Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms. 3 I am grateful for the time and energy that the commentators put into reading and responding to the target article. They raised many significant issues and made many excellent suggestions. Even the strongest criticisms were useful in clarifying misunderstandings and in addressing matters of importance. I have organized the commentaries into two general groups: (1) commentaries that defended amodal symbol systems, (2) commentaries that addressed the properties of perceptual symbol systems. Because the second group was so large, I have divided it into four smaller sections. In the first, I address a wide variety of issues surrounding perceptual symbol systems that include perception, features, simulators, frames, introspection, and so forth. The final three sections address the topics raised most often: abstract concepts, language, and computational mechanisms. No commentator responded systematically to the criticisms of amodal symbol systems listed in 1.2.2 and 1.2.3 of the target article: little direct evidence exists for amodal symbols; it is difficult to reconcile amodal symbols with evidence from neuroscience; the transduction and grounding of amodal symbols remain unspecified; representing space and time computationally with amodal symbols has not been successful; amodal symbol systems are neither parsimonious nor falsifiable. Expecting that a single commentator address all of these concerns in the space provided would certainly be unfair. I was struck, however, by how little attempt overall there was to address them. Having once been deeply invested in amodal symbol systems myself (e.g., Barsalou, 1992) , I understand how deeply these convictions run. Nevertheless, defending amodal symbol systems against such criticisms seems increasingly important to maintaining their viability. A handful of commentators defended amodal systems in various other ways. Although most of these defenses were concrete and direct, others were vague and indirect. In addressing these defenses, I proceed from most to least concrete. R1.1. Amodal symbols reside in sensory-motor areas of the brain. The defense that struck me as most compelling is the suggestion that amodal symbols reside in sensory-motor regions of the brain (Aydede; Zwaan, Stanfield, & Madden). A related proposal is that perceptual representations are not constitutive of concepts but only become epiphenomenally active during the processing of amodal symbols (Adams & Campbell). In the target article, I suggested that 4 neuroscience evidence implicates sensory-motor regions of the brain in knowledge representation (2.1, 2.2, 2.3). When a lesion exists in a particular sensory-motor region, categories that depend on it for the perceptual processing of their exemplars can exhibit knowledge deficits. Because the perception of birds, for example, depends heavily on visual object processing, damage to the visual system can produce a deficit in category knowledge. Neuroimaging studies of humans with intact brains similarly show that sensory-motor areas become active during the processing of relevant categories. Thus, visual regions become active when accessing knowledge of birds. Several commentators noted correctly that these findings are consistent with the assumptions that (a) amodal symbols represent concepts, and (b) these symbols reside in sensory-motor regions (Aydede: Zwaan, Stanfield, & Madden; Adams and Campbell). If these assumptions are true, then damage to a sensory-motor region could produce a deficit in category knowledge. Similarly, sensory-motor regions should become active when people with intact brains process categories. This is an important hypothesis that requires careful empirical assessment. Damasio's (1989) convergence zones provide one way to frame the issue. On his view, local associative areas in sensorymotor regions capture patterns of perceptual representation. Later, associative areas reactivate these sensory-motor representations to simulate experience and thereby support cognitive processing. As the quote from Damasio (1989) in 2.1 illustrates, he believes that reactivating sensory-motor representations is obligatory to representing knowledge-activation in a nearby associative area never stands in for them. In principle, however, activation in local associative areas could stand in for sensory-motor representations during symbolic activity, thereby implementing something along the lines of amodal symbols, with perceptual representations ultimately being epiphenomenal. Behavioral findings argue against this proposal. Studies cited throughout the target article show that perceptual variables predict subjects' performance when they perform conceptual tasks. For example, Barsalou, Solomon, and Wu (1999) report that occlusion affects feature listing, that size affects property verification, and that detailed perceptual form predicts property priming. The view that activation in associative areas represents concepts does not predict these effects or explain them readily. Instead, these