting judgments in a task, due to contradictory evidence from the separate systems. One hallmark of rules that divorces them from perceptual information is precisely their generality and universal applicability regardless of domain. In discussing criterial evidence for rule use (as opposed to similarity), Smith, Langston, and Nisbett (1992) argue that when people use rules, they are as accurate with unfamiliar as with familiar material, and with abstract information as with concrete information. All of these researchers believe that they find evidence for the use of such rules in human reasoning.
In short, a wealth of evidence and intuition suggests that superficial perceptual similarity does not always determine categories. According to many philosophers and psychologists, abstract, rule-based reasoning is often at odds with perceptual data, and must be marshaled in order to counter the misleading influences of superficial percepts. Still, returning to the Greeks (Kirk & Raven, 1962), Democritus, speaking on behalf of the senses, chastises abstract reasoning thusly: "Wretched mind, do you, who get your evidence from us, still try to overthrow us?"
Like Democritus, we believe that our conceptual structures develop from our perceptual processes, and continue to bear vestiges of this legacy. At one level, this is an uncontroversial statement, given that many of our concepts are clearly characterized by perceptual properties. Perceptual properties are often good indicators of important, category-defining properties, and our perceptual systems may have evolved so that they would establish useful categories (Medin & Ortony, 1989). Objects that belong to psychologically important categories often have similar shapes (Rosch et al, 1976), and our perceptual systems offer a tremendous amount of data that is probably underestimated by the use of overly sparse experimental materials (Jones & Smith, 1993).
One could take many possible approaches to reuniting perception with conception. We explore two here: the eliminative view and the agnostic view. According to the eliminative view, perceptual representatations constitute all knowledge. Human knowledge contains no non-perceptual representations. According to the agnostic view, human knowledge has major perceptual components and may or may not also contain non-perceptual components. One of us has developed the eliminative view elsewhere (Barsalou, 1993, 1996; Barsalou & Prinz, in press; Barsalou, Yeh, Luka, Olseth, Mix, & Wu, 1993; Prinz & Barsalou, in press). Thus, we do not pursue this view here, except to summarize it briefly as a boundary case in the realm of possibilities. Instead, we focus on the agnostic view that perceptual representations are central to conceptual knowledge, without making a commitment to whether non-perceptual representations exist as well. In making this argument, we address both similarity and rules, demonstrating that each has important perceptual origins.
The Eliminative View
Prior to the twentieth century, theories of mind typically assumed that human knowledge is inherently perceptual. Not only did the British Empiricists believe this, but so did most other theorists of mind thereafter, including philosophers such as Kant (1787/1965) and Russell (1919/1956). In the early twentieth century, ordinary language philosophy and behaviorism attempted to expunge mental states and mechanisms from theories of mind. As part of their strategy to elimininate mentalism, they frequently criticized mental images. When the cognitive revolution occurred fifty years later, theorists were reluctant to view the cognitive mechanisms that they readopted as inherently perceptual. Although theorists once again became comfortable with cognitive mechanisms, they remained wary of mental images. Furthermore, the proliferation of formal languages and the computer metaphor made possible new ways of thinking about knowledge in non-perceptual formats. Whereas earlier approaches assumed that aspects of perceptual states become stored in memory to form concepts, these new approaches assumed that perceptual states are transduced into amodal symbols. Much like the words of a language, amodal symbols are assumed to bear arbitrary relations to perceptions and to their referents in the world. Attractive properties of this approach included the abiliies to form propositional representations of the world productively, to represent abstract as well as concrete concepts, and to implement these languages on computer hardware.
Early critics in the twentieth century often construed the perceptual view in limited and overly simplistic manners, a tradition many modern critics have continued. For example, critics often construe the perceptual approach as containing images that are only conscious, that are only drawn from sensory states, and that are only holistic. Rejection of the perceptual view is typically based on this formulation of the position. Actually, many other possible formulations are possible, some of which were the theories that the British Empiricists actually proposed. For example, Locke's (1690/1959) theory assumed that images in knowledge could come from internal cognitive states, not only from sensations of the external world. He also argued that images could be analytic and productive, not holistic and unproductive. More recently, a wide variety of researchers across the cognitive sciences, especially in cognitive linguistics, have proposed increasingly sophisticated and powerful theories of perceptually-based knowledge.
As an example of these more modern views, consider perceptual symbol systems (Barsalou, 1996; Barsalou & Prinz, in press; Prinz & Barsalou, in press; Prinz, in preparation; for earlier formulations, see Barsalou, 1993; Barsalou et al., 1993). This theory's first assumption is that perceptual representations are not necessarily conscious images but are unconscious states of perceptual systems specified neurally. For example, the representation of a chair might be specified as a configuration of neurons active in the visual system rather than as a consious mental image. Second, a perceptual representation is not necessarily holistic. Instead, it can be a schematic aspect of a perceptual state extracted with selective attention and stored in long-term memory. For example, selective attention might focus on the form of an object, storing only its shape in memory and not its color, texture, position, size, and so forth. This schematic extraction process not only operates on sensory states, it also operates on internal mental events, extracting aspects of representational states, cognitive operations, and emotions. Once these schematic perceptual representations become established in memory, they can function as symbols. They can refer to entities in the world, they can combine productively using combinatoric and recursive mechanisms, and they can implement propositional construals of situations. Furthermore, they can represent abstract concepts such as truth, negation, and disjunction, by capitalizing on perceptual symbols for internal mental events and simulated external events.
One purpose of developing this theory is to establish an existence proof that a completely perceptual approach is sufficient for establishing a fully functional symbolic system. Because this theory can implement reference, productivity, propositions, and abstract concepts, it appears comparable to amodal symbol systems in expressive power. As a result, amodal symbols could be eliminated because they are not necessary. Of course, many sorts of evidence must be considered to determine whether eliminativism is justified, but, again, the primary purpose of this theory is to serve as an existence proof that one can develop a fully functional symbolic system that is inherently perceptual.
If one were to push for the eliminative view, additional sources of evidence could be brought to bear, besides the argument that perceptual symbol systems have sufficient expressive power. First, the amodal view suffers serious problems. These include lack of accounts for how amodal symbols become transduced from perceptual states, and, conversely, for how reference from amodal symbols to perceptual states is established (Harnad 1987; Searle, 1980). Furthermore, there is no direct empirical evidence that symbols are inherently amodal. Instead, the primary evidence for amodal symbols is indirect, namely, systems having sufficient expressive power can be constructed from amodal symbols. Finally, the amodal view is too powerful. It can explain virtually any finding post hoc, yet fails to predict many perceptually-based phenomena a priori or provide insight into them. In contrast, the perceptual symbols view does not suffer from these problems. It provides natural accounts of how conceptual symbols are linked to perceptual states, and there is considerable evidence that conceptual symbols are perceptual (as we review shortly). Furthermore, the perceptual view is falisfiable, it provides a priori predictions for observed perceptual phenomena, and it provides insights into them.
