In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.

Within the cognitive sciences, most researchers assume that it is the job of linguists to investigate how language is represented, and that they do so largely by building theories based on explicit judgments about patterns of acceptability – whereas it is the task of psychologists to determine how language is processed, and that in doing so, they do not typically question the linguists' representational assumptions. We challenge this division of labor by arguing that structural priming provides an implicit method of investigating linguistic representations that should end the current reliance on acceptability judgments. Moreover, structural priming has now reached sufficient methodological maturity to provide substantial evidence about such representations. We argue that evidence from speakers' tendency to repeat their own and others' structural choices supports a linguistic architecture involving a single shallow level of syntax connected to a semantic level containing information about quantification, thematic relations, and information structure, as well as to a phonological level. Many of the linguistic distinctions often used to support complex (or multilevel) syntactic structure are instead captured by semantics; however, the syntactic level includes some specification of “missing” elements that are not realized at the phonological level. We also show that structural priming provides evidence about the consistency of representations across languages and about language development. In sum, we propose that structural priming provides a new basis for understanding the nature of language.

Studies of visual space perception have been assuming that people have an internal representation of the physical space that surrounds them. A variety of psychophysical procedures has been used in an attempt to measure the properties of visual space. The goal of the present study was to evaluate the accuracy of the mental representation and the strategies adopted to acquire and retain visuo-spatial information of a configuration as a function of two types of instructions. Thirty-eight undergraduate and graduate students participated in the study and were distributed in perceptive and mnemonic experimental conditions. The effect of the instructions (intentional and incidental) on the representation of the distances among the objects of the scene was estimated using exponents of power function, based on the reproduction of the distances among the stimuli of the scene. The results revealed that judgments made under intentional instructions were more frequently based on strategies related to the location of the stimuli, whereas judgments originating from incidental instructions were based on strategies related to the name of the stimuli. It was observed that the intentional instruction facilitated a more accurate mental representation of the observed experimental configuration, enhancing participants' performance.

We report on two experiments that ask when and under what linguistic conditions comprehenders construct detailed shape representations of mentioned objects, and whether these can change over the course of a sentence when new information contradicts earlier expectations. We used Japanese because the verb-final word order of Japanese presented a revealing test case where information about objects can radically change with a subsequent verb. The results show that language understanders consistently generate a distinct and detailed shape for an object by integrating the semantic contributions of different sentential elements. These results first confirm that the tendency to generate specific shape information about objects that are involved in described events is not limited to English, but is also present in Japanese, a typologically and genetically distinct language. But more importantly, they shed light on the processing mechanism of object representation, showing that mental representations are initiated sentence medially, and are rapidly revised if followed by a verb that implies a change to an object shape. This work contributes to ongoing research on incremental language processing – comprehenders appear to construct extremely detailed semantic representations early in a sentence, and modify them as needed.

When speakers describe motion events using different languages, they subsequently classify those events in language-specific ways (Gennari, Sloman, Malt & Fitch, 2002). Here we ask if bilingual speakers flexibly shift their event classification preferences based on the language in which they verbally encode those events. English–Spanish bilinguals and monolingual controls described motion events in either Spanish or English. Subsequently they judged the similarity of the motion events in a triad task. Bilinguals tested in Spanish and Spanish monolinguals were more likely to make similarity judgments based on the path of motion versus bilinguals tested in English and English monolinguals. The effect is modulated in bilinguals by the age of acquisition of the second language. Late bilinguals based their judgments on path more often when Spanish was used to describe the motion events versus English. Early bilinguals had a path preference independent of the language in use. These findings support “thinking-for-speaking” (Slobin, 1996) in late bilinguals.

Since the 1990s, the number of websites and web users, especially older users, has increased extensively. Despite the rapid growth in the number of websites, a significant number of ergonomic violations still hinder the information search activity performed by web users. As ageing is associated with reduced working memory capacity, inhibition failure, slowing of processing speed and more generally impaired executive functioning, older adult web users may experience difficulties while searching for information, especially when the website includes ergonomic violations, such as usability and accessibility violations. In this experiment, the navigation activities of younger and older web users were compared while they were searching for information on a website that met ergonomic guidelines and on a website that included ergonomic violations. The participants then performed a free, delayed-recall task to assess their mental representation of the website they had just navigated. The main findings showed that ageing had a negative impact on search performance but few effects on mental representation built by participants. On the contrary, the ergonomic quality of the website had an impact on search performance and mental representation built by the participants.

What might a theory of mental imagery look like, and how might one begin formulating such a theory? These are the central questions addressed in the present paper. The first section outlines the general research direction taken here and provides an overview of the empirical foundations of our theory of image representation and processing. Four issues are considered in succession, and the relevant results of experiments are presented and discussed. The second section begins with a discussion of the proper form for a cognitive theory, and the distinction between a theory and a model is developed. Following this, the present theory and computer simulation model are introduced. This theory specifies the nature of the internal representations (data structures) and the processes that operate on them when one generates, inspects, or transforms mental images. In the third, concluding, section we consider three very different kinds of objections to the present research program, one hinging on the possibility of experimental artifacts in the data, and the others turning on metatheoretical commitments about the form of a cognitive theory. Finally, we discuss how one ought best to evaluate theories and models of the sort developed here.