effects are more consistent with the view subjects are reactivating sensory-motor 5 representations. Studies on language comprehension similarly exhibit such effects (4.1.6, R4.5, Further empirical evidence is clearly required to resolve this issue with confidence. Neuroimaging studies that assess both sensory-motor and local associative areas during perception and conception should be highly diagnostic. Similarly, lesion studies that assess simultaneous deficits in perception and conception should be useful as well. If sensory-motor representations are epiphenomenal, then patients should frequently be observed who exhibit deficits in sensory-motor representation but not in corresponding knowledge. Aydede claims that I only addressed the form of perceptual symbols and ignored their reference. From these flawed premises, he proceeds to the conclusion that a causal theory of amodal symbols must be correct. To the contrary, the target article clearly addressed the reference of perceptual symbols and agreed with Aydede that causal relations-not resemblances-are critical to establishing reference. Section 2.4.3 states that simulators become causally linked to physical categories, citing Millikan (1998). Similarly, 3.2.8 is entirely about the importance of causal mechanisms in establishing the reference of perceptual symbols, citing Dretske (1995) . Indeed, 3.2.8 makes Aydede's argument that resemblance is inadequate for establishing a symbol's reference. Although resemblance is often associated with perceptual views of knowledge, I took great pains to dissociate the two. Although I would have valued Aydede's analysis of my actual proposal, reminding him of it provides an opportunity for elaboration. Like many philosophers, I agree that much conceptual knowledge is under the causal or nomic control of physical categories in the environment. Indeed, this assumption underlies all learning theories in cognitive science, from exemplar theories to prototype theories to connectionism. In this spirit, 2.4.3 proposed that simulators come under the control of the categories in the world that they represent. As a person perceives the members of a category, sensorymotor systems are causally driven into particular states, which are captured by various associative areas. The target article does not specify how these causal processes occur, claiming explicitly not to provide a theory of perception (2). Nor does it make any claim that perceptual states resemble the environment (although they do in those brain regions that are topographically mapped). Regardless, it is safe to 6 assume that causal processes link the environment to perceptual states. The critical claim of perceptual symbol systems is that these causally-produced perceptual states, whatever they happen to be, constitute the representational elements of knowledge. Most critically, if the environment is causally related to perceptual states, it is also causally related to symbolic states. Thus, perceptual symbol systems constitute a causal theory of concepts. Simulators arise in memory through the causal process of perceiving physical categories. In contrast, consider the causal relations in the amodal theories that Aydede apparently favors (as do Adams & Campbell). On these views, physical entities and events become causally related to amodal symbols in the brain. As many critics have noted, however, this approach suffers the vagaries of symbol grounding. Exactly what is the process by which a physical referent becomes linked to an amodal symbol? If philosophers want to take causal mechanisms seriously, they should provide a detailed process model of this causal history. On attempting to do so, they will discover that perception of the environment is essential (Harnad, 1987 (Harnad, , 1990 Höffding, 1891; Neisser, 1967) , and that some sort of perceptual representation is necessary to mediate between the environment and amodal symbols. Aydede notes that perceptual representations could indeed be a part of this sequence, yet he fails to consider the implication that potentially follows: Once perceptual representations are included in the causal sequence, are they sufficient to support a fully functional conceptual system? As I argue in the target article, they are. If so, then why include an additional layer of amodal symbols in the causal sequence, especially given all the problems they face? Nothing in Aydede's reply makes a case for their existence or necessity. Two commentaries suggest that spatial processing and temporal processing reside in amodal systems (Freksa, Barkowsky, & Klippel; Mitchell & Clement). On this view, a single spatial system subserves multiple modalities, enabling coordination and matching across them. Thus, the localization of a sound can be linked to an eye movement that attempts to see its source. Similarly, a single temporal system subserves multiple modalities, coordinating the timing of perception and action. Because these two systems are not bound to any one modality, they are amodal, thereby constituting evidence for amodal symbol