This, then, is the flavor of the eliminative position. Clearly, theories of perceptual symbols and the evidence for them remain to be developed considerably in many regards. At this time, this approach primarily provides an existence proof that, in principle, perception and conception can be united in a way that does not require amodal symbols.
The Agnostic View
A more moderate approach to reuniting perception and conception is to propose that perceptual information plays a major role in conceptual knowledge, which may or may not also include amodal symbols. Our primary purpose is to convince the reader of this and only this point. In the remaining sections, we review a large diversity of empirical phenomena that implicate perception in conception. One might wish to follow these phenomena to the stronger eliminativist conclusion, but we will not go that route here. Instead, we will remain content to conclude that conceptual processing at all levels reflects perceptual mechanisms in unexpected ways.
The distance between percepts and many of concepts may seem insurmountable. However, many properties of percepion prove very useful, in fact irreplaceable, in constructing concepts. In the remaining sections, we demonstrate how concepts rely on implicit information in perceptual representations, how perceptually-based holistic similarity plays important roles in cognition, how learned perceptual similarities become conceptual biases, how various abstractions (including rules) originate in perception, how perceptual simulation can underlie conceptual processing, and how various perceptual mechanisms enter into higher cognition.
Freeloading with Analogical Systems
Systems that reflect perceptual similarities in their conceptual structures have a major advantage over those that only incorporate amodal representations-the same advantage that analog systems have over digital systems. Perceptual and analogical representations, because they preserve aspects of the external object in a relatively raw form, can represent certain aspects of the represented object without explicit machinery to do so (Palmer, 1978). For example, to decide that a particular couch belongs to the category things that will fit through the front doorway, a good strategy is to manipulate an analog representation of the couch's shape in reference to an analog representation of the doorway. If shape and relative size information is preserved in one's representation, then one can be confident that conclusions drawn from mental manipulations will be applicable to the real-world couch.
The alternative to computing using perceptually-based representations is to reason from symbolic representations that either completely remove perceptual information or begin with a symbolic representation. One prominent example of the latter approach is Lenat's CYC project, an attempt to build common sense reasoning into a computer by having "knowledge engineers" input symbol-level knowledge (Lenat & Guha, 1994). The main problem with this approach is that a tremendous number of facts are needed to represent the same information conveyed efficiently by shape. A picture is indeed worth thousand symbols, provided that there are processes (such as rotating, scanning, and zooming) that take advantage of the picture's analog format.
Furthermore, it is surprising how many seemingly abstract properties can be computed by analog devices. For example, many people would assume that if one wished to find the correlation between two variables (the height and weights of a sample of college students), it would be necessary to symbolically represent the variables by numbers, and use mathematical equations to derive a measure of correlation. Dewdney (1985) describes an analog alternative (shown in Figure 1). Partially drive in a nail on a wood surface at each point representing an individual height-weight pair. Fit a single rod approximately into place between the nails, and attach a rubber band to each nail and the rod. When the rod is released, it quickly falls into an equilibrium position such that the angle of the rod represents the best fitting linear regression of height on weight, and total slackness of the rubber bands represents the correlation between height and weight.
Analog representations such as this are often efficient representations, because properties (e.g. correlation, slope, and intercept) "ride for free" within the representation without explicitly being computed. Relatively raw, analog representations are particularly useful when one does not know what properties will be needed at a later point, or explicitly how to compute the needed properties. As such, ironically enough, analogical representations are often times most useful for our more complex concepts-those without simple definitions. Although it is unclear what class of object properties are well handled by analog representations, we suspect that many of the properties useful for categorizing objects (e.g. shape, size, pattern of motion, texture, density, and curvature) fall into this camp. Certainly, other researchers have gotten mileage from analog representations that preserve in a relatively raw form many of the properties (Finke, 1986) and spatial relations (Shepard, 1984) present in visual percepts.
The Primitive Appeal of Overall Similarity
To the extent that concepts can be characterized thoroughly by simple rules or verbal definitions, the role of perceptual similarity in structuring concepts is weakened. If "unmarried male human" adequately captures the concept bachelor, then sophisticated perceptual representations seem unnecessary. Similarity might be said to explain the concept bachelor, because members of the bachelor category are similar in all being unmarried men, but this hardly salvages similarity as useful for explanation. As Goodman (1972) criticizes similarity: "When to the statement that two things are similar we add a specification of the property that they have in common, .. rather than supplementing our initial statement, we render it superfluous" (p. 444-445). That is, if the similarity of concept members that determines categorization is only with respect to particular properties of commonality, why not just dispense with similarity altogether and discuss the common properties instead?
Our reply to this is to deny the premise. Similarity often involves not only single properties, but integration across many properties. Many of the most important disputes in the field of categorization concern exactly how to integrate across several properties when calculating similarity. Some approaches list features of the two objects, and integrate the overlapping features and the distinctive features to determine overall similarity (Tversky, 1977). Other approaches represent objects as points in a multidimensional space and calculate similarity as an inverse function of the points' distances (Ashby, 1992; Nosofsky, 1992). Still other approaches posit that similarity is proportional to the degree to which the parts of the two compared objects can be aligned with one another (Goldstone, 1994a; Markman & Gentner, 1993; Medin, Goldstone, & Gentner, 1993). In all of these approaches, several sources of information (features, dimensions, or parts, respectively), not single criterial properties, are the basis for determining similarity.
Similarity assessments may typically integrate across many properties, because it is natural for people to form impressions of overall similarity. In fact, evidence suggests that in many situations, it is easier for people to base similarity and categorization judgments on more, rather than fewer, properties (Goldstone, 1994b; Kemler, 1983). For example, Sekuler and Abrams (1968) report cases in which people are faster to respond that two displays are identical along all their elements than that two displays have a single common element. Nickerson (1972) reviews evidence in favor of a fast, "sameness detector" that allows people to quickly assess overall similarity between displays before being able to respond to particular dimensions. Individuals whose cognitive judgments are impeded, because they are young (Smith & Kemler, 1978; Kemler, 1983; Smith, 1989), inexperienced (Foard & Kemler, 1984), hurried (Ward, 1983), or distracted (Smith & Kemler Nelson, 1984), seem to rely on "holistic" rather than "analytic" processes. Thus, responding on the basis of overall, undifferentiated similarity may be a more primitive computation than responding on the basis of particular properties.
