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“What is mind?” When you are asked such a question, you may be probably confused, because you do not exactly know how to answer, though you frequently use the word “mind” in daily conversation to describe your conditions, experiences, feelings, mental states, and so on. On the other hand, many people have so far tackled the topic of how science can handle the mind and its operation.

This chapter begins by briefly summarising some of the well-known classical connectionist/artificial neural network models such as multi-layered perceptron neural networks (MLP-NNs), radial basis function neural networks (RBF-NNs), self-organising feature maps (SOFMs), associative memory, and Hopfield-type recurrent neural networks (HRNNs). These models are shown to normally require iterative and/or complex parameter approximation procedures, and it is highlighted why these approaches have in general lost interest in modelling the psychological functions and developing artificial intelligence (in a more realistic sense).

In this chapter, the general concept of kernel memory (KM) is described, which is given as the basis for not only representing the general notion of “memory” but also modelling the psychological functions related to the arti- ficial mind system developed in later chapters.

In the previous chapter, various topological representations in terms of the kernel memory concept have been discussed together with some illustrative examples. In this chapter, a novel unsupervised algorithm to train the link weights between the KFs is given by extending the original Hebb’s neuropsychological concept, whereby the self-organising kernel memory (SOKM)^1 is proposed.

The previous two chapters have been devoted to establishing the novel artificial neural network concept, namely the kernel memory concept, for the foundation of the artificial mind system (AMS).

In any kind of creature, both the mechanisms of sensation and perception are indispensable for continuous living, e.g. to find edible plants/fruits in the forest, or to protect themselves from attack by approaching enemies. To fulfill these aims, there are considered to be two different kinds of information processes occurring in the brain: 1) extraction of useful features amongst the flood of information coming from the sensory organs equipped and 2) perception of the current surroundings based upon the features so detected in 1) for planning the next actions to be taken. Namely, the sensation mechanism is responsible for the former, whereas the latter is the role of the perception mechanism.

In this chapter, we dig further into the notion of “learning” within the AMS context. In conventional connectionist models, the term “learning” is almost always referred to as merely establishing the input-output relations via the parametric changes within such models, and the parameter tuning is typically performed by a certain iterative algorithm, given a finite (and mostly static) set of variables (i.e. both the training patterns and target signals). However, this interpretation is rather microscopic and hence still quite distant from the general notion of learning, since it only ends up with such parameter tuning, without giving any clear notions or clues to describe it at a macroscopic level, e.g. to explain the higher-order functions/phenomena occurring within the brain (see e.g. Roy, 2000).

As the philosopher Miguel de Umamuno (1864-1936) once said, “We live in memory and memory, and our spiritual life is at bottom simply the effort of our memory to persist, to transform itself into hope ... into our future. ” from “Tragic Sense of Life” (Unamuno, 1978), the “memory” is an indispensable item for the description of the mind. In psychological study (Squire, 1987), the notion of “learning” is defined as the process of acquiring new information, whereas “memory” is referred to as the persistence of learning in a state that can be revealed at a later time (see also Gazzaniga et al., 2002) and the outcome of learning. Thus, both the principles of learning, as described in the previous chapter, and memory within the AMS context are closely tied to each other.

In this chapter, we focus upon the two modules which are closely tied to the concept of “action planning”, i.e. the 1) language and 2) thinking modules.

This chapter is devoted to the remaining four modules within the AMS, i.e. 1) attention , 2) emotion , 3) intention , and 4) intuition module, and their mutual interactions with the other associated modules. Then, the four modules so modelled represent the respective abstract notions related to the mind.