systems. To my knowledge, the nature of spatial and temporal systems remains an open question. To date, there is no definitive answer on whether each modality has its own spatial and temporal processing system, or whether general systems serve all modalities. Indeed, both modality-specific and modalitygeneral systems may well exist. Ultimately this is an empirical question. Imagine, however, that domain-general systems in the brain are found. In the framework of the target article, they would not constitute amodal systems. As defined in 1.2, an amodal symbol system contains representations that are transduced from perceptual states-they do not contain representations  intelligence (4.4), Landauer takes me to mean that no silicon-based system could acquire human knowledge. He then shows how computers that implement LSA mimic humans on a wide variety of knowledge-based tasks. I never claimed, however, that amodal systems cannot mimic humans behaviorally! Indeed, 1.2 noted that amodal symbols have so much power that they can probably mimic all human behavior. My claim in 4.4 was different, namely, if knowledge is grounded in sensory-motor mechanisms, then humans and computers will represent knowledge differently, because their sensorymotor systems differ so radically. I acknowledged the possibility that future technological developments might produce computers with sensory-motor systems closer to ours, in which case their knowledge could be represented similarly. No one is currently under the illusion, however, that the input-output 8 systems of today's computers are anything like human sensory-motor systems. Thus, if knowledge is implemented in perceptual systems, it would have to take very different forms. In making his case for LSA, Landauer fails to address the problems I raise for amodal symbol systems in 1.2. (1) What is the direct evidence that cooccurrence frequency between words controls performance? A system based on cooccurrence frequencies can mimic human behavior, thereby providing indirect evidence that this construct is useful, but is there evidence implicating this basic unit of analysis directly? (2) What's the neural story? Do lesion and neuroimaging data provide support? (3) How are perception and conception linked? Everyone would agree that a conceptual system helps us interpret perceived events in the world, yet how do word cooccurrences map onto perceived individuals in the world? When we perceive an object, how is it categorized using LSA? When we conceptualize an object, how do we find its referent in the world? (4) How does LSA represent knowledge about space and time? It is difficult to see how a system that only tracks word cooccurrence can represent these basic aspects of perception. Other concerns about LSA arise as well. How does LSA accomplish the conceptual functions addressed in the target article, such as distinguishing types and tokens, implementing productivity, and representing propositions? Using only word correlations, it does not seem likely that it can to implement these basic human abilities. Finally, are cooccurrences only tracked between words, or between corresponding amodal symbols as well? If the latter, what is the nature of this amodal system, and how is it related to language? It is important to note that correlation-not causation-underlies LSA's accounts of human data. Because word cooccurrence exists in the world and is not manipulated experimentally, infinitely many potential variables are confounded with it. To appreciate this problem, recall how LSA works. On a given task, the likelihood of a particular response (typically a word) is predicted by how well it correlates with the overall configuration of stimulus elements (typically other words). Thus, responses that are highly correlated with stimulus elements are more likely to be produced than less correlated responses. The problem is that many, many other potential variables might well be correlated with word cooccurrence, most notably, the perceptual similarity of the things to which the words refer. It is an interesting finding that computer analyses of word correlations produce a system that mimics human behaviors. It does not follow, though, that these correlations are causally important. Of course the 9 human brain could work like LSA, but to my knowledge, no evidence exists to demonstrate this, nor to rule out the possibility that variables correlated with word cooccurrence are the critical causal factors. A number of empirical findings suggest that knowledge is not grounded in word cooccurrence. First, aphasics typically just lose language, they do not lose knowledge (Lowenthal). If knowledge were simply represented in word cooccurrence, they should lose knowledge as well. Second, Glenberg reports that perceptual affordances enter into language comprehension, and that word cooccurrence cannot explain these results (also see 4.1.6, R4.5). Landauer believes that this is because words for certain idioms, metaphors, and modern phrases have not been included in the language corpus that underlies LSA, but