Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

A great deal of philosophy of mind in the modern era has been driven by an intense aversion to Cartesian dualism. In the 1950s, materialists claimed to have succeeded once and for all in exorcising the Cartesian ghost by identifying the mind with the brain. In subsequent decades, cognitive science put scientific meat on this metaphysical skeleton by explicating mental processes as digital computation implemented in the brain's hardware.

Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...) of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources. (shrink)

Recent theories of cognition have argued that embodied experience is important for conceptual processing. Embodiment can be contrasted with linguistic factors such as the typical order in which words appear in language. Here, we report four experiments that investigated the conditions under which embodiment and linguistic factors determine performance. Participants made speeded judgments about whether pairs of words or pictures were semantically related or had an iconic relationship. The embodiment factor was operationalized as the degree to which stimulus pairs were (...) presented in the spatial configurations in which they usually occur (i.e., an iconic configuration, e.g., attic presented above basement). The linguistic factor was operationalized as the frequency of the stimulus pairs in language. The embodiment factor predicted error rates and response time better for pictures, whereas the linguistic factor predicted error rates and response time better for words. These findings were modified by task, with the embodiment factor being strongest in iconicity judgments for pictures and the linguistic factor being strongest in semantic judgments for words. Both factors predicted error rates and response time for both semantic and iconicity judgments. These findings support the view that conceptual processing is both linguistic and embodied, with a bias for the embodiment or the linguistic factor depending on the nature of the task and the stimuli. (shrink)

Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...) a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information. (shrink)

The hypothesis that perceptual mechanisms could have more representational and logical power than usually assumed is interesting and provocative, especially with regard to brain evolution. However, the importance of embodiment and grounding is exaggerated, and the implication that there is no highly abstract representation at all, and that human-like knowledge cannot be learned or represented without human bodies, is very doubtful. A machine-learning model, Latent Semantic Analysis (LSA) that closely mimics human word and passage meaning relations is offered as a (...) counterexample. (shrink)

The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...) test the model on a variety of different datasets from grounded cognition experiments and demonstrate that this diverse set of results can be explained as perceptual simulation (cf. Barsalou, Simmons, Barbey, & Wilson, 2003) within a global memory model. (shrink)

In Being There, Andy Clark weaves these several threads into a pleasing whole and goes on to address foundational questions concerning the new tools and..

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...

Concepts are the elementary units of reason and linguistic meaning. They are conventional and relatively stable. As such, they must somehow be the result of neural activity in the brain. The questions are: Where? and How? A common philosophical position is that all concepts—even concepts about action and perception—are symbolic and abstract, and therefore must be implemented outside the brain’s sensory-motor system. We will argue against this position using (1) neuroscientific evidence; (2) results from neural computation; and (3) results about (...) the nature of concepts from cognitive linguistics. We will propose that the sensory-motor system has the right kind of structure to characterise both sensory-motor and more abstract concepts. Central to this picture are the neural theory of language and the theory of cogs, according to which, brain structures in the sensory-motor regions are exploited to characterise the so-called “abstract” concepts that constitute the meanings of grammatical constructions and general inference patterns. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...) in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

Evidence from functional neuroimaging of the human brain indicates that information about salient properties of an object¿such as what it looks like, how it moves, and how it is used¿is stored in sensory and motor systems active when that information was acquired. As a result, object concepts belonging to different categories like animals and tools are represented in partially distinct, sensory- and motor property-based neural networks. This suggests that object concepts are not explicitly represented, but rather emerge from weighted activity (...) within property-based brain regions. However, some property-based regions seem to show a categorical organization, thus providing evidence consistent with category-based, domain-specific formulations as well.Acronyms and DefinitionsBiological motion: motion of animate agents characterized by highly flexible, fully articulated motion vectors, in contrast to the rigid, unarticulated motion vectors associated with most tools.Category-specific disorder: a relatively greater impairment in retrieving information about members of one superordinate object category (e.g., animals) as compared with other categories following brain injury or diseaseIPS: intraparietal sulcusLO: lateral occipital cortexObject concept: memory representations of a class or category of objects. Necessary for numerous cognitive functions including identifying an object as a member of a specific category and drawing inferences about object propertiespMTG: posterior middle temporal gyruspSTS: posterior superior temporal sulcusRepetition suppression: decreased neural response associated with repeated presentation of an identical, or a semantically/conceptually related, stimulusSD: semantic dementiaSemantic memory: a large division of long-term memory containing knowledge about the world including facts, ideas, beliefs, and conceptsSemantic priming: a short-lasting facilitation in processing a stimulus due to the prior presentation of a semantically related stimulusTMS: transcranial magnetic stimulationVPMC: ventral premotor cortex. (shrink)