In a similar vein, Brooks (1978) argued that judging category membership by overall similarity is an often used strategy, particularly when the category members are rich and multi-dimensional and the category rules are complicated. Determining overall similarity across many properties is efficient relative to determining similarity with respect to a particular property when it is difficult to break an object down into separate features or aspects. Such a state of affairs is likely to occur for many real-world objects. Laboratory stimuli often "wear their featural compositions on their sleeves," but natural objects are seldom so obliging (Schyns, Goldstone, & Thibaut, in press; Goldstone & Schyns, 1994).
In other work, Rosch found that the members of "basic level" categories such as chair, trout, bus, apple, saw, and guitar are characterized by high within-category overall similarity (Rosch, 1975; Rosch & Mervis, 1975). Items within a basic-level category tend to have several features in common, in contrast to the members of metaphor-based categories (such as "situations that are 'time bombs' waiting to go off," Glucksberg & Keysar, 1990; Ortony, 1979) or ad-hoc categories ("things to take from a burning house," Barsalou, 1991). Rosch argues that basic-level categories are defined by family resemblance; category members need not all share a definitional feature, but they tend to have several features in common (Rosch & Mervis, 1975; Rosch et al, 1976). Not coincidentally, it is precisely these categories that permit many inductive inferences. If we know that something belongs to the category bird, then we know that it probably has two legs and two eyes, nests, flies, is smaller than a desk, and so on. Basic level categories allow many inductions because their members share similarities across many features. If we want to rapidly learn and deploy these categories, it greatly behooves us to pay attention to overall similarity.
As all of this work illustrates, people have a strong disposition to process overall similarity, and doing so appears to serve a number of important cognitive functions. This is not to say that people do not also process similarity more analytically, a topic to which we turn shortly. Nevertheless, people do appear to process similarity holistically on many occasions. We propose that this basic tendency has its origins in perception, and that perception places important constraints on it, thereby mitigating the Goodman problem of unsconstrained similarity. Overall similarity may arise out of perception, because of the importance of parallel comparison in object categorization. Rapid categorization is possible when multiple perceived properties can be matched in parallel to potentially corresponding properties in category knowledge. If processing were serial, processing would not likely not be as efficient, and survival might be compromised. Thus, overall similarity may have evolved in perception to optimize categorization, having the additional result of producing a comparison mechanism that is used broadly across many other cognitive tasks as well.
Because the overall similarity process has its roots in perception, the features it utilizes are biased towards features that perception makes available. Thus, features like shape, color, size, texture, and position are important, because perceptual systems make them readily available to the overall comparison process. Other features are less likely to be incorporated into overall similarity, because they are not as available. The consequence is that natural constraints are placed on overall similarity, thereby mitigating the problem that it is unconstrained.
Learned Perceptual Similarities
One reason why perceptual similarity is more powerful than might be thought is that it is not inflexible and insensitive to contextual factors. Although similarity certainly affects categorization, there is also an influence, albeit attenuated, in the reciprocal direction. Lassaline (in press) reports that judgments of induction across categories (e.g. if horses have property X, how likely is it that cows do?) have an influence on subsequent similarity assessments involving the same items. Kelly and Keil (1987) find that exposure to metaphors can even influence similarity judgments to different materials. For example, subjects who received the metaphor "The New Yorker is the quiche of newspapers and magazines" gave higher similarity ratings to food-periodical pairs that had similar values on a tastefulness dimension (e.g. steak and Sports Illustrated) than did subjects who were not given these metaphors. In short, impressions of similarity are educated by the more sophisticated tasks that use them.
In the above examples, one could charge that the similarity assessments are influenced by high-level tasks simply because they are quite sophisticated judgments themselves. While similarity ratings do certainly seem sophisticated and cognitively penetrable, effects of categorization have also been found on tasks that tap more perceptually-based similarities. Goldstone (1994c) first trained subjects on one of several categorization conditions in which one physical dimension was relevant and another was irrelevant. Subjects were then transferred to same/different judgments ("Are these two squares physically identical?"). Ability to discriminate between squares in the same/different judgment task, measured by Signal Detection Theory's d', was greater when the squares varied along dimensions that were relevant during categorization training. In one case, experience with categorizing objects actually decreased people's ability to spot subtle perceptual differences between the objects, if the objects belonged to the same category. Along longer time courses, work in categorical perception (Harnad, 1987) indicates that discriminations involving pairs of stimuli that straddle category boundaries are more easily made than are discriminations involving stimuli that fall within the same category, equating for physical dissimilarity between the pairs.
The argument that perceptual similarity is powerful, because it can be tuned to an organisms' needs, is a two-edged sword. Turned around, similarity's critic can argue, "The flexibility of similarity only exposes its inadequacy as a solid ground for explaining cognitive processes." Certainly, similarity's explanatory value is attenuated if it is based on exactly those processes that it attempts to explain (similar arguments are presented by Goodman, 1972; Rips, 1989; Shanon, 1988). However, we believe the impact of this second edge can be somewhat blunted by a metaphor: Perceptual similarity is to higher cognition as mainstream culture is to the artistic avant-garde (Goldstone, 1995a). Both domains have a conservative, slow-moving force (perceptual similarity and the mainstream) and an exploratory vehicle of change (context-dependent cognition and the avant-garde). The avant-garde and task-dependent categorizations (e.g. categorizing a chair as a device for reaching a lightbulb, Barsalou, 1991) explore many avenues that are never followed by their more conservative partners. However, the conservative partners may follow up on a minority of these idiosyncratic directions. That is, if a task-dependent categorization is frequently made, or is particularly promising for its organizing power, then it may eventually change perceptual similarities that are noticed. Experts may eventually come see the objects in their domain of expertise in a different way than novices (Biederman & Shiffrar, 1987; Burns & Shepp, 1988), and perceptual development in children may involve spotting new perceptual commonalities (Schyns et al, 1995).
In general, perceptual similarity, like the mainstream, may change, but with a relatively protracted time course or small range. As such, it can serve as a point of departure for highly context-dependent cognitive process such as goal-driven categorization, metaphorical comparison, and analogical reasoning. Because perception is flexibly tuned, the departure point itself moves, making often traveled points more accessible. Similarities that were once effortfully constructed, become second nature to the organism. In this manner, perceptual similarity can provide a useful starting-off point for specialized cognitive processes-useful because it has been tuned, although perhaps slowly, to the tasks that use it.