again the problem of correlation versus causation arises. Landauer wants to believe that these missing entries have causal significance, when actually their status is merely correlational. Because Glenberg's affordances are also correlational, more powerful laboratory techniques are needed to tease these factors apart. To the extent that sensory-motor affordances are implicated in conceptual processing, however, LSA has no way of handling them. In this spirit, Solomon and Barsalou (1999a,b) controlled the strength of lexical associations in the property verification task and found effects of perceptual variables. In a task involving only linguistic stimuli, nonlinguistic factors robustly affected performance when linguistic factors were held constant. One way to extend LSA would be to apply its basic associative mechanism to perceptual elements. Just as cooccurrences of words are tracked, so are cooccurrences in perception. Depending on the specific details of the formulation, such an approach could be virtually identical to my proposal about frames in the target article. As described in 2.5, a frame accumulates components of perception isolated by selective attention, with the associative strength between components reflecting how often they are processed together. Thus, the cooccurrence of perceptual components lies at the heart of these perceptually-grounded frames. Notably, however, these structures are proposed to be organized by space and time, not simply by associative strength. To later simulate perceptual experience, the mere storage of associations will not be sufficient-extensive perceptual structure will be necessary as well. If so, a simple extension of LSA from words to perception will probably not work. Furthermore, we have known since Chomsky (1957) that the human brain is not simply a generalization mechanism. Besides generalizing around known exemplars, the brain productively 10 produces infinite structures from finite elements. Not only is this true of language, it is also true of conception (Fodor & Pylyshyn, 1988) . People can conceive of concepts that they have never encountered and that are not generalizations of past concepts. Neither a word-based nor a perceptionbased version of LSA can implement productivity. There is no doubt that the human brain is an associative device to a considerable extent, yet it is also a productive device. Although LSA may explain the associative aspects of cognition, it appears to have no potential for explaining the productive aspects. Finally, in a superb review of picture-word processing, Glaser (1992) concluded that a mixture of two systems accounts for the general findings in these paradigms. Under certain conditions, a system of word associations explains performance; under other conditions, a conceptual system with a perceptual character explains performance; under still other conditions, mixtures of these two systems are responsible. Based on my own research, and also on reviewing other relevant literatures, I have become increasingly convinced of Glaser's general thesis, although perhaps in a somewhat more radical form: People frequently use word associations and perceptual simulations to perform a wide variety of tasks, with the mixture depending on task circumstances (e.g., Barsalou et al., 1999; Solomon & Barsalou, 1999a,b;. Furthermore, I have become convinced that LSA provides an excellent account of the word association system (as does HAL; Burgess & Lund, 1997). When people actually use word associations to perform a task, LSA and HAL provide excellent accounts of the underlying processing. However, when a task demands true conceptual processing, or when the word association strategy is blocked, the conceptual representations required are implemented through perceptual simulation. Although such simulations may rely on structures of associated perceptual components, they also rely on nonassociative mechanisms of perception and productivity. Adopting the framework of connectionism, Gabora argues that amodal symbols arise as similar experiences are superimposed onto a common set of units. As idiosyncratic features of individual experiences cancel each other out, shared features across experiences coalesce into an attractor that represents the set abstractly. I strongly agree that such learning mechanisms are likely to exist in the brain, and that they are of considerable relevance for perceptual symbol systems. Most importantly, however, this process does not satisfy the definition of amodal symbols in 1.2. To the extent that Gabora's learning process occurs in the original units that 11 represent perception, it implements a perceptual symbol system, even if generic attractors evolve over learning. As described in 2.5 and shown in Figure 3 , repetitions of the same perceptual symbol are superimposed to produce increasingly generic components of frames. Conversely, Gabora's learning process would only become an amodal symbol system should a population of nonperceptual units recode perceptual activity, as in a feed forward net with hidden