Semantics, experience and time

Robbins, Stephen E (2002) Semantics, experience and time. Cognitive Systems Research, 3 (3). pp. 301-337.

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Abstract

The computational hypothesis, with its inherent representationalism, and the dynamical hypothesis, with its apparent absence of representations and its commitment to continuous time, stand at an impasse. It is unclear how the dynamical stance can handle representational thought, or how computationalism can deal effectively with a tightly coupled, reciprocally causative agent-environment system in continuous transformation. Underlying this dilemma is the complex relation of mind to time, a relation encoded in the word experience. We must ask if any hypothesis describes a "device" capable of experience? Yet what is an intelligence and its thought without experience? Is a computational device, whether supporting a symbolic processor or connectionist net, intrinsically condemned to a zero degree of experience? What is required of a dynamical device? It is argued here that “semantic” intelligence and thought rests upon experience, fundamentally upon the invariance laws defined over time within conscious perception. The structure of experience is intrinsically unavailable to the computational device, limiting it to a “syntactic” intelligence. An alternative conception of a device is offered, based on Bergson conjoined with Gibson, which supports the qualitative and structural aspects of experience and the semantic. It frames a dynamical model of perception and memory in which invariance laws are intrinsic, creates a deeper notion of situatedness, and supports a concept of semantically based, representative thought founded upon perception.

Comments/Discussion

The impasse between the dynamical hypothesis, connectionism, and the computational hypothesis, is a function of the unresolved hard problem, i.e., the unsolved problem of the perception of the external world. It is not generally understood that a solution will imply an entirely new theory of memory, cognition and semantics. The solution to the hard problem described here thus outlines a broadly computational "device," beyond machines of the Turing class.

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