We further suggest that the distinction between mainstream and avante-garde similarity is closely related to the associative/rule distinction (Sloman, in press; Smith & Sloman, 1994). In general, we suspect that associative mechanisms are, in general, a large class of relatively automatic processes, and that rule mechanisms are, in general, a large class of relatively controlled processes (Shiffrin & Schneider, 1977). Associative mechanisms tend to be those that process bursts of features made available automatically, either through parallel processing in perception or automatic activation in long-term memory. Rule mechanisms tend to be those that process individual features selectively through serial processing in working memory, using limited attentional resources. Thus, our notions of overall and mainstream similarity fall into the cluster of associative mechanisms, whereas our notion of avante-garde similarity falls into the cluster of rule mechanisms.
Abstractions From Perception
Until now, we have concentrated on the usefulness of perceptual information in conceptual representations. However, the importance of perceptual information in concepts is underestimated if we restrict ourselves to situations where it is represented directly. In this section, we consider situations in which perception less directly, but not necessarily less strongly, 'jump starts' our concepts, by motivating, informing, and providing procedures for abstract thoughts.
The original aircrafts built by the Wright brothers are a far cry from our current airplanes. Airplanes are now differentiated into classes, such as passenger, military, and delivery, and have many new structures, such as bathrooms, in-flight movies, and stewards. This evolution provides an analogy for understanding the development of many abstract concepts. If we focus on the current state of abstract concepts, we risk ignoring the simpler, less sophisticated starting points that were necessary for its development.
Developmental evidence provides several examples of abstractions evolving out of what appear to have been perceptually-based concepts. For example, infants seem to be biased to treat parts of a display that move together as being part of the same object (Spelke, 1990). Once spatially separated parts of a display are joined together in the same object because of their common motion, other properties of objects, such as that they tend to be uniformly colored and possess edges or smoothly varying contours, can be detected. The detection of motion is also instrumental in acquiring the distinction between living things and human-made objects (Gelman, 1990; Mandler, 1992). Not all living things move, and not all human-made objects are static, but Gelman and Mandler suggest that the original inspiration for this latter distinction may be based on movement patterns. In fact, children often treat human-made objects with irregular patterns of motion as being alive. The distinction between living things and human-made objects, in turn, is probably important in developing the even more abstract distinction between natural kinds and artifacts (Keil, 1989). This distinction has significant impact on our use of concepts, determining how extensively we infer properties from one member of a category to others, and whether we believe the categories to be organized around essences. This elevated role in inductive reasoning belies the "lowly," motion-based origin of the natural kind/artifact distinction.
We do not necessarily disagree with theorists who argue that biological categories are organized around "theories" that involve genetic heritage, internal structure, birth, death, and reproduction (Carey, 1985; Keil, 1989; Rips, 1989). For example, Carey's finding that children and adults are more likely to extend an unfamiliar property from humans to worms than from humans to toy monkeys may derive from knowledge concerning internal organs. However, we argue that in many cases, the original inspiration for constructing the theory comes from relatively simple, perceptual cues such as motion and the presence of internal fluids. Moreover, perceptual representations of events may underlie many abstract concepts (Barsalou, 1993, 1996; Barsalou & Prinz, in press). In the spirit of the earlier section on "Freeloading with Perception," perceptual representations of reproductive events, such as mating and giving birth, may provide large amounts of implicit information that underlie biological theories. Without perceptual knowledge of these events, we suspect that people would have little understanding of biological concepts.
The evolution of abstract concepts from perceptual roots has other applications. Pure mathematical concepts are often originally inspired by perceptual evidence. In the development of number concepts, there is strong evidence that children rely on perceptual representations. (Huttenlocher, Jordan, & Levine 1994; Stigler, 1984). Similarly, mathematicians frequently report first creating a visual "proof" of a theorem, and only subsequently derive the symbolic, and publishable, version, written out in theorems and lemmas (Barwise & Etchemendy, 1990, 1991). In problem solving, one of the most effective ways of deriving an abstract schema such as "overcoming an object by converging weak intensity forces from several pathways onto the object" is to use several concrete examples (Gick & Holyoak, 1983). Categorizations that are based on a highly complex abstract rule may initially be solved by using perceptual similarity between items to be categorized and known category members (Brooks, 1978).
Another body of evidence indicates that experimentally noticed perceptual similarities alter more abstract processes. Requiring subjects to perform similarity judgments on pairs of scenes makes subjects more likely to treat the scenes in an abstract manner subsequently (Markman & Gentner, 1993). For example, early similarity judgments promote responses based on common roles ("These two things correspond to each other because they are both donors") rather than on superficial attributes ("these go together because they are both women"). As another example, when people are shown an object to be categorized, they are often reminded of a superficially similar object. Once reminded, they try to come up with an abstract description for the category that encompasses both objects (Ross, Perkins, & Tenpenny, 1990).
In sum, even when concepts eventually come to be characterized by abstractions, these abstractions may be based on perceptual similarity. Two consequences follow: First, conceptual end states do not imply an absence of perceptual origins. Even if the end-state of a concept were purely free of perceptual information, perceptual processing may have been required to build it. The current view stresses the evolution of concepts, and the processes that could achieve the eventually abstract end states. Second, perceptually inspired abstractions can provide a mechanism for developing abstractions not currently within the abstract system's powers. Fodor (1975) has argued that it impossible for a symbolic representation system to learn concepts that have expressive powers not already present in the system. However, the current suggestion is that perceptual similarity can cause the abstract system to entertain hypotheses that it would not otherwise have been able to entertain. New expressive capacities arise when abstractive processes create new uses and descriptions for concepts that have been established perceptually. To borrow an example from evolution, where we have good reasons to think that genuinely new structures and functions arise, lungs probably evolved from swim bladders of fish. The oxygen-respirating function of the swim bladder/lung was only selected for once the balance-preserving function of the swim bladder had already established the swim bladder's basic shape. Likewise, the starting shape of our concepts may be perceptually specified initially, but can be transformed in quite different directions once developed. Later structures (lungs and abstractions) depend on and grow out of earlier structures (swim bladders and perceptually-based concepts) for their very existence even as they acquire radically different functions.
Perceptual Simulation in Conceptual Tasks
Standard conceptual tasks that lack pictorial materials offer a means of assessing how far perception extends into conceptual processing. In the feature listing task, for example, subjects receive the word for a concept and list features typically true of its instances (e.g., list features typically true of "watermelons"). Similarly, in the property verification task, subjects receive the word for a concept and verify whether a second word specifies a property true of the concept (e.g., for "watermelon," is "seeds" a property?). In neither task do subjects recieve pictures, nor are they asked to perform perceptual processing.