units (1.2). As long as no such recoding takes place, however, I view Gabora's proposal as highly compatible with the proposal that abstractions are grounded perceptually. Schwartz, Weaver, and Kaplan make virtually the same proposal as Gabora but view it as an implementation of a perceptual symbol system, not as an implementation of an amodal symbol system. The commentaries by Landau and Ohlsson raise specific criticisms of perceptual symbol systems addressed later. Here I address their general belief that amodal symbol systems must be correct. Landau and Ohlsson both view perceptual symbol systems as essentially a classic empiricist theory. Because they believe that prior empiricist theories have been completely discredited, they believe that perceptual symbol systems could not possibly work either. First, these commentaries prove my point in the target article that perceptual approaches to knowledge are often oversimplified for the sake of criticism (1.3). I do not have the sense that Landau and Ohlsson have appreciated this point, nor made much of an attempt to entertain anything but an overly simplistic perceptual view. I continue to believe that when more substantial and ambitious perceptual theories are entertained, they are not only plausible and competitive, they are superior. Second, the theory in the target article departs significantly from classic empiricist theories. Whereas prior theories focused on consciously experienced images, perceptual symbol systems focus on neural states in sensory-motor systems (2.1). To my mind, this is the most exciting feature of this proposal, primarily because it leads one think about the perceptual view in provocative new ways. Third, I explicitly stated that my theory is not a simple empiricist view of knowledge, appealing to genetically-regulated mechanisms that structure brain development (2.4.1). In particular, I proposed that genetically-based mechanisms for space, objects, movement, and emotion organize perceptual symbols. Landau and Ohlsson seem to believe that nativism is the answer to the ills of empiricism. However, it is one thing to view nativism naively as a panacea for every difficult problem, and quite 12 another to specify the complex epigenetic processes that characterize the genetic regulation of development (Elman, Bates, Johnson, Karmiloff-Smith, Parisi, & Plunkett, 1996) . Fourth, perceptual symbol systems go considerably further than previous theories in laying out a fully-functional theory of concepts, largely because the criteria for such a theory were not known until modern times. To my knowledge, no prior perceptual theory of knowledge has accomplished this. Although prior theories have included mechanisms that had potential for implementing these functions (1.3), they were typically not developed in these ways. Finally, in embracing amodal symbol systems as the only reasonable alternative, Landau and Ohlsson fail to address the problems raised for amodal symbol systems in 1.2. Without resolving these major problems, it is difficult to see how anyone could have such complete confidence in a theory. The large majority of the commentaries addressed specific aspects of perceptual symbol systems. Some commentaries attempted to show that empirical findings or theoretical arguments support some aspect of the theory. Other commentaries developed a particular aspect in a positive manner. Still other commentaries attempted to undermine the theory by raising problems for some aspect. I have organized these remarks around each aspect of the theory addressed. For each, I have combined the relevant commentaries with the hope of clarifying and developing that part of the theory. claims that there is no way to distinguish perceptual and nonperceptual systems independently of modal and amodal symbols. Given the huge behavioral and neural literatures on perception, this is a rather surprising claim. A tremendous amount is known about the perceptual systems of the brain independently of anything anyone could possibly say about conceptual systems. Clearly, we know a lot about perceptual abilities and the neural mechanisms that implement them. Identifying these abilities and mechanisms independently of perceptual symbols is not only possible but has long since been accomplished in the scientific disciplines that study perception. In defining perceptual symbols, I simply utilized these well recognized findings. Thus, 2.1 proposed that well established sensory-motor mechanisms not only represent edges, vertices, colors, and movements in perception but also in conception. Perceptual symbols follow the well-trodden paths of perception researchers in claiming that 13 the representational mechanisms of perception are the representational mechanisms of knowledge. What is perceptual about perceptual symbol systems is perception in the classic and well-established sense. Brewer similarly worries that perceptual symbols, neurally defined, are of little use because we currently have no knowledge of the neural circuits that underlie them. Again, however, we do have