The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach (the “proprietary vocabulary hypothesis”) is that there is a natural domain of human functioning (roughly what we intuitively associate with perceiving, reasoning, and acting) that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis.

Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the “cognitive impenetrability condition.” Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind (called its “functional architecture”) from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the “degrees of freedom” problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories.

The intentional stance is the strategy of prediction and explanation that attributes beliefs, desires, and other “intentional” states to systems – living and nonliving – and predicts future behavior from what it would be rational for an agent to do, given those beliefs and desires. Any system whose performance can be thus predicted and explained is an intentional system, whatever its innards. The strategy of treating parts of the world as intentional systems is the foundation of “folk psychology,” but is also exploited (and is virtually unavoidable) in artificial intelligence and cognitive science more generally, as well as in evolutionary theory. An analysis of the role of the intentional stance and its presuppositions supports a naturalistic theory of mental states and events, their content or intentionality, and the relation between “mentalistic” levels of explanation and neurophysiological or mechanistic levels of explanation. As such, the analysis of the intentional stance grounds a theory of the mind and its relation to the body.

This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory and, with the help of evolutionary biology and neurophysiology, to attempt a characterization of the adaptive processes involved in visual perception. Visual representations are explained as patterns of cortical activity that are enabled to focus on objects in the changing visual environment by constantly adjusting to maintain levels of mutual information between pattern and object that are adequate for continuing perceptual control. In these terms, the answer proposed to (1) is that the intentional functions of vision are those involved in the establishment and maintenance of such representations, and to (2) that semantic features are added to the information processes of vision with the focus on objects that these representations accomplish. The article concludes with proposals for extending this account of intentionality to the higher domains of conceptualization and reason, and with speculation about how semantic information-processing might be achieved in mechanical systems.

The present article examines how people's belief in an afterlife, as well as closely related supernatural beliefs, may open an empirical backdoor to our understanding of the evolution of human social cognition. Recent findings and logic from the cognitive sciences contribute to a novel theory of existential psychology, one that is grounded in the tenets of Darwinian natural selection. Many of the predominant questions of existential psychology strike at the heart of cognitive science. They involve: causal attribution (why is mortal behavior represented as being causally related to one's afterlife? how are dead agents envisaged as communicating messages to the living?), moral judgment (why are certain social behaviors, i.e., transgressions, believed to have ultimate repercussions after death or to reap the punishment of disgruntled ancestors?), theory of mind (how can we know what it is “like” to be dead? what social-cognitive strategies do people use to reason about the minds of the dead?), concept acquisition (how does a common-sense dualism interact with a formalized socio-religious indoctrination in childhood? how are supernatural properties of the dead conceptualized by young minds?), and teleological reasoning (why do people so often see their lives as being designed for a purpose that must be accomplished before they perish? how do various life events affect people's interpretation of this purpose?), among others. The central thesis of the present article is that an organized cognitive “system” dedicated to forming illusory representations of (1) psychological immortality, (2) the intelligent design of the self, and (3) the symbolic meaning of natural events evolved in response to the unique selective pressures of the human social environment.

The target article is an attempt to make some progress on the problem of color realism. Are objects colored? And what is the nature of the color properties? We defend the view that physical objects (for instance, tomatoes, radishes, and rubies) are colored, and that colors are physical properties, specifically, types of reflectance. This is probably a minority opinion, at least among color scientists. Textbooks frequently claim that physical objects are not colored, and that the colors are “subjective” or “in the mind.” The article has two other purposes: First, to introduce an interdisciplinary audience to some distinctively philosophical tools that are useful in tackling the problem of color realism and, second, to clarify the various positions and central arguments in the debate.

The first part explains the problem of color realism and makes some useful distinctions. These distinctions are then used to expose various confusions that often prevent people from seeing that the issues are genuine and difficult, and that the problem of color realism ought to be of interest to anyone working in the field of color science. The second part explains the various leading answers to the problem of color realism, and (briefly) argues that all views other than our own have serious difficulties or are unmotivated. The third part explains and motivates our own view, that colors are types of reflectances and defends it against objections made in the recent literature that are often taken as fatal.

We propose that the isomorphism generally observed between the representations composing our momentary phenomenal experience and the structure of the world is the end-product of a progressive organization that emerges thanks to elementary associative processes that take our conscious representations themselves as the stuff on which they operate, a thesis that we summarize in the concept of Self-Organizing Consciousness (SOC).

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many have been doing recently, there are two fundamentally distinct approaches available. Consciousness is to be explained either in terms of the nature of the representational vehicles the brain deploys or in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, although there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research that purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. Two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies used in the dissociation studies – so critical, in fact, that it is no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this target article we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists of the explicit representation of information in neurally realized parallel distributed processing (PDP) networks. This hypothesis leads us to reassess some common wisdom about consciousness, but, we argue, in fruitful and ultimately plausible ways.