Perceptual mechanisms play no role in standard theories of these tasks. Instead, these theories assume that subjects access feature lists, frames, and semantic nets that only contain amodal symbols. Recent evidence, however, strongly implicates perceptual mechanisms (for a review, see Barsalou, Solomon, & Wu, 1996). Rather than accessing amodal representations, subjects appear to be simulating referents of the concepts perceptually and using these simulations to produce the required information.
Wu (1995) found evidence of perceptual simulation in the feature listing task. Subjects produced features for nouns (e.g., "watermelon") and related noun phrases (e.g., "half watermelon"). Two sources of evidence indicated that subjects simulated the referents of these nouns and noun phrases to list features. First, subjects who received neutral instructions produced essentially the same features as subjects asked to construct and describe images, suggesting that the neutral subjects adopted images spontaneously. Second, the visibility of features in real-world referents (not encountered in the experiment) predicted prediction. For example, the feature seeds is occluded in the perception of a whole watermelon but is visible in the perception of a half watermelon. Across four experiments, noun phrases, for referents with revealed internal properties (e.g., "half watermelon") produced much higher rates of internal feature listing than nouns for referents with occluded internal properties (e.g., "watermelon"). These results strongly suggest that subjects simulated referents of the concepts perceptually to produce features.
Perhaps subjects in Wu's experiments performed perceptual simulation, because feature listing is a deliberate, recall-oriented task that allows time for such simulations. If so, then we shouldn't observe such effects in faster, recognition-oriented tasks that produce reaction times under a second. To explore this issue, Solomon (1996) explored the role of perceptual simulation in the property verification task (for a preliminary report, see Olseth & Barsalou, 1995). Although Kosslyn (1976, 1980) found no evidence of perceptual simulation when neutral subjects performed property verification, the easy false trials in his experiments made such simulation unnecessary. Like Kosslyn, Solomon found that when the false trials were easy (e.g., does a crab have a brick?), subjects adopted a linguistic associative strategy that bypassed conceptual knowledge and perceptual simulation. However, when the false trials were difficult (e.g., does a crab have a fin?), subjects could not use the linguistic associative strategy and had to use conceptual knowledge instead. Under these conditions, perceptual factors, such as property size, provide the best prediction of reaction time. Thus, when subjects were forced to perform conceptual processing, they resorted to perceptual simulation to find and verify property information about concepts.
Further evidence for this conclusion comes from Solomon and Barsalou (1996). In these experiments, the similarity of parts is manipulated across trials. Whereas one subject might first verify the concept-property pair, PONY-mane, and later the pair, HORSE-mane, another subject might first verify LION-mane and later HORSE-mane. If subjects perceptually simulate the concepts to verify the parts, then they should be faster for the PONY-HORSE sequence than for the LION-HORSE sequence, because pony manes are more similar to horse manes than are lion manes. When subjects process the HORSE-mane pair, they are reminded of the earlier pair involving mane, which either facilitates or inhibits processing. Across three experiments, we have observed this result, suggesting that subjects perceptually simulated the concepts to perform verifications. One could argue that this effect results from HORSE being more similar to PONY in general than to LION. To assess this possibility, all experiments include materials in which the part is equally similar to all three concepts. For example, the part, back, is roughly the same across PONY, HORSE, and LION. For these materials, there is no difference between sequences such as PONY-HORSE and LION-HORSE, indicating that part similarity, not concept similarity, is the important factor.
Together, the results from these three projects indicate that perceptual simulation is central to conceptual processing. Even when subjects receive no pictorial materials and are not asked to use imagery, they nevertheless perform perceptual simulation spontaneously.
Perceptual Mechanisms in Conceptual Processing
Contrary to the Greek philosophers' polarized dichotomy between perception and cognition, we have seen that there is good reason to believe that cognitive processes borrow from perceptual ones. Cognitive economy and evolutionary considerations (large frontal lobes being a relatively recent evolutionary advance) encourage the co-option (borrowing) of perceptual processes for symbolic cognition. Thus, although some symbolic reasoning operations (e.g. deduction and modus tollens) appear to have no perceptual equivalent (but see Johnson-Laird, 1983 for concrete models of these abstract operations, and Rips, 1986 for a reconsideration), many suggestive parallels can be drawn. Table 1 lists several of these, which we consider next.
Selectivity. A hallmark of abstract, rule-like cognition is that it emphasizes certain properties over others. To apply the rule "an island is any piece of land completely surrounded by water" to a particular plot of land, one must emphasize this criterial attribute and ignore characteristic island features such as tropical and sandy (Keil, 1989). This selective highlighting of important attributes has a clear parallel in the considerable body of work on selective attention (for a review, see Johnston & Dark, 1986). Many of the properties of perceptual attention make it a promising candidate for subserving situations where more abstract highlighting of properties is needed, and indeed recent theories of knowledge and language have incorporated it (e.g., Barsalou, 1993; 1996; Langacker, 1986; Mandler, 1992; Talmy, 1983) . First and most basically, perceptual selection of relevant information is highly effective. Researchers have found that when people are instructed to respond to one of two overlapping shapes, there is very little processing of the irrelevant shape (Rock & Gutman, 1981; also Garner, 1974, 1978; Melara & Marks, 1990), and very little performance decrement compared to when just one shape is shown (Neisser & Becklen, 1975). Second, attention can be directed to particular stimulus properties (Treisman & Gelade, 1980), and properties automatically capture attention if they have been important during prolonged training (Shiffrin & Schneider, 1977). As applied to abstract cognition, this latter capacity could underlie people's ability to learn new criterial definitions. Third, attention is not only directed by simple stimulus properties, but also by semantic coherence and context (Triesman, 1960). If perceptual selection processes were not capable of being driven by higher-level properties, then it would clearly have limited application to more strategic cognition.
From this description of perceptual selection, it is clear that ,at a minimum, we cannot use selectivity as evidence for symbolic, rule-like cognition. Perception also benefits from a sharp, efficacious form of selectivity. Our critic then continues, "Just because both abstract reasoning and perception have mechanisms for highlighting relevant properties does not mean that the abstract ability derives from the perceptual ability." Although far from definitive, evidence exists for the co-option of perceptual selective attention by cognitive selective attention. Some evidence comes from an examination of individual differences and mental disorders. For example, schizophrenics have characteristic attentional and cognitive defecits that parallel each other in interesting ways. Cognitive correlates of schizophrenia include abnormal word associations, problems with developing coherent discourses, and difficulties with abstract thought that stem from intrusions of superficial information (Schwartz, 1982). Perceptual correlates of schizophrenia include problems with allocating attention to relevant stimulus attributes, driving attention by informative cues, and inhibiting irrelevant attributes (Liotti, Dazzi, & Umilta, 1993).