considerable knowledge of the neural circuits that underlie sensory-motor processing. Because perceptual symbols are supposed to utilize these same circuits to some extent, we do know something about the neural configurations that purportedly underlie them. Although perceptual symbols utilize perceptual mechanisms heavily, this does not mean that conceptual and perceptual processing are identical. In the spirit of Lowenthal's commentary, important differences must certainly exist. As noted in 2.1 and 4.3 of the target article, perceptual symbols probably rely more heavily on memory mechanisms than does standard perception. Furthermore, the thesis of 2.4.7 is that a family of representational processes capitalizes on a common set of representational mechanisms in sensory-motor regions, while varying in the other systems that utilize them. This assumption is also implicit throughout the subsections of 4.1 which show how basic cognitive abilities could draw on the same set of representational mechanisms. The point is not that these abilities are identical, only that they share a common representational basis. Mitchell and Clement claim that I never define "simulation" adequately. It is clear throughout the target article, however, that a simulation is the top-down activation of sensory-motor areas to reenact perceptual experience. As 2.4 describes in some detail, associative areas reactivate sensory-motor representations to implement simulations. Wells claims that the extraction of perceptual symbols (2.2) is not an improvement over the unspecified transduction process associated with amodal symbols (1.2). The improvement, as I see it, is that the extraction of perceptual symbols is a well-specified process. It is easy to see how selective attention could isolate and store a component of perception, which later functions as a perceptual symbol. In contrast, we have no well-specified account of how an amodal symbol develops. Finally, I would like to reiterate one further point about my use of "perception." The use of this term in "perceptual symbol systems" is not meant to imply that movements and action are irrelevant to knowledge. To the contrary, many examples of perceptual symbols based on movements and actions 14 were provided throughout the target article. Rather "perception" refers to the perception of experience from which perceptual symbols are extracted, including the perception of movement and action. Thus, "perception" refers to the capture of symbolic content, not to the content itself. For additional discussion of perceptual representations in knowledge, see Brewer and Pani (1983) and Pani (1998). One could take Wells' concern about perceptual symbol extraction being unconstrained to mean that it provides no leverage on the problem of what can be a feature. Landau makes this criticism explicitly. Notably, the amodal account that Landau apparently prefers places no constraints on features at all. As the introduction to section 2 in the target article noted, amodal theories have not resolved the feature issue themselves. It is puzzling that what counts as evidence against the perceptual view is not also counted as evidence against the amodal view. Furthermore, as Landau notes, perceptual views contain the seeds of a solution to this problem. Specifically, she notes that classic empirical theories define conceptual features as features that arise from the interaction of the sense organs with the physics of the environment. In other words, the features of conception are the features of perception. Clearly not all sensory distinctions make their way to the conceptual level, such as opponent color processing and center-surround inhibition. Yet many conceptual features do appear to have their roots in sensory systems, including the features for shape, color, size, and texture that occur frequently in reports of conceptual content. Although Landau does not credit perceptual symbol systems with drawing on perception for features, they certainly do, given the explicit mention of perceptually-derived features in 2.1. I did not attempt to resolve the feature problem in the target article because of its complexity. The features of conception clearly go beyond perceptual features, as I noted in footnote 5. Most importantly, high-level goals and conceptual structures capture aspects of perception through selective attention that become features. In most cases, the information selected is not a feature computed automatically in sensory processing but is instead a higher-order configuration of basic sensory-motor units. It was for this reason that I cited Schyns, Goldstone, and Thibaut (1998) as showing that conceptual features go clearly beyond perceptual features. The architecture of perceptual symbol systems is ideally suited towards creating new features that exceed sensory-motor features. Because high-level frames and intuitive theories are represented as 15 perceptual simulations (2.5, 4.1.2), they are well suited towards interacting with perception to isolate critical features. Furthermore, the symbol