Abstract not found

We simulated organisms with an arm terminating with a hand composed by two fingers, a thumb and an index, each composed by two segments, whose behavior was guided by a nervous system simulated through an artificial network. The organisms, which evolved through a genetic algorithm, lived in a bidimensional environment containing four objects, either large or small, either grey or black. In a baseline simulation the organisms had to learn to grasp small objects with a precision grip and large objects with a power grip. In Simulation 1 the organisms learned to perform two tasks: in Task 1 they continued to grasp objects according to their size, in Task 2 they had to decide the objects ' color by using a precision or a power grip. Learning occured earlier when the grip required to respond to the object and to decide the color was the same than when it was not, even if object size was irrelevant to the task. The simulation replicates the result of an experiment by Tucker & Ellis (2001) suggesting that seeing objects automatically activates motor information on how to grasp them.

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.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statistics, 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 recording 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 storage and reactivation of perceptual symbols operates at the level of perceptual components -- not at the level of holistic perceptual experiences. 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 common 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 introspection (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 abstract concepts, thereby implementing a fully functional conceptual sy...