How Does a Brain Build a Cognitive Code?

Abstract

This article indicates how competition between afferent data and learned feedback expectancies can stabilize a developing code by buffering committed populations of detectors against continual erosion by new environmental demands. Tille gating phenomena that result lead to dynamically maintained critical pe-ri(Jlds, and to attentional phenomena such as overshadowing in the adult. The fuillctional unit of cognitive coding is suggested to be an adaptive resonance, or amplification and ,prolongation of neural activity, that occurs when afferent data and efferent expectancies reach consensus through a matching process. The resonant state embodies the perceptual event, or attentional focus, and its amplified and sustained activities are capable of driving slow changes of long-term mem-or:r"' Mismatch between afferent data and efferent expectancies yields a global sulppression of activity and triggers a reset of short-term memory, as well as raJ~id parallel search and hypothesis testing for uncommitted cells. These mechanisms help to explain and predict, as manifestations of the unified theme of stable code development, positive and negative aftereffects, the McCollough effect , spatial frequency adaptation, monocular rivalry, binocular rivalry and hysteresis, pattern completion, and Gestalt switching; analgesia, partial reinforcement acquisition effect, conditioned reinforcers, underaroused versus overaroused depression; the contingent negative variation, P300, and ponto-ge]lliculo-occipital waves; olfactory coding, corticogeniculate feedback, matching of proprioceptive and terminal motor maps, and cerebral dominance. The psy-chophysiological mechanisms that unify these effects are inherently nonlinear and parallel and are inequivalent to the computer, probabilistic, and linear models currently in use. How do internal representations of the environment develop through experience? How do these repJ,esentations achieve an impressive measure of global self-consistency and stabi!ity despite the :inability of individual nerve cells to discern the behavioral meaning of the representations? How are coding errors corrected , or adaptations to a changing environment effected, if individual nerve cells do not know that these errors or changes have oc-curred? ThIs article describes how limitations in the types of information available to individual cells can be overcome when the cells act together in suitably designed feedback schemes. The designs that emerge have a natural neural interpretation, and enable us to explain and predict a large variety of psychological and physiological data as manifestations of mechanisms that have evolved