formation process in 2.2 is well-suited to creating new features through selective attention. Clearly, a great deal remains to be specified about how this all works. Conversely, it is much less clear how amodal symbols systems accomplish the construction of novel features, again because they lack an interface between perception and cognition. Schwartz, Weaver, and Kaplan argue that I fail to specify how attention is controlled during the formation of perceptual symbols, such that perceptual symbols are as arbitrary as amodal symbols. Again, however, perceptual systems provide natural constraints on the formation of perceptual symbols. One prediction is that when empirical studies examine this process, they will find that perceptual symbols exhibit a weak bias for following the structure of perception. By no means, however, must perceptual symbols only follow this structure. Because selective attention is so flexible, it can focus on complex configurations of perceptual information to establish symbols that serve higher goals of the system. The open-endedness of the symbols that people use should not be viewed as a weakness of perceptual symbol systems. Instead, it is something that any theory needs to explain, and something that perceptual symbol systems explain through the flexible use of selective attention. It would be much more problematic if the theory were simply limited to symbols that reflect the computations of low-level sensory-motor processing. Ultimately Schwartz, Weaver, and Kaplan have another agenda. Rather than selective attention being responsible for schematic category representations, they argue that the Hebbian mechanisms of recruitment and fractionation are responsible. Implicit in this view is that holistic perceptual states are recorded during learning, with differences across them canceling out to produce schematic representations of perceptual symbols. Gabora similarly argues for the importance of these mechanisms. While I endorse these mechanisms, I strongly disagree with the view that selective attention plays no role in learning. As discussed in 2.2 and 4.1.3, selective attention has been strongly implicated in learning across decades of research in many areas. Furthermore, it is naive to believe that holistic recordings of perception occur, as Hochberg argues eloquently (also see Hochberg, 1998). Just because selective attention is highly flexible does not mean that it is irrelevant to conceptual processing. Instead, it is absolutely essential to creating a conceptual system (1.4, 4.1.3). Hochberg and Toomela each propose that active information-seeking and goal pursuit guide selective attention and therefore the acquisition of perceptual symbols. Glenberg's arguments about the importance of situated action are also consistent with this view. I agree strongly that top-down goalachievement often guides selective attention, such that perceptual information relevant to these pursuits becomes acquired as perceptual symbols. However, bottom-up mechanisms may produce perceptual symbols as well. For example, attention-grabbing onsets and changes in perception may attract processing and produce a perceptual symbol of the relevant content. Similarly, the perceptual layout of a physical stimulus may guide attention and the subsequent extraction of perceptual symbols. In this spirit, Brewer notes that physical proximity better predicts recall order for the objects in a room than does their conceptual relatedness. Thus, bottom-up, as well as top-down, factors are probably important to selecting the content of perceptual symbols. Furthermore, appealing to the importance of top-down factors pushes the difficult issues back a level. Clearly, attention is under the control of goals and action to a considerable extent, yet how do we characterize the mechanisms underlying goals and actions that guide attentional selection? R2.3. Abstraction, concepts, and simulators. I am the first to admit that specifying the mechanisms underlying simulators constitutes perhaps the most central challenge of the theory, and I noted this throughout the target article. Later I will have more to say about implementing simulators in specific mechanisms (R5.3). Here I focus on some misunderstandings about them. Without citing any particular statement or section of the target article, Ohlsson claims that I equated selection with abstraction. According to him, I believe that selectively storing a wheel while perceiving a car amounts to creating an abstraction of cars in general. Nowhere does the target article state that selection is abstraction. To the contrary, this is a rather substantial and surprising distortion of my view. What I did equate roughly with selection was schematization, namely, the information surrounding focal content is filtered out, leaving a schematic representation of the component (2.2).
The Perceptual Symbol Systems theory holds that all of cognition, language, reasoning, and mem-ory, is grounded in perceptual features REF .
Perceptions of Perceptual Symbols
Behavioral and Brain Sciences
Psychology