The parallel between these high- and low-level defecits is that both involve problems with selective attention. The pattern of correlations is well explained if the same selective attention processes are at work for surprisingly different levels of processing, among tasks that many have claimed to be handled by special modules (e.g. for language and visual information). Further, there is suggestive evidence that perceptually-based selective attention is borrowed for more conceptual selective attention, rather than vice versa. Schreiber, Stolz-Born, Kornhuber, and Born (1992) found that schizophrenics show impairments on a visual selection task before cognitive impairments arise, even when the tasks are roughly equated for their sensitivity at diagnosing abnormalities. This result parallels the finding that children who are deaf but have normal I.Q.s show defecits in non-auditory selective attention tasks (Quittner, Smith, Osberger, Mitchell, and Katz, 1994). The implication from both results is that if perceptually-based selective attention processes go awry, then more general cognitive impairments of selective attention may arise.
Selective attention to important stimulus aspects may emerge from one of two processes-a process that focuses on important, criterial, or goal-based aspects, or a process that actively suppresses irrelevant aspects. While many theories of abstract reasoning focus on the former, recent perceptually-based work has found a strong presence of the latter. For example, research on "negative priming" (Neill, 1977; Tipper, 1992) has shown that people are slower to respond to a target if it was a distractor on previous trials. Again, non-perceptual equivalents are available. Processes exist to inhibit irrelevant memories (Anderson & Spellman, 1995) and words (Gernsbacher & Faust, 1991) depending on how much they compete with other, potentially more appropriate items. The perceptual and cognitive defecits of schizophrenics seem to be attributed to attentional processes that are insufficiently selective (Beech, Powell, McWilliam, & Claridge, 1989). That is, schizophrenics have particular difficulty with inhibiting inappropriate thoughts and irrelevant perceptions. Conversely, many of the perceptual and cognitive symptoms of childhood autism, including hyper-sensitivity to sensory stimulation, abnormally narrow generalizations from training, and lack of productive language, may be traced to an overly selective attentional process (Lovass, Koegel, & Schreiman, 1979).
In short, there appear to be strong correlations between classes of cognitive and perceptual behavior that stem from shared processes of selective attention to relevant, and selective inhibition of irrelevant, properties. If this speculation is correct, then we might expect interference between cognitive and perceptual tasks that both make demands on the same selective attention process. Focusing on a target location or property (as with Flanker and Stroop tasks) should interfere with focusing on criterial properties in a categorization task. Regardless of the empirical outcome, given the functional similarities between perceptual and conceptual processes for highlighting relevant information, it is plausible that some of the early (phylogentically and developmentally) processes for allocating perceptual attention would be co-opted for later processes.
Blurring. Turning to some of the other parallels in Table 1, it is somewhat surprising to note a link between abstraction, often considered the epitome of cognition that has transcended perception, and the "lowly" process of blurring. To abstract is to distill the essence from its superficial trappings. The conventionally considered way to do this is by developing a "schema" that is tuned to the essence (Gick & Holyoak, 1983). Another way is to blur over the irrelevant aspects. Blurring has the advantage that it can operate even when the essential schema cannot be formulated. Three-year old Amelia does not need to know what makes something a dog in order to categorize the neighbor's poodle as one, as long as she knows that her beagle is a dog, and is able to ignore (blur over) the differences between poodles and beagles. Blurring is a particularly appropriate technique when the superficial aspects are details, and the global structure is correlated with important abstractions. Furthermore, strategic blurring may cause only particular features to be ignored, thereby permitting features known to be irrelevant to exert little influence on behavior, even if the relevant features have not been identified. Strategic blurring is probably instantiated by attentional, rather than visual, means. Our perceptual/attentional system shows flexibility in being able to allocate intermediate degrees of attention to stimulus features. At times, attention given to a stimulus feature nearly optimally matches the feature's diagnosticity for a task (Nosofsky, 1986; Shaw, 1982). Thus, ignoring is not necessarily all-or-none. Whereas amodal abstractions must either represent or not represent a property, an advantage of incorporating perceptual attention in high-level cognition is that it provides a mechanism for partially representing a property.
Structure and binding. The bindings of arguments to values is often suggested as a structure that distinguishes high-level cognition from its lower-level counterpart. The proposition Loves(John, Mary) means something different from Loves(Mary, John), much to John's dismay. The predicate Loves takes arguments that are ordered by their roles, with the "agent" role bound to John, and the "patient" role bound to Mary. This binding of objects to roles establishes a structure in propositions that goes beyond the representational capacities of "flat" representations such as feature lists (Barsalou, 1992; 1993; Barsalou & Hale, 1993). Although structured representations are necessary for orderly thought, abstract thought is not alone in this regard. Detailed mechanisms are now available to describe the role of binding in perception. Elements from a person's left eye image are bound to their corresponding elements from the right eye's image, such that globally harmonious structures, and the perception of depth, are established (Marr & Poggio, 1979). Similarly, elements from one "frame" of an event are bound to their corresponding elements from the subsequent frame on the basis of local similarity and their respective roles within their frames, in order to establish the perception of motion (Dawson, 1991). Finally, successful models of object recognition have worked by binding parts of an image to be analyzed to the parts of an internal object model (Hummel & Biederman, 1992). Thus, a suitcase is recognized by binding some of an image to a "handle" role and the rest of the image to a "container" role. Bindings are created by first passing inhibitory and excitatory signals between image elements so that elements that belong to a single part will be activated in phase with each other. Bindings, thus, can be implemented by synchronized patterns of activity between the elements (two image parts, or an image part and an object's role) to be bound.
While these models of perceptual binding can offer mechanisms for the structuring of abstract thought (for an application to analogical reasoning, see Hummel, Burns, & Holyoak, 1994), they also offer the exciting possibility of intermediate degrees of binding. This possibility is neglected by standard propositional representations because of their explicit and all-or-none assignment of arguments to roles. In contrast, most perceptually motivated models of binding describe a dynamic and temporally extended mechanism for establishing structures, with completely unambiguous, one-to-one bindings as perhaps only an end-state. For example, in Goldstone and Medin's (1994) model of similarity assessments, parts of two scenes are gradually placed into alignment to the extent that they are similar and play the same role in their respective scenes. Until the alignments are fully established, a particular part may be bound 70% to another part. Such an approach holds promise for abstract cognition as well. Even in the abstract domains of analogical reasoning and problem solving, the determination of abstracts correspondences is imperfect (Gentner & Toupin, 1986), and sensitive to superficially misleading features (Holyoak & Koh, 1987; Ross, 1987). In reading comprehension, fast processing is characterized by minimal inferences that can be established by unstructured priming rather than thoroughly worked out conceptual assignments (McKoon & Ratcliff, 1992). People's primitive preferences also seem to use imperfect bindings. Drinks labeled "not poisonous" are rated as undesirable, presumably because "poisonous" is somewhat free-floating, not completely bound to "not" (Rozin, Markwith, & Ross, 1990). In sum, perception requires structured binding as much as does abstract cognition, and mechanisms of perceptual binding may better explain cases of intermediate degrees of binding than do amodal propositions.
