Analog Resonance Computation: A New Model for Human Cognition

Abstract

Early models of human cognition appeared to posit the brain as a collection of discrete digital computing modules with specific data processing functions. More recent theories such as the Hierarchically Mechanistic Mind characterize the brain as a massive hierarchy of interconnected and adaptive circuits whose primary aim is to reduce entropy. However, studies in high workload/stress situations show that human behavior is often error prone and seemingly irrational. Rather than regarding such behavior to be uncharacteristic, this paper suggest that such "atypical" behavior provides the best information on which to base theories of human cognition. Rather than using a digital paradigm, human cognition should be seen as an analog computer based on resonating circuits whose primary driver is to constantly extract information from the massively complex and rapidly changing world around us to construct an internal model of reality that allows us to rapidly respond to the threats and opportunities.

Keywords: behavior; cognition; decision making; human error; learning.

FIGURE 1

Simple analogue model with each resonant circuit represented by a pendulum. Sensory input is represented by (A,B). Motion is transmitted through elastic to pendulums (C) with the final result expressed by the motion of pendulum (D).