1   Introduction

Progress in science is reflected in a corresponding development of language. The vistas opened up by the microscope, the telescope, tomography and other sensing devices have resulted in the naming of new entities and processes. Quantum theory has led to the su­persession of the classical atomic picture and one speaks in terms of tangled processes and non-binary logic. Quantum theory has also led to deep questions related to the definition of the observer and the observed. This has been one path to the examination of the mystery of mind. The other paths are rooted in ancient philosophical traditions and the psychological theories of the past century.

The language for the description of the mind in scientific discourse has not kept pace with the developments in the physical sciences. The mainstream discussion has moved from the earlier dualistic models of common belief to one based on the emergence of mind from the complexity of the parallel computer-like brain processes. The two old paradigms of determin­ism and autonomy, expressed sometimes in terms of separation and interconnectedness, show up in various guises. Which of the two of these is in favor depends on the field of research and the prevailing fashions. Although quantum theory has provided the foundation for physical sciences for seventy years, it is only recently that holistic, quantum-like operations in the brain have been considered. This fresh look has been prompted by the setbacks suffered by the various artificial intelligence (AI) projects and also by new analysis and experimental findings. It is being recognized that stimulus-response constructs such as "drive" are often inadequate in providing explanations; and one invokes the category "effort" to explain au­tonomous behavior. The languages used to describe the workings of the brain have been modeled after the dominant scientific paradigm of the age. The rise of mechanistic science saw the conceptu­alization of the mind as a machine. In our present computer age, the brain is often viewed as a computing machine; Neural networks are the engines of this machine. Although the neural network approach has had considerable success in modeling many counterintuitive illusions, there exists other processes in human and nonhuman cognition that appear to fall outside the scope of such models. Scholars have expressed the opinion that brain processing cannot be described by Turing machines. We do not wish to go into the details of these arguments; rather we will examine the question in the broadest terms. Briefly, the classical neural network model does not provide a resolution to the question of binding of patterns: How do the neuron firings in the brain come to have specific mean­ings or lead to specific images? The proposal that 40-Hz waveforms are characteristic of consciousness and may somehow bind the activity in different parts of the brain is too vague to be taken seriously. Furthermore, machines have been unable to match many computing capabilities of nonhumans. Is that because computers lack the self-organizational feature of biological systems? In unified theories of physics one speaks of a single force that, upon symmetry breaking, manifests itself into three or four distinct forces. Analogously, we argue that the quantum language of the brain manifests itself in terms of other languages. In this paper I consider the computational aspects of the problems of perception and adaptation in light of dual and associative processes. The central insight obtained from the study of animal intelligence is that it is predicated on continual self-organization, as seen, for example, in superorganisms. That biological processing has a quantum basis has been argued by several authors. Quantum models provide a natural explanation for the unity of awareness in addition to explaining other puzzling features of brain behavior. In one class of such models, quantum behavior is postulated within neurons. But this does not resolve the question of the continuing self-organization of a biological system. Since self-organization is the basic feature of biological processing, one needs to consider an explicit signaling scheme for this. This additional signal provides a dual to the usual neural transmissions in parallel with the many-component vectors of a quantum description. This bottom-up dual signaling regime for the brain may be taken to complement the top-down quantum view. This paper also considers the associative learning problem, that deals with the most basic linguistic category of how  associations are implemented. When a pattern is presented to a system for the first time, the synaptic weights that can resonate, or generate, this in the neural circuitry don't exist. So notions of supervised learning cannot be realistic biologically. A scheme is presented that can find synaptic weights instantaneously. In summary, our paper speaks of the three languages of brain: quantum, reorganizational, and associative. Our learning scheme provides a basis for the interiorization of the last two languages. How these languages fit into the overarching quantum framework remains to be investigated.