Differentiation, subcategories, and dimensions. A primary methods for refining thought is to differentiate-to take a rough category and sub-divide it into smaller categories. Smith, Carey and Wiser (1985) describe the development of children's concepts as differentiation. Children (and cultures), who originally confuse weight and density, or heat and temperature, cognitively develop by creating distinct concepts for the originally confounded entities. Interestingly, child development also involves a parallel process of dimensionalization-breaking apart dimensions that were originally fused. Evidence suggests that dimensions that are easily separated by adults, such as the brightness and size of a square, are treated as fused together for children (Kemler, 1983; Smith, 1989). For example, children have difficulty identifying whether two objects differ on their brightness or size even though they can easily see that they differ in some way. Both differentiation and dimensionalization occur throughout one's lifetime. Tanaka and Taylor (1991) show that experts in a domain become increasingly adept at making subordinate-level categorizations. A dog expert, for example, can distinguish between German shepards and golden retrievers as fast as they can distinguish between dogs and cats; the nonexpert is much faster at the latter discrimination. Likewise, dimensions that are psychologically fused for most adults, such as the chroma and hue of a color, can become separated with practice (Goldstone, 1994c). Artists and scientists who deal with colors regularly are better than nonexperts at extracting dimensional information about chroma while ignoring hue differences (Burns & Shepp, 1988).
The primary reason to think that cognitive differentiation and perceptual dimensionalization have deep properties in common is that it is simply hard to make a principled distinction between dimensions (features) and concepts. Many of the features that have been proposed for describing concepts (e.g. "nests" and "lays eggs" for "bird," "has wheels" and "has engine" for "car," and "sit on" and "legs" for "chair") are concepts in their own right (Schyns, Goldstone, & Thibaut, in press). If we dispense with the traditional division between concepts and dimensions, then we can potentially take advantage of the interesting work on computational mechanisms that underpin perceptual dimensionalization in understanding concept differentiation (de Sa & Ballard, in press; Rumelhart & Zipser, 1985). Furthermore, the link also explains why the left hemisphere seems to be specialized for both fine perceptual discriminations, such as when stimuli have high spatial frequencies or when subjects are told to use a strict criteria for identification (Robertson & Lamb, 1991), and relatively differentiated language use, such as choosing the appropriate meaning of an ambiguous word (Burgess & Simpson, 1988). Conversely, the right hemisphere seems better tuned to relatively global, multi-dimensional perceptual aspects and relatively undifferentiated, even metaphoric, linguistic categories (Brownell, Simpson, & Bihrle, 1990). Thus, it is at least plausible to believe that the processes that split percepts into dimensions and more abstract categories into sub-categories may be related.
Synesthesia and productivity. ability to reason analogically may borrow from perceptual processes that underlie synesthesia, which is characterized by subjective sensations along a sensory modality other than the one externally stimulated. Although often considered an anomalous phenomenon, even normal adults seem predisposed to directly perceive relations between separate sensory modalities such as loudness, pitch, and color (Melara, 1989).
The operation of combination is important for abstract thought because it provides productivity; a potentially infinite number of new thoughts can be generated by recombining existing thoughts in new arrangements. Although productivity is typically associated with symbolic systems, perceptual representations can support an equivalent operation: spatio-temporal concatenation (Barsalou, 1993, 1996; Barsalou & Prinz, in press; Prinz & Barsalou, in press). Separate images can be juxtaposed to produce new images. Furthermore, evidence exists that new interpretations can accompany spatial concatenations. When asked to form an image of a "D" rotated counter-clockwise 90 degrees, and intersect an image of a "J" such that the top of the "J" touches the top of the rotated "D," subjects often are able to reinterpret their concatenation as an umbrella (Finke, Pinker, & Farah, 1989).
The significance of these parallels is two-fold. The first point is that many properties of abstract cognition, when explored from the perspective of processes that could furnish them, are also found in perceptual systems (for other parallels between perceptual and conceptual categories, see Medin & Barsalou, 1987). Claims that abstract cognition is special because it highlights relevant properties, is abstractive, has argument structure, permits analysis into components, or allows productivity are weakened by the presence of perceptual equivalents. In several cases, much more is known about the mechanisms of these perceptual processes than is known about their conceptual counterparts (Ullman, 1984), and pragmatically speaking, we would be well advised to use this knowledge to guide our understanding of abstract thought. The second, more speculative, point is that these parallels are hardly coincidental but arise because the same processes are being used. An extreme version of this position is unlikely, but an examination of individual differences, task manipulations, and neuropsychological data provides enough evidence for correlations between perceptual and conceptual tasks to encourage exploration of the possibility that some of the process pairs of Table 1 are linked by identity rather than analogy.
In an attempt to reunite perceptual and conceptual processing, we have argued that perceptual processing can provide us with unexpectedly rich and useful information in concepts. First, relatively raw perceptual representations are powerful, because they can implicitly represent properties of the external world in an analog fashion. Fairly abstract properties, such as regression coefficient and minimal spanning distance, can be represented in analog systems without being calculated explicitly. Second, impressions of overall, undifferentiated similarity seem to be perceptually primary, and to be exactly the perceptually-constrained type of similarity that is useful in creating many common categories. Third, perceptual similarity itself is not static, but changes as a function of the categorization demands confronting an organism. This flexibility reduces that gap between perception and sophisticated analytic concepts. Fourth, even when concepts seem to have little perceptual basis, their origins can often be traced to perceptual processing. Fifth, people appear to perform perceptual simulation in conceptual tasks that have no perceptual demands. Sixth, striking commonalities exist between the mechanisms that process abstract information and those established for perception. Correlations between cognitive tasks (e.g. problem solving, language comprehension and production, and reasoning) and perceptual tasks suggest that the same processes may underlie both types of task.
