Perceptual symbol systems

Lawrence W. Barsalou

doi:10.1017/s0140525x99002149

journal article Aug 01, 1999

Perceptual symbol systems

Lawrence W. Barsalou

Behavioral and Brain Sciences Vol. 22 No. 4 pp. 577-660 · Cambridge University Press (CUP)

View at Publisher Save 10.1017/s0140525x99002149

Abstract

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 system. Productivity results from integrating simulators
combinatorially 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 perceptual theory of knowledge can implement a fully functional
conceptual system while avoiding problems associated with amodal symbol
systems. Implications for cognition, neuroscience, evolution,
development, and artificial intelligence are explored.

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