The Perceptual/Conceptual Distinction
Is there a continuum from perceptual to conceptual representations, and if so, what varies along this continuum? Given the top-down influences of concepts on perception (e.g. Goldstone, 1995b) and compelling research suggesting surprisingly low-level influences of expectations on perceptual judgments (e.g. Peterson & Gibson, 1994), searching for the boundary between perception and conception is most likely futile. However, it may be useful to describe a continuum from perceptual to conceptual. What varies along this continuum is how much and what sort of processing has been done to input information. Specifying exactly where expectations and conceptual pressures influence processing along the perception/conception continuum is a real, although highly empirical, question. The general principle that conceptual processes are more perspective-dependent and strategically tuned than are perceptual processes probably has some validity. When we categorize "T"s and tilted "T"s as belonging to the same letter category, it increases their rated similarity (here, similarity ratings, despite "similarity" in their name, are relatively conceptual tasks!), although it has little influence on perceptual measures of similarity such as our ability to quickly spot the borderline between a group of "T"s and a group of tilted "T"s (Beck, 1966). The momentary perspective that one takes toward an object has clear and large influences on cognitive processes such as description and inference (Barsalou, & Sewell, 1984), but it has less influence, or it is surprising when it does have an influence (Moscovici & Personnaz, 1991), on perceptual processes such as figure/ground segmentation, color afterimages, edge detection, and same/different discrimination.
We also assume that conception differs from perception in degree of productivity. In conception, one can combine perceptual representations in ways that go far beyond perception. In imagining the Cheshire Cat from Alice in Wonderland, productivity is freed from the constraints of actual perception. Real cats don't have human smiles, and their bodies don't fade in and out while their smiles remain. Although conception may have fundamental underpinnings in perception, its ability to manipulate schematic perceptual representations productively allows it to go considerably beyond.
The Perceptual/Abstract Distinction
We are less sanguine about the usefulness of a perceptual/abstract continuum. Perception often involves the abstraction of certain elements. Concrete details are often blurred over, ignored, or actively suppressed in perception. As Arnheim (1969) points out, the difference between realistic and abstract art is not one of concreteness or relevance of perception. Abstract art is, of course, concrete. An understanding of perceptual processes is often of fundamental importance for creating and appreciating abstract art. The abstractions at work in a piece of music or painting are often not the same sort of abstractions present in a novel. For example, in a painting, the abstractions often deal with spatial relations, relations between colors, and the manner of dividing and integrating different areas on the canvas. The banality of programmatic music (music based on a verbalizable theme, such as "A young man combatting a large dragon") and allegorical paintings may be due to strained efforts to capture the abstractions better suited for one medium (verbalizations, propositions) in a more visual or auditory medium. Similarly, it is notoriously difficult to verbally describe the abstractions created by Picasso. The representations in a particular perceptual domain (hearing or seeing) may be highly abstract even though they are constrained by concrete qualities of the particular domain.
A corollary to the notion that abstractions can be abstract even if they are tied to their particular perceptual domain is that it is misleading to equate "perceptual" and "superficial." A traditional assumption, particularly in research on analogical reasoning, is that comparisons based on perceptual aspects are superficial. Accordingly, the deep similarity between time bombs and cigarettes is that they both cause delayed damage; the fact that they both involve fire is deemed superficial (and hence less interesting or important). In contrast, following Bassok (Bassok & Olseth, 1995; Bassok, Wu, & Olseth, 1995), we believe that perceptual aspects typically are at least cues for the abstraction that is built, and often times are never removed from the abstraction. The similarity between diving boards and bed springs depends on perceptual aspects (a bouncing motion with gradual deceleration and acceleration). These perceptual aspects are "deep" in the sense that they permit widespread causal inferences between highly dissimilar objects and are the result of general physical laws. Many physical, biological, and psychological principles that are discussed in every-day life produce perceptual effects; our perceptual systems have been refined to make this so. Given this, perceptually-based comparisons probably yield more reliable inferences than those produced by analogies that are completely stripped of their perceptual grounds.
The Eliminativist /Agnostic Distinction
In endorsing the perceptual/conceptual distinction, we essentially argue that being conceptual is being abstract. By increasingly discarding perceptual information, the cognitive system uses the remaining perceptual information to form the abstractions that constitute conceptual knowledge. In not endorsing the perceptual/abstract distinction, we implicitly deny the requirement that abstract knowledge need be amodal. Instead, we argue that abstract knowledge can be constituted from perceptual bases.
We are left with the question of what role, if any, do amodal symbols play in conceptual knowledge? We have seen that perceptual mechanisms can accomplish many of the functions that are well-known for amodal symbol systems. On the basis of such observations, one might be inclined to adopt the eliminativist view that amodal symbols are unnecessary. The problems raised earlier for amodal symbols might further encourage this conclusion.
We can think of two reasons why one might want to maintain amodal symbols. First, if there is some necessary function of intelligence that amodal symbols can accomplish that perceptual ones cannot, then this is an obvious reason for maintaining amodal symbols. Thus, far we remain unaware of any such function. We hasten to add that accounts of how perceptual symbols would account for many phenomena remain to be developed, although we have not observed any failures thus far that suggest serious limitations. Nevertheless, we may well discover such limitations, in which case amodal symbols might become necessary, assuming that independent empirical evidence justifies their existence.
Second, we might find that amodal symbols deliver certain conceptual functions more efficiently that perceptual symbols. Even though perceptual symbols could implement the same functions, amodal symbols may do so more optimally. For such an argument to go through, though, we would need both theoretical analysis and empirical data as evidence.
Regardless of where one comes down on the eliminative/agnostic distinction, we believe that perceptual mechanisms underlie conceptual processing to a considerable degree. From overall similarity to analytic rules, many sources of evidence implicate perception in conception.
Table 1. Parallels between cognitive and perceptual processes.
Highlighting Selective attention
Abstraction Blurring, ignoring
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We would like to thank Douglas Medin and Linda Smith for useful comments. This research was supported by National Science Foundation grant SBR-9409232 to Robert Goldstone and by National Science Foundation grant SBR-9421326 to Lawrence Barsalou. Correspondence concerning this article should be addressed to Robert L. Goldstone, Psychology Department, Indiana University, Bloomington, Indiana 47405, or Lawrence W. Barsalou, Department of Psychology, 5848 S. University Ave., University of Chicago, Chicago, IL 60637. Electronic mail may be sent via Internet to email@example.com or L-Barsalou@uchicago.edu.
Figure 1. Although normally computed by amodal mathematical systems, properties such as the correlation between height and weight can also be computed by an analog device consisting of a rod, rubber bands, tacks, and a cork board.